CN117099165A - Systems, devices, and methods related to medication dose guidance - Google Patents

Abstract

Systems, devices, and methods for determining a medication dose for a patient or user are provided. Dose determination may take into account recent and/or historical analyte levels of a patient or user. The dose determination may also take into account other information about the patient or user, such as physiological information, dietary information, activity, and/or behavior. A number of different dose-determining embodiments are set forth relating to a wide variety of aspects of the system or environment in which the embodiments may be implemented. Systems, devices, and methods are provided for displaying information related to glucose levels, including a time display within a target range and a graph containing identified analyte levels for a pattern type for a time period of a day.

Description

Systems, devices, and methods related to medication dose guidance

Cross Reference of Related Applications

The present application claims priority from the following applications: U.S. application Ser. No. 63/237,769, filed on 8.27, 2021, U.S. application Ser. No. 63/225,140, filed on 7.23, 2021, and U.S. application Ser. No. 63/145,131, filed on 2.3, 2021, all of which are expressly incorporated herein by reference in their entirety for all purposes. The application also relates to the following applications: U.S. application Ser. No. 29/817,852, filed on 3 months 12 and 2021, U.S. application Ser. No. 29/817,851, filed on 11 and 2021, and U.S. application Ser. No. 29/813,998, filed on 11 and 2021, are expressly incorporated herein by reference in their entirety for all purposes. The application also relates to U.S. application Ser. No. 16/944,736, filed on 31/7/2020, which claims priority and benefit from the following applications: U.S. provisional application Ser. No. 62/882,249, U.S. provisional application Ser. No. 62/979,578, U.S. provisional application Ser. No. 62/979,594, U.S. provisional application Ser. No. 62/979,618, and U.S. provisional application Ser. No. 63/058,799, all of which are incorporated herein by reference in their entirety for all purposes, filed on 8/2/21/2020.

Technical Field

The subject matter described herein relates generally to systems, devices, and methods related to drug dosage guidance, such as determining insulin dosages for treating elevated glucose levels caused by diabetes.

Background

For the health of individuals suffering from diabetes, detection and/or monitoring of analyte levels (e.g., glucose, ketone, lactate, oxygen, hemoglobin A1C, etc.) may be critical. Patients with diabetes can experience complications including loss of consciousness, cardiovascular disease, retinopathy, neuropathy, and nephropathy. It is generally desirable to monitor the glucose level of diabetics to ensure that their glucose levels remain within a clinically safe range, and this information may also be used to determine if and/or when insulin is needed to reduce the glucose level in diabetics or when additional glucose is needed to increase the glucose level in their bodies.

More and more clinical data indicate a strong correlation between glucose monitoring frequency and glycemic control. Despite this correlation, many individuals diagnosed with diabetic conditions do not monitor their glucose levels as frequently as possible due to a combination of factors including inconvenience, test judgment, pain associated with glucose testing, and cost.

For patients who rely on administration of a drug (e.g., insulin) to treat or manage diabetes, it is desirable to have a system, device, or method that can automatically utilize glucose information collected by an analyte monitoring system to provide drug dosage guidance on demand in a readily available manner. What is further desired is a system, apparatus or method that allows for the physiology, diet, activity and/or behavior of the user or patient to be treated in providing such medication dosage guidance so that accuracy and reliability may be improved. Furthermore, in some instances, it is also desirable for such a system, device or method to be able to automatically deliver a selected dose of medication.

For these and other reasons, there is a need for improved systems, methods, and apparatus related to medication dose guidance.

Disclosure of Invention

Provided herein are exemplary embodiments of systems, devices, and methods relating to providing drug dosage guidance and, in some embodiments, drug delivery. According to one aspect, many embodiments described herein include a Dose Guidance System (DGS) comprising: a display device, a sensor control device and a drug delivery device. The dose guidance system may include a dose guidance application (e.g., software) that may determine and output dose guidance (e.g., recommendations regarding dosages, corrections, and adjustments) to the patient. Furthermore, according to some embodiments, the dose guidance system may learn the patient's dosing strategy during a learning period in which the dose guidance system may estimate critical dosing parameters. According to some embodiments, once the system is configured with the patient's current dosing strategy, the dose guidance system may also provide guidance for adjustments and corrections. The dose guidance system may also provide guidance for different meal dosing scenarios. For example, in some embodiments, the dose guidance system may provide dose guidance at or before the beginning of a meal or after the beginning of a meal. The dose guidance system may also provide dose guidance for compound meals (e.g., desserts) or "replenishment" doses to address high postprandial glucose levels. Exemplary systems and safety features of the dose guidance system are also described.

Many of the embodiments provided herein include improved software features or graphical user interfaces for use with analyte monitoring systems that are highly intuitive, user friendly, and provide quick access to physiological information of a user. More specifically, these embodiments allow a user (or HCP) to quickly determine an appropriate medication based on information about the user's physiological condition, historical dosing patterns, and other factors, without the laborious task of the user (or HCP) to check large amounts of analyte data. Furthermore, some of the GUI and GUI features allow the user (and his caregivers) to better understand and improve the user's dosing pattern and subsequent hypoglycemic and hyperglycemic episodes. Likewise, many other embodiments provided herein include improved software features for dose guidance systems that are improved as follows: dose guidance provided to a user by a safety regulation strategy that allows minimizing hypoglycemic episodes, a method for changing dose guidance as a function of when a dose is administered relative to a meal start time (e.g., before, at or after the start of a meal), consideration of real world events affecting dosing strategies, post-meal alerts based on predicted event probabilities other than thresholds, to name a few.

Furthermore, many of the embodiments described herein are improvements over conventional bolus calculators (bolus calculators) in which HCPs need to configure many settings, a very time-consuming process. Alternatively, the system may require the patient to enter a number of settings, which is often confusing to the patient. Because no input from the patient is required or minimal input from the patient (e.g., typical meal doses) is required, the embodiments described herein significantly reduce the manual input required by both the patient and the HCP, and the system is able to learn other necessary parameters related to the patient's insulin administration practices. Furthermore, many patients have low compliance with recommended dosing regimens. During the learning period, the system may collect data regarding both glucose levels and insulin administration, and may assess the patient's compliance level. Thus, features of the system may address issues related to the time investment of the HCP and the complexity level of the patient. Another advantage of the system is that: while conventional bolus calculators use fixed parameters that need to be configured, the system can automatically adjust (i.e., optimize) the dose regimen parameters over time; that is, the dosage regimen is optimized to reduce the pattern of low or high glucose. This reduces the time burden that the HCP must perform the task of periodically viewing the patient's glucose data and updating its dosage parameters to address the blood glucose problem. Another advantage is: in the event that many patients frequently miss their insulin dosage, the system may detect and alert the patient that they missed the dosage, and may provide the patient with a means of late safety dosing that will help reduce their overall glucose level. Current standard practice is that patients wait two or more hours after they begin a meal before taking a correction dose, or only waiting until their next meal dose corrects for high glucose; the ability to safely administer the drug after a meal but within 2 hours will help reduce the overall glucose level with minimal additional risk of hypoglycemia. Other improvements and advantages are also provided. Various configurations of these devices are described in detail by way of example only.

Other systems, devices, methods, features and advantages of the subject matter described herein will be or become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, devices, methods, features and advantages be included within this description, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Where these features are not explicitly recited in the claims, the features of the exemplary embodiments should not be construed in any way as limiting the appended claims.

Drawings

Details of the subject matter set forth herein, both as to its structure and operation, may be apparent from consideration of the accompanying drawings, in which like reference numerals refer to like parts throughout. The components in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the subject matter. Moreover, all illustrations are intended to convey concepts, wherein relative sizes, shapes, and other detailed attributes may be illustrated schematically rather than literally or precisely.

Fig. 1A and 1B are block diagrams of exemplary embodiments of a dose guidance system.

Fig. 2A is a schematic diagram depicting an exemplary embodiment of a sensor control device.

FIG. 2B is a block diagram depicting an exemplary embodiment of a sensor control device.

Fig. 3A is a schematic diagram depicting an exemplary embodiment of a drug delivery device.

Fig. 3B is a block diagram depicting an exemplary embodiment of a drug delivery device.

Fig. 4A is a schematic diagram depicting an exemplary embodiment of a display device.

Fig. 4B is a block diagram depicting an exemplary implementation of a display device.

FIG. 5 is a block diagram depicting an exemplary embodiment of a user interface device.

Fig. 6A-1 through 6A-4 are exemplary layouts of glucose mode reports.

FIGS. 6B-1 through 6B-2, 6C-1 through 6C-2, 6D-1 through 6D-2, 6E-1 through 6E-2, 6F-1 through 6F-2, and 6G-1 through 6G-2 are exemplary glucose concentration profiles.

Fig. 7A is a flowchart depicting an exemplary embodiment of a process flow of a portion of a learning method for estimating insulin delivery practices of a patient for a dose guidance application.

Fig. 7B is a flow chart depicting an exemplary embodiment of a method for parameterizing a patient's drug administration practices to configure dose guidance settings.

Fig. 7C-7E are flowcharts depicting additional optional elements of the method shown in fig. 7B.

Fig. 8A is a flow chart depicting an exemplary embodiment of a process flow for evaluating meal bolus adjustments for a Multiple Daily Injection (MDI) dosing therapy performed by a dose guidance application.

Fig. 8B is a flowchart depicting an exemplary embodiment of a process flow of operations performed by a dose guidance application for Glucose Pattern Analysis (GPA).

FIG. 8C is an exemplary embodiment depicting a graph of information for determining risk of hypoglycemia and other indicators for GPAs.

Fig. 8D-8H are flowcharts illustrating aspects of various exemplary embodiments of algorithms for assessing meal bolus adjustment of MDI insulin administration therapy.

FIGS. 9A-1 and 9A-2 depict exemplary reports for viewing by the HCP.

Fig. 9B is an exemplary compliance report.

FIG. 9C is an exemplary chart associated with cluster analysis of meal periods.

Fig. 9D is an exemplary graph of dining dose clusters and doses.

FIG. 9E is an exemplary chart associated with a pre-meal correction factor determination.

Fig. 9F is a flowchart depicting an exemplary embodiment of a process for facilitating access of electronic cases by a HCP.

Fig. 9G is an exemplary summary report for the HCP.

Fig. 9H is an exemplary chart demonstrating patient compliance with a recommended dosing regimen.

Fig. 9I is an exemplary summary report of patient treatment.

Fig. 10 is a state transition diagram of when the insulin delivery algorithm may be invoked at the lead time.

FIG. 11 is a flow chart depicting an exemplary embodiment of a method for displaying a dose guidance associated with a meal dose and a correction dose.

Fig. 12 is a flowchart depicting an exemplary embodiment of a method for displaying a dose guidance screen.

FIG. 13 is a flow chart depicting an exemplary embodiment of a method for displaying a plurality of meal icons.

Fig. 14 is a flow chart depicting an exemplary embodiment of a method for displaying dose calculations.

FIG. 15 is a flow chart depicting an exemplary embodiment of a method for issuing a missed meal dose alert.

Fig. 16A-16D are flowcharts depicting exemplary embodiments of methods for revoking a missed meal dose alert.

FIG. 17 is a flow chart depicting an exemplary embodiment of a method for issuing a corrected dose alert.

Fig. 18A-18C are flowcharts depicting exemplary embodiments of methods for overriding a corrected dose alert.

Figure 19 is a plot of IOB remaining from a previous injection as a function of DIA for fast acting insulin.

Fig. 20A-20C are flowcharts depicting exemplary embodiments of methods for classifying doses.

Fig. 21A is a flow chart depicting an exemplary embodiment of a method for providing dose guidance in response to analyte data.

FIG. 21B is a flow chart depicting an exemplary embodiment of a method for determining a glucose mode indicator.

Fig. 21C and 21D are flowcharts illustrating certain additional operations that may be performed in connection with one or more of the methods illustrated in fig. 21A and 21B.

Fig. 21E is a flow chart illustrating additional or alternative operations for glucose mode indication.

Fig. 22A and 22B are exemplary data flow diagrams.

Fig. 22C is a flow chart depicting an exemplary embodiment of a method for a delivery device to determine whether stored dose data is complete.

Fig. 22D and 22E are flowcharts depicting exemplary embodiments for applying a method of determining whether received dose data is complete.

23A and 23B are flowcharts depicting exemplary embodiments of methods for labeling meals of recommended dosages.

Detailed Description

Before the present subject matter is described in detail, it is to be understood that this disclosure is not limited to particular embodiments described herein, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

In general, embodiments of the present disclosure include systems, devices, and methods related to medication dose guidance. The dose guidance may be based on various types of information specific to the user and categories of information, such as the user's current and previous analyte levels, the user's current and previous diets, the user's current and previous physical activities, the user's current and previous medication history, and other physiological information about the user. According to one aspect of an embodiment, the dose guidance provided by the systems, devices, and methods of the present disclosure may be based not only on individual categories of information, but also on predictive impact of these categories of information on a user's future analyte level.

The dose guidance function may be implemented as a Dose Guidance Application (DGA) comprising software and/or firmware instructions stored in a memory of a computing device for execution by at least one processor or processing circuitry thereof. The computing device may be owned by a user or a Health Care Professional (HCP), and the user or HCP may interact with the computing device through a user interface. According to some implementations, the computing device may be a server or trusted computer system accessible over a network, and the dose guidance software may be presented to the user in the form of an interactive web page through a browser executing on a local display device (with a user interface) that communicates with the server or trusted computer system over the network. In this and other embodiments, the dose guidance software may be executed across multiple devices, or partially on processing circuitry of a local display device and partially on processing circuitry of a server or trusted computer system. Those skilled in the art will appreciate that when a DGA is described as performing an action, this action is performed in accordance with instructions stored in a computer memory (including instructions hard-coded in a read-only memory), which when executed by at least one processor of at least one computing device, cause the DGA to perform the action described above. In all cases, the acts may alternatively be performed by hardwired hardware to implement the acts (e.g., dedicated circuitry), as opposed to performing the acts by instructions stored in memory.

Further, as used herein, the system on which DGA is implemented may be referred to as a dose guidance system. The dose guidance system may be configured for the sole purpose of providing dose guidance, or may be a multi-functional system in which dose guidance is only one aspect. For example, in some embodiments, the dose guidance system is also capable of monitoring the analyte level of the user. In some embodiments, the dose guidance system is also capable of delivering a drug to a user, such as with an injection or infusion device. In some embodiments, the dose guidance system is capable of monitoring an analyte and delivering a drug.

These and other embodiments described herein represent improvements in the field of computer-based dose determination, analyte monitoring, and drug delivery systems. Specific features and potential advantages of the disclosed embodiments are discussed further below.

Before describing the dose guidance embodiments in detail, it is first desirable to describe examples of dose guidance systems on or through which a dose guidance application may be implemented.

Exemplary embodiments of the dose guidance System

Fig. 1A is a block diagram depicting an exemplary embodiment of a dose guidance system 100. In this embodiment, the dose guidance system 100 is capable of providing dose guidance, monitoring one or more analytes, and delivering one or more drugs. This multifunction example is used to illustrate the high degree of interconnectivity and performance available through the system 100. However, in the embodiments described herein, the analyte monitoring component, the drug delivery component, or both may be omitted, if desired.

Here, the system 100 includes: a Sensor Control Device (SCD) 102 configured to collect analyte level information from a user; a drug delivery device (MDD) 152 configured to deliver a drug to a user; and a display device 120 configured to present information to a user and receive input or information from the user. The structure and function of each device will be described in detail herein.

The system 100 is configured for highly interconnected and highly flexible communications between devices. Each of the three devices 102, 120, and 152 may communicate directly with each other (without going through an intermediate electronic device) or indirectly with each other (such as through the cloud network 190 or through another device and then through the network 190). The bi-directional communication capability between devices and between a device and the network 190 is shown in fig. 1A as bi-directional arrows. However, those skilled in the art will appreciate that any of the one or more devices (e.g., SCD) is capable of unidirectional communication, such as broadcast, multicast, or advertisement propagation. In each instance, the communication may be wired or wireless, whether bi-directional or uni-directional. The protocols governing the communication on each path may be the same or different and may be proprietary or standardized. For example, wireless communication between devices 102, 120, and 152 may be performed according to the bluetooth (including bluetooth low energy) standard, the Near Field Communication (NFC) standard, the Wi-Fi (802.11 x) standard, the mobile phone standard, and so forth. All communications on the various paths may be encrypted and each device of fig. 1A may be configured to encrypt and decrypt those communications transmitted and received. In each instance, the communication path of fig. 1A may be direct (e.g., bluetooth or NFC) or indirect (e.g., wi-Fi, mobile phone, or other internet protocol). Embodiments of the system 100 need not have the ability to communicate across all paths shown in fig. 1A.

Further, although FIG. 1A depicts a single display device 120, a single SCD 102, and a single MDD 152, one skilled in the art will appreciate that system 100 may include a plurality of any of the foregoing devices. By way of example only, the system 100 may include a single SCD 102 in communication with multiple (e.g., two, three, four, etc.) display devices 120 and/or more MDDs 152. Alternatively, system 100 may include multiple SCDs 102 in communication with a single display device 120 and/or a single MDD 152.

Furthermore, each of the plurality of devices may be the same or different device types. For example, system 100 may include a plurality of display devices 120, including smartphones, handheld receivers, and/or smartwatches, each of which may be in communication with SCD 102 and/or MDD 152, as well as each other.

Analyte data may be transferred between each device within system 100 in an autonomous manner (e.g., automatically transmitted according to a schedule) or in response to a request for analyte data (e.g., a request for analyte data is sent from a first device to a second device, then analyte data is transmitted from the second device to the first device). Other techniques for communicating data may also be used to accommodate more complex systems, such as cloud network 190.

Fig. 1B is a block diagram depicting another exemplary embodiment of a dose guidance system 100. Here, system 100 includes SCD 102, MDD 152, first display device 120-1, second display device 120-2, local computer system 170, and trusted computer system 180 accessible by cloud network 190. SCD 102 and MDD 152 are capable of communicating with each other and with display device 120-1, which may act as a communication hub for aggregating information from SCD 102 and MDD 152, processing and displaying the information when needed, and transmitting some or all of the information to cloud network 190 and/or computer system 170. Rather, display device 120-1 may receive information from cloud network 190 and/or computer system 170 and communicate some or all of the received information to SCD 102, MDD 152, or both. The computer system 170 may be a personal computer, a server terminal, a laptop computer, a tablet, or other suitable data processing device. Computer system 170 may include or present software for data management and analysis and communication with components in system 100. The computer system 170 may be used by a user or medical professional to display and/or analyze analyte data measured by the SCD 102. Further, while FIG. 1B depicts a single SCD 102, a single MDD 152, and two display devices 120-1 and 120-2, one skilled in the art will appreciate that the system 100 may include a plurality of any of the above devices, wherein each plurality of devices may include the same or different types of devices.

Still referring to FIG. 1B, according to some embodiments, trusted computer system 180 may be physically or virtually owned by the manufacturer or distributor of the components of system 100 through a secure connection and may be used to perform authentication of devices of system 100 (e.g., devices 102, 120-n, 152), secure storage for user data, and/or as a server serving a data analysis program (e.g., accessible via a web browser) for performing analysis on measured analyte data and medication history of a user. The trusted computer system 180 may also act as a data hub for routing and exchanging data between all devices in communication with the system 180 over the cloud network 190. In other words, all devices of system 100 that are capable of communicating with cloud network 190 (e.g., directly through an internet connection or indirectly via another device) are also capable of communicating with all other devices of system 100 that are capable of communicating with cloud network 190 directly or indirectly.

Display device 120-2 is depicted as being in communication with cloud network 190. In this example, the device 120-2 may be owned by another user authorized to access analyte and drug data of the person wearing the SCD 102. For example, the person having the display device 120-2 may be: parents of children wearing SCD 102, as one example; or caregivers of elderly patients wearing SCD 102, as another example. The system 100 may be configured to: analyte and drug data about the wearer is communicated over the cloud network 190 (e.g., via the trusted computer system 180) to another user authorized to access the data.

Exemplary embodiments of analyte monitoring devices

The analyte monitoring function of the dose guidance system 100 may be implemented by including one or more devices capable of collecting, processing, and displaying analyte data of a user. Exemplary embodiments of such devices and methods of their use are described in international publication No. WO 2018/152241 and U.S. patent publication No. 2011/0213225, the entire contents of both of which are incorporated herein by reference for all purposes.

Analyte monitoring can be performed in a number of different ways. For example, a "continuous analyte monitoring" device (e.g., a "continuous glucose monitoring" device) may continuously or repeatedly transmit data from a sensor control device to a display device, with or without prompting, e.g., automatically according to a schedule. As another example, a "flash analyte monitoring" device (e.g., a "flash glucose monitoring" device or simply a "flash" device) may transmit data from a sensor control device through a display device (e.g., a scan) in response to a user-initiated request for data, such as with Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocols.

An analyte monitoring device that utilizes a sensor configured to be partially or fully disposed within a user's body may be referred to as an in-vivo analyte monitoring device. For example, an in-vivo sensor may be placed in a user's body such that at least a portion of the sensor is in contact with a bodily fluid (e.g., interstitial (ISF) fluid, such as dermal fluid in the dermis layer or subcutaneous fluid below the dermis layer, blood, or other), and an analyte concentration in the bodily fluid may be measured. In vivo sensors may use various types of sensing technologies (e.g., chemical, electrochemical, or optical). Some systems that utilize in vivo analyte sensors may also operate without finger stick calibration.

"in vitro" devices are those devices in which a sensor is in contact with a biological sample outside the body (or rather "ex vivo"). These devices typically include a port for receiving an analyte test strip carrying a user's body fluid, which test strip can be analyzed to determine the user's blood glucose level. Other ex vivo devices have been proposed that attempt to non-invasively measure analyte levels in the user's body, such as by using optical techniques that can measure analyte levels in the body without mechanically penetrating the user's body or skin. In vivo and ex vivo devices typically include in vitro capabilities (e.g., in vivo display devices that also include a test strip port).

The subject matter will be described with respect to a sensor capable of measuring glucose concentration, although detection and measurement of concentrations of other analytes are within the scope of the disclosure. These other analytes may include, for example, ketone, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotrophin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, peroxide, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, and the like. The concentration of the drug may also be monitored, such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drug abuse, theophylline, and warfarin. The sensor may be configured to measure two or more different analytes at the same or different times. In some embodiments, the sensor control device may be coupled with two or more sensors, wherein one sensor is configured to measure a first analyte (e.g., glucose) and the other one or more sensors are configured to measure one or more different analytes (e.g., any of the analytes described herein). In other embodiments, a user may wear two or more sensor control devices, each capable of measuring a different analyte.

The embodiments described herein may be used with all types of in vivo, in vitro, and ex vivo devices capable of monitoring the aforementioned analytes and other analytes.

In many embodiments, sensor operation may be controlled by SCD 102. The sensors may be mechanically and communicatively coupled with the SCD 102, or may be communicatively coupled with the SCD 102 only using wireless communication technology. SCD 102 may include electronics and a power supply that enable and control analyte sensing performed by the sensor. In some embodiments, the sensor or SCD 102 may be self-powered, thereby eliminating the need for a battery. The SCD 102 may also include communication circuitry for communicating with another device (e.g., a display device) that may or may not be local to the user's body. The SCD 102 may reside on the body of the user (e.g., attached to or otherwise placed on the skin of the user, carried on the user's clothing, etc.). The SCD 102 may also be implanted in the user's body along with the sensors. The functionality of the SCD 102 may be divided between a first component (e.g., a component that controls a sensor) that resides on or otherwise outside the body and a second component (e.g., a relay component that communicates with the first component and also communicates with an external device such as a computer or smart phone) that is implanted in the body. In other embodiments, the SCD 102 may be external to the body and configured to non-invasively measure the analyte level of the user. Depending on the actual implementation or implementation, the sensor control device may also be referred to as a "sensor control unit," "on-body electronics" device or unit, "on-body" device or unit, "in-body electronics" device or unit, or "sensor data communication" device or unit, to name a few.

In some implementations, the SCD 102 can include a user interface (e.g., a touch screen) and can process the analyte data and display the resulting calculated analyte level to a user. In such a case, the dose guidance embodiments described herein may be implemented directly, in whole or in part, by the SCD 102. In many embodiments, the physical form factor of the SCD 102 is minimized (e.g., to minimize the appearance of the user's body), or the sensor control device may be inaccessible to the user (e.g., if fully implanted), or other factors may make it desirable to have a display device available to the user to read analyte levels and interact with the sensor control device.

Fig. 2A is a side view of an exemplary embodiment of SCD 102. The SCD 102 may include a housing or mounting 103 for sensor electronics (fig. 2B) that may be electrically coupled with an analyte sensor 101, which is configured herein as an electrochemical sensor. According to some embodiments, the sensor 101 may be configured to reside partially within the user's body (e.g., through the outermost surface of the skin), wherein the sensor may be in liquid contact with the user's body fluid and may be used with the sensor electronics to measure analyte related data of the user. An attachment structure 105, such as a patch, may be used to secure the housing 103 to the skin of the user. The sensor 101 may extend through the attachment structure 105 and protrude away from the housing 103. Those skilled in the art will appreciate that other forms of attachment to the body and/or housing 103 may be used in addition to or in lieu of adhesive and are well within the scope of the present disclosure.

The SCD 102 may be applied to the body in any desired manner. For example, an insertion device (not shown), sometimes referred to as an applicator, may be used to position all or a portion of analyte sensor 101 through the outer surface of the user's skin and into contact with the user's body fluid. In so doing, the insertion device may also position the SCD 102 onto the skin. In other embodiments, the insertion device may first position the sensor 101, and then accompanying electronics (e.g., wireless transmission circuitry and/or data processing circuitry, etc.) may be coupled (e.g., inserted into the mounting frame) with the sensor 101 later, either manually or by means of a mechanical device. Examples of insertion devices are described in U.S. patent publication nos. 2008/0009692, 2011/0319729, 2015/0018639, 2015/0025345, 2015/0173661, and 2018/0235218, the entire contents of which are incorporated herein by reference for all purposes.

Fig. 2B is a block diagram depicting an exemplary embodiment of SCD 102 having analyte sensor 101 and sensor electronics 104. The sensor electronics 104 may be implemented in one or more semiconductor chips (e.g., application Specific Integrated Circuits (ASICs), processors or controllers, memories, programmable gate arrays, etc.). In the embodiment of fig. 1B, the sensor electronics 104 includes: advanced functional units, including: an Analog Front End (AFE) 110 configured to interact with the sensor 101 in an analog manner and convert analog signals to and/or from digital form (e.g., using an a/D converter); a power supply 111 configured to provide power to components of SCD 102; a processing circuit 112; a memory 114; timing circuitry 115 (e.g., oscillators and phase-locked loops for providing clocks or other timing to components of SCD 102); and communication circuitry 116 configured to communicate with one or more devices external to SCD 102 (e.g., display device 120 and/or MDD 152) in a wired and/or wireless manner.

SCD 102 may be implemented in a highly interconnected manner, wherein power source 111 is coupled with each of the components shown in fig. 2B, and wherein those components that pass or receive data, information, or commands (e.g., AFE 110, processing circuitry 112, memory 114, timing circuitry 115, and communication circuitry 116) may be communicatively coupled with each of the other components via, for example, one or more communication connections or buses 118.

The processing circuitry 112 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a discrete chip or distributed among a plurality of different chips (and portions thereof). The processing circuitry 112 may include on-board memory. The processing circuitry 112 may interact with the communication circuitry 116 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuitry 112 may also interact with the communication circuitry 116 to perform the inverse functions necessary to receive wireless transmissions and convert them to digital data or information.

The processing circuitry 112 may execute instructions stored in the memory 114. These instructions may cause processing circuitry 112 to process raw analyte data (or pre-process analyte data) and reach a final calculated analyte level. In some implementations, the instructions stored in the memory 114, when executed, may cause the processing circuitry 112 to process the raw analyte data to determine one or more of: calculating an analyte level, calculating an average analyte level over a predetermined time window, calculating a rate of change of the analyte level over the predetermined time window, and/or calculating whether the analyte indicator exceeds a predetermined threshold condition. The instructions may also cause the processing circuitry 112 to read and act on the received transmissions, adjust the timing of the timing circuitry 115, process data or information received from other devices (e.g., calibration information received from the display device 120, encryption or authentication information, etc.), perform tasks to establish and maintain communications with the display device 120, interpret voice commands from a user, cause the communication circuitry 116 to transmit, etc. In embodiments where the SCD 102 includes a user interface, these instructions may then cause the processing circuitry 112 to control the user interface, read user input from the user interface, cause information to be displayed on the user interface, format data for display, and so forth. The functions described herein as encoded in instructions may alternatively be implemented by SCD 102 using a hardware or firmware design that does not rely on executing stored software instructions to implement the functions.

The memory 114 may be shared by one or more of the various functional units present within the SCD 102, or may be distributed among two or more of them (e.g., as separate memories present within different chips). The memory 114 may also be a separate chip of its own. Memory 114 is non-transitory and may be volatile (e.g., RAM, etc.) and/or nonvolatile memory (e.g., ROM, flash memory, F-RAM, etc.).

The communication circuitry 116 may be implemented as one or more components (e.g., a transmitter, receiver, transceiver, passive circuit, encoder, decoder, and/or other communication circuitry) that perform communication functions over respective communication paths or links. The communication circuit 116 may include or may be coupled to one or more antennas for wireless communication.

The power source 111 may include one or more batteries, which may be rechargeable or single use disposable batteries. A power management circuit may also be included to regulate battery charging and monitor usage of the power supply 111, boost power, perform DC conversion, etc.

Further, skin or sensor temperature readings or measurements may be collected by an optional temperature sensor (not shown). Those readings or measurements may be communicated (alone or as aggregated measurements over time) from SCD 102 to another device (e.g., display device 120). However, instead of or in addition to actually outputting temperature measurements to the user, temperature readings or measurements may be used in conjunction with software routines executed by the SCD 102 or the display device 120 to correct or compensate for analyte measurement output to the user.

Exemplary embodiments of drug delivery devices

The drug delivery function of the dose guidance system 100 may be achieved by including one or more drug delivery devices (MDDs) 152. MDD 152 may be any device configured to deliver a particular dose of medication. MDD 152 may further include a device (e.g., a pen cap) that transmits data regarding the dose to the DGA even though the device itself may not be capable of delivering the drug. MDD 152 may be configured as a Portable Injection Device (PID) that may deliver a single dose at each injection (e.g., bolus). The PID may be a substantially manually operated syringe in which the drug is preloaded in the syringe or the syringe must be inhaled from the container prior to injection. However, in most embodiments, the PID includes electronics for interacting with the user and performing drug delivery. PID is commonly referred to as a medication pen, although it need not be of a pen-like appearance. The PID with user interface electronics is commonly referred to as a smart pen. The PID may be used to deliver a single dose and then disposed of, or may be durable and reusable so as to deliver a number of doses over the course of a day, week or month. PID is typically relied upon by users practicing Multiple Daily Injection (MDI) treatment regimens.

The MDD may further include a pump and infusion set. The infusion set includes a tubular cannula that resides at least partially within the subject. The tubular cannula is in fluid communication with a pump capable of repeatedly delivering a drug through the cannula and into the subject with a small increase in time. The infusion set may be administered to the recipient's body using an infusion set applicator, and the infusion set typically remains implanted for 2 to 3 days or more. A pump device comprising electronics for interacting with a user and for controlling a slow infusion of a drug. Both the PID and the pump can store the drug in a drug reservoir.

MDD 152 may be used as part of a closed loop system (e.g., an artificial pancreas system that operates without user intervention), a semi-closed loop system (e.g., an insulin loop system that operates with little user intervention (e.g., confirming a dose change)), or an open loop system. For example, the analyte level of diabetes may be monitored by SCD 102 in a repeated automated fashion, and this information may be used by the dose guidance embodiments described herein to automatically calculate or otherwise determine an appropriate drug dose to control the analyte level of a diabetic patient and subsequently deliver that dose into the diabetic patient. This calculation may occur within the MDD 152 or any other device of the system 100, and the resulting determined dose may then be transferred to the MCD 152.

In many embodiments, the dose guidance provided by the embodiments described herein will be for one type of insulin (e.g., rapid Acting (RA), short acting insulin, medium acting insulin (e.g., NPH insulin), long Acting (LA), ultra-long acting insulin, and mixed insulin), and will be the same drug delivered by MDD 152. Types of insulin include human insulin and synthetic insulin analogues. Insulin may also include premix formulations. However, the dose guidance embodiments and drug delivery capabilities of MDD 152 set forth herein are applicable to other non-insulin drugs. Such drugs may include, but are not limited to: exenatide, exenatide sustained release agent, liraglutide, cable Ma Lutai, pramlintide, metformin, SLGT1-i inhibitor, SLGT2-i inhibitor and DPP4 inhibitor. The dose guidance embodiments may further comprise combination therapies. Combination therapies may include, but are not limited to: insulin and glucagon-like peptide-1 receptor agonists (GLP-1 RA), insulin and pramlintide.

For ease of description of the dose guidance embodiments herein, MDD 152 will often be described in terms of a PID, particularly a smart pen. However, those skilled in the art will readily appreciate that MDD 152 may alternatively be configured as a pen cap, pump, or any other type of drug delivery device.

Fig. 3A is a schematic diagram depicting an exemplary embodiment of an MDD 152 configured as a PID, specifically a smart pen. MDD 152 may include a housing 154 for electronics, an injection motor, and a drug reservoir (see fig. 3B) from which drug may be delivered through a needle 156. The housing 154 may include a removable or detachable cap or cover 157 that, when attached, shields the needle 156 when not in use and is removed for injection. MDD 152 may also include a user interface 158, which may be implemented as a single component (e.g., a touch screen for outputting information to and receiving input from a user) or as multiple components (e.g., a touch screen or display in combination with one or more buttons, switches, etc.). MDD 152 may further include an actuator 159 that may be moved, depressed, touched, or otherwise activated to initiate the delivery of drug from the internal reservoir and into the subject through needle 156. According to some embodiments, cap 157 and actuator 159 may further include one or more safety mechanisms to prevent removal and/or actuation, thereby mitigating the risk of harmful drug injections. Details of these safety mechanisms and other mechanisms are described in U.S. patent publication No. 2019/0343385 (' 385 disclosure), the entire contents of which are incorporated herein for all purposes.

Fig. 3B is a block diagram depicting an exemplary embodiment of MDD 152 having electronics 160 coupled to a power source 161 and a power injection motor 162, which in turn is coupled to power source 161 and a drug reservoir 163. Needle 156 is shown in fluid communication with reservoir 163, and a valve (not shown) may be present between reservoir 163 and needle 156. The reservoir 163 may be permanent or may be removable and replaceable with another reservoir containing the same or a different medicament. The electronic device 160 may be implemented in one or more semiconductor chips (e.g., an Application Specific Integrated Circuit (ASIC), a processor or controller, a memory, a programmable gate array, etc.). In the embodiment of fig. 3B, electronics 160 may include high-level functional units including: a processing circuit 164; a memory 165; communication circuitry 166 configured to communicate with one or more devices external to MDD 152, such as display device 120, in a wired and/or wireless manner; and user interface electronics 168.

MDD 152 may be implemented in a highly interconnected manner, wherein a power source 161 is coupled with each of the components shown in fig. 3B, and wherein those components that pass or receive data, information, or commands (e.g., processing circuitry 164, memory 114, and communication circuitry 166) may be communicatively coupled with each of the other such components via, for example, one or more communication connections or buses 169.

The processing circuitry 164 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a separate chip or distributed among a plurality of different chips (and portions thereof). The processing circuitry 164 may include on-board memory. The processing circuitry 164 may interact with the communication circuitry 166 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuitry 164 may also interact with the communication circuitry 166 to perform the inverse functions necessary to receive wireless transmissions and convert them to digital data or information.

The processing circuitry 164 may execute software instructions stored in the memory 165. The instructions may cause the processing circuit 164 to receive a selection or provision of a specified dose from a user (e.g., typed in via the user interface 158 or received from another device), process a command to deliver the specified dose (such as a signal from the actuator 159), and control the motor 162 to deliver the specified dose. The instructions may also cause the processing circuitry 164 to read and act on receiving transmissions, processing data or information received from other devices (e.g., calibration information received from the display device 120, encryption or authentication information, etc.), performing tasks to establish and maintain communications with the display device 120, interpreting voice commands from a user, causing the communications circuitry 166 to transmit, etc. In embodiments where MDD 152 includes user interface 158, the instructions may cause processing circuitry 164 to control the user interface, read user input from the user interface (e.g., enter a medication dose for administration or enter a confirmation of a recommended medication dose), cause information to be displayed on the user interface, format data for display, and so forth. The functionality described herein as encoded in instructions may alternatively be implemented by MDD 152 using a hardware or firmware design that does not rely on executing stored software instructions to implement the functionality.

Memory 165 may be shared by one or more of the various functional units present within MDD 152, or may be distributed among two or more of them (e.g., as separate memories present within different chips). The memory 165 may also be a separate chip of its own. The memory 165 is non-transitory and may be volatile (e.g., RAM, etc.) and/or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

The communication circuitry 166 may be implemented as one or more components (e.g., a transmitter, receiver, transceiver, passive circuitry, encoder, decoder, and/or other communication circuitry) that perform communication functions over respective communication paths or links. The communication circuitry 166 may include or may be coupled to one or more antennas for wireless communication. Details of an exemplary antenna may be found in the' 385 publication, which is incorporated herein in its entirety for all purposes.

The power source 161 may include one or more batteries, which may be rechargeable or single use disposable batteries. A power management circuit may also be included to regulate battery charging and monitor the use of the power source 161, boost power, perform DC conversion, etc.

MDD 152 may also include an integrated or attachable external blood glucose meter, including an external test strip port (not shown) that receives an external glucose test strip for performing an external blood glucose measurement.

Communication function

The connected insulin pen and cap device is one type of MDD 152 that measures the amount of insulin injected by the patient and then transmits that data to the display device 120 (e.g., a smart phone). With the pen connected, the electronics and mechanisms required to transmit data are built into the insulin pen. With the cap attached, the electronics and mechanisms are contained in a "cap" that attaches to the insulin pen.

The connected MDD 152 is an important part of the DGS 100. Traditionally, bolus calculator applications require the patient to manually enter their dosing information, which limits the usability of the application. The use of connected MDD 152 to automatically transmit insulin delivery data to DGA significantly improves the usability of DGS 100.

The function of how and what information is transferred between the DGA and the connected MDD 152 can have a significant impact on the degree of availability of the DGS 100.

The currently connected MDD 152 may include circuitry that broadcasts a record of the insulin dose once it has been administered. In addition, many designs may replay the record until the receiving application has confirmed that it has received the dose record. As shown in fig. 22A, in data flow design 2300, MDD 152 may broadcast dose information 2302 to an application, and the application may send a receipt acknowledgement to MDD 152. For applications that use dose information for certain functions (e.g., dose logging functions), the data flow design may work well.

However, this data flow design may not be sufficient to address the needs of the software application that is intended to provide insulin administration guidance. In particular, dose calculation typically requires knowledge of the previous insulin dose over a time frame of insulin action time (e.g., typically about 4.5 hours for the most rapid acting insulin). The dose calculator may record an index commonly referred to as IOB (insulin-on-board). The IOB is typically subtracted from the calculated dose before being displayed to the user. When the user requests a dose guidance, the application needs to calculate the insulin dose and then the patient's IOB. The problems with the data flow design 2300 are: if MDD 152 is not active or the communication path is interrupted, the application may not receive information about the recent dose and will then miscalculate the user's IOB, potentially causing overdosing by the user.

In one embodiment, the hazard may be alleviated by having the DGA display a prompt that asks the user to confirm that no other dose is taken than the dose received by the DGA. To make dose guidance calculations, the DGA may provide a tool for the patient to manually key in dose and time when the DGA lacks recent dose records. In another embodiment, the DGA may provide the user with instructions to correct communication disruption and a tool to heavy reagent quantity guidance calculation. However, this approach can be cumbersome and adds an extra user step in requesting the dose guidance, which can severely reduce the availability of DGA.

In another embodiment, as seen in FIG. 22B, MDD 152 may be designed to provide a way for the DGA to query the pen for the latest dose record. In addition to or instead of DGA monitoring MDD 152 for data stream 2300 of alert conditions, in data stream 2320, after a user initiates (e.g., requests) a dose guidance, DGA may send a query to MDD 152 to request dose information 2322. The query may be for recent dose information or for dose information from a particular time period, e.g. a time period since last received insulin dose data as specified in the communication protocol. MDD 152 may transmit requested dose information 2324 back to the DGA in response to the query, and the DGA may transmit a receipt acknowledgement 2326 back to MDD 152. When the DGA has received all recent dose records, the DGA may calculate IOBs and dose guide amounts as described elsewhere in the present application for display to the user.

The data flow design 2320 may account for situations where the communication channel has been interrupted. However, to ensure that the IOB is accurate, the DGA may need to confirm from the MDD 152 that the DGA has received all of the recent dose information, including confirming that no other dose was provided by the MDD 152 recently or within a certain period of time. Some scenarios in which this may occur include: (a) The battery in MDD 152 is dead, or some other temporary malfunction prevents MDD 152 from properly recording the delivered dose; or (b) for a cap delivery device, the cap may not have been engaged with the insulin pen.

In one exemplary embodiment, in a method 2340 as seen in fig. 22C, in a first step 2342, MDD 152 may store data of doses administered over a period of time. The data may include the dose and time of administration. The data may further comprise a remaining amount of drug, e.g. insulin, remaining in the drug delivery device. In step 2344, MDD 152 may determine whether the stored data includes all doses delivered during the time period. To make this determination, MDD 152 may include self-test circuitry that may periodically ensure proper function and battery power. The self-test circuit may maintain a counter that is incremented at a fixed period (e.g., every minute) after each self-test period. Upon querying MDD 152, MDD 152 may check the self-test counter to confirm that the counter value is equal to the estimated counter value based on the current elapsed time, which may be provided by a separate circuit in the MDD 152 electronics. In another implementation, MDD 152 may transmit the counter value to the DGA as part of the query, and the DGA may perform the counter value check: the counter value of MDD 152 is compared to an estimated counter value based on the elapsed time of the clock in the DGA. DGS100 may include a degree of temporal tolerance between the DGA clock and the MDD clock.

In step 2346, if it is determined that the stored data contains all doses delivered during the time period, the MDD may transmit the stored data to the application that sent the query.

If it is determined that the stored data does not contain all doses delivered during the time period, then in step 2348, MDD 152 may create an indication of incomplete dose data. In step 2350, MDD 152 may transmit an indication of the incomplete dose data to the application that sent the query.

In alternative embodiments, the DGA may include circuitry to determine whether the data transmitted from the MDD 152 contains all doses administered during that time period. In an exemplary method 2360, as shown in fig. 22D, in step 2362, the DGA may query the MDD 152 (e.g., insulin pen) and receive a first self-test counter value. The first counter value may be a current counter value at the time of the query. At a later time, in step 2364, the DGA may send additional queries to the MDD 152 and receive a second self-test counter value. The additional query may be in response to a request from the user for dose guidance. The second self-test counter value may be the current counter value at the time of the additional query. In step 2366, DGA may calculate an estimate of the second self-test counter value. The estimate may be calculated based on a first counter value + (elapsed time between query and additional queries/self-test period). The self-test period may be a period of time that the MDD 152 self-test circuitry is configured to increment a counter value by "1".

In step 2368, the DGA may compare the second counter value to the estimated counter value to determine whether the values are within tolerance. If the comparison of the values (e.g., the difference) is within a tolerance, then in step 2370, the DGA may calculate insulin dosage guidelines that may be displayed to the user. If the comparison is not within tolerance, the DGA may request that the user confirm that no other dose is delivered than the dose recorded by the DGA (e.g., received in the data transfer) in step 2372. If the user confirms that no other doses are delivered, the DGA may calculate insulin dose guidance that may be displayed to the user in step 2370. If the user does not confirm that additional doses were not delivered, the DGA may not calculate and display the dose guidance and the system may recheck the counter value.

In another alternative embodiment, in a method 2380 as seen in fig. 22E, the DGA may query the MDD 152 for dose data over a period of time in step 2382. In step 2384, DGA may receive data from MDD 152. The received data may include dose data and may further include an indication of incomplete dose data. In step 2386, the DGA may determine whether an indication of an incomplete dose has been received from the MDD 152. If no indication of an incomplete dose is received, the DGA may calculate a dose guidance based on the data transmitted from the MDD 152 in step 2388. If the DGA has received an indication of an incomplete dose, then in step 2390 the DGA may output a prompt seeking confirmation from the user, i.e., confirming that the received dose data includes all doses administered during that period. If, in step 2392, it is determined that the DGA has received confirmation from the user that no further dose has been delivered within the period of time, then in step 2388 the DGA may calculate a dose guidance. If it is determined that the DGA has not received confirmation from the user that no other dose has been delivered within the period of time, the DGA may again query the MDD 152 for dose data, as in step 2382.

For DGS100 that includes a pen cap system as MDD 152, when the cap has been removed from the insulin pen for a period of time and then reattached, the system may be able to detect that a dose has been delivered and may also be able to detect a cumulative dose (if more than one dose delivery occurs). However, the actual time of these one or more doses may not be known. In one embodiment, the cap may include a mechanism to detect when the cap is attached to or detached from the insulin pen in addition to the mechanism to detect the current insulin remaining in the pen. The cap controller system may store the date and time of the last instance that the cap was reattached and the insulin level was different from the insulin level at the last time the cap was detached. The date and time of the error indication may be sent to the DGA in response to the query. For the special case when the pen cap is detached when the pen is empty (or almost empty) and when the pen is reattached to a full pen, the cap controller system may exclude the storage of this error indicator. Similar to the description of the self-test indicator, the DGA may process the indicator.

Exemplary embodiments of a display device

The display device 120 may be configured to display information related to the system 100 to a user and accept or receive input from the user that is also related to the system 100. The display device 120 may display recently measured analyte levels to the user in any number of forms. The display device may display the user's historical analyte level as well as other indicators describing the user's analyte information (e.g., time in range (AGP), dynamic glucose profile (AGP), hypoglycemia risk level, etc.). The display device 120 may display drug delivery information such as historical dose information and administration time and date. The display device 120 may display alarms, warnings, or other notifications related to analyte levels and/or drug delivery.

Display device 120 may be dedicated for use with system 100 (e.g., an electronic device designed and manufactured for the primary purpose of interacting with an analyte sensor and/or a drug delivery device), as well as a multi-function device, a general-purpose computing device (such as a handheld or portable mobile communication device (e.g., a smart phone or tablet)), or a laptop computer, personal computer, or other computing device. The display device 120 may be configured to move smart wearable electronic components, such as smart glasses or smart watches or bracelets. The display device and its variants may be referred to as a "reader device," "reader," "handheld electronics" (or handheld device), "portable data processing" device or unit, "information receiver," "receiver" device or unit (or simply receiver), "relay" device or unit, or "remote" device or unit, to name a few.

Fig. 4A is a schematic diagram depicting an exemplary implementation of display device 120. Here, the display device 120 includes a user interface 121 and a housing 124 in which display device electronics 130 (fig. 4B) are held. The user interface 121 may be implemented as a single component (e.g., a touch screen capable of input and output) or as multiple components (e.g., a display and one or more devices configured to receive user input). In this embodiment, the user interface 121 includes a touch screen display 122 (configured to display information and graphics and accept user input by touch) and input buttons 123, both of which are coupled to a housing 124.

The display device 120 may have software stored thereon (e.g., provided by a manufacturer or downloaded by a user in the form of one or more "applications" or other software packages) that interact with the SCD 102, MDD 152, and/or user. Additionally or alternatively, the user interface may be affected by a web page displayed on a browser or other internet interactive software that may be executed on the display device 120.

Fig. 4B is a block diagram of an exemplary implementation of display device 120 with display device electronics 130. Here, the display device 120 includes: a user interface 121 including a display 122 and input components 123 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, scroll wheels, microphones, speakers, etc.); a processing circuit 131; a memory 125; communication circuitry 126 configured to communicate to and/or from one or more other devices external to display device 120; a power supply 127; and timing circuitry 128 (e.g., oscillators and phase-locked loops for providing clocks or other timing to components of SCD 102). Each of the above components may be implemented as one or more different devices, or may be combined into a multi-functional device (e.g., integrated processing circuit 131, memory 125, and communication circuit 126 on a single semiconductor chip). The display device 120 may be implemented in a highly interconnected manner, wherein a power supply 127 is coupled with each of the components shown in fig. 4B, and wherein those components that pass or receive data, information, or commands (e.g., the user interface 121, the processing circuitry 131, the memory 125, the communication circuitry 126, and the timing circuitry 128) may be communicatively coupled with each of the other such components via, for example, one or more communication connections or buses 129. Fig. 4B is a abbreviated representation of typical hardware and functionality residing within a display device, and one of ordinary skill in the art will readily recognize that other hardware and functionality (e.g., codec, driver, glue logic) may also be included.

The processing circuit 131 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a discrete chip or distributed among a plurality of different chips (and portions thereof). The processing circuitry 131 may include on-board memory. Processing circuitry 131 may interact with communication circuitry 126 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). Processing circuitry 131 may also interact with communications circuitry 126 to perform the inverse functions necessary to receive wireless transmissions and convert them to digital data or information.

Processing circuitry 131 may execute software instructions stored in memory 125. These instructions may cause processing circuit 131 to process raw analyte data (or pre-process analyte data) and reach a corresponding analyte level suitable for display to a user. These instructions may cause processing circuitry 131 to read, process, and/or store dosage instructions from a user, and because the dosage instructions are to be communicated to MDD 152. The instructions may cause the processing circuitry 131 to execute user interface software adapted to present the set of interactive graphical user interface screens to the user for the following purposes: configuring system parameters (e.g., alarm thresholds, notification settings, display preferences, etc.), presenting current and historical analyte level information to the user, presenting current and historical drug delivery information to the user, collecting other non-analyte information from the user (e.g., information about meals, performing activities, administering drugs, etc.), and presenting notifications and alarms to the user. The instructions may also cause the processing circuitry 131 to cause the communication circuitry 126 to transmit, may cause the processing circuitry 131 to read and act on the received transmission, read input from the user interface 121 (e.g., enter a confirmation of a medication dose to be administered or a recommended medication dose), display data or information on the user interface 121, adjust timing of the timing circuitry 128, process data or information received from other devices (e.g., analyte data received from the SCD 102, calibration information, encryption or authentication information, etc.), perform tasks to establish and maintain communication with the SCD 102, interpret voice commands from a user, etc. The functions described herein as encoded in instructions may alternatively be implemented by display device 120 using a hardware or firmware design that is not dependent on executing stored software instructions to implement the functions.

The memory 125 may be shared by one or more of the various functional units present within the display device 120, or may be distributed among two or more of them (e.g., as separate memories present within different chips). The memory 125 may also be a separate chip of its own. The memory 125 is non-transitory and may be volatile (e.g., RAM, etc.) and/or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

The communication circuitry 126 may be implemented as one or more components (e.g., a transmitter, receiver, transceiver, passive circuit, encoder, decoder, and/or other communication circuitry) that perform communication functions over respective communication paths or links. The communication circuit 126 may include or may be coupled to one or more antennas for wireless communication.

The power source 127 may include one or more batteries, which may be rechargeable or single use disposable batteries. A power management circuit may also be included to regulate battery charging and monitor the use of the power supply 127, boost power, perform DC conversion, etc.

Display device 120 may also include one or more data communication ports (not shown) for wired data communication with external devices, such as computer system 170, SCD 102, or MDD 152. The display device 120 may further include an integrated or attachable external blood glucose meter including an external test strip port (not shown) that receives an external glucose test strip for performing an external blood glucose measurement.

The display device 120 may display measured analyte data received from the SCD 102 and may also be configured to output an alarm, a warning notification, a glucose value, etc., which may be visual, audible, tactile, or any combination thereof. In some implementations, the SCD 102 and/or MDD 152 may also be configured to output an alert or warning notification in visual, audible, tactile form, or a combination thereof. Further details and other display embodiments may be found, for example, in U.S. patent publication No. 2011/0193704, the entire contents of which are incorporated herein by reference for all purposes.

Exemplary embodiments related to dose guidance

The following exemplary embodiments relate to dose guidance functionality provided by the dose guidance system 100. In many embodiments, the dose guidance functionality will be implemented as a set of software instructions stored and/or executed on one or more electronic devices. This dose guidance function will be referred to herein as Dose Guidance Application (DGA). In some implementations, the DGA is stored, executed, and presented to the user on the same single electronic device. In other implementations, the DGA may be stored and executed on one device and presented to the user on a different electronic device. For example, the DGA may be stored and executed on the trusted computer system 180 and presented to the user via a web page displayed by an internet browser executing on the display device 120. The DGA may be a stand-alone application or may be wholly or partially incorporated into another software application.

Thus, there are many different implementations relating to the number and types of electronic devices used to store, execute, and present DGAs to a user. With respect to presentation to a user, a device configured to implement this capability will be referred to herein as a User Interface Device (UID) 200. Fig. 5 is a block diagram depicting an exemplary embodiment of UID 200. In this embodiment, UID 200 includes a housing 201 coupled with a user interface 202. The user interface 202 is capable of outputting information to a user and receiving input or information from the user. In some implementations, the user interface 202 is a touch screen. As shown herein, the user interface 202 includes a display 204, which may be a touch screen, and input components 206 (e.g., buttons, actuators, touch sensitive switches, capacitive switches, pressure sensitive switches, scroll wheels, microphones, touch pads, soft keys, keyboards, etc.).

Many of the devices described herein may be implemented as UID 200. For example, in many implementations, display device 120 will be used as UID 200. In some implementations, MDD 152 may be implemented as UID 200. In embodiments where SCD 102 includes a user interface, SCD 102 may be implemented as UID 200. Computer system 170 may also be implemented as UID 200.

Purpose(s)

The dose guidance system (DGS 100) uses glucose and insulin data to learn, provide and adjust insulin doses. DGS includes applications such as mobile applications based on smart phones integrated with connected insulin pens and continuous glucose sensors to improve the management of treatment of insulin-fortified diabetics (PWD) with Multiple Daily Injections (MDI).

DGS100 may perform three main tasks. First, DGS100 can learn the patient's insulin dosage regimen (i.e., how often and at what dose the insulin dose is administered) during the DGS "learning phase". Second, DGS100 may provide patients with dose recommendations for both meal dosing and post-meal correction. Third, the DGS100 may adjust the current dose setting to maximize glycemic control. The second and third tasks may occur in parallel during the "lead period" of the DGS.

Continuous glucose data in various forms (scan, history, and stream) as well as insulin data may be used as inputs to perform all three of the above functions. DGS100 may receive glucose data in different ways and in various forms, including scanning, history, and streaming. The scan data (including the latest glucose values and trend values) may be retrieved by the user as desired. Historical glucose data may be generated by components of the DGS, which may generate and record glucose and trend values at regular intervals (e.g., every 15 minutes). Past history data may be retrieved by a user using a scan. The streaming data may include glucose and trend values generated and recorded at regular intervals (e.g., every minute) and automatically sent to the DGA. Similarly, insulin data can come from a variety of sources. Insulin data may be manually recorded or transmitted from MDD152 (e.g., insulin pen). Glucose data and insulin data may be transmitted by any known means, for example wireless communication techniques such as bluetooth or NFC.

During the "learning period", the DGA may determine the user's insulin dosage regimen by clustering the insulin data by time of day (time of day) and then curve fitting when the insulin data is combined with the glucose data. This learning portion of the DGA may require glucose data and insulin data from the user over a period of time (e.g., about 14 days).

A coaching period may be initiated after the learning period is completed and an initial insulin dosage regimen has been determined, the coaching period including providing a dosage recommendation and adjusting the dosage setting. During the coaching period, the DGS may provide meal dose recommendations upon user request. The user may request a dose recommendation for a meal that the DGS has determined that the user is currently being treated with insulin. Dose calculation may utilize bolus calculator form to provide a dose recommendation that modifies the learned fixed dose to account for pre-meal glucose and residual insulin in the blood stream remaining from a previous injection.

The DGS may also inform the user whether to miss a meal dose and recommend a modified dose. It is recommended to take the fast acting insulin analogue for administration at the meal or just before the meal. Missing meal insulin doses is common and treatment compliance is known to be a factor affecting glycemic management. To accommodate these behavioral trends, DGS may utilize streaming data (e.g., data sent at regular intervals, such as every minute) to detect periods of considerable glucose fluctuations (glucose excursion) without an associated insulin dose. Such a time period may indicate a situation where the user has a meal once but does not take their prescribed insulin dose. In this case, a prompt may appear to notify the user. Once the missed meal event has been confirmed by the user, a modified meal insulin dose may be recommended to account for the time mismatch between meal start and insulin injection.

The DGS may also inform the user when it is appropriate to take a correction dose and recommend a dose to correct high glucose between meals. The DGS may process the streaming data every minute to identify periods of time when the user is presenting high glucose between meal doses. In this example, the DGS may display a prompt to inform the user of the occurrence of high glucose. The user may then request a dose recommendation from the DGS to correct for high glucose. Alternatively, the DGS may provide dose recommendations within the notification and without the user requesting dose recommendations.

The DGS may also adjust the user's insulin dosage regimen in order to reduce the user's glucose level while avoiding excessive times below a low threshold (e.g., 70 mg/dL), and thereby maximize blood glucose within a target range (e.g., 70 to 180 mg/dL). Once the user has transitioned to the "lead period," the DGS may periodically analyze the insulin data and glucose data to adjust previously learned or adjusted insulin dosage regimen parameters.

Detection of MDI dose strategy

Turning now to aspects of DGA, and more particularly, DGA can leverage knowledge of patient dosing strategies and analyte levels to provide accurate dose guidance. Described herein are exemplary embodiments for automatically detecting patient administration strategies that can facilitate and speed up setup of DGA. The detection of a dose strategy may be based on monitoring a number of characteristics of the drug (e.g., insulin) dose. For example, an embodiment may identify a dose as a base dose or bolus dose based on the MDD 152 used to administer the dose. Some patients may have more than one MDD 152. For example, a patient may have one MDD that administers long acting insulin (e.g., basal dose) and another MDD that administers fast acting insulin (e.g., meal dose). The count (e.g., number of doses) and timing of basal doses per administration can also be used to classify basal strategies as either 'single' or 'split' basal dosing strategies. For example, in a 'split' basal dosing strategy, a daily basal dose of 20U may be split into two doses of 10U, where one dose may be administered before sleep and the other dose may be administered upon waking.

When consecutively administering consecutive bolus doses, the system may attempt to distinguish between the original meal doses, increase the original meal doses, or correct doses for high glucose between meals. When the DGA detects a small dose followed by a larger dose (both occurring near the beginning of a meal), the DGA may group the doses together into a single meal dose, even though the first dose may be the first dose that was not injected into the patient. Thereafter, if a dose occurs long after a known meal and/or a dose (group) labeled as a meal dose, the DGA may label the later dose as a corrected dose of high postprandial glucose for the meal or an increase to the previous meal dose to account for additional ingested food. When a meal event is recognized based on a meal detector algorithm or a user-entered meal event, the DGA may use the amount of previous dose event and the time relative to the current meal detected in order to help explain whether the previous dose is the first dose of the multiple meal doses compared to the correction for high glucose between meals. The correction dose is assumed to be smaller in size than the meal dose. Furthermore, if the time elapsed between the previous dose and the current meal event is long enough, it is reasonable to assume that these two events are not correlated with the same glycemic volatility event of the treatment, thereby eliminating the possibility that the previous dose is the first dose of the multiple doses for the given meal. Thus, if the earlier dose is sufficiently less than the recorded meal dose within the window for the first few days, and is sufficiently long from the current meal, the previous dose may be classified as a corrected dose event.

The DGA may be configured to use a real-time meal detection algorithm and dose time to identify doses other than the base dose as breakfast, lunch and/or dinner bolus doses and/or correction doses. DGA may also be configured to use the number of bolus doses per day to identify the dosing strategy as basal only, basal plus one, basal plus two, etc.

These different scenarios and aspects of DGA are discussed in more detail elsewhere in the specification.

Training system

To increase the safety profile of DGA, HCPs may approve learned insulin administration parameters and subsequent adjustments calculated by DGA. The DGA embodiments include a number of methods of interaction between the HCP and DGA such that the HCP is provided with relevant evidence to approve recommended dose learning and adjustment in a concise informative manner to improve workflow.

For diabetics who are already in an insulin dosing regimen, the HCP can utilize existing reports that provide insight into the glucose patterns of the patient to identify users who may benefit from dosage guidance. Embodiments of DGA provide a learning period that can categorize patient dosing strategies and trends (e.g., when DGS100 is used). If the combined insulin and glucose data further confirms that the user is a good candidate for a DGA, e.g., a candidate from which the DGA can learn its particular dosing strategy, the insulin dosage parameters learned during the learning period can serve as initial conditions for dosage guidance that can be adjusted by the DGA as needed. HCP notification methods for dose parameter initialization and DGA adjustment may also be presented. The process may assist both the HCP and the user by simplifying DGA training and adjustments, while also helping to ensure that DGA is only used by those indicated by it. When DGA is unable to learn the patient's dosing parameters, DGA may indicate patient dosing inconsistencies, which may be used by HCPs to resolve the patient dosing inconsistencies.

The first step of identifying potential DGA users may involve: an introductory analysis of a patient's glycemic control via the patient's glucose concentration profile. To facilitate DGA access to as many users as possible, this process is agnostic to the user's current glucose monitoring method.

For current diabetic patients using SCD 102, glucose pattern reports (as discussed in further detail below with respect to fig. 6A-1 through 6G-2) may be available, including key indicators, glucose concentration profiles (e.g., dynamic glucose profile (AGP)), patterns identified for different times of day, and regulatory and lifestyle recommendations to improve the situation where glucose is always outside of a target range. The pattern may be identified using the GPA algorithm, as described in more detail elsewhere.

For persons not currently using a device or system (e.g., SCD 102) associated with an application that may generate glucose mode report 250 as described above, the HCP may suggest that the patient be monitored by a different device or system so that report 250 or the like may be generated. For example, the patient may wear the SCD 102 in order to collect glucose data over a period of days or weeks, wherein the SCD 102 is configured in a shielded or blind mode, wherein the user cannot access the measured glucose level and thus cannot change his or her behavior at that time. From these data, a glucose pattern report may be generated. If recommended insulin regulation is included in the glucose mode report, glucose mode report 250 may also include recommendations that the patient is a good candidate for DGS100, and may suggest a learning period for the drug administration strategy.

During the learning period, MDD 152 may be combined with a glucose sensing system for initial screening to provide a more complete description of insulin-fortified diabetes management. The learning period may utilize algorithms (such as those described elsewhere herein) to detect insulin administration strategies of the user. During the learning period, the DGA may be configured to determine the manner in which the user determines the dining dose. For example, DGA may determine: whether the user determines the bolus dose based on carbohydrate counting techniques, empirical techniques (such as techniques in which the user learns to administer the appropriate bolus based on past experience with meals or meals similar thereto); whether the user takes a fixed amount of insulin for a meal; whether the user modifies the insulin meal dose (determined by fixed administration, carbohydrate count, or empirical administration) based on the pre-meal glucose value; the user may consider residual Insulin (IOB) from a previous injection, or another technique, in determining the dose (determined by fixed administration, carbohydrate count, or empirical administration). DGA may also determine: whether the user's meal dose is fixed according to meal type (e.g., breakfast, lunch, and dinner), or whether the meal dose varies. The determination of the change in the bolus dose may be an indication of the bolus dose by the user based on carbohydrate counting techniques. DGA may also determine whether the user is adjusting the meal dose to account for pre-meal high glucose. In some embodiments, the DGA may also determine a target glucose level, wherein the user adjusts or corrects the dining dose when its glucose level is above or predicted to be above the target glucose level. DGA can also determine which meals are associated with insulin doses. DGA may also determine patterns of missed meal doses. For example, DGA may detect whether a user has not administered an insulin dose associated with a meal or time period for at least two, alternatively at least three times, over a time period (e.g., one or two weeks).

In some embodiments, the DGS may also prompt the user to type in a typical meal dose and a typical period of time for which the meal dose is typically administered. In some embodiments, the DGS may prompt the user to type in the amount of fast acting insulin they typically take per meal when their glucose level is at a certain level. For example, DGS may prompt the user to type in the amount of fast acting insulin they typically take per meal when their glucose level is about 120 mg/dL. For fast acting insulin, DGA may prompt the user for a start and end time period for each of breakfast, lunch and dinner. Thereafter, the quick-acting dose in the time period assigned to each of the meals may be recorded as the dose for that meal. In some embodiments, the quick-acting dose may be recorded as a dose for the meal without additional input from the user, e.g., the user may not be prompted to verify that the dose is for a particular meal after administration of the dose. For example, a bolus time period for breakfast may be from about 2:00 a.m. to about 11:00 a.m.. The bolus time period for lunch may be about 11:30 a.m. to about 2:00 a.m.. The bolus time period for the evening meal may be about 5:00 pm to about 8:00 pm. In this exemplary embodiment, the quick-acting insulin dosage administered at 12:30 pm may be automatically recorded as a lunch dosage without any additional confirmation from the user.

In some embodiments, the DGS may also prompt the user for a typical basal dose to administer or a period of time for a typical basal dose to administer. The DGS may also prompt the user to view and verify all doses and administration times and periods before ending.

The learning period may last for any period of time sufficient to enable the necessary information. In many embodiments, the period of time is at least two days, more preferably one week or more (e.g., 14 days), and may vary depending on the extent to which DGA may learn the trend. The results may be compiled as summary reports for both the user and the physician.

Glucose mode reporting

As seen in fig. 6A-1 through 6G-2, glucose mode report 250 may include various elements that may be arranged in different layouts. Those skilled in the art will appreciate that glucose mode report 250 may be a graphical user interface that is output to a display of a computing device. These elements may include: the identification of the most important glucose patterns 278, medication notes 260, change statements 286, lifestyle notes 284, and fluctuation statements 288. The glucose mode report 250 may further include: the report covers an identification of a time period 264, an identification of an amount of time 266 of CGM sensor activity, an average of daily scans or views 268, a glucose indicator or statistics portion 270, a time within target range (TIR) portion 272, a clinician notes 276 portion, and a glucose mode 282 portion. The clinician notes 276 may include, in part, medication notes 260, change statements 286, lifestyle notes 284, and fluctuation statements 288.

The period of time 264 covered by the report may be included in the glucose mode report 250. The period 264 may be about 7 days, about 14 days, about 1 month, about 2 months, or alternatively about 3 months. Time period 264 may be reported as a start and end date, a total number of days, and/or both a start date and end date and a total number of days (e.g., "2018, 5, 31, 6, 13 days (14)"). The time period 264 may be listed at the top of the report (e.g., under the report name), or alternatively, at the bottom of the report, at the header or footer, or elsewhere in the layout of the report.

In the glucose mode report 250, the amount of time 266 that the CGM sensor is active may also be reported as a percentage, for example. The amount of time 266 that the CGM sensor is active may be listed at the top of the report, e.g., near time period 264. Alternatively, the amount of time 266 that the CGM sensor is active may be listed at the top of the report (e.g., under the report name), or alternatively, at the bottom, header or footer of the report or elsewhere in the layout of the report.

Average number 268 of scans or views daily over period 264 may also be included in glucose mode report 250. The average number of scans or views per day 268 may be listed at the top of the report, e.g., near the amount of time 266 that the CGM sensor is active. Alternatively, average number of scans or views per day 268 may be listed at the top of the report (e.g., under the report name), or alternatively, at the bottom, header or footer of the report, or elsewhere in the layout of the report.

Glucose indicator portion 270 may also be included in glucose mode report 250. Glucose indicator portion 270 may include average glucose over time period 264. The glucose indicator portion 270 may further include a Glucose Management Indicator (GMI) over time period 264. The targets for each of the average glucose and GMI are optionally listed next to the average glucose and GMI values so that the user quickly sees how close or far they are to achieve their targets within time period 264. The targets may be displayed in different colors (e.g., in gray and black fonts for actual calculated average glucose and GMI values), and may also be displayed in smaller font sizes.

The target in-range Time (TIR) portion 272 may include a TIR graphical display 252 and a text component 274 describing the amount of time in each of the different ranges. TIR graphical display 252 may be a bar graph, pie graph, histogram, or any other graphical representation showing the relative amounts of time in a plurality of different concentration ranges. The TIR graphical display 252 may include at least 3, alternatively at least 4, alternatively at least 5, alternatively at least 6 different concentration ranges. The graphical display of each concentration range may reflect the time within the target range within that concentration range. For example, the relative area or relative height of the graphical display for each concentration range may be proportional or related to the percentage of time determined for that concentration range over time period 264.

These ranges may include: below very low threshold 290 (e.g., below about 54 mg/dL), between very low threshold 290 and low threshold 291 (e.g., between about 54mg/dL and about 69 mg/dL), between low threshold 291 and high threshold 292 (e.g., between about 70mg/dL and 180 mg/dL), between high threshold 292 and very high threshold 293 (e.g., between about 181mg/dL and about 250 mg/dL), and above high threshold 293 (e.g., above about 250 mg/dL).

Very low threshold 290 may be between about 50mg/dL and about 65mg/dL, alternatively between about 50mg/dL and about 60mg/dL, alternatively about 53mg/dL, alternatively about 54mg/dL, alternatively about 55mg/dL, alternatively about 56mg/dL, alternatively about 57mg/dL, alternatively about 58mg/dL, alternatively about 59mg/dL, alternatively about 60mg/dL, alternatively about 61mg/dL, alternatively about 62mg/dL, alternatively about 63mg/dL, alternatively about 64mg/dL, alternatively about 65mg/dL. The low threshold may be between about 60mg/dL and about 75mg/dL, alternatively between about 65mg/dL and about 75mg/dL, alternatively between about 67mg/dL and about 72mg/dL, alternatively about 67mg/dL, alternatively about 68mg/dL, alternatively about 69mg/dL, alternatively about 70mg/dL, alternatively about 71mg/dL, alternatively about 72mg/dL, alternatively about 73mg/dL, alternatively about 74mg/dL, alternatively about 75mg/dL. The high threshold may be between about 170mg/dL and about 190mg/dL, alternatively between about 175mg/dL and about 185mg/dL, alternatively about 175mg/dL, alternatively about 176mg/dL, alternatively about 177mg/dL, alternatively about 178mg/dL, alternatively about 179mg/dL, alternatively about 180mg/dL, alternatively about 181mg/dL, alternatively about 182mg/dL, alternatively about 183mg/dL, alternatively about 184mg/dL, alternatively about 185mg/dL. The very high threshold may be between about 230mg/dL and about 270mg/dL, alternatively between about 240mg/dL and about 260mg/dL, alternatively about 245mg/dL, alternatively about 246mg/dL, alternatively about 247mg/dL, alternatively about 248mg/dL, alternatively about 249mg/dL, alternatively about 250mg/dL, alternatively about 251mg/dL, alternatively about 252mg/dL, alternatively about 253mg/dL, alternatively about 254mg/dL, alternatively about 255mg/dL. The thresholds and limits for the various ranges may be user-defined. Alternatively, the thresholds and limits for the various ranges may not be customizable by the user.

Different intensity ranges in the TIR graphical display may each have a different color. For example, the graphical display for a graphical display below a very low threshold may be dark red or maroon, the graphical display for a range between a very low threshold and a low threshold may be light red, the graphical display for a range between a low threshold and a high threshold may be green, the graphical display for a range between a high threshold and a very high threshold may be yellow, and the graphical display for a range above a very high threshold may be orange.

Text component 274 of TIR display 272 may include labels for each range. Ranges below the very low threshold may be labeled "very low", ranges between the very low and low thresholds may be labeled "low", ranges between the low and high thresholds may be labeled "target", ranges between the high and very high thresholds may be labeled "high", and ranges above the very high threshold may be labeled "very high". Text component 274 of TIR display 272 may also or alternatively include a description of numerical limits for the concentration ranges of the multiple ranges. The different concentration ranges may be listed next to or in close proximity to the corresponding graphical elements and/or indicia for that range. For example, for graphical elements below the very low threshold 290 time, the text may be read as "very low <54" or "below 54mg/dL", "low 54-69" or "54-69mg/dL", "target 70-180" or "target 70-180mg/dL", "high 181-250" or "181-250mg/dL" and/or "very high >250" or "above 250mg/dL". Text component 274 may also contain values, such as percentages, for the time spent in each of the concentration ranges. Alternatively or in addition to individual values for each concentration range, text component 274 may include a combined value, such as a combined percentage, of two or more ranges. For example, values below the very low threshold 290 and between the low threshold 291 and the very low threshold 290 may be reported as a single combined value. Furthermore, values above the very high threshold 293 and between the high threshold 292 and the very high threshold 293 may be reported as a single combined value. For each respective concentration range(s), the number or combined value may be located next to or in close proximity to the graphical element(s) and/or the explanatory text. In the case where both individual values and combined values are reported, the combined values may differ from the individual values in explicit display. For example, the combined values may be bolded, italic, or differently colored. The sum of the reported numerical values or combined numerical values may be equal to 100 or may be equal to a numerical value other than 100. The text component 274 may also contain a target 275 (see, e.g., fig. 6A-2 and 6A-3), such as a percentage, for each concentration range. Alternatively, text component 274 may also include a combined target of two or more concentration ranges. For example, targets below the very low threshold and between the low and very low thresholds may be reported as a single target. Furthermore, targets that are above the very high threshold and between the high and very high thresholds may be reported as a single target. The targets for each concentration range and/or the combined targets may be listed next to or in close proximity to the determined number of time periods 264 for each respective concentration range.

Portions detailing notes 276 for a clinician, HCP, or patient may also be included in the glucose mode report 250. The clinician notes 276 portion may include a most important mode portion 278, a medication notes portion 260, and a lifestyle notes portion 284.

The most important patterns section 278 may identify the most important patterns for the time period 264 determined by algorithms, including but not limited to the GPA algorithm described elsewhere in this disclosure. The most important pattern portion 278 may identify pattern(s) as including, but not limited to, "hypoglycemic," "hyperglycemia predominates," and occasionally hypoglycemic (Highs with Some lows) "as well as" hyperglycemic. The most important patterns section 278 may also identify time periods during the day when the most important patterns occur, such as, for example, night, morning, afternoon, and/or evening. Each of the patterns and the time period during which the identified pattern has occurred in the day may be identified in text (e.g., a sentence or phrase) or may be identified in tag(s) 280. When multiple patterns are detected, including "hypoglycemic" patterns, the glucose pattern report 250 may identify the "hypoglycemic" patterns in the most important pattern portion 276, such that the clinician may first resolve any "hyperglycemia-based," occasional hypoglycemic "or" hyperglycemic "patterns before resolving these. In the case where multiple patterns are identified, the patterns identified in the most important pattern section 278 may be prioritized such that the "hypoglycemic" pattern is identified first, then the "hyperglycemic-based, even hypoglycemic" pattern is identified, and then the "hyperglycemic" pattern is identified. However, additional patterns may be identified in contours or boxes in glucose pattern curve 256, even if they are not identified in most important pattern declarations 278. The mode (multi-mode) labels 280 in the most important mode portion 278 may be color coded to match the color of the outline or box or partial box or bracket for highlighting those portions in the glucose concentration curve 256 in the glucose mode portion 282. For example, the label 280 that is identified as "low blood glucose" in the evening may be colored red (e.g., a red background with a white font), and the frame or portion of the frame surrounding the evening period of the glucose profile 256 may have a red line color, and the color-coded red label that identifies low blood glucose may be located at the top of the frame. The time period(s) in which the most important pattern occurs may be listed next to the tag 280. The time period(s) may be displayed as text according to the order on glucose concentration curve 256, e.g., left to right: "night", "morning", "afternoon" and "evening". Alternatively, the time period(s) may be identified in a tag (not shown), but in a different color than the mode tag. For example, the time period label may be gray. If the pattern occurs for all four time periods of the day, the most important pattern portion 278 may include two tags: "all day" and "night". If the pattern occurs during all but "night" of the day, the most important pattern portion 278 may include a single tag for "whole day" or it may include three tags for "morning", "afternoon" and "evening". If the pattern occurs in multiple time periods, a single box or portion of a box may cover adjacent time periods with a single label (see, e.g., fig. 6C-6E).

Medication notes 260 may also be provided in glucose mode report 250 if the patient's current treatment (e.g., basal plus RA insulin, basal only, basal plus SU, etc.) is known. The medication guidance may be provided in the form of text recommendations. General advice regarding adjusting insulin dosage may be provided based on the identified high glucose mode and low glucose mode highlighted in box 258 in glucose concentration curve 256. However, this general advice may have been determined without having access to data regarding the actual insulin dosage administered. The recommendation may generally follow a rule that any hypoglycemic pattern is first alleviated before the hyperglycemic pattern is alleviated. If the glucose mode report contains advice for insulin dosage adjustment(s), the glucose mode report 250 may further include advice that the patient is a good candidate for the DGS100, thereby facilitating conversation between the HCP and the patient prior to transitioning to the learning phase.

The medication notes section 260 may include different statements and/or observations regarding medications administered over a period of time 264. The medication attention portion 260 may include questions asking whether the medication contributes to hypoglycemia. Alternatively, the medication attention portion 260 may include a statement that a medication added to address hyperglycemia may exacerbate hypoglycemia. Alternatively, the medication note portion 260 may include the following statement: if the patient starts or adjusts the medication to address hyperglycemia, consider how the medication may cause hypoglycemia. The medication note portion 260 may further include the following statements: it is recommended that the clinician and/or patient should consider different treatments to account for glucose changes. The medication note portion 260 may also state: for T1 patients, insulin adjustment is considered. For T2 patients, the medication note section may include advice: for T2 patients currently taking insulin or sulfonylurea, consider adjusting the statement of medication; or for other T2 patients, consider adjusting the drug or starting a drug other than insulin or sulfonylurea. The medication note portion 260 may also state: for other T2 patients, insulin is considered to be initiated.

The medication notes section 260 may include, but is not limited to, one or more statements related to the following subject matter:

is the drug contributing to hypoglycemia?

Drugs added to address hyperglycemia may exacerbate hypoglycemia.

If the drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia.

Consider different treatments to account for glucose changes.

For T1 patients, consider insulin adjustment.

For T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication.

For other T2 patients, insulin is considered to be initiated.

For other T2 patients, consider adjusting the drug or starting a drug other than insulin or sulfonylurea.

The lifestyle notes section 284 may include change declarations 286, fluctuation declarations 288, and self-care notes 262. If it is determined that the change over a period(s) is low, then no statement regarding the change may be included. In some implementations, the change statement 286 may not be displayed if no pattern is detected for any period of time. In some implementations, the change statement 286 can be displayed when it is determined that there is a high change and also a mode(s) is determined to be present. If it is determined that the change over a period(s) is high, glucose mode report 250 may include a change statement 286. The change declaration 286 may declare: hypoglycemia is often associated with high glucose changes. The change declaration 286 may alternatively or additionally declare: hyperglycemia is often associated with high glucose changes. The change declaration 286 may also declare: certain actions may contribute to high glucose changes and may contain a list of certain actions. The change declaration 286 may also declare: certain actions may contribute to glucose changes, and may include a list of certain actions that contribute to glucose changes.

The change declarations 286 may include, but are not limited to, one or more declarations related to the following topics:

hypoglycemia is often associated with high glucose changes.

The following behavior may contribute to glucose changes.

The following behavior may contribute to high glucose changes.

Hyperglycemia is often associated with high glucose changes.

Determination of the change is described elsewhere in the present application. Alternative determinations for determining changes are described in WO 2014/145335 and WO 2014/106263, the entire contents of both applications are expressly incorporated herein by reference for all purposes.

As seen in fig. 6A-3, when a fluctuation is detected, a fluctuation declaration 288 may be included in the glucose mode report 250. Fluctuations may be an example of detecting a glucose level below a very low threshold. The very low threshold may be between about 50mg/dL and about 65mg/dL, alternatively between about 50mg/dL and about 60mg/dL, alternatively about 53mg/dL, alternatively about 54mg/dL, alternatively about 55mg/dL, or as described in other parts of the application. If fluctuations are detected, the fluctuation declaration 288 may suggest that the clinician discusses with his patient sporadic hypoglycemic episodes below a very low threshold, and may consult weekly summary reports or daily review reports, which may list fluctuations below a very low threshold (e.g., below about 54 mg/dL). Alternatively, the fluctuation declaration 288 may declare an sporadic hypoglycemic occurrence below the very low threshold 290, and may reference a weekly summary report or a daily review report (e.g., "sporadic hypoglycemic below 54mg/dL". See daily review report "). When one or more fluctuations below a very low threshold are detected, a fluctuation declaration 288 may be included in the glucose mode report 250. In some implementations, if a hypoglycemic pattern is detected, the fluctuation statement is not included in the glucose pattern report 250 even if one or more fluctuations below a very low threshold are detected. In some embodiments, upon detecting a "hyperglycemia" mode, a "hyperglycemia-dominant," occasional hypoglycemic "mode or no mode, a fluctuation statement 288 may be displayed.

Self-care notes 262 may also be included in glucose mode report 250. When the GPA algorithm has identified a pattern with high variance, self-care notes 262 may be displayed in glucose pattern report 250. Alternatively, the glucose concentration curve 256 may have such high variation that the logic behind the report cannot make specific suggestions, but default users and their HCPs discuss lifestyle or treatment changes.

The self-care notes 262 displayed in the glucose pattern report 250 may depend on the type of pattern detected, the amount of change, the median glucose value, the presence or absence of a risk of hypoglycemia, and the presence or absence of fluctuations. Self-care notes 262 may include one or more statements related to the following subject matter:

is sometimes missed a meal or carbohydrate change?

Is the activity level changed daily?

Is alcohol intake changed daily?

Is sometimes missing a drug?

Is often the carbohydrate of a meal or snack very high?

Is sometimes very high the carbohydrates of meals or snacks?

Glucose mode portion 282 may include glucose concentration curve 256, which displays glucose data over a 24 hour period. Glucose concentration curve 256 may be a dynamic glucose profile (AGP). Alternatively, the glucose mode curve may display the various data points as graphically points or dots (dots). These points or dots may or may not be connected with a line(s) to show daily analyte profiles. An exemplary glucose concentration curve 256 is depicted in FIGS. 6B-1 through 6G-2. Glucose concentration curve 256 may be a graph of glucose data for a time period 264 of report 250, wherein various data points of the graph may be color coded to correspond to a concentration range (not shown) within which a glucose analyte level falls. The color coding may correspond to the color coding of the TIR display 252. The box or portion of box 295 surrounding the different portions of glucose concentration curve 256 highlights the detected patterns (e.g., "hyperglycemia", "hypoglycemia", and "hyperglycemia predominately, and sometimes hypoglycemia"). Various thresholds and boundaries for different concentration ranges may be highlighted in glucose concentration curve 256, including marking or otherwise highlighting (e.g., text marking and/or horizontal lines) very low threshold 290, low threshold 291, high threshold 292, and very high threshold 293. The target range between the low threshold 291 and the high threshold 292 may also be marked on the glucose concentration curve 256, for example, shaded or highlighted in a different color (e.g., green). The median glucose level for each time may be highlighted with a solid line 294 and marked as median or 50%. The glucose median line 294 may change color depending on the median value in that portion of the line. For example, for those medians below a very low threshold, the glucose median line 294 may be colored dark chestnut or dark red; for those medians that fall between the very low and low thresholds, the glucose median line may be colored red; for those medians that fall between the low and high thresholds, the glucose median line may be colored green; for those medians that fall between the high and very high thresholds, the glucose median line may be colored yellow; and for those medians above a very high threshold, the glucose median line may be colored orange. The four time periods may be night 296 (e.g., 12 am to 8 am), am 297 (e.g., 8 am to 12 pm), pm 298 (e.g., 12 pm to 6 pm), and evening 299 (e.g., 6 pm to 12 am).

For each of these time periods, the algorithm may determine that one of three possible modes exists or that no mode exists within the time period. Thus, each of the four time periods may be determined to have the following allocation pattern: (1) hypoglycemia 281, (2) hyperglycemia, occasional hypoglycemia 283, (3) hyperglycemia 285, or (4) no adverse pattern detected. Each pattern identified may be marked with a color label over the appropriate portion of the graphic, and the time period may be covered with a box or portion of a box, which may also be color coded. The "hypoglycemic" pattern 281 may appear red (see, e.g., fig. 6B-1 through 6B-2). "hyperglycemia predominates, and occasional hypoglycemia" 283 may appear tan or maroon (see, e.g., FIGS. 6E-1 through 6E-2). The "hyperglycemic" pattern 285 may appear orange (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). As seen in fig. 6B-1 through 6B-2, in the event that multiple modes are detected, multiple boxes or more partial boxes 295 may display all modes in relevant portions on glucose concentration curve 256. In some implementations, the "low blood glucose" mode 281 may be prioritized and highlighted in a different color (e.g., red) than boxes and labels for other modes. As seen in fig. 6C-1 through 6C-2, if the same pattern occurs within two or more adjacent time periods, a single box or partial box 295 may encompass all relevant time periods with a single mark on top of that box. As seen in fig. 6D-1 through 6D-2 and fig. 6E-1 through 6E-2, if the same pattern occurs during all time periods of the day (night, morning, afternoon, and evening), a single or partial box 295 may encompass all time periods, with box 295 having a single flag.

In some embodiments, the mode may be determined according to the GPA algorithm discussed elsewhere in this specification. A number of variables may be used to determine the output of the GPA algorithm and the content of the glucose mode report 250. The variables may include, but are not limited to: priority mode, additional modes combined with priority mode, change (high or low), median glucose (e.g., above or below 180 mg/dL), moderate risk of hypoglycemia (yes or no), and fluctuations below 54mg/dL (yes or no). The layout and text associated with each element may vary depending on the output of the GPA algorithm.

The particular instructional text displayed on any given report may be based on a combination of the three modes described herein: a "hidden" mode defined as a "moderate hypoglycemic risk" mode determined according to the GPA algorithm discussed elsewhere in this specification, glucose change, and hyperglycemia as defined by the median total glucose compared to the 180mg/dL threshold. In some embodiments, the patterns and metrics described herein may be replaced with similar patterns and metrics. In some embodiments, the hypoglycemic pattern may be replaced by a calculation that the number of hypoglycemic events (e.g., glucose less than 70 mg/dL) exceeds a threshold. For example, the threshold may require that the number of hypoglycemic events be at least 4 hypoglycemic events within 14 days. In some embodiments, the change index may be replaced by any other common glucose change index. In some embodiments, the overall hyperglycemia marker may be defined as mean glucose instead of median glucose. In some embodiments, the pattern of "hyperglycemia predominates, with occasional hypoglycemia" may be replaced with a calculation of about 2-3 hypoglycemic events and about 4 or more hyperglycemic events occurring over a time of day period within 14 days, where a hyperglycemic event may be defined as glucose >180mg/dL. Thus, the guidance may be driven by comparable patterns and metrics.

Table 1 below outlines exemplary mappings of guidelines or look-up tables that may be provided in a glucose mode report. The inputs include: (1) whether there is a high variance; (2) Determining that there is a low pattern, hyperglycemia predominates, and there is an occasional low, high pattern or no pattern; (3) whether there is a risk of hypoglycemia; (4) median glucose (G) _med ) Whether greater or less than 180mg/dL. Table 1 may define a instructional text output based on the input. The output may include: (1) medication notes; (2) lifestyle statements and notes; (3) low ripple; and (4) most important pattern recognition. In other embodiments, the look-up table may have fewer inputs. In some embodiments, the table may exclude, for example, overall hyperglycemia and corresponding guidelines or additional inputs. The various scenarios summarized in table 1 are described in more detail below.

Table 1. Exemplary glucose map look-up table.

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At least one of the "arbitrary" =4 TOD periods

"none" =0 of 4 TOD periods

G _med Median concentration of =glucose

Drug notes:

statement A1: for T1 patients, insulin adjustment is considered.

Statement A2: for the T2 patient currently taking insulin or sulfonylurea, the adjustment of the medication is considered.

Statement A3: for other T2 patients, it is contemplated to adjust the drug or to start drugs other than insulin or sulfonylurea.

Statement A4: for other T2 patients, consider starting a new drug, such as insulin.

Statement A5: if the drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia.

Statement A6: different treatments are considered to account for glucose changes.

Statement A7: is the drug contributing to hypoglycemia?

Statement A8: drugs added to address hyperglycemia may exacerbate hypoglycemia.

Lifestyle declarations and notes

Statement B1: the following behavior may contribute to high glucose changes.

Statement B2: the following actions may contribute to glucose changes.

Statement B3: hypoglycemia is often associated with high glucose changes.

Statement B4: hyperglycemia is often associated with high glucose changes.

Statement B5: is often the carbohydrate of a meal or snack very high?

Statement B6: is sometimes very high a meal or snack carbohydrate?

Statement B7: is the drug sometimes missed?

Statement B8: is sometimes missed a meal or a change in carbohydrates?

Statement B9: is the activity level changed daily?

Statement B10: is alcohol intake changed daily?

As seen in fig. 6A-1 to 6A-4, the layout of the glucose mode report 250 may have a time period 264 and a time 266 of CGM activity, which may occur, for example, at the top of the report, e.g., under the heading of the report. In some embodiments, daily average scans/views may also appear at the top of the report. The time period 264, the time 266 of CGM activity, and the average scan/view per day 268 may be listed next to each other and may be separated by a line or outlined by a box. Glucose indicator portion 270 and TIR portion 272 may appear side-by-side under the list of time period 264, time 266 of CGM activity, and average scan/view 268 per day. The glucose indicator portion 270 may display an average glucose value 271a and a target value 271b, and display a GMI 273a and a target value 273b. In some embodiments, the average glucose value and the target value may be displayed above the display of the GMI with the target value. The TIR display portion 272 may be displayed on the left side and the glucose indicator portion 270 may be displayed on the right side. Alternatively, the TIR display portion 272 may be displayed on the right side and the glucose indicator portion 270 may be displayed on the left side. Under the glucose indicator portion 270 and the TIR display portion 272, the glucose mode report 250 may include a clinician notes portion 276. The clinician notes section 276 may include: the identification of the most important pattern 278 at the top of the portion 276, and the medication notes 260 and lifestyle notes 284 may be arranged below the most important pattern 278. Within the lifestyle notes section 284, when all three are reports, the change statement 286 may be listed first, the self-care notes 262 may be listed second, and the fluctuation statement 288 may be listed last. Alternatively, each of the three declarations 286, 262, and 288 may be listed in a different order, for example, self-care notes 262 listed first, middle, or last, change declarations 286 listed first, middle, or last, and fluctuation declarations 288 listed first, middle, or last. A glucose mode portion 282, which may include the glucose concentration curve 256, may appear below the clinician notice 276. Thus, in one embodiment, glucose indicator portion 270 and TIR display portion 272 may appear in the top third of report 250, clinician notes 276 may appear in the middle third of report 250, and glucose mode portion 282 may appear in the bottom third of report. Alternatively, in other embodiments, the glucose indicator portion 270 and the TIR display portion 272 may appear in any of the top third, middle third, or bottom third of the report 250; clinician notes 276 may appear in the top, middle, or bottom third of report 250; and glucose mode portion 282 may appear in the top, middle, or bottom third of the report.

In some embodiments, where no pattern is found and no fluctuation is detected within time period 264, clinician notice 276 may include a statement that no adverse glucose pattern is detected. Further, the glucose concentration profile may not include any boxes highlighting any time period of the day.

In some embodiments, in the event that no pattern is found but at least one fluctuation is detected within time period 264, clinician notes 276 may include a most important pattern statement 278 stating that no adverse glucose pattern is detected. Clinician notes 276 may also include a wave statement 288 that may suggest that the clinician discusses with the patient that occasional hypoglycemia occurs below a very low threshold and directs the clinician to view additional reports, such as weekly summary reports. The glucose concentration curve may not include any boxes highlighting any time period of the day, but may include some data points below a very low threshold 290 highlighted in dark red or maroon, which correspond to one or more detected fluctuations.

In some embodiments, in the event that a "low blood glucose" mode 281 and a low change over a period of time are detected, clinician notes 276 may include highlighting the most important mode statement 278 for "low blood glucose" mode 281 with a red label, and may also include a label for the period of time during which the "low blood glucose" mode occurred during the day. If a "low blood glucose" pattern 281 occurs during each time period of the day, the most important pattern declaration 278 may include two labels: "all day" and "night". As seen in fig. 6F-1 through 6F-2, if the "hypoglycemic" pattern 281 occurs at every time period of the day, the glucose concentration curve may contain a single box 295, such as a red box, highlighting the entire graphic with a "hypoglycemic" heading on top. Alternatively, as seen in fig. 6G-1 through 6G-2, if the "hypoglycemic" pattern 281 occurs within at least two time periods that are not adjacent, then these time periods will be highlighted by a separate or partial box 295.

In some embodiments, in the event that a "hypoglycemic" pattern 281 and a high change in at least one time period is detected, or in the event that a "hypoglycemic" pattern 281 and any other pattern are detected, clinician notes 276 may include a most important pattern statement 278 that may highlight "hypoglycemic" pattern 281 with a red label 280, and may also include an identification of the time period during the day that the "hypoglycemic" pattern occurred, which may be colored a different color, such as gray. Medication notes 260 may include statement of clinician notes when determining treatment for the "hypoglycemic" pattern. These statements may include, but are not limited to: is the drug contributing to hypoglycemia? "; and "drugs added to address hyperglycemia may exacerbate hypoglycemia. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hypoglycemia is often associated with high glucose changes"; and "the following actions may contribute to glucose changes", which may be followed by a list of actions. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care 262 may include, but is not limited to: "do you sometimes miss meals or change carbohydrates? "; "does the activity level change daily? "; is the alcohol intake changed daily? ". In the glucose concentration curve, the time periods with "hypoglycemic" pattern, with "hypoglycemic" heading on top, can be highlighted with a red box, as seen for example in fig. 6G-1 to 6G-2. In some embodiments where adjacent time periods have the same pattern, a single box may cover adjacent time periods in the same pattern (see, e.g., fig. 6F-1 through 6F-2). As seen in fig. 6G-1 through 6G-2, if different types of patterns are detected within that time period, all patterns may be identified with a box covering the relevant time period of the day and an appropriate heading mark identifying the pattern type. In some embodiments in which the "hypoglycemic" mode 281 and the "hyperglycemic" mode 285 and/or the "hyperglycemic" mode 283 are detected, the "hypoglycemic" mode 281 may be highlighted in a different color (e.g., red) than the colors (e.g., gray) of the other modes (see, e.g., fig. 6B-1 through 6B-2 and fig. 6G-1 through 6G-2).

In some embodiments, in the event that a "hyperglycemia predominate, a pattern 283 with occasional hypoglycemia" and a high variance are detected, or in the event that a "hyperglycemia predominate, a pattern 283 with occasional hypoglycemia" and a "hyperglycemia" pattern 285 are detected, clinician notes 276 may include a most important pattern statement 278 that may highlight the "hyperglycemia predominate, the pattern 283 with occasional hypoglycemia" with a red tab 280, and may also include an identification of the time period during the day that the "hyperglycemia predominate, the pattern 283 with occasional hypoglycemia" occurred. In some embodiments, the "hyperglycemia dominant, and sometimes hypoglycemia" mode may be preferred over the "hyperglycemia" mode, as it is more important for the clinician to first address the "hyperglycemia dominant, and sometimes hypoglycemia". Medication notes 260 may include when determining treatment for the hypoglycemic pattern, statement of clinician notes. These statements may include, but are not limited to: "if a drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia"; and "consider different treatments to account for glucose changes". Clinician notice 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: the following actions may contribute to high glucose changes, which may be followed by a list of actions. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "does a drug sometimes miss? "; "do you sometimes miss meals or change carbohydrates? "; "does the activity level change daily? "; is the alcohol intake changed daily? ". In glucose concentration curve 256, the time period with the "hyperglycemia dominant, occasional low blood glucose" mode 283 may be highlighted with a dark red box or partial box with a "hyperglycemia dominant, occasional low blood glucose" header at the top (see, e.g., fig. 6E-1 through 6E-2), and the time period with the "hyperglycemia" mode 285 is highlighted with a box 295 (e.g., a gray box) with a "hyperglycemia" header at the top of the box.

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a low change, the median glucose is < 180mg/dL, there is no risk of hypoglycemia, and no fluctuation is detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. To distinguish between the "hyperglycemic" mode 285 and the "hypoglycemic" mode 281 and the "hyperglycemic dominant" mode, the modes of the "hypoglycemic" 283 are occasional, each of the "hyperglycemic" modes 285 may be highlighted in orange, while the "hypoglycemic" mode 281 may be highlighted in red, and the "hyperglycemic dominant," the mode 283 of the "hypoglycemic" is occasional may be highlighted in dark red/maroon, at which time the particular mode is detected and reported and identified as the most important mode. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for a T1 patient, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, it is contemplated to adjust the drug or to begin the drug in addition to insulin or sulfonylurea. "because a low change is detected, the glucose mode report 250 may not contain a change statement 286. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "is the carbohydrate of a meal or snack often very high? The time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) in the glucose concentration curve. As seen in fig. 6D-1 through 6D-2, adjacent time periods are each determined to have a "hyperglycemic" pattern 285, then a single box 295 may encompass adjacent time periods.

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a low change, the median glucose is >180mg/dL, there is no risk of hypoglycemia, and no fluctuation is detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. Medication notes 260 may include statement of clinician notes when determining treatment for the "hyperglycemic" mode. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. ### "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "A kind of" a kind of the method comprises the steps of carrying out a first treatment on the surface of the; and "for other T2 patients, consider insulin priming. "clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "carbohydrate for meals or snacks" is the compound often very high? The time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) in the glucose concentration curve (see, e.g., fig. 6C-1 to 6C-2 and fig. 6D-1 to 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single or partial box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some embodiments, where only a "hyperglycemic" pattern is detected, with low variance, any median glucose, moderate risk of hypoglycemia, and no fluctuation is detected, clinician notes 276 may include a most important pattern statement 278 identifying a "hyperglycemic" pattern 285, and a period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "if a drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia. "clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "is the carbohydrate of a meal or snack often very high? The time period with "hyperglycemic" mode may be highlighted with an orange box "in the glucose concentration curve" (see, e.g., fig. 6C-1 to 6C-2 and fig. 6D-1 to 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern, a single box may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a high variance, the median glucose is < 180mg/dL, there is no risk of hypoglycemia, and no fluctuation is detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. Medication notes 260 may include statement of clinician notes when determining treatment for the "hyperglycemic" mode. These statements may include, but are not limited to: "for a T1 patient, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, consider modulating the drug or starting a drug other than insulin or sulfonylurea. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high blood sugar" glucose change associated "; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. The statement regarding self-care may include: "does a drug sometimes miss? "; and "do sometimes miss meals or carbohydrate changes? ". In the glucose concentration profile, the time period with the "hyperglycemic" mode 285 may be highlighted with a box 295 (e.g., orange box) (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a high change, the median glucose is >180mg/dL, there is no risk of hypoglycemia, and no fluctuation is detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, consider insulin priming. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high glucose changes"; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "does a drug sometimes miss? "; and "is the carbohydrate of a meal or snack sometimes very high? ". In the glucose concentration profile, the time period with the "hyperglycemic" mode may be highlighted with an orange box 295 (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some embodiments, where only the "hyperglycemic" pattern 285 is detected, there is a high change, any median glucose, no risk of hypoglycemia, and no fluctuation is detected, the clinician notes 276 may include the most important pattern declaration 278 identifying the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. Medication notes 260 may include statement of clinician notes when determining treatment for the "hyperglycemic" mode. These statements may include, but are not limited to: "if a drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high glucose changes"; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. The statement regarding self-care may include: "does a drug sometimes miss? "; and "is the carbohydrate of a meal or snack sometimes very high? ". In glucose concentration curve 256, the time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some embodiments, where only the "hyperglycemic" pattern 285 is detected, there is a low change, median glucose < 180, no risk of hypoglycemia, and low fluctuation is detected, clinician notes 276 may include a most important pattern statement 278 identifying the "hyperglycemic" pattern 285, and a period of time that the pattern is detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; "for other T2 patients, consider modulating the drug or starting a drug other than insulin or sulfonylurea. "because a low change is detected, the glucose mode report 250 may not contain a change statement 286. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "is the carbohydrate of a meal or snack sometimes very high? ". Clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode may be highlighted with a box (e.g., orange box) in glucose concentration curve 256 (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a low change, the median glucose is > 180mg/dL, there is no risk of hypoglycemia, and low fluctuations are detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern is detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, consider insulin priming. "because a low change is detected, the glucose mode report 250 may not contain a change statement 286. Clinician notes 276 may also include self-care notes 262. The statement regarding self-care may include: "is the carbohydrate of a meal or snack often very high? "clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) in glucose concentration curve 256 (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some embodiments, where only the "hyperglycemic" pattern 285 is detected, with low variance, any median glucose, moderate risk of hypoglycemia, and low fluctuations are detected, the clinician notes 276 may include the most important pattern declaration 278 identifying the "hyperglycemic" pattern 285, and the period of time that the pattern was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "if a drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia. "because a low change is detected, the glucose mode report 250 may not contain a change statement 286. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "is the carbohydrate of a meal or snack often very high? "clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) in glucose concentration curve 256 (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a low change, the median glucose is < 180mg/dL, there is no risk of hypoglycemia, and low fluctuations are detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern is detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, consider modulating the drug or starting a drug other than insulin or sulfonylurea. "clinician notes 276 may also include information about a change statement 286 of the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high glucose changes"; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "does a drug sometimes miss? "; and "is the carbohydrate of a meal or snack sometimes very high? ". Clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode 285 may be highlighted with a box (e.g., orange box) in glucose concentration curve 256 (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some implementations, where only the "hyperglycemic" pattern 285 is detected, there is a high change, the median glucose is > 180mg/dL, there is no risk of hypoglycemia, and low fluctuations are detected, the clinician notes 276 can include the most important pattern declaration 278 that identifies the "hyperglycemic" pattern 285, and the period of time that the pattern is detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "for T1 patients, insulin is considered to be regulated. "; "for T2 patients currently taking insulin or sulfonylurea, consider adjusting the medication. "; and "for other T2 patients, consider insulin priming. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high glucose changes"; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "does a drug sometimes miss? "; and "is the carbohydrate of a meal or snack sometimes very high? ". Clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode 285 may be highlighted with an orange box "in glucose concentration curve 256 (see, e.g., fig. 6C-1 to 6C-2 and fig. 6D-1 to 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

In some embodiments, where only a "high" mode 285 is detected, there is a high change, any median glucose, moderate risk of hypoglycemia, and low fluctuations are detected, clinician notes 276 may include a most important mode statement 278 identifying a "hyperglycemic" mode 285, and a period of time that the mode was detected. The "hyperglycemia" mode tab 280 may be highlighted in orange, while the time period may be identified in text form. The medication notes 260 may include a statement of clinician notes when determining treatment for the "hyperglycemic" mode 285. These statements may include, but are not limited to: "if a drug is started or adjusted to address hyperglycemia, consider how the drug may cause hypoglycemia. "clinician notes 276 may also include a change statement 286 regarding the detected high change. These statements may include, but are not limited to: "hyperglycemia is often associated with high glucose changes"; and "the following behaviors may contribute to high glucose changes," which may be followed by a list of behaviors. Clinician notes 276 may also include self-care notes 262. Statement regarding self-care may include, but is not limited to: "does a drug sometimes miss? "; and "is the carbohydrate of a meal or snack sometimes very high? ". Clinician notes 276 may also include fluctuation declarations 288. The declaration may include, but is not limited to: "sporadic hypoglycemia is below 54mg/dL. See weekly summary report. The time period with "hyperglycemic" mode 285 may be highlighted with an orange box "in glucose concentration curve 256 (see, e.g., fig. 6C-1 to 6C-2 and fig. 6D-1 to 6D-2). When the adjacent time periods are all determined to have a "hyperglycemic" pattern 285, a single box 295 may encompass the adjacent time periods (see, e.g., fig. 6C-1 through 6C-2 and fig. 6D-1 through 6D-2).

As seen in fig. 6B-1 through 6B-2, when multiple types of patterns are detected, all patterns may be identified in the glucose concentration curve 256. In some embodiments, glucose concentration curve 256 may include up to 3 modes per patient. A box or partial box or bracket (black et) 295 covering the various modes may be prioritized. The order of priority from first to last may be "hypoglycemia", "hyperglycemia predominately", occasional hypoglycemia "and" hyperglycemia ". The various modes may be color coded based on priority. In some implementations, the highest priority mode of the detected modes may be the only mode highlighted with color. The remaining lower priority mode(s) may be colored a different color, such as gray or black. For example, in the event that at least one of each of the "hypoglycemic" 281, "hyperglycemic predominate," occasional hypoglycemic "283 and" hyperglycemic "285 modes is detected, the" hypoglycemic "mode 281 may be displayed in red (in part of box 295 and indicia), while the" hyperglycemic predominate, "occasional hypoglycemic" 283 and "hyperglycemic" mode 285 may be displayed in gray on glucose concentration curve 256. In other embodiments, in the event that at least one of each of the "hyperglycemia dominant", and "hyperglycemia" 285 modes is detected, the "hyperglycemia dominant", and "hypoglycemia dominant" 283 may be displayed in a dark red or maroon color (as part of box 295 and the indicia), while the "hyperglycemia" mode 285 may be displayed in gray on the glucose concentration curve 256.

If the patient is found to have met all of the TIR targets for a period of time, but a "hypoglycemic" pattern is detected for at least one period of time, the report may still identify the "hypoglycemic" pattern in all appropriate parts, including a box or partial box or brackets 295 covering the "hypoglycemic" pattern on the glucose concentration curve 256, and the most important pattern declaration 278. In an alternative embodiment, if the patient is found to have met all of the TIR targets within a period of time, no mode may be identified or highlighted in the report.

In embodiments where only a single mode is detected, the single mode may be displayed in color on the glucose concentration curve 256. For example, the "low blood sugar" mode 281 may be displayed in red (with a portion of box 295 and indicia), the "high blood sugar dominant", the occasional low blood sugar "mode 283 may be displayed in dark red or maroon (with a portion of box 285 and indicia), and the" high blood sugar "mode 285 may be displayed in orange (with a portion of box 295 and indicia).

Learning method

Manual configuration of the DGS100 may require time for the HCP to spend, but the HCP may not have sufficient time available. Furthermore, even if the HCP's time is available, the configuration may be complex and may be prone to error. To alleviate these problems, a patient Parameter Initialization (PI) module that requires no or only minimal settings may be included in the DGA. The PI module learns the patient's dosing strategy, which may include, for example, base only, base plus one, base plus two, etc., and parameterizes the patient's drug dosing practices for configuring the dose guidance settings by DGA.

According to an aspect of an embodiment, the learning process of the PI module may include automatically configuring patient dose guidance settings from the observed data. Once the settings are successfully learned, DGS100 may enter a coaching mode in which the patient may request a dosing coaching and receive notification regarding dosing. During a learning period prior to the coaching mode, the DGA can process glucose data and insulin data collected by the patient's SCD 102, UID 202, and/or other devices, and determine dosing information based on the processed data.

The dosing information may include, for example, dosage regimen, meal dosage type, dosage parameters, and dosage ranges. Dosage regimens may include, for example, base dose plus BF, base plus LU, base plus DI, base plus BF/LU, base plus BF/DI, base plus LU/DI, and base plus 3, where BF means "breakfast", LU means "lunch", and DI means "dinner". Additional regimens, such as a afternoon tea snack dose, may also be included. Meal dosage types may include, for example, fixed meal dosages or variable meal dosages. Dose parameters may include, for example, nominal fixed dose or carbohydrate ratio for each meal, pre-meal Correction Factor (CF), and postprandial CF. The dose range may include an estimate of the lowest meal dose. CF is also known as insulin sensitivity factor. This factor is the ratio reporting how much 1U of insulin will lower blood glucose in the fasting or preprandial state. DGA can have two CF values to explain fasting and preprandial physiology: insulin sensitivity difference between pre-meal and post-meal. The unit of CF is (mg/dL)/unit of insulin.

For each of the administration information types described above, the DGA may determine whether the accumulated data is sufficient or insufficient to determine administration information. In some embodiments, the patient's SCD 102 may be configured to run for a predefined period of time, for example 14 days. In these embodiments, the DGA may determine after a predetermined period of time (or earlier if the sensor stops working before the end of the period of time) whether the available analyte and dosing data are sufficient to determine each of the above dosing information. If it is sufficient to determine, the DGA may perform the parameterization method 300 and allow the start of the dose guiding mode. In alternative embodiments, the DGA may periodically (e.g., once a day) determine during the learning period whether the data is sufficient to determine each of the above dosing information. In either case, when the collected data is sufficient, the DGA may end the learning period, perform parameterization, and begin the coaching period. If the data is insufficient, the DGA may continue the learning process.

Referring to fig. 7a, dga may be configured to perform method 300 on a suitable computing device (e.g., UID 200, SCD 102, MDD 152) alone or in any combination. Program instructions for performing method 300 may be grouped in PI modules or any other suitable code configuration. In general, the method 300 may include: in step 302, each of the drug doses received by the patient during the analysis period is classified by the DGA based on data characterizing the patient's analyte and the drug dose received by the patient during the analysis period. The method 300 may further include: in step 304, each of the doses is grouped into one of a set of dining groups. The method may further comprise: in step 306, a dose parameter for the patient is generated at least in part by applying the data for each of the dining groups to the model. The method may include: in step 308, the dose parameters are stored in computer memory for configuring the dose guidance settings. In embodiments described herein, the analyte may be glucose, or may include an indicator of the patient's glucose level, and the drug may be, or may include, insulin. The dose guidance settings may be used by the DGA to develop dose guidance, or provided to an interface device (e.g., UID 200 or a healthcare practitioner's terminal) for output. More detailed aspects of each of the operations in method 300 are described below. As used herein, a "PI module" refers to a portion or portions of DGA that performs the operations of method 300 and any ancillary operations. The PI module is not limited to a particular configuration and may include various arrangements of computer code.

In one aspect, the classification operation 302 may include: each dose of medication (e.g., insulin) is classified as one of a meal dose, a correction dose, and/or a fuzzy dose. If the DGA is unable to classify a medication dose as a dining dose or a correction dose with a defined confidence, the DGA may classify the dose as ambiguous and may omit use of the dose in generating a dose parameter for dose guidance.

DGA can perform drug dose classification through a sequence of two operations, referred to herein as feature extraction and classification. Associating this with fig. 7A, the classifying operation 302 may include: a feature matrix is generated that associates a set of classification features with each dose. In some embodiments, the DGA may configure the vector of insulin injection time stamps, the data file including analyte measurements from the patient's SCD 102, and the results from the meal detection algorithm module discussed elsewhere herein as inputs to a function that outputs a feature matrix for insulin dose classification. The number of rows of the feature matrix may indicate the amount of injection or equivalent drug administration event during the relevant analysis period. Each row in the feature matrix may be or may include a feature vector for a single administration event. In embodiments for classifying insulin injections, each vector may include elements referred to herein as classification features as described below. The DGA may determine each of the elements of the feature vector based on a corresponding glucose monitoring data segment in a time range between-2.5 and 1.5 hours relative to insulin injection time.

In an embodiment, the classification features may include: for the time of administration of each dose, for example, insulin injections are recorded by MDD 152 or the time of day of insulin injections are recorded by the patient using UID 200.

The classification features may further include: the time-filtered analyte value is, for example, a glucose value filtered using a Savitsky-Gao Lei (Savitsky-Golay) filter, a low-pass filter, a band-pass filter, a non-parametric smoothing filter (e.g., locally estimated scatter plot smoothing), or other filter. In one aspect, the savitz-Gao Lei filter may employ a 2 nd order, a frame length of 7, at 15 minute sampling intervals.

The classification features may further include: the rate of change of the analyte value closest to the time of administration, e.g., the rate of change of the analyte (e.g., glucose) value calculated by linear regression of five analyte data points (e.g., using 15 minute sampling intervals) centered on the data point closest to the time of administration (e.g., injection).

The classification features may further include: an area under the left curve (AUC) index indicating the integrated difference between the analyte value and the analyte value closest to the dosing time in the interval preceding the dosing time. For example, to obtain a left AUC indicator, DGA may calculate the left AUC indicator by: all data points are collected from the filtered analyte data over a time window (e.g., 2.5 hours) counted down from the injection time, then the difference between the average analyte value of the collected data points and the data point closest to the injection time (i.e., the reference data point) is calculated, and the incremental AUC on the left is calculated by multiplying the difference by the duration of the time window.

The classification features may further include: a right AUC index indicating the integrated difference between the analyte value and the analyte value closest to the dosing time in the interval following the dosing time. For example, DGA may calculate the right AUC index by: all data points are collected from the filtered analyte data over a time window (e.g., 1.5 hours) counted down from the injection time, then the difference between the average analyte value of the collected data points and the data point closest to the injection time (reference data point) is calculated, and the incremental AUC on the right is calculated by multiplying the difference by the duration of the time window.

The classification features may further include: the time elapsed between the administration times. For example, for each injection time, the DGA may calculate the elapsed time between the previous injection time and the current injection time by subtracting the previous injection time from the current injection time. For the first injection time in the insulin log, the DGA can calculate the time elapsed from the first SCG time data point to the current injection time, as there is no previous injection time available. Further, for another example, the DGA may calculate the time elapsed between the current injection time and the next injection time by subtracting the current injection time from the next injection time. For the last injection time in the insulin log, the DGA can calculate the elapsed time from the current injection time to the last SCG time data point, since there is no next injection time available. In the backward and forward calculations, if the elapsed time is greater than a predetermined maximum value, for example 12 hours, the DGA may set the elapsed time value equal to the maximum time.

The classification features may further include: the probability of beginning a meal within a defined interval prior to the time of administration, e.g., the maximum probability of beginning a meal within a time window (e.g., 1.5 hours) prior to injection. The probability may be calculated by a meal detection module described elsewhere herein.

The classification features may further include: the most likely time interval elapsed since the last meal, e.g., the time elapsed from the point of maximum meal start probability (e.g., determined by the meal detection module) relative to the injection time.

The classification features may further include: probability of beginning a meal within a defined interval after the administration time, e.g., maximum probability of beginning a meal within 2 hours after injection (determined by meal detection module).

The classification features may further include: until the most likely time interval for the next meal, e.g., a predicted elapsed time from the time of injection to the point of maximum meal start probability after the meal injection (e.g., as determined by the meal detection module).

As noted, calculating some of the classification features includes: the time of each meal of the patient's meal during the analysis period is estimated, and the method for estimating meal time is described in more detail below. Briefly, estimating the time for each meal may further comprise: a feature matrix is generated by the DGA based on the time-dependent analyte data, wherein the feature matrix associates an analyte (e.g., glucose) data feature set with each of the different regions classified as rising, falling, before falling, and falling. The analyte data feature set may be or may include a maximum analyte change rate, a maximum analyte acceleration, an analyte value at a point of maximum analyte acceleration, a duration of the region, a height of the region, a maximum deceleration, an average change rate in the region, and a time of the maximum analyte acceleration. Estimating may further include generating an estimated meal time based on the feature matrix using an algorithm as described below.

More detailed aspects of the retrospective meal time detection algorithm for the method 300 or for other uses are described in the following paragraphs. Thereafter, other aspects of the method 300 are described. The DGA may perform retrospective meal time detection based on time-dependent analyte data by executing one or more code modules (e.g., a feature extraction module and meal detection module). The feature extraction module, when executed by the DGA, may cause the DGA to receive the glucose time series as input and output a feature matrix to be passed to a retrospective meal detection module to detect glucose fluctuations in response to a meal event.

DGA may perform feature extraction using the following operations, which may be divided into a sequence of three sub-operations: smoothing, segmentation and extraction.

In the smoothing sub-operation, the DGA may use a Savitzky-Golay filter (order 2) to smooth the analyte (e.g., glucose) time series and calculate the rate of change and acceleration at each analyte data point. The frame length parameter of the filter may be the number of data points collected in a first time interval (e.g., 60 minutes); thus, the samples are interval dependent. DGA may calculate the rate of change by taking the average of the front-to-back differences between the point of interest and the smoothed analyte values between points within a second interval (e.g., 15 minutes) before and after the point of interest, where the second interval is less than the first interval, e.g., equal to one-fourth of the first interval. Similarly, DGA may calculate acceleration by taking an average of the front-to-back differences in analyte change rate between the point of interest and points within a second interval (e.g., 15 minutes) before and after it.

In a split sub-operation, the DGA may split the smooth analyte trace into monotonically increasing (i.e., rising) and monotonically decreasing (i.e., falling) regions. Each ascending region is considered a candidate for glucose excursion in response to a meal event.

In the extraction sub-operation, the DGA may extract features from the data, e.g., sixteen (16) features, which may be or may include features from each of the rise regions (e.g., eight (8) features), the pre-fall regions (e.g., four (4) features), and the post-fall regions (e.g., four (4) features). Features that DGA may extract from the rising features may include, for example: 1) maximum analyte change rate, 2) maximum analyte acceleration, 3) analyte value at the point of maximum analyte acceleration (reference point), 4) duration of rising region (time elapsed from reference point to last point of region), 5) height of region (difference of smoothed analyte value between last point and reference point), 6) maximum deceleration (negative acceleration with maximum absolute value), 7) average change rate in region (height/duration), and 8) time of data of reference point. For another example, four (4) features extracted from the pre-descent region and the post-descent region may include: 1) height of the drop zone, 2) duration of the drop zone, 3) average rate (height/duration) of the zone, and 4) maximum absolute value of the rate of glucose change. The number of rows in the feature matrix output by the feature extraction module may be the same as the number of rising regions in the smoothed glucose time series.

According to another aspect of an embodiment, the retrospective meal detection module may take as input the feature matrix and output a binary detection result for each ascending region. The output may include: the binary classification result, and each ascending region is a probability value of an analyte (e.g., glucose) fluctuation in response to a meal event. The DGA may assign a probability value for each rising region to its reference point. In some embodiments, for example, the pre-trained machine learning model for meal detection may be implemented using a random forest classifier learned by scikit (https:// scikit-learn. Org/stable/modules/generated/sklearn. Ensemb. Random f orestClassifier. Html). The meal detection module may detect postprandial glucose excursions caused by a meal based on a number of decision trees constructed and optimized during the training process. In alternative implementations, the DGA may build a pre-training model based on alternative classification algorithms (e.g., gradient lifting, ada lifting, artificial neural networks, linear discriminant analysis, and additional trees).

Referring again to the method 300 of fig. 7A, the classification operation 302 may take as input a feature matrix of the patient and output a binary classification result for each relevant medication event (e.g., for each insulin injection). For example, DGA may output binary data '1' indicative of meal doses and '0' indicative of non-meal doses. According to some embodiments, the sorting operation 302 may use meal detection results, in which case meal detection may be performed prior to insulin dose sorting. As noted for retrospective meal detection, the classification operation 302 may include pre-training a machine learning model, e.g., a model implemented with a random forest classifier learned by scikit (referenced above). The machine learning model implemented by DGA may perform classification based on tree construction rules and thresholds for various features in each decision tree that are optimized during the training process. Alternatively, the model may be trained by other machine learning algorithms, including gradient boosting, ada boosting, artificial neural networks, linear discriminant analysis, and additional trees. After the DGA successfully classifies each dose, the determination of the dose regimen and dose parameters may continue.

In step 304, the method 300 may include: the DGA groups each of the doses in one of a set of meal groups or clusters. For example, DGA may determine dosing strategies by performing cluster analysis of the time of a meal dose of drug (e.g., injection). The DGA may execute a clustering module implemented using a K-means algorithm and elbow rules that takes injection times as input and outputs an optimal cluster number K (max 3) and cluster index for each injection time. The optimal cluster number K may be the number of dining doses taken by the patient per day. Using the cluster index for each injection, DGA can divide the meal doses into K groups according to the cluster index.

DGA can identify these groups as breakfast, lunch or dinner (B, L, D) as follows: for each group, DGA may determine a typical TOD by calculating a time of day (TOD) median for that group. Alternatively, DGA may use some other centroid metric (centroid metric). If k=3, DGA may associate breakfast with the group after the longest period of time between typical group TODs. Then, the next group is lunch, and the last group is dinner. If k=2, DGA can estimate which group is related to breakfast, lunch or dinner using the assumed rules regarding the time between meals. For example, if two groups are more than six (6) hours apart from each other, the DGA may identify the groups as breakfast and dinner. Otherwise, if the first group occurs before 10 am, the DGA may identify the groups as breakfast and lunch; otherwise, it is identified as lunch and dinner. In an alternative embodiment, after the DGA identifies typical times for a meal event, the DGA may prompt the user to identify meals associated with each typical time. For another example, in alternative embodiments, DGA may combine the two methods described herein by estimating meal associations and then prompting the user for confirmation. Further alternative methods may include: glucose data is analyzed to identify meals and to cluster meal times to detect typical meal times. This may be useful for distinguishing meals in the case of k=2; i.e. identifying meals for which no dosage is taken.

Once the doses are grouped in the temporal clusters, the DGA may generate dose parameters for the patient at step 306 at least in part by applying the data for each of the temporal groups to the model. For example, for each meal group (B, L, D), the DGA may pair each corresponding set of pre-meal glucose levels with a corresponding meal dose. The DGA may fit each group using a suitable model, for example, a linear function with zero slope, a linear function with non-zero slope, a piecewise linear function connected at a single point, or a nonlinear function that approximates a connected piecewise model but has a smooth curvature around the connection points. Other models are also suitable.

DGA may perform model fitting and parameter estimation by minimizing the Sum of Squares Residuals (SSR) in terms of model parameters. DGA may then use a search algorithm to find the best parameters that lead to the minimum value of SSR. For a linear model, DGA may be fitted using the Nelder-Mead simplex method. For nonlinear models, DGA may use the Levenberg-Marquardt algorithm. That is, DGA may use the Nelder-Mead simplex numerical optimization method for linear models and the Levenberg-Marquardt optimization method for nonlinear models. Alternative methods of fitting data to these models are also possible.

When the number of iterations during optimization exceeds the convergence criterion, the model cannot fit, and DGA may exclude the model that fails to fit as a candidate model. Furthermore, the DGA may apply certain rules to minimize uncertainty in parameter estimation, for example, by requiring at least three pre-meal glucose data points that are greater than an estimated glucose threshold to verify an estimated correction factor; validating the estimated fixed dose by requiring at least three pre-meal glucose data points that are less than the estimated glucose threshold; requiring that the 95% confidence interval for the parameter intercept exclude zero; or require that the 95% confidence interval for the model slope exclude zero.

Insufficient data may lead to failure of model fitting and thus exclude a particular model as a candidate model. DGA may evaluate each model with the erythro pool information criteria (AIC, akaike Information Criterion) and select the model with the lowest AIC value as the preferred model for each dining group.

Once a model has been selected for each of the dining clusters, the DGA may determine dose parameters including, for example, fixed dose insulin amounts, target glucose levels, and correction factors based on the model selected for each of the dining clusters. DGA may determine the target glucose level and correction factor as individual values for each of all groups, as described in more detail in the following paragraphs. In alternative embodiments, the DGA may determine target glucose levels and correction factors for each group separately and use the separately determined parameters for downstream dose-guiding operations.

According to another aspect of the embodiments, the DGA may form a combined data set for obtaining more accurate correction factors for the patient. For example, after fitting dose data to the various models of each meal group to select the best model and estimate the fixed insulin dose, the DGA may subtract the fixed insulin dose from the relevant meal dose of each meal group. The remaining non-zero values correspond to doses with correction amounts. Those non-zero values may then be combined from all three meal groups (B, L, D) to form a combined group. If a fixed insulin dose cannot be determined for a group, the DGA may exclude the data for that group from the combined group. The system may then repeat the operation of finding the best fit model for the combined set, or the same model identified when analyzing the set alone may be used. The use of this combination set approach assumes that the patient has the same (or constant) correction factor and target glucose throughout all meals, and that the combination set provides a larger sample size to potentially make a more accurate fit. After determining the target glucose level and correction factor based on the best fit model, DGA has completed estimation of the dose parameters. The DGA may then store the dose parameters in computer memory for configuring the dose guidance settings in step 308.

In further aspects, DGA may determine whether a patient is potentially carbohydrate counting (e.g., changing their meal dose to account for carbohydrate intake) by comparing the AIC value of the preferred model to a threshold, such as 50, or 75, or 100. If the AIC value is greater than the threshold, the DGA may determine that the patient is performing a carbohydrate count and require the patient to confirm via UID 200.

In alternative embodiments, one or more of the above-described operations may be omitted and replaced by requesting the patient or HCP to manually provide information, or by extracting information from another source (e.g., EMR or another software program). Nonetheless, the method 300 should be useful for a variety of applications that do not exceed the information that can be provided by the SCD and MDD.

Alternative learning method

In alternative embodiments, DGS100 (e.g., SCD 102, display device 120, or MDD 152) may be configured using an automatic or semi-automatic learning method that classifies and characterizes drug doses based on patient inputs and patient GPA analyzed during the learning period. Alternative learning implementations include regimen input from the user, processing of recorded glucose measurements (glucose readings and associated date/time stamps), and insulin administration information (doses and associated date/time stamps). However, the inputs and algorithms may be optimized to reduce the burden on the patient and HCP. For example, in the presently described embodiments, HCP participation may not be required to initiate dose guidance. This is advantageous because HCPs often have no time to configure an existing system for a patient. Furthermore, the present embodiment provides an additional safety mechanism compared to conventional practices that have a patient or HCP directly configure a dose guidance system (or more generally referred to as a bolus calculator). In particular, as connected insulin pens become more common and have been used in research recently, it is clear that many MDI patients do not follow their dosage regimen, such as missing a meal dose, dosing late, or taking less insulin than needed. The first two are due to inconvenience or forgetfulness, while the third is due to fear of hypoglycemia, plus a lack of confidence in managing the hypoglycemia.

The basic problem with conventional practice is that the dose guidance system may be configured based on what the patient or HCP deems necessary based on his blood glucose profile. However, the curve may be based on previous schemes with poor compliance. For example, HCP may recommend that patients increase the fixed insulin dose of their breakfast from 10 to 12 to mitigate high post-breakfast glucose, which is actually caused by patients missing 50% of their breakfast dose or often taking only 8 units and under-dosing. This may lead to hypoglycemia problems if the patient suddenly decides to improve their compliance. Thus, it is important that the dose guidance setting procedure comprises: prior compliance was analyzed prior to presentation of dose guidance to the patient.

As previously mentioned, the patient or HCP may enter all or only a portion of the key dose guidance parameters. Here, as an example, it is assumed that the patient types in the following part of the dose guidance parameters—other examples are conceivable. In particular, the system provides means for typing in the following parameters before providing a dose guidance: typical fixed doses for breakfast, lunch and dinner; and the typical time of day that breakfast (and/or each meal) occurs.

The foregoing illustrates a compact set of parameters that should be sufficiently easy to understand and correctly entered for most patients. It avoids complex parameters that many patients do not understand, such as correction factors. Alternatively, for patients with more complex dosage regimens and/or with sufficient understanding of the regimen details to correctly key in parameters, the system may allow the patient to optionally key in more parameters. In another embodiment, the patient may enter information about their compliance level, such as how often they miss doses at certain times of the day or how often they skip meals and thus do not need to administer insulin. Another embodiment is a system for prompting a patient to manually sort recorded doses. However, for values that are not received immediately, this type of system would likely require a high degree of interaction with the patient and therefore may be less preferred for most patients.

During a learning mode for analyzing patient compliance with a user-entered regimen, the system will acquire glucose data and insulin dosage information over a period of time (e.g., two weeks). If the system does not initiate a dose guidance after this period, the patient may be allowed to initiate a subsequent observation period.

At the end of the learning period, the system uses an analysis algorithm executed by the processor to process the entered protocol parameters and the observed glucose and insulin dosage information. The algorithm may include classifying the dose as previously described. Alternatively or additionally, the classification may be based on dosing information entered by the user, and may take into account absolute time of day and relative time between measured analyte (e.g., glucose) data and user supplied dose data. Alternatively or in addition, the classification may be based on a dynamic relationship between different data inputs, e.g., a rate of change of glucose. The system may perform classification using a classification model that may be developed and trained using common machine learning techniques applied to clinical and simulated data.

Once the system processor classifies the doses, it associates each dose with one of several dining events in the day. The cluster analysis described previously is one method for performing the association. For example, the system may associate breakfast with one of the clusters whose time-of-day center metric (e.g., median, average) is closest in time to the breakfast value entered by the user; the next temporal cluster is associated with lunch and the next cluster is associated with dinner.

Alternative clustering methods may include glucose data and dose information in algorithmic cluster determination for identifying clusters, and which doses are associated with which clusters. Further, the processor may further include data indicating absolute time of day and relative time between glucose and dose data in its cluster analysis. For example, if 10 units are a common dose for clusters other than breakfast clusters and if the dose is taken late enough in the morning, then a dose of 10 units in the morning may be associated with a lunch cluster. The association may also be based on dynamic relationships between different data inputs, such as the rate of change of glucose. The determination is made by a classification model that can be developed and trained using common machine learning techniques applied to clinical and simulation data.

Once the clustering process is complete, the system estimates the scheme parameters as described previously. The parameter estimation process may include: standard numerical analysis techniques are used to determine confidence in the parameter estimates. For the following description, for simplicity, the confidence may be described as a confidence interval; however, there are other common confidence measures that may be used. These parameters are referred to herein as "learned" recipe parameters, which include parameter values and associated confidence intervals.

Once the solution is learned, the system may perform the following checks between the typing parameters and the learning parameters. The system processor may check the Confidence Interval (CI) for the learned meal dose parameters and typical dose times by comparing to a maximum threshold applicable to each parameter. If the CI exceeds the threshold, the processor may flag the system configuration as suspicious. For a meal dose, the appropriate maximum CI is +/-30%, or 20%, or 50%; for typical meal dose times, the appropriate maximum CI is +/-1/2 hours, 1 hour, 2 hours.

In another aspect, the system processor may compare the learning parameter to a user entered parameter; in particular, for breakfast, lunch and dinner. If any of the parameters differ between the typed parameter and the learned parameter by more than a confidence interval (or some multiple of the confidence interval), the processor may flag the system configuration as suspicious.

The system processor may further compare the learned meal dose times that entered the typical breakfast dose time with the temporally closest dose cluster. If the parameters differ by more than a confidence interval, the processor may flag the confidence interval (or some multiple of the confidence interval) as suspicious.

Furthermore, the system may check the rationality of the additional learning parameters. For example, the maximum gap between learned typical meal dose times should occur before learned typical meal dose times to ensure that the night time period is properly considered. If any of the plausibility checks fails, the system configuration may be marked as suspect. Some parameters do not have to be estimated with high confidence, so that the system configuration is not marked as suspicious. For target Glucose (GT), pre-meal CF, and post-meal CF, if CI is within a maximum threshold for the parameter, then using the learned value in a dose guidance regimen; otherwise, a conservative default is used. Suitable maximum CI thresholds for GT and CF are +/-2, 5 or 10mg/dL or +/-5, 10 or 20 mg/dL/unit, respectively. Suitable default values for GT, pre-meal CF or post-meal CF are 120, 125, or 130mg/dL, or 40, or 50 mg/dL/unit, or 60, or 70 mg/dL/unit.

Furthermore, the system may evaluate the acquired data for patient compliance with its typing regime. For example, the processor of the system may estimate the frequency of missed meals (or meals having less impact on postprandial glucose levels). This is useful for calculating the subsequent index. The estimate may be calculated using a model developed from glucose data and insulin dose data and meal records using machine learning or other techniques—the model may be trained using clinical, real world, or simulated data. Typically, during the time of day of the expected meal dose, if the insulin dose does not occur and the pattern of blood glucose during this time does not show an increase in glucose, this indicates a missed meal.

The system processor may estimate the frequency of missed meal doses for breakfast, lunch, and dinner. For example, the frequency may be calculated as (number of missed dose time periods-number of missed meal time periods-flag) divided by (total number of time periods-number of missed meal time period-flag).

In another aspect, the system may estimate the difference between the median dose and the entered dose for each meal (breakfast, lunch, and dinner); i.e., the degree to which the patient takes an underdose or overdose compared to the dosage entered by the patient. The system processor may output dose compliance survey results to a display device or equivalent device prior to initiating the dose guidance. Similarly, the system may report to the patient the estimated impact on their glycemic management and A1c due to lack of compliance; or conversely, if the patient is to correct their compliance problem, they are indicated a potential improvement in their glucose index (e.g., average glucose or time within target range) or laboratory measurements (e.g., A1 c). This information may be generated by the processor through a model developed to correlate each diabetes control measure with a compliance metric for a particular regimen. The simple model will be based on real or simulated demographic data and it can be found that the correlation parameter correlates the compliance metric with the blood glucose metric, which in turn is correlated with the A1c measurement. More advanced models may be developed and implemented by the system processor for correlating specific patient characteristics (e.g., protocols followed) and blood glucose curves with compliance metrics.

Summarizing and by way of additional example, a DGS100 or component thereof (e.g., one or more of SCD 102, display device 120, or MDD 152) for parameterizing a patient's drug administration practices for configuring dose guidance settings may include: an input component configured to receive measured analyte data, meal data, and medication administration data; a display component configured to visually present information; and one or more processors coupled with the input component, the display component, and the memory. The memory may hold instructions that, when executed by the one or more processors, cause the apparatus to perform the method 1000 as shown in fig. 7B, as well as time-related data characterizing an analyte of a patient over an analysis period. In one aspect, the drug may be or may include insulin.

The method 1000 may include: at 1002, patient dose regimen information for an analysis period is received by one or more processors. The one or more processors may store patient dose regimen information in memory for processing. The method 1000 may further include: at 1004, a measure of correspondence between the time-related data and patient dose regimen information is evaluated. In one aspect, the patient dose regimen information may be or may include: typical fixed doses of medication taken at meals and typical times of day when breakfast is taken. For another example, the patient dose regimen information may be or may include: information defining the frequency of patient compliance with a planned dose or meal. In some embodiments, the patient dosage regimen may include information regarding the type, amount, and/or time of the drug. In particular, information about a schedule for administration of one or more medicaments.

The consistency metric may include any numerical metric for comparing consistency between data sets, such as mean and standard deviation or interquartile spacing (IQR). Evaluating the consistency metric may include comparing to a fixed or variable threshold. The consistency measure may also be a measure of variation between, for example, time-dependent data and expected data calculated based on the patient dose regimen. The variable threshold may be determined using machine learning or other algorithms. More specific examples are provided above and below.

The method 1000 may further include: at 1006, dose guidance information is determined based on the consistency metric. Once determined, the processor may output the dose guidance information to a display or other output device for use by the patient or HCP, or stored in memory for later use. In an aspect, the dose guidance information may be or may include dose guidance information as exemplified above. The dose guidance may also be information that alters the type, amount or time of the dose of a particular drug.

The method 1000 may include additional operations 1100, 1200, and/or 1300 illustrated in fig. 7C-7E. Additional operations may be performed by one or more processors of DGS100 in any operable order, and the presence or absence of any one or more of the operations shown in any figure does not necessarily imply the corresponding presence or absence of other operations shown in that figure. Instructions for performing method 1000 and/or any one or more of additional operations 1100, 1200, and 1300 may be stored in memory for execution by one or more processors of the DGS.

As shown in fig. 7C, operations 1100 of the method 1000 may include: in 1102, dose guidance information is output to a display or other output device. The method may further comprise: in 1104, patient dose regimen information is received from an input component, for example, via input from a touch screen of a display device. Alternatively or additionally, the method may comprise: at 1106, patient dose regimen information is received via transmission from a remote data server.

The method 1000 may further include an operation 1200 for estimating a consistency metric as shown in fig. 7D. The method 1000 may include: in 1202, each dose of a patient dose regimen is classified in a medication category based on time-related data. The method may include: in 1204, each of the doses is grouped in one of a set of dining groups, such as breakfast, lunch, and dinner, based on time of day and/or other factors. Alternatively, the method may comprise: each of the doses is grouped by time period (e.g., an analysis once per hour). The method may include: in step 1206, a dose parameter for the patient is generated at least in part by applying the data for each of the dining groups to the model. In some implementations, the model may be based on historical data from the user or from the population study. The method may further comprise: in 1208, dose parameters for configuring the dose guidance settings are stored.

The method 1000 may include further operations 1300 as shown in fig. 7E. The method 1000 may include: in 1302, time-related data characterizing an analyte of a patient over a period of, for example, 10 days, 14 days, or 21 days is accumulated prior to estimating a consistency metric. The method may include: at 1304, dose guidance information is determined at least in part by reducing dosing recommendations based on detecting that fluctuations in analytes in the time-dependent data exceed a low threshold. The recommendation should be related to the meal dose, which is related to the fluctuations.

The method may include: at 1306, patient compliance with the patient dose regimen information is determined based on the time-related data. The method may include: in 1308, based on the consistency metric, it is determined whether to output a dose guidance parameter. For example, if the compliance metric indicates adequate patient compliance, dose guidance information as determined by the DGS is provided. If compliance is slightly consistent, DGS provides dose guidance with warnings regarding compliance. If compliance is inconsistent, the DGS provides information to the patient or HCP that the correction is needed before guidance can be provided.

In an aspect, in 1310, the method may include: if the compliance metric indicates an unreliable system configuration (e.g., unreliable data), a dose guidance parameter is output that includes a predetermined dose recommendation. When a dose guidance is not available by the method in use, the predetermined dose suggestion may be retrieved from memory as a contingency plan (fallback).

HCP participation in learning phase

If the system configuration is marked as suspicious, then dose guidance may not be automatically initiated. DGS100 may provide the patient with two options: (a) Solving the drawbacks described in the learning/compliance report, and repeating the learning period; or (b) the next time the patient accesses his HCP, looking at the results/output of the learning period. For the second option, the system will allow the HCP to configure the system and initiate the dose guidance, or provide advice to the patient and repeat the learning period on how to address the deficiencies covered in the report.

Many HCPs lack the time to acquire web portal accounts and configure patient dose guidance systems. Thus, the DGS100 may provide an efficient tool to facilitate the assistance of the HCP to the configuration. When the learning period has been completed and the system configuration is marked as suspicious, the DGA may display control features that will allow the patient to initiate the following process that facilitates HCP configuration of the system.

When the patient next contacts his HCP (e.g., with a meeting or via telemedicine), the patient may press a button or select an option that will begin the process. Subsequently, the DGA may display URLs and/or codes for the HCP to enter its web browser. The code may be randomly generated by the DGA and associated with the patient. When the HCP types the URL into its internet browser, the DGA may open a web page that provides UI controls for typing these codes. For example, the code may be 4 alphanumeric characters or any other acceptable form. DGA may only consider this code valid for a limited time (e.g., 5 minutes or 15 minutes). When the HCP types in the code, the DGA may check if the code is valid and, if so, provide the HCP with access to the patient's dose guidance learning and configuration information. In alternative embodiments, HCPs may be additionally required to type in their medical licenses, and the system may only have access to the entered license if they match the format requirements of the medical license. The browser of the HCP may store the medical license number for subsequent interactions with the web page.

When the HCP has access rights, the DGA may display a GUI report 1330, an example of which is presented in fig. 9A-1 and 9A-2, which provides information about what 1332 the patient typed into the DGS100 and what 1334 the DGS100 observed and learned during the learning period determines. The parameters listed in report 1330 may include a dose 1342, a dose time 1341 (time to take various doses or typically take various doses), a dosing time range 1344 (time to start and end dosing for each meal dose), and correction parameters 1345. Dose 1342 may include amounts for the base dose and the dose per meal (breakfast, lunch, and dinner). Correction parameters 1345 may include target glucose (mg/dL), pre-meal correction factors (mg/dL/U), and post-meal correction factors (mg/dL/U). Patient entered data 1332 may include a dose 1342, a dosing time 1341 (e.g., a time at which a base dose and a dose per meal (breakfast, lunch, and dinner) are typically taken), and a dosing time range 1344 (e.g., a start time and an end time defining a time range during which a meal dose for each meal is typically taken). The observed or learned values 1334 may include a dose 1342 for a base dose and a per meal dose, a dosing time 1341 for the base dose and the per meal dose, a dosing time range 1344 (observed start and end times of the per meal dosing time range), and correction parameters 1345 (target glucose, pre-meal correction factors, and post-meal correction factors). Report 1330 can also provide information and/or alerts regarding compliance issues for any or all of the parameters listed, for example, in observation notes 1336. The report may also provide conservative values 1340 of correction parameters 1345 or any other parameters listed in report 1330. For example, if compliance observations indicate under-dosing, an alert may be provided to indicate to the HCP that it may be safer to set the initial dose regimen to a lower learning dose (e.g., a conservative value) 1340. Report 1330 may also provide a means for the HCP to manually enter the dose regimen parameters, or to copy the values into the report from other appropriate portions, for example by entering the values in fields under the initial treatment for dose guidance 1338. The report may further include means for the HCP to approve the initial protocol; when the HCP selects the approve UI feature 1337, the system may download the initial dose regimen into the patient's device and initiate a dose guidance. The HCP may also reject the proposed treatment in report 1330 by selecting reject treatment option 1339. If the dosage regimen parameters are not fully filled or out of range, an error message may be provided to the HCP. In another alternative embodiment, the control features in the DGA may initiate the same process on the patient's phone (i.e., a user interface capable of typing in dose guidance parameters) except that a URL and code for the HCP to type in is displayed. In yet another embodiment, rather than (or in addition to) displaying URLs and codes, the system may provide a UI tool that types in an email address, where the patient may type in an email address such as a HCP, and the system may then send an email to that address embedded with a hyperlink that, when selected by the user receiving the email, may open a report web page with patient data and information on the user's web browser.

In some embodiments, the DGA may provide a status overview of patients under the care of the HCP or treatment site. The DGA may display a GUI report 1450, an example of which is presented in fig. 9G, which provides an overview of information about all patients under the care of the HCP or treatment site, as well as various statistics related to the patient's diabetes management. Report 1450 may include records for multiple subjects including a column or field of subject identifier 1452, study type 1454, status 1456 of the subject, pending approval request 1458, time within goal (TIR) 1460, time below low threshold 1462 (e.g., 70 mg/dL) (TB 70), time above high threshold 1464 (e.g., 180 mg/dL) (TA 180), basal dose% 1466 taken, average of bolus doses taken per day 1468, glucose capture (%) 1470, and number of days 1472 the subject is in the study. Any field may include a filter option that allows the HCP to order the order of records as desired. Report 1450 may display at least one, alternatively at least two, alternatively at least three, alternatively at least four, alternatively at least five, alternatively at least six of the time in the target range, the time below the low threshold, the time above the high threshold, the percentage of basal doses taken, and the average bolus dose taken per day.

Report 1450 may display a column including subject identifier 1452, such as the subject's number or name.

Report 1450 may display a column containing study type 1454 the subject was enrolled in. The study may be a exploratory study or other type of study.

Report 1450 may display a column containing status 1456 of the subject. The status may be one of "pre-view", "view 1", "view 2", "protocol approval", "protocol update", or "end of study". If the status is "recipe approval" or "recipe update," then the date 1474 that the recipe was approved or updated may be listed below the status. In some embodiments, date 1474 may be grayed out or a lighter font than status.

Report 1450 may display column 1458 regarding the status of approving the request. Approval request column 1458 may include an icon 1476 (e.g., an orange triangle with "|" in the center) indicating an action that the HCP needs to take if there is an outstanding request waiting for the HCP to view and approve. The report may also contain a statement 1478 indicating how many subjects have a dosage regimen that requires approval by the HCP.

Report 1450 may display a column reporting the amount of time that the subject's glucose level is within a target range ("time within target range" or "TIR") 1460, as described elsewhere in this disclosure. TIR may be calculated for the number of days the subject participated in the study and/or the number of days the subject is on the current dosing regimen.

The report 1450 may display a column reporting the amount of time that the subject's glucose level is below a low threshold (e.g., 70mg/dL ("time below 70 mg/dL" or "TB 70") 1462).

Report 1450 may display a column reporting the amount of time that the subject's glucose level is above a high threshold, such as 180mg/dL ("time above 180 mg/dL" or "TA 180") 1464. The time above the high threshold may be calculated for the number of days the subject participated in the study and/or the number of days the subject is on the current dosing regimen. In some implementations, the high threshold may be about 170mg/dL, alternatively about 175mg/dL, alternatively about 180mg/dL, alternatively about 185mg/dL, alternatively about 190mg/dL, alternatively about 195mg/dL, alternatively about 200mg/dL.

Report 1450 may display a column reporting the basal dose 1466 taken, for example, as a percentage. The% 1466 of the basal dose taken may be calculated for the number of days the subject participated in the study and/or the number of days the subject was on the current dosing regimen.

Report 1450 may display a column reporting average 1468 of bolus doses taken per day. Average number of bolus doses taken per day 1468 may be calculated for the number of days the subject participated in the study and/or the number of days the subject is on the current dosing regimen.

Report 1450 may display a column reporting glucose capture 1470, e.g., as a percentage. The glucose capture percentage 1470 may be calculated as a percentage of glucose readings received by the system from the sensor over a set period of time (e.g., the number of days the subject participated in the study and/or the number of days the subject was on a current dosing regimen (e.g., prior to adjustment).

Report 1450 may display a column reporting the number of days 1472 that the subject has registered in the study.

In some embodiments, DGA may provide reports summarizing approved treatments, as well as statistical analyses of glucose levels and dosages different from approved treatments. The DGA may display a GUI report 1486, an example of which is presented in fig. 9I, which provides information regarding approval of the treatment 1488 for a particular patient 1496, as well as an AGP chart 1490, a missed dose graph 1492, and a user override (override) graph 1494. The clinician 1498 may switch back to its complete list of patients to view another patient's GUI report 1486.

Approving the treatment 1488 may be presented in a table that includes the following columns for: the date of treatment approval, the type of dosing strategy (e.g., initial, adjusted, manual override), various insulin doses, the length of the treatment period (e.g., days), the average number of bolus doses per day, the number of low glucose events, TIR (%), time below low threshold (e.g., TB70 (%)), time above high threshold (e.g., TA180 (%)), and glucose capture (%). The types of administration strategies may include, but are not limited to, initial, regulated, and manual override. The types of insulin doses whose amounts are reported include, but are not limited to: a basal dose; fixed doses of breakfast, lunch and dinner; and doses with correction factors for breakfast, lunch and dinner. The percentages in the table may be calculated for the time period for which the subject was enrolled in the study or for the time period for which the subject was in a particular dosing strategy, for data indicative of the time period (e.g., data of the last two weeks).

The AGP graph 1490 depicts a dynamic glucose map (AGP) showing glucose readings of the 5 th, 25 th, 50 th (median), 75 th and 95 th percentile per hour presented over a 24 hour "typical" full day based on all days within a selected timeframe. Alternatively, the AGP may display other percentiles presented within 24 hours of a "typical" full day based on all days within the selected timeframe, such as glucose readings at 10 th, 25 th, 50 th (median), 75 th and 90 th percentiles per hour. The AGP graphic may further include two horizontal lines indicating the boundary of the target range. For example, the first line may correspond to a lower boundary of the target range (e.g., 70 mg/dL) and the second line may correspond to an upper boundary of the target range (e.g., 180 mg/dL). The first and second lines may also be color coded. Data points falling below the lower boundary may be colored in a different color (e.g., red) than other data points to highlight the data points. Thus, the AGP graphics readily show the amount of time spent within and outside of the target range (or the amount of readings falling within the target range). Other exemplary AGP graphics can be found, for example, in US 2018/0235218, US2014/0188400, US2014/0350369, US2018/0226150, all of which applications are expressly incorporated herein by reference in their entirety for all purposes.

The graph 1492 of missed doses represents the percentage of the total number of doses received or administered for each of the base dose, breakfast dose, lunch dose, and dinner dose over a period of time. The graph can easily show to the HCP whether the subject has missed a large number of doses of a particular type and recommend correction metrics. The percentage in the graph may be calculated for a time period in which the subject was enrolled in the study or for a time period in which the subject is in a particular dosing strategy, for data indicative of the time period (e.g., data of the last two weeks). The graphic may be configurable so that the HCP may select a desired time window from a drop down menu. The graph 1492 of missed doses may be a bar graph in which one axis is the dose type (basal, breakfast, lunch and dinner doses) and the other axis is the percentage of missed doses in the total dose.

The graph 1494 of user override indicates that the subject did not take the percentage of recommended doses for each of the basal, breakfast, lunch, and dinner doses in the total number of doses received or administered. The graph can easily show to the HCP whether the subject is disregarding a recommended dose for a particular type of dose or meal and recommends a correction metric. The percentage in the graph may be calculated for a time period in which the subject was enrolled in the study or for a time period in which the subject is in a particular dosing strategy, for data indicative of the time period (e.g., data of the last two weeks). The graphic may be configurable so that the HCP may select a desired time window from a drop down menu. The user override graph 1494 may be a bar graph in which one axis is the dose type (basal, breakfast, dose, and dinner dose) and the other axis is the percentage of the user override dose in the total dose.

In some embodiments, the DGA may provide a graph showing the correlation between the recommended dose and the actual dose taken by the subject. The DGA may display a GUI 1480 of recommended and taken doses, an example of which is presented in fig. 9H, which represents the dose taken at different times of the day and the dose taken relative to the recommended dose. As seen in GUI 1480, x-axis 1481 may be time in minutes, hours, days, or weeks. In some embodiments, the graphic may span 12 a.m. to 12 a.m.. The graph may display data accumulated over a period of days, weeks or months, and the dose may be represented on the same graph. For example, the profile may include all doses administered during the period that the subject is in a particular study period or that the subject is following a particular dosing regimen.

y-axis 1482 may be the difference between the taken dose and the recommended dose. If the dose is taken the same as the recommended dose, the y-coordinate for that dose will be zero and the x-coordinate will be the time the dose was administered or taken by the user. If the dose is taken X1 units greater than the recommended dose, the y-coordinate of the dose will be X1. (e.g., if the dose is taken 2 units greater than the recommended dose, then the y-coordinate of the dose will be 2.) if the dose is taken X2 units less than the recommended dose, then the y-coordinate of the dose will be-X2. (e.g., if the administered dose is 1 unit less than the recommended dose, then the y-coordinate of the dose would be-1.) for example, if the administered dose is the same as the recommended dose of the fixed breakfast dose at 7:50am, then the corresponding point 1483 on the graph would be (7:50 am, 0). If the basal dose taken at 9pm is 2 units greater than the recommended basal dose, the graphically corresponding point 1484 would be (9 pm, 2).

The doses that may be presented in the graph include, but are not limited to: a basal dose; fixed doses of breakfast, lunch and dinner; and doses with correction factors for breakfast, lunch and dinner.

In some embodiments, as shown in fig. 9B, a report 1350 detailing the compliance of the regimen may also be displayed. The report 1350 may be accessed by the patient, HCP, or other interested party. The compliance report 1350 may cover a period of time (e.g., one week, alternatively 2 weeks, alternatively learning period duration) and include a table 1352 listing different analyses for each of the different types of doses 1351. Different types of doses 1351 may include basal doses, breakfast doses, lunch doses, dinner doses, and correction (e.g., post-meal correction) doses. Table 1352 may list a dose count 1354 of taken doses, missed doses 1356, requested instructions 1358, an average 1360 of delta of taken and instructed doses, and delta IQR 1362 of taken and instructed doses for each of the basal, breakfast, lunch, dinner, and/or post-meal corrections for different dose types.

The definitions and/or interpretations 1364 of each of these categories may be listed below table 1352. Dose count 1354 may be a simple count of the type of dose during the relevant period (e.g., the last week). Missed dose 1356 may be reported as a percentage and may be calculated by (7-basal count dose)/7 for the basal dose of the last week. The missed dose 1356 for a meal dose can be calculated as (missed dose detection without associated dose)/(all missed dose detections). The missed dose 1356 for postprandial correction may be calculated as (postprandial correction alert without related dose)/(all postprandial correction alerts). The instruction request 1358 may be reported in units of insulin and may be calculated as (dose associated with instruction display)/(all doses). The delta average (administration guidance) 1360 may be reported in units of insulin and calculated by calculating the average difference for the dosages associated with the guidance. The increment IQR (administration guidance) 1362 may be calculated by calculating IQR for the dose associated with the guidance. The increment IQR has the standard meaning of a person skilled in the art and refers to the difference between the third quartile and the first quartile.

Additional metrics 1366 and statistics related to these metrics may also be listed in report 1350, including late dose frequency, postprandial frequency, automatic and manual dose classification, non-meal and non-meal correction frequency, snack dose frequency, percentage of doses taken per instruction (which may be the doses associated with the time instruction, not just aggregate doses), and percentage of doses taken per warning.

FIG. 9C illustrates an exemplary graph 1370 associated with cluster analysis to determine a meal period, which may optionally be displayed to the HCP. Graph 1372 plots cumulative counts of all doses during the learning period versus time of day at which the doses occur (e.g., in ascending order of time of day). The user entered meal time range values 1374 and the user entered meal time intermediate points 1376 may be indicated in the graph 1370, if possible, near the x-axis, for example. The fast increasing portion of the curve indicates each meal time cluster. In alternative embodiments, the graph may be plotted as a histogram or pie chart of counts. The learned typical meal time 1378 may be indicated by a vertical line in the graph 1370. Line segment 1378 has a length equal to the amount of insulin injected. If there is more than one injection in a 15 minute period, this length is equal to the sum of all the injected amounts—this is necessary because sometimes the patient will make multiple injections for a meal, for example if they are using an insulin pen containing less than the full amount covering the required dose, and the patient refill the pen (or obtain a new insulin filled pen) and complete the required dose; or some patients require insulin doses greater than the injection dose capacity of the pen, thus requiring multiple injections; or some patients experience pain due to the injection of large amounts of insulin at one injection, they are administered with multiple injections. The learn meal time range 1380 may be indicated by a horizontal line located at the intersection of the typical time and dose line.

Fig. 9D shows an exemplary graph 1390 associated with dining dose clusters and doses. Graph 1392 plots each of all doses during the learning period, with TOD at the time of dose occurrence. Each dose is indicated by a dot 1394 to indicate different doses at the same time of day. The thickness of the strip associated with the time of day may be varied to correspond to the number of doses in order to show when the same dose is present at the same time of day. As seen in the embedded window, the thickness of the dots 1394 and bars allows the viewer to distinguish between varying doses taken at the same time of day. The doses taken within a short period of the same day (e.g., within about 15 minutes) may be added together (e.g., 7.5+1.5=9 units). The additional dose may be indicated with a different color or type of line 1396. Alternative ways of illustrating this may be used.

A user interface control may be provided to switch between showing the results of all data and showing only the results of doses associated with pre-meal glucose below a certain threshold (e.g., 150 mg/dL). Alternatively, a user interface control may also be provided to allow the user to set the threshold.

Fig. 9E shows an exemplary chart 1400 associated with pre-meal correction factor determination. Graph 1402 plots the point 1404 for each insulin dose for all initial meal doses during the learning period, the pre-meal glucose associated with each dose for a particular meal. The curve fitted by each model performed in the learning analysis is superimposed with the associated learning parameters. The models may include a P1 zero slope model (without correction) 1406, a P2 piecewise linear function 1408, and a P3 nonlinear function 1410. The best fit curve may be highlighted in some way, for example, with a thicker curve (compare 1406 to 108 and 1410). DGA may provide four charts, one for each meal and one for the whole, as previously described.

If DGS fails to adequately learn the patient's dosing regimen and more dose guidance is performed, DGA may provide a tool with which HCPs share reports on learning status. It should be noted that the method can also be used to share any type of report. A method 1420 for facilitating HCP efficient access to reports generated by DGS100 while preserving patient health data privacy is depicted in fig. 9F.

In step 1422, a session with the patient is authenticated by at least one processor of the portable display device. Authentication may be accomplished when the patient logs into the DGA or other known authentication method. Most commonly, this function is served by an authentication function provided by the patient's smart phone.

In step 1424, the at least one processor may determine whether an input is received from the patient during the session indicating a request to share EMR with the HCP. The DGA may provide user interface selectable features to allow a user to indicate a desire to share a report. If it is determined that a request to share EMR has been made, at least one processor can generate a report sharing identification code (ID) in step 1426. The ID may be an alphanumeric code, a numeric code, or any other suitable format. The code and URL of the remote report access server may be displayed together by the DGA.

In step 1428, the at least one processor may provide the ID along with the data required to generate the report to a remote report access server that controls access to the report. In step 1430, the HCP may then launch a standard web browser on their PC and type in the URL displayed by the DGA as appropriate. The report access server may then cause the browser to display a screen to accept the ID. The HCP may type in the ID, the browser may send the ID to the server, and if the ID matches the ID sent from the DGA and has been received a period of time (e.g., 20 minutes) since the DGA was sent, the server may send a report to the browser.

In another aspect, the method 1420 may further include: a step of determining whether the DGS has not reached a consistency condition between the patient typing regime and the learning regime. If it is determined that the EMR fails to meet the consistency condition, the method may include: a step of providing the patient with an option to provide a report of the learning outcome to the HCP. In one embodiment, the step of generating the report access ID may be conditioned on determining that the EMR does not satisfy a consistency condition.

In another aspect, the method may include: the remote server creates a user interface (e.g., a web page) addressed at least in part by the ID for displaying the report. The report may include: determining a dosing parameter of a drug to be administered to a patient at a time within a defined period of time, and determining a measure of compliance with patient supply dosing information for the drug, as seen for example in fig. 9A-1 to 9A-2.

An alternative embodiment includes the DGA providing the patient with a user interface tool for the patient to directly generate a report for display on the dga—the report may be displayed to the HCP. It should be understood that each of these steps is optional and is not necessarily required in the process.

Adjustment timing after learning

During the learning period, insulin dosage may not be fully characterized as during the coaching period. Thus, the initial adjustment using data from the learning period may be different from the adjustment of the coaching period. Specifically, DGS 100 may only adjust fixed doses during learning periods, and CF may not be adjusted. Furthermore, the modulation may be performed only if a hypoglycemic pattern is detected by the fixed dose modulation algorithm. Any pattern of hypoglycemia detected may trigger a dose reduction of the insulin dose prior to the time period described in the fixed dose adjustment section above. The GPA algorithm may be used to estimate a fixed meal time period. The bedtime may be defined as 6 hours after a fixed dinner dosage time or about 6 hours before a fixed breakfast time, whichever occurs earlier.

User feedback during learning

Exemplary embodiments of a method for obtaining user feedback during or after a learning period of a DGA will now be described. DGA may be utilized to prompt the user for feedback during the initial learning period. The user feedback may provide an indication to the user that the system is progressing. The DGA may prompt the user for feedback (e.g., input or confirmation) regarding any aspect of the dose guidance, including: lack of information about the administered dose, analyte history, patient behavior or activity, general dosing strategy, type of particular dose, confirmation that DGA determines (e.g., by system learning) that the dose type or strategy is correct, etc.

During (or after) the learning period, DGA may output prompts or other indications on UID 200 requesting user feedback. The feedback may relate to a dosing strategy, for example, a strategy related to the type of insulin action (e.g., long-acting and/or short-acting or quick-acting). If feedback (or other determinations) indicates that a long-acting strategy is being used, the DGA may monitor the patient's basal dosing pattern over a first period of time, for example, three (3) days, to classify each dose or dosing pattern as a single dose or split-dose type, and/or characterize the dose over time (e.g., single dose in the morning, single dose in the evening, or split-dose (e.g., both in the morning and evening)). DGA may also determine trends regarding dose (e.g., median, average) and related dose changes. Based on this information, DGA can develop the expected basal dose. After the first TOD period, if the actual dose administered (e.g., automatically registered by MDD 152 or entered by the user) is different than expected, feedback may be prompted to the user.

According to one aspect of an embodiment, the user may be prompted in many different situations. For example, the DGA may be configured to detect a missed dose, e.g., when the user does not administer a base dose or bolus dose within a period of time in which the previous base dose or bolus dose was administered. If a missed dose is detected, the DGA may be configured to request input from the user as to whether to administer a basal dose within that period of time. According to some embodiments, the DGA may also be configured to detect differences in dose time. For example, the DGA may be configured to detect when a user is administering a basal dose (e.g., administering a basal dose that is typically administered in the morning in the evening) during a different time of day period than the time of day during which the previous basal dose was administered. When such a difference in administration time is detected, the DGA may be configured to request input from the user as to whether to administer the basal dose within different time periods. In another aspect of an embodiment, the DGA may also be configured to detect when an additional dose has been administered. For example, DGA may be configured to detect changes in the number of basal doses administered in days. In yet another aspect of an embodiment, DGA may be configured to detect whether a dosing strategy on a first day (e.g., administration of one basal dose) is different from a dosing strategy on a second day (e.g., administration of two basal doses). When a different dose strategy is detected, the DGA may be configured to request input from the user as to whether the user has adopted the dosing strategy used the next day as a new dosing strategy. In yet another aspect of an embodiment, the DGA may be further configured to detect whether different doses are administered. For example, DGA may be configured to detect whether a first dose administered during a time of day is different (smaller or larger) than a previous dose administered during the time of day of the previous day. When a different dose is detected, the DGA may be configured to request input from the user as to whether the user has changed the dose.

The user's response to these cues may allow the DGA to confirm that it has identified the correct mode (e.g., the user confirms that they missed taking their breakfast base dose, but they will typically take the dose), or provide the user with the opportunity to correct the mode (e.g., the user notifies the DGA before taking, they adjust the base dose based on their glucose).

For a rapid-acting insulin administration strategy, DGA may include cues regarding dose classification in addition to those described above. Dose classification may include, but is not limited to: bolus, correction, split dose, bolus+correction, bolus+carbohydrate count+correction classifications.

The user may be prompted in many different situations regarding the administration of fast acting insulin. DGA may be configured to detect whether a dose unrelated to a meal is administered. For example, the DGA may be configured to determine whether a dose was taken during a period of time when a meal was not identified or detected. If the DGA detects that a dose is taken and no meal is detected within the administration period (e.g., within about 1 hour of administration), the DGA may request input from the user regarding the reason for administering the dose (e.g., because they eat, because they want to lower glucose, or because they complete an earlier meal dose). The DGA may also be configured to detect if the meal dose does not match a previous meal dose associated with the same meal type. For example, the DGA may be configured to determine whether a bolus dose associated with a first meal type and administered during a time of day is different from a previous bolus dose associated with the first meal type and administered during the time of day of the previous day. When such a difference in bolus doses is detected, the DGA may be configured to determine the cause of the different doses. For example, the DGA may be configured to determine a difference in pre-meal glucose values associated with the bolus dose and the previous bolus dose to determine whether the detected difference is a correction. The DGA may also request input from the user regarding the cause of the bolus dose discrepancy (e.g., because they eat less/more food and/or they are correcting hyperglycemia and/or they are correcting other factors).

In addition to enabling the DGA to determine what type of quick-acting dose to take throughout the day, this enables the DGA to facilitate when to expect a dose. After a learning period in which no cues are provided, the DGA may provide these cues to the user in the event that the dose differs from the intended dose in order to refine a model of the user dosing strategy of the DGA.

For long-acting and quick-acting doses, DGA can be aimed at minimizing the number of prompts over time and as the user responds. The cues may be emphasized frequently at the beginning and gradually reduced as the repetitive pattern is observed.

Glucose profiling and meal bolus modulation for MDI insulin administration therapy

Exemplary embodiments of a method for determining meal bolus adjustments will now be described. Once the system learns (or is configured with) the patient's current dosing strategy, the system may provide regulatory guidelines for Multiple Daily Injection (MDI) dosing therapies. For patients administered with a fixed meal, the fixed dose may be adjusted (e.g., for breakfast, lunch, dinner, snack, etc.). For patients with carbohydrate counts, the carbohydrate ratio may be adjusted for these same meals or at different times of the day. Patients who are administered empirically can adjust their dosage on a per meal basis. Regulatory guidelines for DGA may provide a recommendation to vary the dose or carbohydrate ratio in a particular direction. The amount of change may be a suitable percentage change, e.g., 5%, 10%, 15%, etc. The dose guidance may further comprise beginning a meal dose. For example, if the patient is in a basal plus one (e.g., lunch dose) regimen and breakfast shows a hyperglycemic pattern, DGA may provide a recommendation for the breakfast to administer RA insulin.

DGA may require the definition of administration categories such as time of day (TOD) period, meal type (e.g., breakfast), and composition of meal (e.g., oatmeal milk). For example, the dosing category may be a time of day defined by a time period associated with a meal insulin dose over the time of day period. For another example, a post-breakfast period may be defined as beginning when a meal insulin dose is taken within a defined period of the day (e.g., between 5 am and 10 am), and ending when a post-meal period is defined (e.g., six (6) hours later), or ending when the next meal insulin dose is taken, whichever occurs earlier. One or more indicators may be required to define whether the postprandial glycemic response is nominal or in need of correction, or to rank the postprandial glycemic pattern as more or less favorable than another. The likelihood of a low glucose (LLG) indicator and the median glucose value can be used to quantify the degree of risk of hypoglycemia and risk of hyperglycemia, respectively.

An example of an implementation for deriving and determining risk indicators that can be used for Glucose Pattern Analysis (GPA) of DGA embodiments is described in U.S. patent publication 2018/0188400 (' 400 publication), which is incorporated herein by reference for all purposes. Among other things, the implementation utilizes central trends (e.g., average, median, etc.) and change data from a multi-day period to determine a risk indicator corresponding to the degree of risk of hypoglycemia ("hypo risk"). This implementation is outlined herein and a more detailed description of the implementation and its variants may be obtained therefrom by reference to the' 400 disclosure.

An alternative to the implementation described in the' 400 publication is set forth in U.S. patent publication 2014/0350369, which is also incorporated by reference herein for all purposes. For example, instead of using median and variance, the method may employ any two statistical measures that define the data distribution. As described in the' 369 publication, these statistical metrics may be based on glucose target range (e.g., G _LOW =70 mg/dL and G _HIGH =140 mg/dL). Common metrics related to target range are time within target range (TIR), time beyond target (t _AT ) And a time (t) _BT ). If the glucose data is modeled as a distribution (e.g., gamma distribution), then for a predefined threshold G _LOW And G _HIGH Can calculate t _AT And t _BT . For a threshold, the algorithm may also define t _{BT_HYPO} Wherein if t is _BT Beyond this, it may be determined that the patient is at high risk of hypoglycemia. For example, a high risk of hypoglycemia may be defined as every t _BT Greater than G _LOW When=5% of 70 mg/dL. Similarly, an index t may be defined _{AT_HYPER} Wherein if t is exceeded _AT The patient may be determined to be at high risk of hyperglycemia. By separately adjusting G _LOW Or t _{BT_HYPO} Or G _HIGH Or t _{AT_HYPER} To adjust the degree of risk of hypoglycemia and hyperglycemia. Three metrics (TIR, t) _BT And t _AT ) In (a) and (b)Any two may be used to define a control gate. These alternatives (and others) may be used to determine risk indicators for DGA implementations described herein.

The DGA implementations described herein may operate based on quantitative assessment of the user's analyte data over the TOD period. The quantitative evaluation may be performed in various ways. For example, embodiments described herein may evaluate analyte data over a period of days to determine one or more indicators that describe the associated risk that the analyte data exhibits within a corresponding TOD. These indicators can then be used to classify the analyte data from the TOD period as one of a plurality of modes. For example, the patterns may indicate common or generalized glucose behavior or trends for the TOD. DGA implementations may utilize any number of two or more modes. For ease of reference herein, these modes are referred to as glucose mode types, and embodiments described herein will reference implementations that utilize three glucose mode types (e.g., hypoglycemic mode, hyperglycemic/hypoglycemic mode, and hyperglycemic mode), although other implementations may utilize only two types or more than three types, and these types may be different from those described herein.

For example, using a fixed meal dose, once DGA has learned dosing strategies and dose or carbohydrate ratio amounts, a regulatory assessment can begin, which can be categorized into four regulatory categories: night, after breakfast, after lunch and after dinner. For each of these categories, DGA may map the two metrics (LLG and median glucose) described above to four logical "mode" variables according to the GPA method described below. Fig. 8A illustrates the operation of an exemplary method 400 of evaluating meal bolus adjustments for a Multiple Daily Injection (MDI) dosing therapy by DGA. The method 400 may include: in 402, an analyte pattern type for at least one TOD is determined by a DGA by performing a Glucose Pattern Analysis (GPA) algorithm that receives as input time-dependent analyte data originating from a sensor control device worn by a patient during an analysis period. The method 400 may further include: at 404, MDI dosing recommendations are selected by the DGA executing the recommendation algorithm based on the analyte pattern type and the patient's defined dosing strategy over the analysis period. The method 400 may further include: at 406, an indicator of the recommended action is stored by the DGA in computer memory to output at least one of the UID 200 or the MDD 152 to which the medication was administered to the patient. UID 200 may control the user interface using the indicator of the recommended action, for example, by causing a human-readable expression of the indicator to appear on a display, or by generating an audio output that expresses the indicator in human language. MDD 152 may use the indicator to adjust or maintain the next relevant dose administration. Further details of method 400 are described below.

FIG. 8B is a flowchart depicting an exemplary embodiment of a GPA method 410 that may be implemented as the GPA algorithm mentioned in 402. The method 410 may be performed for a particular TOD period, which may be a portion of a full day (e.g., a 24 hour period) or a day depicted by a block of time (e.g., three 8 hour periods) or user activity (e.g., dining, exercising, sleeping, etc.). In many embodiments, the plurality of TOD periods may correspond to meals (e.g., after breakfast, after lunch, after dinner) and sleeping (e.g., at night). These TOD periods may correspond to fixed times of day at which the activity will normally occur (e.g., from 5 a.m. to 10 a.m. after breakfast), wherein such time blocks may be set by the user, or may be dependent on the meal or activity actually having been performed, as determined by automatic detection of the meal or activity or by such user indication (e.g., with UID 200).

The DGA may perform the method 410 independently for each TOD period to obtain a separate mode estimate for that period. At 412, the DGA may determine center trend values and change values from the user's analyte data over a particular TOD period. The user's analyte data may be obtained from the user's own records or records of the user's healthcare professional, or the user's analyte data may have been collected by, for example, DGS 100. The analyte data preferably spans a period of days (e.g., two days, two weeks, one month, etc.) such that there is sufficient data in the TOD period to make a reliable determination. In other embodiments, the method may be performed in real time for limited data. DGA may use any type of central trend indicator related to the central trend of the data, including but not limited to a median or average. Any desired change index may also be used, including but not limited to: a range of variation across the entire dataset (e.g., from a minimum value to a maximum value), a range of variation across most of the data but less than the entire dataset to reduce the importance of outliers (e.g., from 90 th percentile to 10 th percentile, from 75 th percentile to 25 th percentile), or a range of variation targeting a particular asymmetric range (e.g., a low range that may span, for example, a range from a central trend value or from near a central trend value to a lower value of the data, e.g., 25 th percentile, 10 th percentile, or minimum value). The selection of the indicators representing the central trend and variation may vary based on implementation.

In 414, the DGA may evaluate a risk of hypoglycemia (hypo risk) indicator based on the central trend value and the change value. Such a method for determining a risk of hypoglycemia is described with reference to fig. 8C, which illustrates an exemplary embodiment of a framework for determining a risk of hypoglycemia and other indicators. Although FIG. 8C is intended to convey the framework to the reader, the framework may be electronically implemented in many different ways, such as with software algorithms (e.g., mathematical formulas, if-else statement sets, etc.), look-up tables, firmware, combinations thereof, or others.

FIG. 8C is a graph of center trends and changes (e.g., low range changes) that may be used to estimate or identify areas or regions of determined center trend and change data pairs that are stored or correspond to a particular TOD. Any number of two or more zones may be used. In this embodiment, the data pairs may correspond to the target region 425 or three hypoglycemic risk regions: one of low risk area 426, medium risk area 428, or high risk area 430. A first hypoglycemic risk function (e.g., a curve or linear boundary), referred to as a stroke risk function 422, distinguishes low risk region 426 from stroke risk region 428. A second hypoglycemic risk function, referred to as a high risk function 424, distinguishes risk area 428 from high risk area 430. The central trend and change data pairs may be estimated with respect to or compared with these regions to determine a hypoglycemic risk measure over a corresponding TOD period.

The hypoglycemic risk functions 422 and 424 may be explicitly implemented in the DGA as mathematical functions (e.g., polynomials) or may be implicitly implemented, such as by defining each region with its contained pairs, using a look-up table, if-else statement set, threshold comparisons, or otherwise. The hypoglycemic risk functions 422 and 424 may be preloaded into the DGA, or may be downloaded from the trusted computer system 480, or may be set by another party such as a HCP. Once implemented in DGA, the hypoglycemic risk functions 422 and 424 may be considered fixed or adjustable by the user or HCP. An exemplary method for determining a hypoglycemic risk function is described in the' 400 publication.

In 416, the DGA may evaluate a hyperglycemia risk indicator ("hyper risk") based on the central trend value. In this embodiment, the risk of hyperglycemia may be estimated by comparing the central trend value over a particular TOD time period to a central trend target or threshold 432. The magnitude and/or sign of the difference in the central trend value from the target 432 may identify the amount of risk of hyperglycemia. For example, if the central trend value is less than the target 432 (e.g., negative), there may be a risk of hyperglycemia. If the central trend value exceeds the target 432 (e.g., positive value) by less than a threshold amount (e.g., 5%, 10%, etc.), there may be a risk in hyperglycemia. If the central trend value exceeds the value of target 432 by more than the threshold amount, there may be a high risk of hyperglycemia. The use of three discrete groupings for hyperglycemia risk (e.g., low, medium, high) is exemplary, and any number of two or more groupings may be used.

In other embodiments, the DGA may evaluate the hyperglycemia risk indicator at 416 before evaluating the hypoglycemia risk at 414. Alternatively, in another embodiment, the assessment of the risk of hypoglycemia in 414 and the risk of hyperglycemia in 416 may be performed concurrently.

Other indicators, such as risk of change, may also be assessed. For example, a change value less than the first change threshold 434 may indicate a low change risk, a change value greater than the first change threshold 434 and less than the second change threshold 436 may indicate a medium change risk, and a change value greater than the second change threshold 436 may indicate a high change risk. Again, the use of three discrete groupings for varying risk is exemplary. DGA may use any number of two or more packets.

In step 418, the DGA may determine a pattern type for the TOD period based on the evaluated one or more risk indicators. In one exemplary embodiment, the mode determination may be evaluated using a low blood glucose risk indicator and a high blood glucose risk indicator. If the risk of hypoglycemia index is high, this mode may be set to a hypoglycemic mode (or "Lows" mode). Otherwise, if the risk of hypoglycemia is moderate and the risk of hyperglycemia is high or moderate, the mode may be set to a high/low (or moderate) mode (or "hyperglycemia predominates, sometimes hyperglycemia (Lows with Some Highs)" or "hyperglycemia predominates, sometimes hypoglycemia"). Otherwise, if the risk of hyperglycemia is high or medium and the risk of hypoglycemia is low, the mode may be set to a hyperglycemia mode (or "Highs" mode). If both the risk of hyperglycemia and the risk of hypoglycemia are low, the identified pattern may be "no problem" (e.g., displaying an "OK" message we output) (or "no pattern").

Thus, method 410 is an example of how DGA may output one of a plurality of pattern types for each TOD period. The number of pattern types in the pattern types themselves may be different from those described in the present embodiment (e.g., low, high/low, high). Once the pattern type for the TOD period has been determined, the DGA may store an indicator of the pattern type in a memory location for use in determining the adjustment recommendation. Referring again to fig. 8a, dga may continue at 404 to determine adjustment recommendations once GPA is completed for each relevant TOD period.

The recommended method may branch depending on the pattern type (e.g., low, high/low, high) and other factors including TOD period, dosing strategy, compliance with the strategy (e.g., whether a dose was missed), and whether there is sufficient data for evaluation. DGA does not make adjustment recommendations until enough data is available for the respective TOD period, e.g., if less than a threshold amount of data is available (e.g., less than a threshold number (e.g., five) of independent days) and a minimum portion (e.g., 90%) or more of data is available, DGA may omit evaluating and generating error messages.

Fig. 8D-8H illustrate exemplary branches of a recommendation algorithm or method for determining a dose adjustment recommendation based on the above-described input information. Other branches may also be useful. Fig. 8D shows a recommended method branch 440 for TOD with sufficient available data and possible reasons for the type of hypoglycemic pattern including one or more of a higher than optimal basal dose, meal dose, pre-meal correction dose, or post-meal dose. At 442, DGA evaluates whether the pattern type for the night TOD period is low. If the pattern is low, then at 444, the DGA generates a recommendation to reduce all relevant doses by an equal amount (e.g., 10%), including at least the base dose and optionally one or more of the meal dose, the pre-meal correction dose, or the post-meal dose. Regulatory recommendation rules for the hypoglycemic pattern may include: for the nighttime TOD period, a recommendation to decrease the long-acting insulin dose or basal rate is generated at 444. At 446, if any other TOD period has a hypoglycemic pattern, the DGA may generate a recommendation to reduce the fixed meal dose only for the relevant TOD period at 448.

In this embodiment, in 440, if at least one hypoglycemic pattern exists, no regulatory guidance is provided for any hyperglycemic pattern TOD time period. The idea here is to emphasize that hypoglycemia is prevented and that the dose is only increased when the risk of hypoglycemia is low for all TOD periods. Moreover, it is possible that in some cases, when the TOD period has a hyperglycemic pattern, this may be caused by the previous TOD period having a hypoglycemic pattern, and the patient binge eating to compensate for the hypoglycemia, so solving the hypoglycemic pattern itself may help solve the subsequent hyperglycemic pattern. In 449, if the pattern is not a hyperglycemic pattern, the process 440 waits or terminates without generating a recommendation, or proceeds to the hyperglycemic pattern evaluation 450.

Thus, DGA does not generate regulatory guidelines for the high/low glycemic mode. If there are no TOD periods for which regulatory guidelines can be given and the data is sufficient for all periods, the DGA may provide a message to the patient indicating that they need to address glucose changes before further regulatory guidelines can be given. In addition, DGA may provide reports to the HCP of the patient to consider alternative drugs or treatments that can address glucose changes.

Fig. 8E illustrates operations of generating a regulatory recommendation by the DGA for the hyperglycemic mode when the hypoglycemic mode TOD period is not present. At 452, if the overnight period has a hyperglycemic pattern and there are no other periods of time with risk in hypoglycemia, then at 454 DGA may increase the long acting insulin dose or basal rate recommendation. If at 456 the night time period has a hyperglycemic pattern and there is at least one other non-dinner time period with risk in hypoglycemia, then at 458 DGA may reduce the meal insulin dosage associated with any time period with risk in hypoglycemia. If the overnight TOD period has no risk in hypoglycemia and no hyperglycemic pattern at 460, the DGA may generate a recommendation to increase the meal insulin dosage associated with the first TOD period having the hyperglycemic pattern at 462. If the night time period has a risk in hypoglycemia and the only postprandial time period with a hyperglycemic pattern is dinner 464, the DGA may generate a recommendation to increase the prolonged insulin dose or basal rate 466. If the night time period has a risk in hypoglycemia but no post-dinner time period, then the DGA may generate a recommendation to increase the meal insulin dosage associated with the first TOD period having a hyperglycemic pattern at 462.

In alternative embodiments, the pre-meal glucose may be above or below the target glucose (e.g., 120 mg/dL). The calculated glucose data for the facilitated low and high risk indices for each meal may be modified to compensate for the effect of the previous meal or the conditions affecting glucose, which effect is not due to the current meal. DGA can modify these data by subtracting the offset so the resulting starting glucose is the target level. Alternatively, DGA may modify these data by a "triangle" function, where the difference between meal start glucose and target glucose is subtracted for meal start time, but the modification decreases over time; or the modification is linearly decreasing over a defined period of time (e.g., three (3) hours), or the data is modified by another decay function.

Alternatively, the function itself may be a function of meal starting glucose level or glucose trend and/or when a previous meal dose was taken.

According to another aspect of an embodiment, the algorithm for generating meal bolus adjustment recommendations may become more complex when additional aspects take into account, for example, missed meal doses, missed basal doses, post-meal corrections, and pre-meal corrections. If these factors are present, the algorithm for providing the appropriate recommendation may require that some data be excluded while still meeting the data sufficiency threshold after the data is excluded to provide guidance.

For example, referring to fig. 8F, if a hyperglycemic pattern is detected in 461, with some days missing a meal dose, then days with missing meal doses are excluded in 463 and GPA analysis 410 is repeated. If a hyperglycemic pattern is then detected in 465, the dose may be increased based on the pattern identified in the other TODs in 467, or waiting for further input or return in 469. Alternatively, the system may evaluate the hyperglycemic pattern using only data excluding days with missed meal doses. Algorithm 470 with this branching pattern is illustrated in fig. 8F. If the system detects a hypoglycemic pattern at 473, it may execute the hypoglycemic pattern algorithm 472 described in the following paragraphs. If the system does not detect a hyperglycemic or hypoglycemic mode, it may return to block 469 for further input or return.

In 472, for a missed meal dose, if the DGA detects a hypoglycemic pattern in the TOD period, and if the meal dose is missed on some days during the TOD, the DGA may generate a reduced dose recommendation. The recommendation may include, for example, reducing a fixed portion or correcting a dose portion.

Regarding missing the base dose, if DGA detects a low pattern 473 in the night TOD, missing the base dose should not affect the dose adjustment logic. Likewise, if a hypoglycemic pattern is detected in TODs other than the night time period, a missed base dose should not affect the dose adjustment logic.

If the DGA uses data comprising at least one day (or TOD) with a missed base dose to detect hyperglycemic pattern 461 in TOD, then data 463 with any one or more days (or TODs) with a missed base dose may be excluded and pattern analysis 410 repeated. Subsequent actions may depend on the particular TOD in which the hyperglycemic pattern is detected. For example, if the DGA detects a hyperglycemic pattern in the night TOD that includes data for at least one day with a missed basal dose, the data for any one or more days with a missed basal dose may be excluded and the pattern analysis repeated. If a hyperglycemic pattern in nighttime TOD is detected on the day(s) excluding the missed basal dose (doses), the basal dose may be increased because the nighttime TOD results may be used as a guide for adjusting the basal dose. If the hyperglycemic pattern is detected in TODs other than the night time period, days with missed basal doses may be excluded and the pattern analysis repeated. If a hyperglycemic pattern is detected on a day(s) excluding a missed basal dose (doses), the meal dose associated with TOD having the hyperglycemic pattern may be analyzed for adjustment as described herein. In either case, logic flow 470 is illustrated in FIG. 8F.

FIG. 8G illustrates an example of a logic flow 474 for developing a recommendation with post-meal correction. If after GPA 410, DGA detects a hypoglycemic pattern 479 of TOD, some of the days including postprandial correction, the following analysis may be used to adjust the correction or meal dose. If the DGA first detects a hypoglycemic pattern 479, it may exclude the data for days without postprandial correction, at 475, and first test if enough data is available, at 487. If insufficient data is available, the DGA may execute an error recovery routine 489, for example, to display an error message. If sufficient data is available, the DGA may repeat pattern analysis 410. Subsequently, if the DGA detects a hypoglycemic pattern, the post-meal correction dose may be reduced (i.e., the correction factor increased) at 476, which is found by pattern analysis among other TODs. Subsequently, if the DGA does not detect a hypoglycemic pattern, the meal dose may be reduced at 477.

For all embodiments described herein, modifying (e.g., adjusting) the correction dose in one direction may be accomplished by modifying the correction factor in the opposite direction. These two parameters are inversely related such that an increase in correction dose can be achieved by decreasing the correction factor and a decrease in correction dose can be achieved by increasing the correction factor. Thus, in all embodiments described herein, the DGA may recommend or implement a correction by modifying the correction factor or by modifying the correction dose. Thus, to the extent that the correction factors described herein are modified or adjusted, embodiments may be configured to achieve the same effect by an inverse modification to the correction dose; and conversely, to the extent that the correction dose described herein is modified or adjusted, embodiments may be configured to achieve the same effect by an inverse modification to the correction factor. In view of this interchangeability, two options may be used for each of the embodiments described herein, although for ease of description only, these two options will not be described for each embodiment.

Additionally or alternatively, starting from the original data set 491, in 478 the DGA may exclude days with missed meal doses. After finding sufficient data in 487, if pattern analysis 410 of these data with days of postprandial correction excluded in 490 does not indicate a hypoglycemic pattern, DGA may recommend reducing postprandial correction dose 476. Otherwise, DGA may implement logic 510 of fig. 10B published as US application serial No. 16/944,736, published as US 2021/0050085, the disclosure of which is expressly incorporated herein by reference in its entirety for all purposes, which may result in reduced dose-directed recommendations of meal insulin or pre-meal correction portions. If the DGA does not detect hypoglycemia at 479 and any high glucose mode is not detected at 492, further input or return may be waited for at 469. If the DGA detects a hyperglycemic pattern at 492, then process 480 (FIG. 8H) may be implemented at block 471.

Referring to fig. 8H, if DGA detects hyperglycemic pattern 493 of TOD, some of which days include postprandial correction, it may implement the following procedure 480 to develop recommendations for adjusting correction and dining doses. In 481, the DGA may include data for days with missed doses and post-meal corrections, and repeat pattern analysis 410. Subsequently, if the DGA detects a hyperglycemic pattern at 494, the post-meal correction dose may be increased (i.e., the correction factor decreased) at 482, which is subjected to pattern analysis findings in other TODs. If a hyperglycemic pattern is not detected in 494, the hypoglycemic pattern may be checked in 495 and if a hypoglycemic pattern is detected, return to 474 of fig. 8G, or await further input or return at 469. Although not shown in FIG. 8H, after excluding any data for GPA 410 and before executing the GPA, the DGA may test the sufficiency of the data and execute an error recovery routine if the available data is insufficient.

Alternatively or in addition, starting from the original dataset at 493, if pattern analysis excluding data outside the days with missed meal doses at 483 indicates a hyperglycemic pattern along one of branches 2.1 or 2.2, DGA may continue with procedure 480 as follows. On branch 2.1, if pattern analysis 410 of data excluding days of post-meal correction (i.e., data with bolus doses only) at 484 does not indicate a hyperglycemic pattern at 497, DGA may generate a recommendation at 482 to increase the post-meal correction dose, which is subject to pattern analysis by other TODs. Otherwise, the DGA may generate a recommendation to add the meal insulin or pre-meal correction portion according to procedure 550 of FIG. 10C of U.S. application Ser. No. 16/944,736 published as US2021/0050085, the entire contents of which are previously incorporated herein by reference.

On branch 2.2, if the pattern analysis included in 485 on data with days of post-meal correction alone did not indicate hyperglycemic pattern in 496, dga could add a meal insulin or pre-meal correction portion according to procedure 550 of fig. 10C of U.S. application serial No. 16/944,736 published as US2021/0050085, the entire contents of which were previously incorporated by reference. If a hyperglycemic pattern is not detected at 496, DGA may return to block 484.

If the correction factor adjustment recommendations from different TODs are conflicting, and if the patient is currently using the same correction factor for all TODs, the DGA may increase the correction factor. If all three components, i.e., meal dose, pre-meal correction, and post-meal correction are not ideal, program 480 may first increase the meal dose. The pre-meal correction may be adjusted gradually (up-time) after the meal dose has been adjusted. The post-meal correction may be adjusted gradually after the meal dose and pre-meal correction have been corrected.

If during subsequent analysis, within the TOD of the DGA generating the 'increase correction factor' recommendation by the method described above, a 'correction factor unchanged' recommendation will now result. Conversely, if a TOD having a 'reduced correction factor' recommendation by the aforementioned method still received a recommendation of a 'reduced correction factor', then a different TOD would likely be optimized by using a different correction factor.

While fig. 8D-8H illustrate aspects of various recommendation algorithms 404 used in the method 400, it should be understood that these are examples. Various other algorithms may also be suitable.

Instruction period

Description of the algorithm

During the coaching phase, DGS100 can: (1) Providing meal and post-meal corrected insulin dose recommendations to the user, and (2) adjusting the initially learned dose setting from the learning period to improve glycemic control.

Before the coaching period can begin, it may be necessary to initialize the insulin dose setting. Initialization may be accomplished by successful learning or by manually typing in an initialization value if learning is unsuccessful. Once the insulin dose setting is initialized, the user may receive insulin dose advice in a variety of ways. The user may request a particular meal dose via DGA. Alternatively, the DGS100 may detect and notify the user that a meal dose event is missed. If the user confirms that a meal dose event is missed, the DGS100 may provide a "late meal dose" recommendation to account for the fact: insulin administration occurs after the beginning of a meal, rather than at or prior to the beginning of a meal. Alternatively, DGS100 may detect and inform the user whether the glucose between meal doses is too high. If the user confirms that the glucose is too high between meal doses, DGS100 may suggest a "post-meal correction dose" to bring the user's glucose into the target range before the next meal dose.

The frequency of processing the subroutines for dose suggestion may vary. Those subroutines associated with detecting missed meal doses and high postprandial glucose may run continuously, while those subroutines associated with dose calculation may only run upon request by the user. Implementations of when a meal time or post meal dose suggestion may be evoked may be governed by the state transition diagram depicted in fig. 10. DGS100 may periodically adjust the fixed dose and correction factor values to improve the user's blood glucose. These adjustment algorithms may be processed daily and may operate independently of the dosing algorithm discussed above.

Custom settings

In some implementations, the user can customize certain settings. For example, in some embodiments, the user may indicate that certain data should be ignored by the algorithm. Alternatively, the user may be ill or taking a new medication, and the user may want to ignore the period of time. The DGS may include a "holiday mode" that allows the user to inform the DGS that the DGA should ignore certain past or future days or time periods for which the DGA may or may not have been atypical. For example, if the user is riding a cruise ship or wishes to exclude a weekend, the user may select a period of time for the DGS to ignore in determining adjustments.

In some embodiments, the user also adjusts the previously selected dose and time period if the user routine has changed. In some embodiments, if the time at which the user normally has a meal has changed, the user may type in a new time period for that meal so that the DGS can correctly document the meal dose.

In some embodiments, the user may also type in a dose adjustment due to the new medication. In some implementations, the change may only be changed if the user's HCP has reported that they typed the change. For example, HCPs may have developed new oral drugs for diabetes that DGS needs to consider. For example, a 10% increase in typing in a hypoglycemic agent (e.g., a steroid) or a 10% decrease in hypoglycemic agent (i.e., metformin) may be required. In the case of such a change, the DGS may adjust the indicated dose immediately, and the DFA may continue to learn and adjust based on the glucose results of the user after the dose change.

State transition diagram for drug delivery algorithm

DGS100 may recommend two types of fast acting insulin doses to the user, a meal dose and a correction dose. The meal dose is the dose that the user requests to address the glucose increase in response to a meal. The correction dose is the dose recommended by DGS100 for correcting high glucose between meal doses.

It is determined from the state transition diagram when the functions that calculate these meal doses and correct doses can be invoked. Fig. 10 presents state transition diagrams governing insulin administration, collectively referred to as Dose Guidance State Machine (DGSM) 2000.

The current state of dose guidance is determined by two factors: classification of time since dose delivered and recent past dose. Insulin dosage time is defined by the time stamp the system receives from the connected insulin pen. The classification depends on rules for defining insulin doses in other parts of the present discussion of real-time dose classification.

The meal dose state machine 2002, a wait state denoted as "meal dose guidance available state" 2004, may be defined as more than 2 hours from the initial meal insulin dose. In meal dose guidance available state 2004, a user may receive meal dose guidance by querying DGS100 for a dose recommendation. Once the insulin dose (including the timestamp and amount) is received by the DGS100 and correctly classified as the initial meal dose, the transition occurs to the "postprandial dose state" 2006 where the DGS100 will stay for a period of time, e.g. 2 hours, after the timestamp reported by the stylus. In this postprandial dose state 2006, the meal dose may not be recommended to the user. Any additional dose received by the DGS100 while in the post-meal dose state 2006 may also be considered a meal dose and may not affect the time spent in that state.

The corrected dose state machine 2010, in other parts of the application, defines criteria for triggering a corrected dose notification. Once any non-priming dose is received and the dose is classified as a corrected dose, a transition occurs from a wait state (correction dose guide only available) state 2014 to a corrected dose state 2016 where correction dose guide only may not be available. DGS100 may stay in this state for a period of time, e.g., 2 hours, from the time stamp reported by the stylus. While in the post-correction dose state 2016, no correction dose may be recommended. In the event that any non-primary insulin dose is recorded in the corrected dose state 2016, the timer for that period of time (e.g., 2 hours) will restart at the time stamp of the new dose. Performing a primary injection (priming) prior to administration of a dose of insulin will exclude air from the needle and syringe, ensuring that the correct amount of insulin is administered at full dose. The initial dose is part of a safety test in which a small dose of insulin (e.g., 2 units) is directed upward into the air and the user can look at to ensure that the insulin comes out of the tip of the needle.

As shown in fig. 11, in an exemplary method 2020, beginning with step 2022, DGS100 may receive or otherwise access insulin data from a subject (e.g., from MDD 152). For example, the DGA may check for up-to-date insulin delivery information by requesting delivery information from different sources, including but not limited to MDD 152, an application associated with the MDD, or an interface storing up-to-date insulin delivery information (e.g., an MDD application web server), or by checking memory of various applications to obtain up-to-date insulin delivery information.

In step 2024, DGS100 may classify the last drug dose administered as one of a certain class of doses. For example, the last dose administered may be a meal dose, a correction dose, or a prime dose. The most recent doses administered may be classified automatically by DGS100 or manually by the user, as explained elsewhere in the present application. If the most recent dose is the initial dose, no further action may be taken. If the last dose administered is a meal dose or a correction dose, then in step 2026, DGS100 enters a time period in which no recommendations for additional drug doses may be displayed. The period of time may be between about 1 hour and about 3 hours, alternatively between about 1.5 hours and about 2.5 hours, alternatively about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours, alternatively about 3 hours.

In step 2028, DGS100 may determine whether to administer at least one additional drug dose, i.e., after the time of administration of the earlier "last drug dose". If at least one additional drug dose is administered, DGS100 may classify the at least one additional drug dose as either a meal dose or a correction dose in step 2030. If at least one additional drug dose is a meal dose, then in step 2032 the time period during which the recommendation for the additional drug dose may not be displayed is not changed, i.e., the beginning of the time period is still the time when the last drug dose was administered. However, if the at least one additional drug dose is a correction dose, then in step 2034, the start of the recommended period of time for which the additional drug dose may not be displayed may be restarted and set to start when the at least one additional drug dose is administered.

Dining dosage calculation algorithm

Interaction with user interface

When the Dose Guidance State Machine (DGSM) and the dose classification state machine DCMM are up to date, the DGS100 may only display the dose guidance screen. As shown in fig. 12, in an exemplary method 2040, beginning at step 2042, DGS100 may receive or otherwise access insulin data from a subject (e.g., from MDD 152). For example, the DGA may check for up-to-date insulin delivery information by requesting delivery information from different sources, including but not limited to MDD 152, an application associated with the MDD, or an interface storing up-to-date insulin delivery information (e.g., an MDD application web server), or by checking memory of various applications to obtain up-to-date insulin delivery information. The DGA may record the dose at which the DGA electronically receives insulin dose information (e.g., dose and time of administration).

In step 2044, DGS100 may determine whether the received data includes the most recent drug dose data administered, i.e., whether the data is "up-to-date". In order to be up to date, DGSM may require: (a) The last recorded insulin dose confirmed by the user and/or (b) the time since reset (t _reset ) May need to be classified automatically by the system or manually by the user. The confirmation may be performed manually, for example, the DGS100 may prompt the user to confirm whether the last dose recorded was indeed the last dose Amount of the components. Alternatively, the validation may be performed by the DGA interrogating the stylus to ensure that it has the most recent dose information. In some implementations, display device 120 may be configured to transmit a request (e.g., an inquiry) for data to MDD 152 via a wired or wireless communication link. In response to the received request, MDD 152 may transmit data to display device 120. In some implementations, for example, the display device 120 may be configured to communicate with an insulin pen in accordance with a Near Field Communication (NFC) protocol. In other implementations, MDD 152 may autonomously send data to the reader device over a wired or wireless communication link. MDD 152 may be configured to transmit according to a schedule, based on a triggering event or condition, and/or when it comes within wireless communication range of a reader device. In some implementations, MDD 152 may be configured to communicate with display device 120 according to Bluetooth or Bluetooth low-power networking protocols. However, those skilled in the art will recognize that other wireless communication protocols (e.g., infrared, UHF, 802.11x, etc.) may be implemented. DCMM may be at t _reset And reset. Reset time (t) _reset ) May be set to midnight, but may alternatively be a different TOD depending on the time parameters of the dosage regimen. It should be understood that each of these conditions is optional and not necessarily required.

In step 2046, if it is determined that DGS100 has data related to the last drug dose administered, a screen may be displayed. The screen may be a home screen of the DGA that may include dose guidance recommendations. After the user confirms the last dose, the screen may be displayed for only a predetermined period of time. The predetermined period of time may be at least about 10 minutes, alternatively at least about 15 minutes, alternatively at least about 20 minutes, alternatively about 15 minutes. If a bolus record is recorded and the home screen or any of its secondary stream screens (dependent flow screen) is active, the DGA may exit these screens and request new user confirmation (if the dose is not automatically classified) and subsequent stream and logic, so the state machine may be updated before the home screen is displayed again.

Dose classification state machine

DCSM 2000 has information needed to determine how to display the meal carousel icon in the DGA home screen and how the icon functions when selected. As shown in fig. 13, in an exemplary method 2050, beginning with step 2052, DGS100 may receive or otherwise access insulin data from a subject (e.g., from MDD 152). For example, the DGA may check the latest insulin dosage data by requesting delivery information from different sources, including but not limited to MDD 152, an application associated with the MDD, or an interface storing the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications to obtain the latest insulin delivery information. Insulin dosage data may be related to the meal dose administered since the self-setting time.

In step 2054, DGS100 may determine whether the meal dose received from the reset time has been categorized. Meal doses may be categorized as being associated with a particular meal (i.e., breakfast, lunch, or dinner). As discussed with respect to other methods and embodiments described herein, meal doses may be classified automatically by DGS100 or manually by a user.

In step 2056, if it is determined that all meal doses received from the reset time have been categorized, DGS100 may display a screen including a plurality of meal icons. The plurality of meal icons may include icons for breakfast, lunch and dinner, respectively. Each meal icon may have a first appearance or presentation associated with a first state and a second appearance or presentation associated with a second state. The first appearance or presentation may be associated with a first state in which the meal dose administered from the reset time has been classified as a meal type corresponding to a breakfast, lunch or dinner icon. The second appearance or presentation may be associated with a second state in which the meal dose administered from the reset time has not been classified as a meal type corresponding to a breakfast, lunch or dinner icon. For example, if from t for a meal _reset Then there is an associated meal record, then the meal icon (each of breakfast, lunch or dinner) may be "shaded" (first state). If selected, the shadow meal icon may display the recorded dose information and, if available, provide an option for displaying meal dose guidance calculations.If during this period (since t _reset ) There is no associated meal record in (second state), the icon may be unshaded and if available, the meal dose calculation may be displayed when selected. When the time exceeds t _reset When all icons may be reset to unshaded (second state) and available for displaying meal dose calculations.

Variable t _reset Can be determined by the regimen parameters, the dinner dose time range, and the breakfast dose time range. If the dinner dose time frame is the last meal dose time frame before midnight, then t _reset Will be equal to midnight (this is most likely the case). Otherwise, t _reset Should be set to the intermediate point between the end of the dinner dose time range and the beginning of the breakfast dose time range.

Meal dose calculation

When the meal icon has been selected and a meal dose calculation is to be displayed, the DGA may determine whether a normal meal dose calculation should be used or whether a later dose calculation should be used to determine the appropriate dose recommendation. To determine which meal dose calculation is appropriate and retrieve the relevant calculation results, the DGA may determine whether the missed dose warning described elsewhere in the present application is valid.

If a missed meal dose alert is issued, the displayed dose recommendation may be calculated based on the later meal dose. Furthermore, during the last two hours, insulin doses may not be recorded. It should be understood that each of these conditions is optional and not necessarily required. It should be noted that DGA can check to ensure that all warnings are revoked before providing a dose guidance to avoid race conditions (race conditions); for example, when a dose is not received until the user is prompted to connect or scan MDD 152, and the alert may not have been revoked. If the above conditions are not met, the dose displayed by the DGA may be calculated based on the normal meal dose.

As shown in fig. 14, in an exemplary method 2060, beginning at step 2062, DGS100 may receive or otherwise access insulin data from a subject (e.g., from MDD 152). For example, the DGA may check the latest insulin dosage data by requesting delivery information from different sources, including but not limited to MDD 152, an application associated with the MDD, or an interface storing the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications to obtain the latest insulin delivery information.

In step 2064, the DGA may determine whether any insulin doses have been recorded over a period of time (e.g., 2 hours). As seen in step 2066, DGA may not display a dose recommendation if any insulin dose has been recorded within the past 2 hours.

If no insulin dosage has been recorded within the last 2 hours, the DGA may determine if the missed dosage warning is valid in step 2068. If the missed dose alert is valid or has been issued, the DGA may display a dose recommendation calculated based on the later meal dose in step 2070. If the missed dose alert is not valid or not issued, then in step 2072, the DGA may display a dose recommendation calculated based on the normal meal dose.

The normal meal dose calculation (i.e., not the late meal dose calculation) may be based on the recent glucose level (e.g., scanning glucose or streaming glucose) and may be represented by the following logic and equations:

if G _prm ≤G _T

I _guide ＝I _fixed IOB (equation 1)

Then

I _guide ＝I _fixed Iob+correction adj+trend Adj (equation 2)

Wherein,

G _prm current scanned glucose value

G _T Target glucose (scheme parameters lookup)

I _guide Calculation of insulin dosage

I _fixed Fixed insulin dose associated with (e.g. applicable) breakfast, lunch or dinner (regimen parameter lookup)

IOB = active insulin (calculated as active insulin module)

CF _prm =pre-meal correction factor (scheme parameters lookup)

Correction adj= (G _prm -G _T )/CF _prm

Trend adj=based on cf=cf _prm Trend adjustment of (Kudva et al based table lookup)

The passage through CF is explained in the following table from Kudva et al (Approach to Using Trend Arrows in the FreeStyle Libre Flash Glucose Monitoring Systems in Adults, J Endocr Soc.2018;2 (12): 1320-1337) _prm Definition of glucose trend and trend Adj the entire contents of this document are expressly incorporated herein by reference for all purposes.

I _guide May be rounded to the nearest insulin unit according to standard rounding rules. If the calculated dose is negative, the display dose may be set to zero. In some embodiments, the correction Adj may be negative if the pre-meal glucose is less than the target glucose. Note that in alternative embodiments, IOB values are subtracted from correction and trend adjustment only. For example, if there is no correction and trend adjustment, I _guide ＝I _fixed . If there is an adjustment, then at addition I _fixed Previously, the IOB was subtracted from that value. If the adjustment minus IOB is less than zero, I _guide ＝I _fixed 。

As an additional safety measure for the above-described timeout (see, e.g., step 2046 of fig. 12), glucose data received by the DGS according to a schedule or based on triggering events or conditions and/or when it is in wireless communication with the reader device may be used to determine whether a meal dose recommendation is to be calculated and presented to the user. In some embodiments, if the current glucose of the user at the time of the meal dose calculation request is below a threshold, the DGS may not present the calculated dose suggestion to the user. Instead, DGS may present users with alerts regarding their current glucose level suggesting that they raise their current glucose value prior to administration of an insulin dose. In some embodiments, if it is determined that the current glucose value is below the threshold, no dose guidance is displayed. This additional safety measure is to avoid hypoglycemia due to administration of insulin at too low a glucose value. The threshold may be configured based on the user's tolerance to hypoglycemia.

Active Insulin (IOB) is a measure of the residual active insulin remaining in the blood of a user after an injection. The IOB is subtracted from the currently calculated dose to account for active insulin from previous injections, thereby avoiding insulin-induced hypoglycemia. IOB is calculated by multiplying the previous dose by a fraction representing the percentage of insulin remaining at the time point after injection. IOB has insulin units (U).

TABLE 2 definition of trend arrow boxes for glucose change rates from Kudva et al (true arrow bin)

Table 3a. Table of kudva et al defined trend Adj (trend-based postprandial insulin dose adjustment)

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Table 3b table of kudva et al defined trend Adj (trend based pre-meal insulin dose adjustment)

Meal dose calculation may be triggered by a user requesting a meal dose in the DGA. The input data stream may include scan glucose at the time of request, scan glucose trend data at the time of request, IOB at the time of request, and a check for a later dose. The input parameters may be a fixed dose for the requested meal, the meal CF, and the target glucose. The output may be a recommended meal insulin dose.

Dose calculation at dinner

When one selects a meal dose within the DGA, both the normal meal dose and the later meal dose may be calculated. Presentation of the late meal dose value may depend on the DGS100 detecting a missed meal dose event, as described elsewhere in the present application.

The late meal dose calculation may be the same as the normal meal dose calculation explained above, except that G may be calculated using continuous flow glucose data (e.g., once per minute flow glucose, rather than scanning glucose) _prm And trend Adj. For later meal dose calculations, G _prm The glucose value at the estimated meal start time may be and the trend Adj may be determined using the trend of glucose at the estimated meal start time. An estimated meal start time may be calculated based on the once-per-minute flow data and may be an output from the missed meal dose detection algorithm described elsewhere in the present application. Further, for late meal dose calculation, the IOB may be calculated from the time the user requested the late meal dose rather than the estimated meal start time. If the dose calculation is less than the normal meal dose calculation at a later meal, DGS100 may display a lower value.

As an additional safety measure for the above-described timeout (see, e.g., step 2046 of fig. 12), the glucose data received by the DGS according to a schedule or based on triggering events or conditions and/or when it is in wireless communication with the reader device may be used to determine whether a later meal dose recommendation is to be calculated and presented to the user. In some embodiments, if the user's glucose at the estimated meal time is below a threshold, the DGS may not present the calculated dose advice to the user. In contrast, DGS may present the user with an alert to correct for low glucose before insulin is administered instead of a dose recommendation. This additional safety measure is to avoid hypoglycemia due to administration of insulin at too low a glucose value. The threshold may be configured based on the user's tolerance to hypoglycemia.

The later meal dose calculation may be triggered by the user in response to a missed meal dose notification, wherein the user selects a meal and scans glucose. The input data stream may include estimated meal start time, historical glucose at estimated meal start time, and IOB at the time of the requested later dose. The input parameters may be a fixed dose for a requested missed meal, a meal CF, and a target glucose. The output may be a recommended meal insulin dose at the estimated beginning of the meal.

Dose calculation interpretation display

When the user selects a displayed dose, a pop-up screen may show an explanation of how the dose was calculated. The dose may be displayed for two components: i _fixed And I _guide -I _fixed . Interpreted text may also be displayed according to table 4.

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Table 4. Interpretation text displayed for dose calculation.

Tracking and labeling meals

In some embodiments, the user may be able to annotate a meal that has resulted in a variable glucose level (e.g., a glucose level above or below the target range). In some embodiments, the DGS may provide new suggested doses specific to the tagged meal based on the tagged meal and related post-meal glucose data from multiple tagged meals.

In some embodiments, the user may eat the same pancake egg breakfast at the local restaurant once a week. However, each time they eat a pancake egg breakfast, their post-meal glucose level is outside of its target range. In some embodiments, the DGS may inform the user that their meal dose did not return the user to the target range after the meal, and may prompt the user to annotate or track the meal. The user may type a descriptive label for the meal as favorite.

Thereafter, after the user consumes or ingests the same meal, the user may annotate the meal, and the DGS may associate with the tag the meal dose administered in association with annotating the meal and the post-meal glucose dataset associated with the meal. Once the DGS has acquired a sufficiently large dataset, the DGS may determine a particular dose recommendation for the tagged meal based at least on the previous dose administered and the post-meal glucose dataset associated with the tagged meal. In determining a dose recommendation for a labeling meal, the DGS may also consider the current glucose level of the user when making a request for a dose recommendation. Before the DGS can calculate the recommended dose for the labeling meal for pancake eggs, the DGS can require data from at least three meals, alternatively at least four meals, alternatively at least 5 meals. The tag may be used for any food and/or beverage consumed in breakfast, lunch, dinner or snack. The dose recommendation may include a number of components, such as a base dose and a correction dose. The base dose may be a standard or typical meal dose for the user, and the correction dose may take into account the user's previous response to the same tagged meal. In some embodiments, correcting the dose may take into account the user's current glucose level at the time the request for the dose recommendation is made.

In one exemplary embodiment, in method 2400 as seen in fig. 23A, the DGS can prompt the user to enter a tag associated with a meal type in a first step 2402. For example, the meal type may be a description of a particular food and/or beverage when the user is eating or eating a snack, such as "cheese hamburger and French fries", "two-piece cheese pizza", "pancake and egg" or "apple and low sugar soda".

In step 2404, the DGS may receive an input tag for an instance of a meal type. The user may type a descriptive label for the meal as favorite. Alternatively, if the meal type has been entered in the DGS, the user may select the tag from, for example, a list of favorite meal types.

In step 2406, the DGS may determine whether the DGS has received a threshold number of instances of a meal type tag. For example, DGS may require a minimum of three tags for a particular meal type. If the DGS determines that only tags for two instances of the meal type are received, the DGS may not calculate the recommended drug dose for that meal type. However, if the DGS determines that tags for three or more instances of the meal type have been received, the DGS may calculate a recommended medication dose for that meal type, as shown in step 2408.

In some embodiments, after the user marks a meal, the DGS may detect that the administered drug dose is different from the drug dose administered at the previous meal. If the DGS detects a significant difference in drug dose, for example, a 1 unit difference, alternatively a 2 unit difference, alternatively a 3 unit difference, alternatively a 4 unit difference, alternatively a 5 unit difference, alternatively a difference between about 1 and 5 units, alternatively a difference between about 2 and 5 units, alternatively a difference between about 3 and 5 units, the DGS may prompt the user. In some implementations, the DGS may prompt the user to create a new label. The new label may include a description in the label that explains the different drug doses. For example, the new tag may include another reason for a different meal size or drug dosage, e.g., exercise performed prior to a meal. In some embodiments, the DFS may prompt the user in real time (i.e., within 5 minutes, alternatively within 4 minutes, alternatively within 3 minutes, alternatively within 2 minutes, alternatively within 1 minute of the user labeling the meal type).

In one exemplary embodiment, in a method 2420 as seen in fig. 23B, in a first step 2422, the DGS may prompt the user to enter a tag associated with a meal type. For example, the meal type may be a description of a particular food and/or beverage when the user is eating or eating a snack, such as "cheese hamburger and French fries", "two-piece cheese pizza", "pancake and egg" or "apple and low sugar soda".

In step 2424, the DGS may receive a first input tag for a first instance of a meal type. The user may type a descriptive label for the meal as favorite. Alternatively, if the meal type has been entered in the DGS, the user may select the tag from, for example, a list of favorite meal types.

In step 2426, the DGS may associate the first input tag with a first amount of medication administered for the first instance of the meal type. In some embodiments, the first input tag may also be associated with a first post-meal analyte data set for a first instance of a meal type.

In step 2428, the DGS may receive a second input tag for a second instance of the meal type. The user may select the tag from, for example, a list of favorite meal types.

In step 2430, the DGS may associate the second input tag with a second amount of the drug administered for a second instance of the meal type. In some embodiments, the second input tag may also be associated with a second post-meal analyte data set for a second instance of the meal type.

In step 2432, the DGS may determine whether the difference between the administered first and second doses is above a threshold. If the difference is above the threshold, the DGS may prompt the user to enter a modified label that may take into account the different doses in step 2434.

In some embodiments, if the DGS has sufficient data to determine the recommendation, the DGS may output a recommended dose for the meal type in step 2436 if the difference is below a threshold.

Missed meal dose warning

Both the missed meal dose calculation function and the missed dose alert function use a missed meal dose detection algorithm. The missed dose alert function invokes the missed dose detector every minute. When an alert is issued (which may also be referred to as an activity alert or an activation alert), the alert may be presented in a lock screen notification or in-application popup window (in-app mode), and may also be presented in a notification center or notification banner of display device 120. The alert may be dismissed under certain conditions, i.e., if it is determined that the alert should not be active, the alert may become inactive or inactive, or may be removed from the notification center or notification banner of the display device 120.

A missed meal dose alert may be issued if the following conditions are met:

the missed meal dose condition may have been detected within a few consecutive minutes (e.g., 5 minutes or 5 consecutive positive missed meal dose events), or a missed meal dose warning is currently issued.

No insulin dose was recorded over the last 2 hours.

No insulin dose was recorded 45 minutes before the estimated meal start time.

Estimate meal start time within the last 2 hours.

No correction dose warning is issued.

In some embodiments, a missed meal dose alert may not be issued unless all of these conditions are met. In other embodiments, only one or more of these conditions may be met before issuing the missed meal alert. It should be understood that each of these conditions is optional and not necessarily required.

As seen in fig. 15, in an exemplary method 2080, beginning at step 2082, DGA may receive streaming glucose data from sensor control device 102.

In step 2084, the DGA may determine at the current time whether to miss the meal dose associated with the meal having the estimated meal start time by detecting a missed meal dose condition for consecutive minutes (e.g., 5 minutes or 5 consecutive positive missed meal dose events). Alternatively, the DGA may determine whether a missed meal dose alert (not shown) is currently issued.

If step 2084 is answered in the affirmative, in step 2086, the DGA may determine if an insulin dose has been recorded within the previous time period (e.g., within the last two hours).

If step 2086 is negatively answered, in step 2088, the DGA may determine if an insulin dose has been recorded within a time period (e.g., 30 minutes, alternatively 45 minutes, alternatively 60 minutes) before the estimated start time of the meal.

If step 2088 is negatively answered, in step 2090, the DGA may determine if the estimated meal start time is within the last two hours, i.e., two hours from the current time. If step 2090 is answered in the affirmative, then in step 2092 the DGA may determine if a correction dose warning is currently issued. The DGA may prevent the issuance of missed meal dose warnings while issuing correction dose warnings.

If step 2092 is negatively answered, then in step 2094 the DGA may display or issue a missed meal dose warning.

It should be understood that each of the conditions mentioned in the above exemplary method is optional and does not necessarily require that a missed meal dose warning be issued.

If a missed meal dose alert condition is issued, DGS100 may present a lock screen notification to notify the user of the missed meal dose alert. If the missed meal dose alert condition is withdrawn, the DGS100 may then remove the missed meal dose alert notification.

The missed meal dose alert may be revoked if it is currently issued and the following conditions are met:

no missed meal dose condition detected within the last 15 minutes

Or one or more of the following conditions are met:

omicron insulin doses were recorded over the last 2 hours

The insulin dose was recorded either o or 45 minutes before the estimated meal start time

The o or estimated meal start time exceeds 2 hours

As seen in fig. 16A, in an exemplary method 2100, beginning at step 2102, DGA may receive streaming glucose data from sensor control device 102.

In step 2104, DGS100 may issue a missed meal dose alert. Conditions under which a missed meal dose warning may be issued are described elsewhere in the present application.

In step 2106, DGS100 may determine whether a missed meal dose condition has been detected within a few consecutive minutes after the missed meal dose has been issued. For example, DGS100 may determine whether a missed meal dose condition has not been detected within the last 10 minutes, alternatively 15 minutes, alternatively 20 minutes.

If step 2106 is negatively answered, in step 2108, the DGS100 may cancel the missed meal dose warning.

In the event that the streaming glucose data is not available such that a missed warning condition cannot be calculated for, for example, the past 14 minutes and then at 15 minutes, the missed warning condition is calculated and not issued, the DGS100 may undo the missed meal dose warning.

As shown in fig. 16B, in an exemplary method 2110, beginning at step 2112, DGA may receive streaming glucose data from sensor control device 102 as well as insulin dose data from MDD 152 or other means previously described in other embodiments.

In step 2114, the DGS100 may issue a missed meal dose alert. Conditions under which a missed meal dose warning may be issued are described elsewhere in the present application.

In step 2116, the DGS100 may determine whether an insulin dose has been recorded within a period of the current time. The period of time may be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If step 2116 is answered in the affirmative, in step 2108, the DGS100 may cancel the missed meal dose warning.

As shown in fig. 16C, in an exemplary method 2120, beginning with step 2122, DGA may receive streaming glucose data from sensor control device 102 as well as insulin dose data from MDD 152 or other means previously described in other embodiments.

In step 2124, DGS100 may issue a missed meal dose alert. Conditions for issuing missed meal dose alerts are described elsewhere in the present application.

In step 2126, DGS100 may determine whether an insulin dose has been recorded within a period of estimated meal start time. The period of time may be about 30 minutes, alternatively about 45 minutes, alternatively about 60 minutes.

If step 2126 is answered in the affirmative, in step 2128, the DGS100 may cancel the missed meal dose warning.

As seen in fig. 16D, in an exemplary method 2130, beginning with step 2132, DGA may receive streaming glucose data from sensor control device 102.

In step 2134, DGS100 may issue a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal with an estimated start time. Conditions for issuing missed meal dose alerts are described elsewhere in the present application.

In step 2136, DGS100 may determine whether the estimated meal start time is within the time period of the current time. The period of time may be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If step 2126 is negatively answered, in step 2128, DGS100 may cancel the missed meal dose warning.

Missing meal dose condition detection

The missed meal dose condition detection module of DGS100 receives as input streaming glucose data. The missed meal dose condition detection module may be invoked once per minute (e.g., after each minute of receiving streaming glucose) to estimate whether a meal dose has been missed, and if so, the meal start time. The missed meal dose condition detection module may also be invoked as needed to estimate the meal start time of the missed meal.

The missed dose detector uses up to about 4 hours total, alternatively about 4.5 hours total, alternatively about 5 hours total of the streaming glucose (insulin action time used by the IOB calculator) from the last recorded insulin meal dose.

Algorithms describing real-time meal detection and missed meal dose detection are described elsewhere in the present application.

Missed meal dose detection may be triggered by DGA detecting a meal event. The input data stream may include the detected meal event, the estimated meal start time, and the latest insulin dose and timestamp. The output may be a dose-free annotated meal event and a notification to the user.

Correction dose only calculation

Within the DGA, a post-meal correction-only insulin dose guide may be available if (1) the DGSM is in a correction-only guide available state and (2) a correction dose alert is issued.

The display status of the DGA notification (which may be graphically represented as a light bulb) may be determined only when a dose guidance display is initiated (or if another glucose scan is performed). For example, the display state may not be updated each time a new streaming glucose value is received. Furthermore, in one embodiment, it may be required to address all warnings before providing a dose guidance to avoid race conditions.

The correction dose calculation may be triggered by the user in response to a correction dose notification and scan from the correction dose detector. The input data stream may include scan glucose, scan trend arrow, and IOB at the time of the most recent scan. The input parameters may be target glucose and postprandial CF. The output may be a suggested calculation of the correction dose.

Correction-dose warning

When a correction dose alert (which may also be referred to as an activity alert or an activation alert) is issued, the alert may be presented in a lock screen notification or in an in-application pop-up window, and may also be presented in a notification center or notification banner of the display device 120. The alert may be dismissed under certain conditions, i.e., if it is determined that the alert should not be active, the alert may become inactive or inactive, or may be removed from the notification center or notification banner of the display device 120.

A correction dose warning may be issued if the following conditions are met:

the correction dose condition has been issued within all the past 5 minutes, or the correction dose warning is currently issued

No insulin dose recorded over the last 2 hours

No missed meal dose warning is issued. (preventing correction from occurring simultaneously with missed meal dose warning)

In some embodiments, a correction dose warning may not be issued unless all of these conditions are met. In other embodiments, only one or more of these conditions may be met before the correction dose warning is issued. It should be understood that each of these conditions is optional and not necessarily required.

As shown in fig. 17, in an exemplary method 2140, beginning at step 2142, DGA may receive streaming glucose data from sensor control device 102 as well as insulin dose data from MDD 152 or other means previously described in other embodiments.

In step 2144, the DGA may determine at the current time whether to issue a correction dose condition within a few consecutive minutes (e.g., 5 minutes or 5 consecutive positive missed meal dose events). In some embodiments, the correction dose condition may be a condition indicating that the user needs to correct the dose. Alternatively, the DGA may determine whether a correction dose warning (not shown) is currently issued.

If step 2144 is answered in the affirmative, in step 2146, the DGA may determine if an insulin dose has been recorded within the previous time period (e.g. within the last two hours).

If step 2146 is negatively answered, in step 2148, the DGA may display or issue a correction dose warning.

If a correction dose warning condition is issued, the DGS100 may present a lock-in notification to notify the user of the correction dose warning. If the correction dose warning condition is withdrawn, the DGS100 may then remove the correction dose warning notification.

If a correction dose alert is currently issued and any of the following conditions are met, the DGS100 may deactivate the correction dose alert:

no correction dose condition detected at any time within the last 15 minutes

Or the IOB calculation has changed since the last calculation of the corrected dose warning condition

Or the insulin dosage has been recorded within the last 2 hours

As seen in fig. 18A, in an exemplary method 2150, beginning at step 2152, DGA may receive streaming glucose data from sensor control device 102.

In step 2154, the DGS100 may issue a correction dose alert. Conditions for issuing correction warnings are described elsewhere in the present application.

In step 2156, DGS100 may determine whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered. For example, DGS100 may determine whether a corrected dose condition has not been detected within the last 10 minutes, alternatively 15 minutes, alternatively 20 minutes.

If step 2156 is negatively answered, then in step 2158, DGS100 may cancel the correction dose warning.

In the event that the streaming glucose data is not available such that a correction dose condition cannot be calculated for, for example, the past 14 minutes and then calculated at 15 minutes, the correction dose condition is calculated and not issued, then the DGS100 may deactivate the correction dose warning.

As seen in fig. 18B, in an exemplary method 2160, beginning with step 2162, DGA may receive streaming glucose data from sensor control device 102.

In step 2164, the DGS100 may issue a correction dose warning at the first time. Conditions for issuing correction warnings are described elsewhere in the present application.

In step 2166, DGS100 may determine whether the calculation for active Insulin (IOB) has changed since the first time.

If step 2166 is answered in the affirmative, in step 2168, the DGS100 may deactivate the correction dose warning.

As seen in fig. 18C, in exemplary method 2170, beginning with step 2172, DGA may receive streaming glucose data from sensor control device 102.

In step 2174, DGS100 may issue a correction dose alert. Conditions for issuing correction warnings are described elsewhere in the present application.

In step 2176, DGS100 may determine whether an insulin dose has been recorded within a period of the current time. The period of time may be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If step 2176 is answered in the affirmative, then in step 2178, DGS100 may withdraw the correction dose warning.

In some embodiments, the user may customize the conditions under which the correction dose warning is presented to the user. In setting of DGS, the user may set a minimum threshold for correction dose warning so that they are only warned when the correction dose is above the minimum dose. For example, if the suggested correction dose is at least about 2 units, the user may indicate that they only want to receive correction alerts. The minimum dose may be about 1 unit or more, alternatively about 2 units or more, alternatively about 3 units or more, alternatively about 4 units or more, alternatively about 5 units or more, alternatively about 6 units or more. Allowing the user to set the minimum correction dose may prevent the user from being overloaded (warning fatigue) so that the user may only focus on correction conditions that are more severe and require a larger correction dose. This allows the user to receive alerts only when they need a minimum number of units.

Correction dose detector

The correction dose condition detector may indicate whether the condition is appropriate for correction dose. The correction dose condition detector may also provide a calculation for the correction dose. Correction dose calculations may also use flow glucose data, e.g., one minute flow glucose. Thus, the correction dose calculation may match or be consistent with a correction dose warning condition that also uses flow glucose.

An exemplary method for determining a correction dose presented below in pseudo code.

Correction dose= false unless

Wherein,

T _dose time since last recorded insulin dose (not primary if the property is available)

G _corr Current flow glucose value (current meaning at initial display)

G _T Target glucose (scheme parameters lookup)

G _trend Current flow glucose trend value (Current meaning at initial display)

I _guide Calculation of insulin dosage

I _min =minimum correction dose the user wishes to be notified of. This value will be configured by the user when training the mobile application.

Once in the above pseudo code, the required correction dose= true, the correction dose calculation may be performed as follows:

if t _dose >2 hours and less than or equal to 4 hours

I _guide Corrected Adj post-IOB

Otherwise if t _dose >4 hours

I _guide Corrected Adj post + trend Adj post-IOB

Otherwise

I _guide ＝0

Ending

Wherein,

IOB = active insulin at dose calculation

CF _post Postprandial correction factor (scheme parameters lookup)

Corrected adj= (G) _corr -G _T )/CF _post

Trend Adj post = based on CF = CF _post Trend adjustment of (Kudva table lookup)

The glucose trend is explained in tables 1 and 2 adapted from Kudva et al and by CF _post The entire contents of which are previously incorporated by reference herein.

I _guide May be rounded to the nearest integer according to standard rounding rules.

If I _guide ≤I _min Then I _guide ＝0。

The correction dose determination may be run continuously. The input data stream may include streaming glucose, streaming trend arrows, time since last categorized meal dose, and time since last categorized corrected dose. The input parameters may include a minimum correction dose configurable by the user. The output may include a notification to the user.

Correction alert and missed meal dose alert interactions

The warning process for both correcting and missing meal dose warnings may be performed every minute using the streaming glucose data. The missed meal dose alert process may be performed prior to the correction alert process in order to ensure that if the condition is such that both alerts will be issued, the missed meal dose alert will dominate (thereby preventing the correction dose alert from being issued).

Active Insulin (IOB) management and calculation

To prevent dose recommendations based on stale glucose data, the time elapsed between the last scan and the dose request may be no longer than five minutes. IOB can be calculated from the duration of insulin action of 4.5 hours for fast acting insulin. The IOB value to be subtracted in equations 1 and 2 is the% IOB at the meal request times the previous insulin dose. For example, according to the information of fig. 19, a dose of 10U administered at 12:00 would have a residual IOB of 4.7U at 14:15. Further details are provided in fig. 19. The current IOB is the sum of each of these IOB partial calculations for each insulin dose for a duration of less than 4.5 hours.

The Duration of Insulin Action (DIA) is the estimated time a given insulin injection will confer its glucose lowering effect. DGS100 assumes that DIA for fast-acting prandial insulin is 4.5 hours and DIA for long-acting basal insulin is 24 hours. If the DIA of the insulin dose is 4.5 hours, then the IOB is zero 4.5 hours after injection. The units of DIA are hours.

IOB calculations may be triggered each time a dose (meal or correction) is calculated. The input data stream may include insulin doses and time stamps for doses less than or equal to 4.5 hours, and an IOB lookup table. The output may be the remaining insulin units from the previously injected cycle.

Real-time meal detection

In many embodiments, the DGA may be configured to detect missed meal doses using a real-time meal detection algorithm. Systems and methods for detecting missed meal doses and subsequent alerts to patients in real time are described herein. The process for detecting the missed meal dose may be performed periodically (e.g., whenever new glucose data is available to the system). Alternatively, the process may be performed whenever it is appropriate to provide a "missed dose" warning to the patient or when a warning has been enabled. If the alert becomes too cumbersome, the user may enable or disable the alert.

In one exemplary embodiment, real-time meal detection may be performed by the feature extraction module and the meal detection module. As data points become available, the feature extraction module may receive one CGM data point at a time. When the feature extraction module detects that the glucose value is increasing, the feature extraction module may extract a plurality of features and may pass the plurality of features to the meal detection module for meal detection.

In one embodiment, the feature extraction module is configured to perform data smoothing each time a new glucose data point is received by fitting the data over a time window and using a quadratic function to count down from the current data point. The time window may be about 60 minutes. The feature extraction module may be configured to store the fit value at the center of the time window as current smoothed data. The feature extraction module may be further configured to store coefficients of a linear term and a quadratic term of the fitting value at the center of the time window as the latest glucose change rate value and the acceleration value, respectively. In addition to being configured to store the fitting values at the center points, the feature extraction module may be configured to store the fitting values at the closest points for feature extraction. The feature extraction module may be configured to compare the current smoothed glucose value with a previous smoothed glucose value (e.g., a smoothed glucose value immediately preceding the current smoothed glucose value) to determine whether the smoothed glucose data is rising or falling. The feature extraction module may be configured to extract a plurality of features and thereafter pass the plurality of features to the meal detection module after the feature extraction module determines that the current smoothed glucose value is rising compared to the previous smoothed glucose value.

The feature extraction module may be configured to extract a plurality of features from the two segments of smoothed data. The two segments may be a current ascending segment and a previous descending segment. The plurality of features extracted from the current rising segment may include, but are not limited to: 1) Maximum acceleration; 2) Time of maximum acceleration point; 3) Glucose value at the point of maximum acceleration; 4) A height calculated from the difference in glucose value between the current time point (fitting value) and the maximum acceleration point (reference point); 5) A duration of the current segment calculated by an elapsed time from the reference point to the current point; 6) An average rate of rise of the current segment calculated by dividing the altitude by the duration; 7) The maximum increase in acceleration (the increase in acceleration at a given point in time is obtained by subtracting the acceleration at the previous point from the acceleration at that point); and 8) the area of the delta under the curve (the glucose value of the reference point is subtracted from the average glucose value and then the difference is multiplied by the duration of the segment). The plurality of features extracted from the previous descent segment may include, but are not limited to: 1) duration, 2) altitude, 3) average descent rate (altitude/duration), 4) maximum descent rate (maximum value of absolute value of change rate), and 5) maximum deceleration (maximum value of absolute value of acceleration). The feature extraction module may be configured to communicate the plurality of extracted features to the meal dosage module.

The meal detection module may be configured to receive the feature vector as an input and may be configured to output a binary detection result indicating whether the current rising segment is a meal response glucose excursion. The meal detection module may be further configured to output a probability value having a binary detection result. In one embodiment, the pre-trained machine learning model in the meal detection module may be implemented using a random forest class by scikit learning (https:// scikit-learn. Org/stable/modules/generated/sklearn. Ensable. Random f orestclass. Html). The meal detection module may be configured to detect meal onset based on tree construction rules and feature thresholds for each feature in each tree, which may be optimized during training. In one embodiment, the pre-training model may also be constructed based on alternative classification algorithms including gradient boosting, ada boosting, artificial neural networks, linear discriminant analysis, and additional trees.

The meal detection model may be further configured to estimate a start time of a meal if the meal is detected. In one embodiment, the start time of a meal may be estimated as the point in time at which there is a maximum increase in glucose value acceleration, tracing back from the detection point within a time window size of about 1.25 hours. For example, if a missed meal is detected by the algorithm at 1:15 pm, the model may trace back to about 12:00 pm to determine the beginning of the meal. The glucose value acceleration at each point may be determined by using a quadratic function (i.e., y=ax ² +bx+c) fitting five data points centered on the data point of interest. The fitting parameter "a" is the acceleration at the point of interest. The increase in acceleration of the glucose value at time point k may be defined by a (k+1) -a (k).

The meal detection model may also be configured to output a notification to the user on UID 200 regarding the missed dose if no meal insulin dose has occurred is detected within a period of time near the estimated beginning of the meal. In one embodiment, if it is estimated that a meal begins less than two hours ago, the notification may also indicate that the patient is still available with a dose guide for the meal and a later dose.

Details of other types of meal detection methods and algorithms are described in U.S. patent publication No. 2017/0185748 and PCT application serial No. PCT/US 2020/12134, the entire contents of both of which are expressly incorporated herein by reference.

Real-time meal detection may run continuously. The input data stream may include streaming glucose. The output may be a meal event and an estimated meal start.

Real-time dose classification

To drive the dose-guiding state transition diagram described elsewhere in this application and track the dose consistency of the dosing report, the algorithm may sort the incoming dose from the stylus in real-time.

If certain criteria are met, the doses may be automatically classified by an algorithm. The dose may be automatically categorized as a specific meal dose (breakfast, lunch or dinner) or a correction dose only. The automatic meal dose and correction dose categorization has its own logic for categorization.

Automatic real-time dose sorting can be run continuously. The input data stream may include an insulin dose timestamp, an insulin dose, and a previous dose recommendation (meal or correction). The input parameters may include an insulin dosage time window. The output may be a dose classified as dining or correction.

Meal dosage

For automated meal dose classification, the following criteria may be used to determine whether a given dose may be automatically associated with a particular meal (breakfast, lunch or dinner). The logic is presented in the following pseudo code:

· ≡ if the insulin dose timestamp from the stylus is within ∈20 minutes of the time the user requested a meal dose in the mobile application

The dose is exactly the same as the recommended dose &

The timestamp of the meal dose request falls within the approved meal dose time range &

Dose not taken in the postprandial state

Then, the dose is classified as the dose associated with the meal request

Otherwise, the dose is classified as ambiguous.

In one embodiment, all of the above conditions must be true for a dose to be automatically categorized for a given meal. If any of the above are false, the dose may be marked as ambiguous, requiring the user to manually sort the dose before using the application for subsequent dose guidance. The manual classification may be performed via entries in the DGA. In other embodiments, only one or more of these conditions may be met in order to automatically sort the doses. It should be understood that each of these conditions is optional and not necessarily required.

As shown in fig. 20A, in an exemplary method 2180, beginning at step 2182, the DGA may provide a dose recommendation guide for a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide includes a recommended dose.

In step 2184, the DGA may receive insulin dosage data for the user from the connected insulin delivery device. Insulin dosage data may include recent doses including insulin amounts and time stamps.

In step 2186, the DGA may determine whether the timestamp of the recent dose is within a time period of the requested time. The period of time may be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

In step 2188, DGA may determine whether the amount of insulin in the recent dose is the same as the recommended dose recommended by the meal dose guide.

In step 2190, the DGA may determine whether the timestamp of the recent dose is within a determined meal dose time range for the meal type of the recent meal.

In step 2192, the DGA may determine whether a recent dose was taken while the user was in a post-meal state. If the most recent dose is taken while the dose guidance state machine is in the postprandial dose state (i.e., the previous dose has been associated with a meal, triggering a transition to the postprandial dose state), then the most recent dose may be associated with the same meal as the previous dose.

In step 2194, if steps 2186 through 2190 above are all answered in the affirmative and step 2192 is answered in the negative, the DGA may classify the late dose as being associated with the meal type of the recent meal. If either of the above conditions is not met, the DGA may classify the near dose as ambiguous (see step 2196) and prompt the user to manually classify the near dose.

As an example, if the user requests a lunch dose at 12:00 noon and takes a dose consistent with the automatic classification logic described above, it may be classified as a lunch dose. The classification may trigger a transition from a waiting state to a post-meal dose state at a time from the injection time. The algorithm may remain in the post-meal dose state for two hours. If another dose is received at 1:00 pm (while the user is in the post-meal dose state), the dose may be automatically marked as a lunch dose. Notably, the algorithm may not provide a dose guidance to the user when in the post-meal dose state. Any dose received during this period of time may be based solely on user judgment.

In other embodiments, only one or more of these conditions may be met in order to automatically sort the doses. It should be understood that each of these conditions is optional and not necessarily required.

Correction dose

For an auto-correct dose classification, the following criteria must all be true for a given dose to be automatically classified as a correct dose. The logic is presented in the following pseudo code:

if the dose ≡ ≡delivered in 20 minutes recommended by the correction dose in mobile applications ≡

The dose is exactly the same as the recommended dose &

Otherwise, the dose is classified as ambiguous.

In one embodiment, if both of the above conditions are true, the dose may be classified as a correction dose. If any of the above are false, the dose may be marked as ambiguous, requiring manual classification. The manual classification may be performed via entries in the DGA. In other embodiments, only one or more of these conditions may be met in order to automatically sort the doses. It should be understood that each of these conditions is optional and not necessarily required.

As shown in FIG. 20B, in the exemplary method 2200, beginning at step 2202, the DGA may provide a dose recommendation guide for correction at a first time.

In step 2204, the DGA may receive insulin dosage data for the user from the connected insulin delivery device. Insulin dosage data may include recent doses including insulin amounts and time stamps.

In step 2206, DGA may determine whether the timestamp of the recent dose is within a period of the first time. The period of time may be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

In step 2208, DGA may determine whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the correction dose guidance.

In step 2210, if steps 2206 through 2208 above are answered in the affirmative, the DGA may classify the late dose as a corrected dose. If either of the above conditions is not met, the DGA may classify the near dose as ambiguous (see step 2212) and prompt the user to manually classify the near dose.

Because the correction dose can be recommended only when the system is in the waiting state, the classification can trigger a transition from the waiting state to the corrected dose state at a time from the injection time. The categorization may undo any missed meal doses or correction notifications that have been presented to the user that are not complete.

As shown in fig. 20C, in an exemplary method 2220, beginning at step 2222, the DGA may provide dose recommendation guidance at a first time.

In step 2224, the DGA may receive insulin dosage data for the user from the connected insulin delivery device. Insulin dosage data may include recent doses including insulin amounts and time stamps.

In step 2226, the DGA may determine whether the timestamp of the recent dose is within a period of the first time. The period of time may be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

In step 2228, the DGA may determine whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the dose guidance.

In step 2230, the DGA may classify the near dose as a meal dose or a correction dose. If either of the above conditions is not met, the DGA may classify the near-term dose as ambiguous (see step 2232) and prompt the user to manually classify the near-term dose in step 2234.

When a dose is labeled as ambiguous, the user may be required to manually sort the dose before using the DGA for subsequent dose guidance. The user may be provided with the following classification options and may be asked to select one:

Breakfast dose

Lunch dose

Dinner dose

Correction dose

More than expected for last meal

Snack dose

Primary dose

Non-taken dose

In some embodiments, all fuzzy doses within a period of time (e.g., about 4 hours, alternatively about 4.5 hours, alternatively about 5 hours) from when the user turns on the DGA may need to be manually classified before the DGA can provide dose guidance. Unclassified blur doses greater than this period of time may remain blurred. If a time period (e.g., a 4.5 hour window) spans to the previous day, the time period may be shortened to span from only the time the user turned on the DGA to the early morning 00:00:00 of the day.

In some embodiments, if the user manually classifies the ambiguous dose as one of "last meal more than expected", "snack dose", "primary dose", or "no dose taken", no change may be made to the dose guidance state machine.

Method of regulation

As disclosed in U.S. application serial No. 16/944,736, the entire contents of which are previously incorporated herein by reference, a Dose Guidance System (DGS) (e.g., SCD 102, display device 120, or MDD 152) may be configured using an automated or semi-automated learning method that classifies and characterizes drug doses based on patient input and Glucose Pattern Analysis (GPA) of the patient analyzed during the learning period.

Once the current dosing strategy of the patient is learned (or configured), the processor of DGS100 may use the methods as described herein to provide regulatory guidelines for Multiple Daily Injection (MDI) dosing therapies. For patients administered with a fixed meal, the system may determine instructional information for adjusting the fixed dose (e.g., for breakfast, lunch, dinner, snack, etc.). For patients with carbohydrate counts, the carbohydrate ratio may be adjusted for these same meals or at different times of the day. Patients who are administered empirically can adjust their dosage on a per meal basis. Regulatory guidelines for DGA may provide a recommendation to vary the dose or carbohydrate ratio in a particular direction. The amount of change may be a suitable percentage change, e.g., 5%, 10%, 15%, etc. The dose guidance may further comprise beginning a meal dose. For example, if the patient is in a basal plus one (e.g., lunch dose) regimen and breakfast shows a hyperglycemic pattern, DGA may provide a recommendation for the breakfast to administer RA insulin.

As used herein, insulin dosages refer to rapid-acting insulin dosages unless these insulin dosages are expressly referred to as "long acting" or "basal".

In one aspect of the method, several (e.g., six) parameters of the DGS100 provide information to adjust the dose regimen parameters over time while the DGS100 is in the dose-guiding mode. The several parameters may include, for example: fixed basal dose, fixed breakfast dose, fixed lunch dose, fixed dinner dose, fixed preprandial correction factor, and fixed postprandial correction factor.

At least one processor of DGS100, e.g., a processor of a display device coupled to a memory, a wireless interface to a sensor control device, and a display screen, may include instructions that, when executed by the processor, cause DGS100 to perform a method 1500 for providing dose guidance in response to analyte data, as illustrated in fig. 21A. The method 1500 may include: in 1502, time-dependent analyte data of a patient taken during an analysis period is received into a buffer. The method 1500 may further comprise: at 1504, time-dependent analyte data is divided into discrete time of day (TOD) time periods. The method may further comprise: at 1506, a recommended fixed dose of the drug for a respective one of the TOD periods is determined based on the time-dependent analyte data of the patient over the analysis period and at least a portion of the defined dosing strategy by executing an algorithm. The method may further comprise: in 1508, an indicator of the recommended fixed dose is stored in computer memory for output to at least one of a user or a drug administration device.

The DGS100 processor may initiate an adjustment method for adjusting the fixed dose based on parameters configured by an authorized user (e.g., HCP) before the system enters the dose guidance mode. DGS may remain in the dose-guiding mode for a defined period of time, for example 7 days, 14 days or 21 days.

DGS100 may use Glucose Pattern Analysis (GPA) adjustment methods as disclosed in U.S. application serial No. 16/944,736 and elsewhere in the present application to adjust the first four parameters (fixed dose adjustment), wherein the time of day period is defined by a fixed meal dose time defined by a scheme initially set by an authorized user. On the one hand, fix the pre-meal correction factor (CF _pre ) Parameters and fixed postprandial Correction Factors (CF) _post ) Can be calculated by DGS100 using independent adjustment methods. DGS100 may periodically execute an adjustment routine, for example, once a day. For example, DGS100 may perform three different adjustment routines each day; first is a fixed dose routine, then CF _pre Routine then CF _post Routines. The results from each routine may affect each other as described below for each routine. Fig. 21B illustrates the general operation of a method 1600 of adjusting a correction factor or fixed dose using a glucose mode indicator. Further details of method 1600 may be described, for example, in U.S. patent serial No. 16/944,736.

The method 1600 may include: at 1602, each of the drug doses is classified in a drug category based on the time-related data. The method 1600 may further comprise: in step 1604, each of the doses is grouped into one of a set of dining groups. The method 1600 may further comprise: at 1606, determining a glucose pattern that is closest to the fitting time-related data; and in 1608, a glucose mode indicator is selected based on the glucose mode. The glucose mode indicator may be selected from the group consisting of "high", "low", "high/low" or "no mode", for example.

If the authorized HCP approves the adjustment, the DGS100 may update the dose regimen to include the adjustment and use the resulting updated regimen in subsequent dose directions. If the authorized HCP fails to approve the adjustment within the next 24 hours, DGS100 may revoke the adjustment approval request and call the adjustment routine at the next scheduled adjustment time. Thereafter, the DGS may issue a new adjustment approval request. If the authorized HCP expressly denies the adjustment, the DGS may revoke the adjustment approval request and refrain from invoking the adjustment routine until an explicit period of time of at least several days has elapsed (e.g., 7 days), and thereafter issue a new adjustment approval request.

Fixed dose adjustment: DGS may use GPA modulation methods to analyze analyte measurements made at well-defined intervals during an analysis period, e.g., measurements made at 15 minute intervals during a learning period that make up time-dependent analyte (e.g., glucose) data. The GPS may divide the data into discrete time of day (TOD) periods. The TOD period may be defined according to typical meal dose time parameters defined by the authorized HCP as part of an approved dose regimen.

Fixed dose adjustment input: for example, the TOD time period may be defined as follows:

breakfast TOD period (TODBF): the initial record is the first glucose record after a fixed breakfast dose time. The end record is the last glucose record before the fixed lunch dose time.

Postprandial TOD time period (TODLU): the initial record is the first glucose record after a fixed lunch dose time. The end record is the last glucose record before the fixed dinner dose time.

Late postprandial TOD period (TODDI): the initial record is the first glucose record after a fixed dinner dose time. The end record is the last glucose record before bedtime, which DGS can define as for example 6 hours after a fixed dinner dose time or 6 hours before a fixed breakfast time, whichever occurs earlier.

Night TOD period-first half night (TODON 1): the entire night time period may be defined as the time between bedtime and fixed breakfast dose time. The start record is the first glucose record after bedtime. The end record is the last glucose record before the midpoint time of the entire night time period.

Night TOD period-late midnight (TODON 2): the start record is the first glucose record after the midpoint time of the entire night period. The end record is the last glucose record before the typical breakfast dosage period.

DGS100 may impose the following limitations on the user (patient) and authorized HCP typing a fixed meal dose time, for example: the time between the fixed breakfast dose time and the fixed lunch dose time must be not less than 3 hours; the time between the fixed lunch dose time and the fixed dinner dose time must be not less than 3 hours; and the time between the fixed dinner dose time and the fixed breakfast dose time must be no less than 9 hours.

Input validity

Referring to fig. 21C, a method 1500 for providing dose guidance in response to analyte data may include additional operations 1700 in any operable order or combination, any of which may be omitted if not necessary or desired. Operation 1700 may comprise: in 1702, the doses are categorized into categories including a fixed basal dose, a fixed breakfast dose, a fixed lunch dose, a fixed dinner dose for a corresponding one of the TOD periods. For fixed dose adjustments, dose classification (e.g., identifying the initial meal dose) is necessary to determine the necessity to exclude certain TOD periods. Furthermore, fixed dose adjustment algorithms require the identification of post-meal correction doses.

The glucose data segment (one of many that will contribute to the data associated with the TOD time period) may be considered by the processor of DGS100 to be valid under certain conditions, for example, only if all of the following conditions are met:

condition 1: the data segment meets the data sufficiency requirement that there are no gaps in the glucose data that are greater than a threshold, e.g., two consecutive historical glucose values. In a related aspect, the operation 1700 may include: in 1704, it is determined that the time-dependent analyte data segment does not exceed any gap defining a threshold as a condition for determining a recommended fixed dose.

Condition 2: the data segment has an associated initial meal dose. This condition is true if the initial meal dose falls within the fixed meal dose range defined by the authorized HCP upon confirmation of the initial dose regimen setting. For example, if the initial meal dose falls within a fixed breakfast dose range, that dose will be correlated with post breakfast TOD for analysis. In a related aspect, the operation 1700 may include: in 1706, the DGS processor determines that the time-dependent analyte data segment has an associated initial meal dose as a condition for determining the recommended fixed dose.

Condition 3: the data segment has a basal dose during the previous day. For example, operation 1700 may comprise: in 1708, a time-dependent analyte data segment is determined to be associated with a basal fixed dose over a previous 24 hour period as a condition for determining a recommended fixed dose. Alternatively or additionally, for a PM basal regimen, if the basal dose is not recorded, the processor may treat the subsequent TOD period (night, after breakfast, after lunch, after dinner) as invalid. Similarly, for an AM basal regimen, if the basal dose is not recorded, then the subsequent TOD period (after breakfast, lunch, dinner, night) may be considered ineffective.

The following events should not affect the effectiveness of the data segment for fixed dose adjustment: a high alarm or a low alarm occurring in the data segment; postprandial correction doses occurring in the data segment; or other insulin doses occurring in the data segment. If the data segment is invalid, the DGS100 processor may exclude it from the dataset for the TOD time period.

In a related aspect, the operation 1700 may include: at 1710, data for each TOD period is cleared in response to any one or more of: determining a recommended fixed dose, determining a pre-meal correction factor, determining a post-meal correction factor, or determining a manual dose adjustment. For example, the DGS100 processor may clear all TOD data buffers and low alarm and post-meal correction dose counters when any of the following occurs: when a fixed dose adjustment instruction is issued; when issuing a pre-meal CF adjustment instruction; when a postprandial CF modulation instruction is issued; or when a manual dose adjustment is issued.

And (3) data processing: the DGS100 processor may invoke the fixed dose adjustment analysis once a day. Daily, the TOD data buffer, low alarm counter, and post-meal correction counter may be updated to include no more than the last 14 valid data segments for each TOD time period. Note that a low alarm counter and a high alarm counter may be implemented for each TOD. Referring to fig. 21D, a method 1500 for providing dose guidance in response to analyte data may include additional operations 1800 in any operable order or combination, any of which may be omitted if not necessary or desired. Operations 1800 may include: at 1802, a glucose pattern for each TOD period is determined based on the associated valid data segment for the set previous day, wherein the recommended fixed dose is determined further based on the glucose pattern. Operation 1800 may further comprise: in 1804, it is determined that the associated valid data segment is available for a set previous day as a condition for determining a recommended fixed dose. Operation 1800 may further comprise: in 1806, the glucose mode is determined to be low based on the count of low alarms occurring in each TOD time period.

In a related aspect, the fixed dose may be adjusted by the number of occurrences of hyperglycemia or hypoglycemia exhibited by the user over a particular TOD period of a given time period. For example, if the number of hypoglycemic instances that DGS registers within a TOD period of one week exceeds a predetermined threshold, the insulin dosage associated with that TOD may be reduced as a potential remedy. A similar situation of increasing insulin dosage may occur if the number of hyperglycemic instances registered by DGS during the TOD period of one week exceeds a predetermined threshold. Both the threshold number and the time period may be configured to make DGS more or less reactive to instances of dysregulation of glucose. The definition and calculation of the hypoglycemic or hyperglycemic instances may be based on a number of different factors, including but not limited to: exceeding a certain threshold and the time period spent below the threshold.

In a related aspect, the DGS100 processor may integrate fixed dose and correction factor adjustment. Operation 1800 may further comprise: at 1808, a pre-meal correction factor is determined based on the time-dependent analyte data, independent of determining the recommended fixed dose, and the pre-meal correction factor is maintained if both the pre-meal correction factor and the recommended fixed dose indicate an increase in dose. Methods for fixed dose adjustment and pre-meal correction factor adjustment are described above and below. The DGS processor(s) may independently perform both of these adjustments in parallel. Each module may check for data sufficiency and, if appropriate, make adjustments at the same time each day. There may be situations where the fixed dose and pre-meal CF are adjusted on the same day. Some rules may be applicable where both fixed dose and pre-meal correction factor adjustments recommend dose increases. These rules may include, for example: if at any time of the day, the fixed dose adjustment outputs a hypoglycemic pattern, then the pre-meal CF is not reduced; and if the fixed dose adjustment suggests any fixed dose increase, then the pre-meal CF is not reduced.

Postprandial correction factor modulation: the DGS100 processor may recalculate the postprandial correction factor using a predetermined scaling factor each time the postprandial correction factor changes.

For further examples of how GPA may be used in data processing, TOD data buffer content may affect results under certain conditions, such as pp-adjusted fixed dose adjustment guidance may be provided when the glucose pattern analysis for each TOD requires that the associated valid data segment represent at least 7 different days, and only when at least 7 different days are represented in all TODs. Other independent data sufficiency requirements may be imposed within GPA method/module 1600.

For each postprandial TOD, the DGS may maintain a counter that counts the number of low alarms occurring during that TOD. As part of the fixed dose adjustment process, the counter may be checked against a threshold of 4 occurrences, which if met or exceeded, the hypoglycemic pattern for the associated TOD is input to the adjustment mapping module, regardless of the result from the GPA module. As described above, operation 1800 may further include: in 1806, the glucose mode is determined to be low based on the count of low alarms occurring in each TOD time period.

The method 1500 for providing dose guidance in response to analyte data may include additional operations 1900 as shown in fig. 21E in any operable order or combination, any of which may be omitted if not necessary or desired. For each postprandial TOD, DGS100 may maintain another counter that counts the number of postprandial correction doses that occur during that TOD. Operation 1900 may include: in 1902, a glucose mode condition is determined based on the count of low alarms, the count of post-meal corrections within each TOD time period, and the glucose mode indicator. As part of the fixed dose adjustment process, the DGS processor may check this counter for a threshold of 4 occurrences—if this threshold is met or exceeded, then depending on the results from the GPA module, the following modes are input to the adjustment mapping module: if GPA indicates a hypoglycemic mode, inputting the hypoglycemic mode; if GPA indicates a risk in hypoglycemia or a high/low glycemic pattern, a high/low glycemic pattern is entered; if GPA indicates no mode or hyperglycemic mode, hyperglycemic mode is entered.

Consistent with the foregoing and as an additional example, operation 1900 may further comprise: in 1904, it is determined that the glucose mode is low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA analysis indicates a hypoglycemic mode. In 1906, the operations may further include: it is determined that if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA analysis indicates a risk in hypoglycemia or a high/low glycemic pattern, then the glucose pattern is determined to be high/low. In 1908, the operations may further include: it is determined that if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the glucose mode indicator indicates no mode or a hyperglycemic mode, then the glucose mode is determined to be high.

When the adjustment module output indicates an increase or decrease in dose for any corresponding TOD, the DGS processor may adjust the fixed dose regimen for base, breakfast, lunch, and/or dinner as follows. The amounts are described as follows:

and (3) increasing: adjusting fixed dose = current fixed dose + I _delta Wherein I _delta According to G _risk The change is: if G _risk If the concentration is less than or equal to 15mg/dL, I _delta 2U; if G _risk >15mg/dL and G _risk If the concentration is less than or equal to 30mg/dL, I _delta 4U; if G _risk >30mg/dL and G _risk Less than or equal to 60mg/dL, I _delta 6U; if G _risk >60mg/dL, then I _delta 8U.

The method comprises the following steps: adjust fixed dose = current fixed dose-10% of current fixed dose.

In a related aspect, the operation 1800 may further comprise: at 1808, a pre-meal correction factor is determined based on the time-dependent analyte data, independent of determining the recommended fixed dose, and the pre-meal correction factor is maintained if both the pre-meal correction factor and the recommended fixed dose indicate an increase in dose. The DGS processor may calculate the pre-meal correction factor as described, for example, in U.S. application serial No. 16/944,736.

And (3) treatment: the DGS processor may execute the CF once per day _pre And (5) adjusting analysis. Every day, it can update CF _pre Glucose data buffering is adjusted to include the last day of an active glucose pair from a data segment. Two aspects of processing have different requirements related to data segment buffering: continuing the analysis and performing the analysis.

To continue CF on the same day _pre The conditioning analysis, the data buffer should include at least a clear threshold number (e.g., fourteen valid pairs) of pre-dose glucose and post-dose glucose measurements that satisfy the following conditions: these pairs are associated with new initial meal doses, i.e., these pairs have not been used for the previous CF that has been approved _pre Adjusting; and the glucose pair is associated with an initial meal dose having a correction portion, i.e., the dose has an additional amount beyond the fixed dose, and the pre-dose glucose is greater than the target glucose defined by the approval regimen. The above conditions need only be met to initiate analysis. The analysis itself may use all valid pairs of data, whether or not these pairs meet the above conditions.

For glucose pairs where the pre-dose glucose value is less than the target glucose defined by the protocol, the pre-dose glucose should be set to the target glucose value prior to treatment. This change need not occur continuously, but only prior to processing. In some embodiments, when the pre-dose glucose value is less than the target glucose defined by the regimen, the pre-dose glucose should not be set to the target glucose value prior to treatment. This condition indicates that the user's pre-meal glucose value is less than the pre-specified target glucose. Thus, the pre-meal correction for meal doses is negative to reduce the total meal dose and avoid hypoglycemia.

The glucose pairs may be clustered according to the meal category (breakfast, lunch or dinner) with which each glucose pair is associated. The average post-dose glucose value may be calculated for each meal category. The meal category average may be subtracted from each post-dose glucose value of the corresponding cluster. Regression may then be performed on the data from all corrections.

CF _pre The adjustment can be performed in two alternative modes (fast mode and stable mode). The transition between these two modes may be governed by mode transition logic. Both may require a slave CF _pre The pre-dose glucose and post-dose glucose pairs retrieved in the buffer were adjusted to perform linear regression. Linear regression can produce two results: slope estimates and corresponding p values to determine if the estimates are statistically significantly different from zero.

Fast mode: this mode may be used when the system is first started in a guided learning period and is executed daily until mode transition logic as described below is satisfied. In this mode, CF _pre The adjustment output may be determined solely by slope estimation. For example: if the slope is>0, then CF _pre A reduction; otherwise, if the slope < = 0, CF _pre And (3) increasing.

Mode conversion logic: while in fast mode, each approves CF _pre The adjustment may be stored in a buffer. Whenever fast mode CF is issued _pre This buffer may be checked for adjustment. If five most recent regulatory changes are at two CF _pre Continuous oscillation between values, the adjustment scheme may be switched to a steady mode.

Stable mode: can satisfy the mode conversion logicThereafter, the second mode is executed daily. In this mode, the same logic as in the fast mode can be followed, except for the CF _pre Additional criteria that the value will change only when the p value < 0.05.

CF _pre And (3) adjusting and calculating: when C _pre The resulting CF when the regulatory analysis indicates a subsequent change approved by the authorized HCP _pre The value may be calculated as follows: if CF is _pre To increase the value, CF _pre ＝CF _pre，old *1.33; or if CF is to be reduced _pre Value of CF _pre ＝CF _pre，old /1.33. If a fixed dose adjustment analysis performed on the same day results in an effective pattern discovery for all TOD periods and none of the patterns is hypoglycemic, then CF _pre The value should only decrease.

Aspects of the present subject matter are set forth below by way of review and/or supplementation of the embodiments described thus far, with emphasis on the interrelationship and interchangeability of the following embodiments. In other words, the emphasis is on the following facts: each feature of an embodiment may be combined with each other feature unless explicitly stated otherwise or logically unreliable. The embodiments described herein are reiterated and expanded in the following paragraphs without explicit reference to the drawings.

Systems, devices, and methods for determining a medication dose for a patient or user are provided. Dose determination may take into account recent and/or historical analyte levels of a patient or user. The dose determination may also take into account other information about the patient or user, such as physiological information, dietary information, activity, and/or behavior. A number of different dose-determining embodiments are set forth relating to a wide variety of aspects of the system or environment in which the embodiments may be implemented. Systems, devices, and methods are provided for displaying information related to glucose levels, including a time display within a target range and a graph containing identified analyte levels for a pattern type for a time period of a day.

In many systems, an apparatus for parameterizing a patient's drug administration practices to configure dose guidance settings, the apparatus comprising: an input component configured to receive measured analyte data, meal data, and medication administration data; a display component configured to visually present information; and one or more processors coupled with the input, the display, and the memory, the memory storing instructions and time-related data characterizing an analyte of the patient during an analysis period, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform: receiving patient dose regimen information for an analysis period; estimating a measure of correspondence between the time-related data and patient dose regimen information; and determining the dose guidance information based on the consistency metric.

In some systems, the memory holds further instructions for outputting the dose guidance information to the display.

In some systems, the memory holds further instructions for receiving patient dose regimen information from the input component.

In some systems, the memory holds further instructions for receiving patient dose regimen information via transmission from a remote data server.

In some systems, the patient dose regimen information includes: typical fixed doses of medication taken at meals and typical times of day when breakfast is taken.

In some systems, the patient dose regimen information includes: information defining the frequency of patient compliance with a planned dose or meal.

In some systems, the drug comprises insulin.

In some systems, the input is a wireless communication circuit.

In some systems, the instructions for estimating the consistency metric further comprise: classifying each dose of the patient dose regimen in a drug class based on the time-related data; grouping each of the doses in one of a set of dining groups; generating a dose parameter for the patient at least in part by applying the data for each of the dining sets to the model; and storing the dose parameters for configuring the dose guidance settings.

In some systems, the memory holds further instructions for accumulating time-related data that characterizes the patient's analyte over a period of time.

In some systems, the memory holds further instructions for determining the dose guidance information at least in part by reducing the dosing recommendation based on detecting that fluctuations in the analytes in the time-dependent data exceed a low threshold. In some systems, dosing is recommended for only fixed dosing.

In some systems, the memory holds further instructions for determining patient compliance with patient dose regimen information based on the time-related data.

In some systems, the memory holds further instructions for determining whether to output the dose guidance parameter based on the consistency metric.

In some systems, the memory holds further instructions for outputting a dose guidance parameter comprising a predetermined dose recommendation if the compliance metric indicates an unreliable system configuration.

Among the many methods, a method for facilitating effective access by a healthcare provider (HCP) to a patient of an electronic case (EMR) generated by a dose guidance system while preserving patient privacy is described. The method comprises the following steps: authenticating, by at least one processor of the portable display device, a session with the patient; generating, by the at least one processor, an EMR identification code (ID) in response to receiving input from the patient during the session indicating a request to share EMR with the HCP; providing, by the at least one processor, the EMR ID and data required to generate the report to a remote server that controls access to the report; and outputting, by the at least one processor, a report to a display of the portable display device in response to receiving the EMR ID. In some implementations, the portable display device may be a device under user control. In some implementations, the EMR ID can be displayed on a device other than the portable display device. For example, the EMR ID may be sent to a separate device in a separate message for security reasons.

In some methods, the method further comprises: a step of providing EMR to the remote server prior to authentication.

In some methods, the method further comprises: a step of receiving EMR from a dose guidance system.

In some methods, the method further comprises the steps of: a determination is made as to whether the EMR does not satisfy a condition of compliance with patient input indicative of a dosage pattern for the tracked drug. In some methods, the method further comprises the steps of: upon determining that the EMR does not meet the consistency condition, the patient is provided with an option to provide EMR to the HCP. In some methods, wherein generating, providing, and outputting are conditioned on determining that the EMR does not satisfy a consistency condition. In some methods, wherein generating, providing, and outputting are conditioned on determining that the EMR does not satisfy a consistency condition.

In some methods, the method further comprises the steps of: the patient is provided with the option of providing EMR to the HCP. In some methods, wherein the remote server, upon receiving the EMR ID, creates a web page for displaying the EMR addressed at least in part by the EMR ID.

In some methods, wherein the EMR comprises: determining a dosing parameter of a drug to be administered to a patient at a time within a defined period of time, and determining a measure of compliance with patient supply dosing information for the drug. In some methods, wherein the drug is insulin.

In many systems, a system for providing dose guidance to a subject is described. The system comprises: an input configured to receive dose data from a drug delivery device, wherein the dose data includes an amount and time of a last drug dose administered; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: classifying the last drug dose administered as a dining dose or a correction dose; and in response to determining that a period of time has elapsed since the time of administration of the most recent drug dose, displaying additional dose guidance.

In some systems, the period of time is about 2 hours. References herein to a time period of about 2 hours include time periods greater than 1 hour. The period of about 2 hours also includes a period of up to about 6 hours, preferably less than 4 hours.

In some systems, the last drug dose administered is a meal dose, and wherein the dose data further comprises at least one additional drug dose administered after the last drug dose is administered, and wherein the time period is not reset to the time at which the at least one additional drug dose was administered.

In some systems, the last drug dose administered is not the primary dose.

In some systems, the time of the last drug dose administered is a time stamp from the connected drug delivery device.

In some systems, the last drug dose administered is a corrected dose, and wherein the dose data further comprises at least one additional drug dose administered after the last drug dose is administered, and wherein the beginning of the time period is reset to the time at which the at least one additional drug dose was administered.

In some systems, the system further comprises: a drug delivery device configured to deliver at least one dose of a drug to a subject.

In some systems, the input includes wireless communication circuitry.

Among the many methods, a method for providing dose guidance is described. The method comprises the following steps: receiving, by the electronic device, drug dose data of the subject from the drug delivery device, wherein the drug dose data includes an amount and time of a last drug dose administered; classifying the last drug dose administered as a dining dose or a correction dose; in response to determining that a period of time has elapsed since the last drug dose was administered, a dose guide is displayed.

In some methods, the period of time is about 2 hours.

In some methods, the last drug dose administered is a meal dose, wherein the drug dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the beginning of the time period is not reset to the time at which the at least one additional drug dose was administered.

In some methods, the last drug dose administered is not the initial dose.

In some methods, the time of the last drug dose administered is a time stamp from the connected drug delivery device.

In some methods, the last drug dose administered is a corrected dose, wherein the drug dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the beginning of the time period is reset to the time at which the at least one additional drug dose was administered.

In many systems, a system for providing dose guidance to a subject is described. The system comprises: an input configured to receive dose data from a drug delivery device, wherein the dose data comprises data related to a recent drug dose administered; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining whether the last drug dose administered is the last drug dose administered; and in response to determining that the recent drug dose administered is the recent drug dose administered, displaying a screen including a dose guidance recommendation.

In some systems, the last drug dose administered is determined to be the last drug dose administered by confirmation from the user.

In some systems, the dose data further includes data related to at least one drug dose administered from the self-setting time, and wherein the instructions further cause the one or more processors to: the screen is displayed in response to determining that at least one drug dose administered since the self-setting time has been categorized. In some systems, at least one drug dose-related dose data administered from the reset time is automatically categorized. In some systems, the system further comprises: a drug delivery device configured to deliver at least one dose of a drug to a subject, wherein the instructions further cause the one or more processors to: one or more wireless interrogation signals are transmitted to the drug delivery device to determine that the last dose administered has been received. In some systems, dose data related to at least one drug dose administered from the self-setting time has been categorized by the user.

In some systems, the instructions further cause the one or more processors to: a prompt is displayed to allow the user to confirm that the information relating to the last drug dose administered is correct. In some systems, the instructions further cause the one or more processors to: a screen including a dose guidance recommendation is displayed during a period of time from after user confirmation.

In some systems, the system further comprises: a drug delivery device configured to deliver at least one dose of a drug to a subject.

In some systems, the input is further configured to receive measured analyte data and a request for dose guidance, and wherein the instructions further cause the one or more processors to: determining whether the glucose concentration at the time the request for dose guidance is received is below a low threshold; and in response to determining that the glucose concentration at the time the request for the dose guidance is received is below a low threshold, displaying a screen including a message to address the low glucose level prior to administering the drug. In some systems, the instructions further cause the one or more processors to: in response to determining that the glucose concentration at the time the request for dose guidance is received is below the low threshold, no dose guidance recommendation is displayed.

In some systems, the input is further configured to receive the measured analyte data and the request for the dose guidance after a start time of a meal, and wherein the instructions further cause the one or more processors to: determining whether the glucose concentration at the estimated meal start time is below a low threshold; and in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, displaying a screen including a message to address the low glucose level prior to administering the drug. In some systems, the instructions further cause the one or more processors to: in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, no dose-guiding recommendation is displayed.

In some systems, the input includes wireless communication circuitry.

In some methods, the input is further configured to receive measured analyte data and a request for dose guidance, and wherein the instructions further cause the one or more processors to: determining whether the glucose concentration at the time the request for dose guidance is received is below a low threshold; and in response to determining that the glucose concentration at the time the request for the dose guidance is received is below a low threshold, displaying a screen including a message to address the low glucose level prior to administering the drug. In some methods, the instructions further cause the one or more processors to: in response to determining that the glucose concentration at the time the request for dose guidance is received is below the low threshold, no dose guidance recommendation is displayed.

In some methods, the input is further configured to receive the measured analyte data and the request for the dose guidance after a start time of a meal, and wherein the instructions further cause the one or more processors to: determining whether the glucose concentration at the estimated meal start time is below a low threshold; and in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, displaying a screen including a message to address the low glucose level prior to administering the drug. In some methods, the instructions further cause the one or more processors to: in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, no dose-guiding recommendation is displayed.

Among the many methods, a method for providing dose guidance is described. The method comprises the following steps: receiving, by the electronic device, dose data of the user from the drug delivery device, wherein the dose data comprises data related to a recent drug dose administered; determining whether the last drug dose administered is the last drug dose administered; and in response to determining that the recent drug dose administered is the recent drug dose administered, displaying a screen including a dose guidance recommendation.

In some methods, the last drug dose administered is determined to be the last drug dose administered by confirmation from the user.

In some methods, the administered recent drug dose is administered after the reset time, and the method further comprises the steps of: it is determined whether the recent dose administered after the reset time is classified. In some methods, the recent dose administered after the reset time is automatically categorized.

In some methods, the method further comprises the steps of: one or more wireless interrogation signals are transmitted to the drug delivery device to determine that the last dose administered has been received.

In some methods, the recent dose administered after the reset time is categorized by the user.

In some methods, the method further comprises the steps of: a prompt is displayed to allow the user to confirm that the information relating to the last drug dose administered is correct.

In some methods, a screen including a dose guidance recommendation is displayed only for a period of time beginning after the user confirms that the information related to the last drug dose administered is correct.

In many systems, a system for providing dose guidance to a subject is described. The system comprises: an input configured to receive dose data from the drug delivery device, wherein the dose data comprises data related to at least one meal dose administered from a self-setting time; a display configured to visually present a plurality of meal icons; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining whether at least one meal dose administered since the reset time has been categorized; and displaying a screen comprising a plurality of meal icons in response to determining that at least one meal dose administered since the self-reset time has been categorized.

In some systems, the instructions cause the one or more processors to: it is determined whether the at least one meal dose administered since the reset time has been categorized as one of breakfast, lunch or dinner.

In some systems, the plurality of meal icons includes a breakfast icon, a lunch icon, and a dinner icon, and wherein each of the breakfast icon, the lunch icon, and the dinner icon includes a first appearance and a second appearance. In some systems, the first appearance is associated with a first state in which one of the at least one meal doses administered since the reset time has been classified as a meal type corresponding to a breakfast, lunch or dinner icon; and the second appearance is associated with a second state in which one of the at least one meal doses administered since the reset time is not classified as a meal type corresponding to a breakfast, lunch or dinner icon. In some systems, the first appearance comprises a shadow appearance. In some systems, the second appearance comprises an unshaded appearance. In some systems, the second appearance is brighter than the first appearance. The meal icon may be text or alphanumeric characters or may be an image. The icon may also change in form between the first appearance and the second appearance.

In some systems, the instructions further cause the one or more processors to: at least one meal dose administered since the reset time is classified as breakfast, lunch or dinner.

In some systems, the instructions further cause the one or more processors to: an input is received from the user, wherein the input includes classifying at least one meal dose administered from the reset time as a breakfast dose, a lunch dose, or a dinner dose.

In some systems, the reset time is determined based on at least one time range associated with at least one meal.

In some systems, the reset time is determined based on a time range associated with the dinner dose and a time range associated with the breakfast dose.

In some systems, the reset time is midnight.

In some systems, the reset time is about half the time between the end of the dinner dose time range and the beginning of the breakfast dose time range.

In some systems, the system further comprises: a drug delivery device configured to deliver at least one dose of a drug to a subject.

In some systems, the input includes wireless communication circuitry.

Among the many methods, a method for providing dose guidance is described. The method comprises the following steps: receiving, by the electronic device, drug dosage data of the user from the drug delivery device, wherein the dosage data comprises data related to at least one meal dose administered from a self-setting time; determining whether at least one meal dose administered since the reset time has been categorized; in response to determining that at least one meal dose administered since the self-set time has been categorized, a screen including a plurality of meal icons is displayed.

In some methods, the determining step comprises: it is determined whether the at least one meal dose administered since the reset time has been categorized as one of breakfast, lunch or dinner.

In some methods, the plurality of meal icons includes a breakfast icon, a lunch icon, and a dinner icon, and wherein each of the breakfast icon, the lunch icon, and the dinner icon includes a first appearance and a second appearance, wherein the first appearance is associated with a first state in which one of the at least one meal doses administered since the reset time has been classified as a meal type corresponding to the breakfast, lunch, or dinner icon; and the second appearance is associated with a second state in which one of the at least one meal doses administered since the reset time is not classified as a meal type corresponding to a breakfast, lunch or dinner icon. In some methods, the first appearance comprises a shadow appearance. In some methods, the second appearance comprises an unshaded appearance. In some methods, the second appearance is brighter than the first appearance.

In some methods, the at least one meal dose administered since the reset time has been automatically categorized by one or more processors of the reader device.

In some methods, at least one meal dose administered since the reset time has been categorized by the user.

In some methods, the reset time is determined based on at least one time range associated with at least one meal.

In some methods, the reset time is determined based on a time range associated with the dinner dose and a time range associated with the breakfast dose.

In some methods, the reset time is midnight.

In some methods, the reset time is about half the time between the end of the dinner dose time range and the beginning of the breakfast dose time range.

In many systems, a system for providing dose guidance to a subject is described. The system comprises: an input configured to receive dose data from a drug delivery device; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining whether the missed dose alert is valid; in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on the normal meal dose; and in response to determining that the missed dose alert is valid, displaying a dose guidance recommendation calculated based on the later meal dose.

In some systems, the input includes wireless communication circuitry.

In some systems, the instructions further cause the one or more processors to: determining whether dose data received from the drug delivery device includes a dose administered during a period of a current time; and in response to determining that the dose data received from the drug delivery device includes a dose administered within a time period of the current time, displaying a dose guidance recommendation calculated based on the later meal dose. In some systems, the instructions cause the one or more processors to: in response to determining that the missed dose alert is valid and that the dose data received from the drug delivery device includes a dose administered within a time period of the current time, a dose guidance recommendation calculated based on the later meal dose is displayed. In some systems, the period of time is about 2 hours.

In some systems, if the user's current glucose level is below the target glucose value, the normal meal dose calculation is based on the fixed insulin dose and the amount of active insulin associated with the meal.

In some systems, if the user's current glucose level is below the target glucose value, the normal meal dose calculation is based on the fixed insulin dose and the amount of active insulin associated with the meal.

In some systems, if the user's current glucose level is above the target glucose value, each of the normal meal dose calculation and the later meal dose calculation is based on a fixed insulin dose associated with the meal, an amount of active insulin, a correction adjustment, and a trend adjustment. In some systems, the normal meal dose calculation is based on a current glucose level determined from the scanned glucose data. In some systems, the late meal dose calculation is based on a current glucose level determined from the flow glucose data. In some systems, the late meal dose calculation is further adjusted based on a trend determined from the flow glucose data. In some systems, the late meal dose calculation is based on the amount of active insulin calculated from the time of the user's request for the dose guidance recommendation.

In some systems, the system further comprises: a drug delivery device configured to deliver at least one dose of a drug to a subject.

Among the many methods, a method for providing dose guidance is described. The method comprises the following steps: receiving, by the electronic device, drug dosage data of a user from the drug delivery device; determining whether the missed dose alert is valid; and in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on the normal meal dose; and in response to determining that the missed dose alert is valid, displaying a dose guidance recommendation calculated based on the later meal dose.

In some methods, the method further comprises the steps of: determining whether the drug dose data includes data for a dose administered over a period of time; in response to determining that the drug dose data does not include any data for doses administered over a period of time, a dose guidance recommendation calculated based on the later meal dose is displayed. In some methods, the period of time is about 2 hours.

In some methods, if the user's current glucose level is below the target glucose value, the normal meal dose calculation is based on the fixed insulin dose and the amount of active insulin associated with the meal.

In some methods, if the user's current glucose level is above the target glucose value, each of the normal meal dose calculation and the later meal dose calculation is based on a fixed insulin dose associated with the meal, an amount of active insulin, a correction adjustment, and a trend adjustment. In some methods, the normal meal dose calculation is based on a current glucose level determined from the scanned glucose data. In some methods, the late meal dose calculation is based on a current glucose level determined from the flow glucose data. In some methods, the later meal dose calculation is further adjusted based on a trend determined from the flow glucose data. In some methods, the late meal dose calculation is based on the amount of active insulin calculated from the time of the user's request for the dose guidance recommendation.

In many systems, a system for providing a warning to a subject is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes; determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; and in response to detecting a missed meal dose condition for a consecutive number of minutes and determining that no insulin dose has been recorded for about 45 minutes prior to the estimated meal start time, displaying a warning interface associated with the missed meal dose.

In some systems, the consecutive minutes are about 5 minutes.

In some systems, the instructions further cause the one or more processors to: determining whether a meal dose has been recorded within about 2 hours of the current time; and in response to determining that the meal dose has been recorded within about 2 hours of the current time, displaying a warning interface associated with the missed meal dose.

In some systems, the instructions further cause the one or more processors to: determining whether to issue a correction dose alert; and in response to determining that the correction dose alert has not been issued, displaying an alert interface associated with the missed meal dose.

In some systems, the system further comprises: a sensor control device configured to collect data indicative of an analyte level of a subject, the sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of the subject.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for alerting a user to missed a meal dose is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes; determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; and in response to detecting a missed meal dose condition for a consecutive number of minutes and determining that no insulin dose has been recorded for about 45 minutes prior to the estimated meal start time, displaying a warning interface associated with the missed meal dose.

In some methods, the consecutive minutes are about 5 minutes.

In some methods, the method further comprises the steps of: whether a meal dose has been recorded within about 2 hours of the current time; and in response to determining that the meal dose has been recorded within about 2 hours of the current time, displaying a warning interface associated with the missed meal dose.

In some methods, the method further comprises the steps of: whether a meal dose has been recorded within about 2 hours of the current time; and in response to determining that the meal dose has been recorded within about 2 hours of the current time, displaying a warning interface associated with the missed meal dose.

In some methods, determining whether to issue a correction dose alert; in response to determining that the correction dose alert has not been issued, an alert interface associated with the missed meal dose is displayed.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a missed meal dose alert; determining whether a missed meal dose condition has been detected within a few consecutive minutes after a missed meal dose warning has been issued; responsive to determining that the missed meal dose condition is not detected within a consecutive number of minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

In some systems, the continuous minutes are about 15 minutes.

In some systems, missing meal dose conditions include determining that no insulin dose was administered during a period of estimated meal start time.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for overriding a warning of missed meal doses is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a missed meal dose alert; determining whether a missed meal dose condition has been detected within a few consecutive minutes after a missed meal dose warning has been issued; responsive to determining that the missed meal dose condition is not detected within a consecutive number of minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

In some methods, the continuous minutes are about 15 minutes.

In some methods, missing meal dose conditions include determining that no insulin dose was administered during a period of estimated meal start time.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a missed meal dose alert at the current time; determining whether an insulin dosage has been recorded within about 2 hours of the current time; responsive to determining that the insulin dose has been recorded within about 2 hours of the current time, the missed meal dose alert is withdrawn.

In many methods, a method for overriding a warning of missed meal doses is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a missed meal dose alert at the current time; determining whether an insulin dosage has been recorded within about 2 hours of the current time; and in response to determining that the insulin dose has been recorded within about 2 hours of the current time, withdrawing the missed meal dose alert.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time; determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and in response to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, withdrawing the missed meal dose alert.

In many methods, a method for overriding a warning of missed meal doses is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time; determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and in response to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, withdrawing the missed meal dose alert.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a missed meal dose alert at the current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time; determining whether the estimated meal start time is within about 2 hours of the current time; in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, the missed meal dose alert is withdrawn.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for overriding a warning of missed meal doses is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a missed meal dose alert at the current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time; determining whether the estimated meal start time is within about 2 hours of the current time; and in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, withdrawing the missed meal dose alert.

In many systems, a system for providing dose guidance to a subject is described. The system comprises: an input configured to receive dose data from an insulin delivery device; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining whether a correction dose warning has been issued at the current time; determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and in response to determining that the correction dose has been delivered and that the insulin dose has not been administered to the subject within about 2 hours of the current time, displaying a correction dose guide.

In some systems, the instructions further cause the one or more processors to: determining whether the correction dose warning has been resolved; and in response to determining that the correction dose warning has been resolved, displaying a correction dose guidance.

In some systems, the input includes wireless communication circuitry.

Among the many methods, a method for recommending a correction dose is described. The method comprises the following steps: receiving, by the electronic device, insulin dosage data of the subject from the insulin delivery device; determining whether a correction dose warning has been issued at the current time; determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and displaying a correction dose guidance, wherein the correction dose guidance is displayed in response to determining that the correction dose has been delivered and that no insulin dose has been administered to the subject within about 2 hours of the current time.

In some methods, the method further comprises the steps of: a determination is made as to whether the correction dose warning has been resolved, wherein the correction dose guidance is displayed only when the correction dose warning has been resolved.

In many systems, a system for providing a warning to a subject is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device; a display configured to visually present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: determining at the present time whether a correction dose condition has been issued within a succession of minutes; determining whether no insulin dosage has been recorded within about 2 hours of the current time; and responsive to determining that a correction dose condition has been issued within a consecutive number of minutes and that no insulin dose has been recorded or alternatively received within about 2 hours of the current time, displaying a warning interface associated with the correction dose warning.

In some systems, the consecutive minutes are about 5 minutes.

In some systems, the instructions further cause the one or more processors to: determining whether to issue a missed meal dose alert; responsive to determining that a missed dose alert has not been issued, displaying an alert interface related to correcting the dose alert; and in response to determining that the missed dose alert is issued, not displaying an alert interface associated with correcting the dose alert.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for alerting a user about a corrected dose is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device; determining at the present time whether a correction dose condition has been issued within a succession of minutes; determining whether no insulin dosage has been recorded within about 2 hours of the current time; and responsive to determining that a correction dose condition has been issued within a consecutive number of minutes and that no insulin dose has been recorded or alternatively received within about 2 hours of the current time, displaying a warning interface associated with the correction dose warning.

In some methods, the consecutive minutes are about 5 minutes.

In some methods, the method further comprises the steps of: it is determined whether a missed meal dose alert has not been issued before a warning interface associated with the corrected dose alert is displayed. In some methods, an alert interface associated with the corrected dose alert is displayed only in response to determining that the missed meal dose alert has not been issued.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a correction dose warning; determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered; in response to determining that the correction dose condition is not detected within a consecutive number of minutes after the correction dose has been issued, the correction dose warning is withdrawn.

In some systems, the consecutive minutes are 15 minutes.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for overriding a warning of a correction dose is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a correction dose warning; determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered; in response to determining that the correction dose condition is not detected within a consecutive number of minutes after the correction dose has been issued, the correction dose warning is withdrawn.

In some methods, the consecutive minutes are 15 minutes.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device; a display configured to present a warning; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a correction dose alert, wherein the correction dose alert is issued first at a first time; determining whether a calculation of active Insulin (IOB) has changed since a first time; and responsive to determining that the calculation of active Insulin (IOB) has changed since the first time, overriding the correction dose warning.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for overriding a warning of a correction dose is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a correction dose alert, wherein the correction dose alert is issued first at a first time; determining whether a calculation of active Insulin (IOB) has changed since a first time; responsive to determining that the calculation of active Insulin (IOB) has changed since the first time, the corrective dose warning is withdrawn.

In many systems, a system for managing alerts is described. The system comprises: an input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: issuing a correction dose alert at the current time; determining whether an insulin dosage has been recorded within a time period of the current time; in response to the insulin dosage having been recorded within the period of the current time, the corrective dosage warning is withdrawn.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for overriding a warning of a correction dose is described. The method comprises the following steps: receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device; issuing a correction dose alert at the current time; determining whether an insulin dosage has been recorded within a time period of the current time; and if it is determined that the insulin dosage has been recorded within the period of the current time, a corrective dosage warning is withdrawn.

In some methods, the period of time is about 2 hours.

In many systems, a system for classifying doses is described. The system comprises: an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes a recent dose comprising an amount of insulin and a timestamp; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide includes a recommended dose; determining whether a timestamp of the recent dose is within a time period of the requested time; determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and in response to determining that the timestamp of the recent dose is within the time period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, classifying the recent dose as being associated with the meal type of the recent meal.

In some systems, the period of time is less than or equal to about 20 minutes.

In some systems, the meal type is selected from the group consisting of: breakfast, lunch and dinner.

In some systems, the instructions further cause the one or more processors to: determining whether a timestamp of the recent dose is within a meal dose time range determined for a meal type of the recent meal; and in response to determining that the timestamp of the recent dose is within the meal dose time range determined for the meal type of the recent meal, classifying the recent dose as being associated with the meal type of the recent meal.

In some systems, the instructions further cause the one or more processors to: determining whether a recent dose was taken while the user was in a post-meal state; and in response to determining that the recent dose was taken while the user was not in the post-meal state, classifying the recent dose as being associated with a meal type of the recent meal. In some systems, in the post-meal state, the previous dose administered within about 2 hours of the requested time has been associated with the meal type of the recent meal.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for classifying a dose from an attached insulin delivery device is described. The method comprises the following steps: providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide includes a recommended dose; receiving, by the electronic device, insulin dosage data of the user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp; determining whether a timestamp of the recent dose is within a time period of the requested time; determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and in response to determining that the timestamp of the recent dose is within the time period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, classifying the recent dose as being associated with the meal type of the recent meal.

In some systems, the period of time is less than or equal to about 20 minutes.

In some methods, the meal type is selected from the group consisting of: breakfast, lunch and dinner.

In some methods, the method further comprises the steps of: it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

In some methods, the method further comprises the steps of: it is determined whether a recent dose was taken while the user was in a post-meal state. In some methods, in the post-meal state, the previous dose administered within about 2 hours of the requested time has been associated with the meal type of the recent meal.

In many systems, a system for classifying doses is described. The system comprises: an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes a recent dose comprising an amount of insulin and a timestamp; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: providing a dose recommendation guide at a first time; determining whether a timestamp of the recent dose is within a time period of the first time; determining whether the amount of insulin of the recent dose is the same as the recommended dose for meal dose guidance; and classifying the near dose as a meal dose, a correction dose, or a blur dose.

In some systems, the period of time is less than or equal to about 20 minutes.

In some systems, the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

In some systems, the dose recommendation guidance is a dose recommendation guidance for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type. In some systems, the instructions further cause the one or more processors to: it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal. In some systems, the instructions further cause the one or more processors to: it is determined whether a recent dose was taken while the user was in a post-meal state.

In some systems, recent doses are classified as ambiguous.

In some systems, the instructions further cause the one or more processors to: the user is prompted to manually sort the recent doses. In some systems, the instructions further cause the one or more processors to: the user is prompted to manually categorize the near term dose by prompting the user to select a categorization from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose. In some methods, the instructions further cause the one or more processors to: the user is prompted to manually categorize the recent dose by prompting the user to select a categorization from the group consisting of snack dose, initial dose, and non-taken dose. In some methods, the instructions further cause the one or more processors to: the user is prompted to manually categorize the recent dosage by prompting the user to select a categorization that relates more to the last meal than expected.

In some systems, recent doses classified as fuzzy doses must be classified as other than fuzzy doses before additional dose guidance recommendations are provided.

In some systems, the input includes wireless communication circuitry.

In many methods, a method for classifying a dose from an attached insulin delivery device is described. The method comprises the following steps: providing a dose recommendation guide at a first time; receiving, by the electronic device, insulin dosage data of the user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp; determining whether a timestamp of the recent dose is within a time period of the first time; determining whether the amount of insulin of the recent dose is the same as the recommended dose for meal dose guidance; and classifying the near dose as a meal dose, a correction dose, or a blur dose.

In some methods, the period of time is less than or equal to about 20 minutes.

In some methods, the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

In some methods, the dose recommendation guidance is a dose recommendation guidance for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type. In some methods, the method further comprises the steps of: it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal. In some methods, the method further comprises the steps of: it is determined whether a recent dose was taken while the user was in a post-meal state.

In some approaches, recent doses are classified as ambiguous. In some methods, the method further comprises the steps of: the user is prompted to manually sort the recent doses. In some methods, the step of prompting the user to manually categorize the recent dose comprises: the user is prompted to select a category from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose. In some methods, the step of prompting the user to manually categorize the recent dose comprises: the user is prompted to select a category from the group consisting of snack dose, initial dose, and non-taken dose. In some methods, the step of prompting the user to manually categorize the recent dose comprises: the user is prompted to select more categories related to the last meal than expected. In some approaches, the recent dose classified as a fuzzy dose must be classified as a classification other than the fuzzy dose before additional dose guidance recommendations are provided.

In many systems, a device for providing dose guidance in response to analyte data is described. The device comprises: an input configured to receive measured analyte data, meal data, and medication administration data; a display configured to visually present information; and one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform: receiving into a buffer time-dependent analyte data of the patient taken during the analysis period; dividing the time-dependent analyte data into discrete time of day (TOD) time periods; determining, by executing an algorithm, a recommended fixed dose of the drug for a respective one of the TOD periods based on the time-dependent analyte data of the patient over the analysis period and at least a portion of the defined dosing strategy; and storing the indicator of the recommended fixed dose in computer memory for output to at least one of the user or the drug administration device.

In some systems, the memory holds further instructions for determining the recommended fixed dose at least in part by: classifying each of the drug doses in a drug class based on the time-related data; grouping each of the doses in one of a set of dining groups; determining a glucose pattern closest to the fitting time-dependent data; and selecting a glucose mode indicator based on the glucose mode.

In some systems, the memory holds further instructions for classifying the doses into categories including a fixed base dose, a fixed breakfast dose, a fixed lunch dose, a fixed dinner dose for the corresponding TOD period.

In some systems, the memory holds further instructions for determining that the time-dependent analyte data segment does not exceed any gap defining the threshold as a condition for determining the recommended fixed dose.

In some systems, the memory holds further instructions for determining that the time-dependent analyte data segment has an associated initial meal dose as a condition for determining the recommended fixed dose.

In some systems, the memory holds further instructions for determining that the time-dependent analyte data segment is associated with a basal fixed dose within a previous 24 hour period as a condition for determining a recommended fixed dose.

In some systems, the memory holds further instructions for clearing data for each TOD period in response to any one or more of: determining a recommended fixed dose, determining a pre-meal correction factor, determining a post-meal correction factor, or determining a manual dose adjustment.

In some systems, the memory holds further instructions for determining a glucose pattern for each TOD period based on the associated valid data segment for the set previous day, wherein the recommended fixed dose is determined further based on the glucose pattern. In some systems, the memory holds further instructions for determining that the associated valid data segment is available for a set previous day as a condition for determining a recommended fixed dose. In some systems, the memory holds further instructions for determining that the glucose mode is low based on a count of low alarms occurring in each TOD period. In some systems, the memory holds further instructions for determining that the glucose mode is low based on the count of hypoglycemic instances in each TOD period. In some systems, the memory holds further instructions for determining that the glucose mode is high based on the count of hyperglycemic instances in each TOD period. In some systems, the memory holds further instructions for determining a pre-meal correction factor based on the time-dependent analyte data independent of determining the recommended fixed dose, and maintaining the pre-meal correction factor if both the pre-meal correction factor and the recommended fixed dose indicate an increase in dose. In some implementations, the memory holds further instructions for determining that the glucose mode is low based on the count of hypoglycemic instances in each TOD period. In some implementations, the memory holds further instructions for determining that the glucose mode is high based on the count of hyperglycemic instances in each TOD period.

In some systems, the memory holds further instructions for determining a glucose mode condition based on the count of low alarms, the count of post-meal corrections within each TOD period, and the glucose mode indicator. In some systems, the memory holds further instructions for determining that the glucose mode is low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA method indicates a hypoglycemic mode. In some systems, the memory holds further instructions for determining that the glucose mode is high/low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA method indicates a risk in hypoglycemia or a high/low glucose mode. In some systems, the memory holds further instructions for determining that the glucose mode is high if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the glucose mode indicator indicates no mode or a hyperglycemic mode.

In some systems, the input is a wireless communication circuit.

In some embodiments, at least a portion of the time-dependent analyte data does not include a portion of the time-dependent analyte data selected to be excluded by the user.

In many systems, a drug delivery device is described. The apparatus includes: an input configured to receive a query for dose data for a time period, wherein the dose data includes an amount and a time of all doses delivered within the time period; one or more processors coupled with the input and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: storing data of the dose administered during the period of time to create stored data; determining whether the stored data includes all doses delivered during the time period; and in response to determining that the stored data does not include all doses delivered during the time period, creating an indication of incomplete dose data.

In some systems, the instructions further cause the one or more processors to: in response to a query for dose data, an indication of incomplete dose data is transmitted.

In some systems, the indication of incomplete dose data is a counter value.

In some systems, the indication of incomplete dose data is based on a counter value.

In some systems, the indication of incomplete dose data is based on a comparison of a counter value to an estimated counter value. In some systems, the estimated counter value is calculated based on a previous counter value and an elapsed time since the previous counter value was received.

In some systems, the drug delivery device is a connected insulin pen, and wherein the connected insulin pen is configured to wirelessly transmit the dose data.

In some systems, the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit dose data.

In some systems, the indication of incomplete dose data is based on detecting that the cap is not attached to the insulin pen for a different period of time. In some systems, detecting that the cap is not attached to the insulin pen for a different period of time comprises: it is determined that the insulin pen contains a first amount of insulin before the cap is unattached and that the insulin pen contains a second amount of insulin after the cap is reattached to the insulin pen, wherein the first amount is different than the second amount.

In some systems, the input is a wireless communication circuit.

In many embodiments, a method of transmitting data includes the steps of: receiving a query for dose data over a period of time, wherein the dose data includes an amount and time of all doses delivered over the period of time; storing data of the dose administered during the period of time to create stored data; determining whether the stored data includes all doses delivered during the time period; and in response to determining that the stored data does not include all doses delivered during the time period, creating an indication of incomplete dose data.

In some embodiments, the method further comprises the steps of: in response to a query for dose data, an indication of incomplete dose data is transmitted.

In some embodiments, the indication of incomplete dose data is a counter value.

In some embodiments, the indication of incomplete dose data is based on a counter value.

In some embodiments, the indication of incomplete dose data is based on a comparison of a counter value to an estimated counter value. In some implementations, the estimated counter value is calculated based on a previous counter value and an elapsed time since the previous counter value was received.

In some embodiments, the indication of incomplete dose data is based on detecting that the cap is not attached to the insulin pen for a different period of time. In some embodiments, detecting that the cap is not attached to the insulin pen for a different period of time comprises: it is determined that the insulin pen contains a first amount of insulin before the cap is unattached and that the insulin pen contains a second amount of insulin after the cap is reattached to the insulin pen, wherein the first amount is different than the second amount.

In some embodiments, a query for dose data from the time period is sent from an application providing dose guidance.

In many systems, a system for providing dose guidance to a subject, comprising: an input configured to receive, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data includes data related to at least one dose administered over a period of time; a display configured to visually present a dose guidance; one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: querying a drug delivery device for dose data, the dose data comprising data relating to at least one dose administered during the period of time; determining whether an indication of incomplete dose data is received from the drug delivery device; in response to determining that an indication of incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received during the time period includes dose data for all doses administered during the time period; and in response to determining that no indication of incomplete dose data was received, calculating a dose guide.

In some implementations, the instructions further cause the one or more processors to: the dose guidance is output on a display.

In some embodiments, the indication of incomplete dose data is based on a counter value.

In some embodiments, the indication of incomplete dose data is based on a comparison of a counter value to an estimated counter value. In some implementations, the estimated counter value is calculated based on a previous counter value and an elapsed time since the previous counter value was received.

In some embodiments, the drug delivery device is a connected insulin pen, and wherein the connected insulin pen is configured to wirelessly transmit the dose data.

In some embodiments, the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit dose data.

In some embodiments, the system further comprises a drug delivery device, and wherein the drug delivery device further comprises: an input configured to receive a query for dose data, the dose data comprising data relating to at least one dose administered during the time period; one or more processors coupled with the input and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: storing data of the dose administered during the period of time to create stored data; determining whether the stored data includes all doses delivered during the time period; in response to determining that the stored data does not include all doses delivered during the time period, an indication of incomplete dose data is created. And transmitting an indication of incomplete dose data in response to a query for dose data and in response to a determination that the stored data does not include all doses delivered during the time period.

In some embodiments, the input is a wireless communication circuit.

In many embodiments, a method for providing dose guidance to a subject comprises the steps of: receiving, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data comprises data relating to at least one dose administered over a period of time; querying a drug delivery device for dose data, the dose data comprising data relating to at least one dose administered during the period of time; determining whether an indication of incomplete dose data is received from the drug delivery device; in response to determining that an indication of incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received during the time period includes dose data for all doses administered during the time period; and in response to determining that no indication of incomplete dose data was received, calculating a dose guide.

In some embodiments, the method further comprises the steps of: the dose guidance is output on a display.

In some embodiments, the indication of incomplete dose data is based on a counter value.

In some embodiments, the indication of incomplete dose data is based on a comparison of a counter value to an estimated counter value. In some implementations, the estimated counter value is calculated based on a previous counter value and an elapsed time since the previous counter value was received.

In some embodiments, the drug delivery device is a connected insulin pen, and wherein the connected insulin pen is configured to wirelessly transmit the dose data.

In some embodiments, the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit dose data.

In many embodiments, a method for recommending a dose for a meal comprises the steps of: prompting the user to enter a tag associated with the meal type; receiving an input tag for an instance of a meal type; associating the input tag with the amount of drug administered for the instance of the meal type and the post-meal analyte data set administered for the instance of the meal type; determining whether a threshold number of instances associated with the meal type is met; and if a threshold number of instances are met, determining a recommended medication dose for the meal type.

In some embodiments, the recommended drug dose for the meal type is based at least in part on the amount of drug administered for the instance of the meal type and the post-meal analyte data set for the instance of the meal type.

In some embodiments, the recommended drug dose for the meal type is based at least in part on a plurality of drug amounts administered for the plurality of instances of the meal type and a plurality of post-meal analyte data sets for the plurality of instances of the meal type. In some implementations, the instance of the meal type is a first instance of the meal type, and wherein the plurality of instances of the meal type includes the first instance of the meal type.

In some embodiments, the method further comprises the steps of: analyte data is received from the sensor control device. In some embodiments, the recommended medication dose for the meal type is based at least in part on analyte data received from the sensor control device.

In some methods, the method further comprises the steps of: the recommended medication dose for the meal type is visually output to the display.

In some embodiments, the method further comprises the steps of: the user is prompted with an option to track the meal type. In some implementations, wherein the user is prompted to enter a tag associated with the meal type in response to the user selecting an option to track the meal type.

In many embodiments, a system for determining a recommended medication dose includes: one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: prompting the user to enter a tag associated with the meal type; receiving an input tag for an instance of a meal type; associating the input tag with the amount of drug administered for the instance of the meal type and the post-meal analyte data set administered for the instance of the meal type; determining whether a threshold number of instances associated with the meal type is met; and if a threshold number of instances are met, determining a recommended medication dose for the meal type.

In some embodiments, the recommended drug dose for the meal type is based at least in part on the amount of drug administered for the instance of the meal type and the post-meal analyte data set for the instance of the meal type.

In some embodiments, the recommended drug dose for the meal type is based at least in part on a plurality of drug amounts administered for the plurality of instances of the meal type and a plurality of post-meal analyte data sets for the plurality of instances of the meal type. In some implementations, the instance of the meal type is a first instance of the meal type, and wherein the plurality of instances of the meal type includes the first instance of the meal type.

In some embodiments, the system further comprises: a wireless communication circuit configured to receive data indicative of an analyte level from the sensor control device; in some embodiments, the recommended medication dose for the meal type is based at least in part on data received from the sensor control device indicating the analyte level.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the recommended medication dose for the meal type is visually output to the display.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the user is prompted with an option to track the meal type. In some implementations, the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted to enter a tag associated with the meal type only when the user has selected an option to track the meal type.

In many embodiments, a system for intelligent meal annotation comprises: one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: prompting the user to enter a tag associated with the meal type; the method includes receiving an input tag for an instance of a first meal type, wherein the first meal type is associated with one or more previous input tags, determining whether a meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, and associating the input tag with a second meal type, wherein the second meal type is different from the first meal type.

In some implementations, the meal type characteristics of the instance are based at least in part on a difference between a meal size associated with one or more previously entered tags and a meal size of the entered tag for the instance.

In some implementations, the meal type characteristic of the instance is based at least in part on a difference between an amount of medication associated with one or more previously entered tags and an amount of medication associated with an entered tag for the instance.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: if the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, the user is prompted with an option to create a second tag.

In some implementations, the instructions, when executed by the one or more processors, cause the one or more processors to: the input tag is associated with the second meal type only when the user has selected the option to create the second tag.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: if the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, the input tag for the instance is disassociated from the first meal type. In some implementations, the instructions, when executed by the one or more processors, cause the one or more processors to: the input tag for the instance is disassociated from the first meal type before the input tag is associated with the second meal type.

In many embodiments, a method for recommending a dose for a meal comprises the steps of: prompting the user to enter a tag associated with the meal type; receiving a first input tag for a first instance of a meal type; associating the first input tag with a first amount of medication administered for a first instance of the meal type; receiving a second input tag for a second instance of the meal type; associating a second input tag with a second amount of the drug administered for a second instance of the meal type and a second post-meal analyte data set for the instance of the meal type; and in response to determining that the difference between the second quantity of medication and the first quantity of medication is greater than a predetermined threshold difference, prompting the user to enter a modification tag associated with the meal type.

In some embodiments, the modification tag includes a meal of a different size.

In some embodiments, the predetermined threshold difference is at least about 2 units.

In some implementations, prompting the user to enter a modification tag associated with the meal type occurs in real-time.

In some implementations, prompting the user to enter the modified tag associated with the meal type occurs within about 5 minutes or less of receiving the second input tag.

In some implementations, prompting the user to enter the modified tag associated with the meal type occurs within about 2 minutes or less of receiving the second input tag.

In some methods, the method further comprises the steps of: associating the first input tag with a first post-meal analyte data set for a first instance of a meal type; and associating the second input tag with a second post-meal analyte data set for a second instance of the meal type.

In many embodiments, a system for meal annotation comprises: one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: prompting a user to enter a label associated with a meal type, receiving a first entry label for a first instance of the meal type, associating the first entry label with a first quantity of medication administered for the first instance of the meal type, receiving a second entry label for a second instance of the meal type; associating a second input label with a second amount of medication administered for a second instance of the meal type; and in response to determining that the difference between the second quantity of medication and the first quantity of medication is greater than a predetermined threshold difference, prompting the user to enter a modification tag associated with the meal type.

In some embodiments, the modification tag includes a meal of a different size.

In some embodiments, the predetermined threshold difference is at least about 2 units.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the user is prompted to enter a modification tag associated with the meal type, the prompting occurring in real-time.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the user is prompted to enter a modified tag associated with the meal type within about 5 minutes or less of receiving the second input tag.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the user is prompted to enter a modified tag associated with the meal type within about 2 minutes or less of receiving the second input tag.

In some implementations, the instructions, when executed by the one or more processors, further cause the one or more processors to: the first input tag is associated with a first post-meal analyte data set for a first instance of a meal type and the second input tag is associated with a second post-meal analyte data set for a second instance of the meal type.

In many embodiments, an analyte monitoring system includes: a sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of a subject; a reader device. The reader device includes: a wireless communication circuit configured to receive an analyte level from the sensor control device; and one or more processors coupled to the memory, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: determining a pattern type for at least one time period of a day based on the hypoglycemic risk index and the hyperglycemic risk index for the at least one time period of the day; and outputting a user interface to the display, the user interface comprising: at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device; a time range display comprising a graph including a target range time of a plurality of graph portions, wherein each graph portion of the plurality of graph portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graph portion, wherein the plurality of graph portions includes at least 4 graph portions; and a graph including a graph of analyte levels represented by the user's levels across a plurality of time periods of the day and identification of a determined pattern type for at least one time period.

In some embodiments, the at least one glucose indicator comprises a glucose average.

In some embodiments, the at least one glucose indicator comprises a glucose management indicator.

In some implementations, the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

In some implementations, the plurality of graphics portions includes at least 5 graphics portions;

in some implementations, the plurality of graphical portions includes at least four graphical portions selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low threshold and the low threshold, a portion of the graph between the low threshold and the high threshold, a portion of the graph between the high threshold and the very high threshold, and a portion of the graph above the very high threshold.

In some embodiments, the target in-range time display further comprises: a description of the predefined analyte range associated with each graphical section.

In some implementations, the in-target-range time display further includes a value for each of the plurality of graphical portions that relates to an amount of time that the user's analyte level is within a predefined analyte range associated with the graphical portion within the time period. In some embodiments, the value is a percentage value.

In some implementations, the in-target-range time display further includes a combined value for at least two of the plurality of graphical portions that correlates to a sum of the amounts of time the user's analyte level is within a predefined analyte range associated with the at least two graphical portions over the period of time.

In some embodiments, the graph of time within the target range includes a histogram. In some embodiments, each graphical portion of the histogram is arranged in a vertical layout, wherein the graphical portion below the very low threshold is below the graphical portion between the very low threshold and the low threshold, the low threshold is below the graphical portion between the low threshold and the high threshold is below the graphical portion between the high threshold and the very high threshold, and the very high threshold is below the graphical portion above the very high threshold.

In some implementations, the identifying of the determined pattern type for the at least one time period includes: at least a partial outline of the time period in the graph. In some implementations, the identifying of the determined pattern type for the at least one time period further includes: a flag of the pattern type is determined.

In some embodiments, the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately occasional hypoglycemic pattern, a hyperglycemic pattern, or a no pattern.

In some implementations, the graph includes a plurality of determined pattern types, and wherein the identification of a single pattern type is distinct from other identifications of the plurality of determined pattern types.

In some implementations, the instructions further cause the one or more processors to: a display of the identification of the most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for at least one time period of the day. In some implementations, the identification of the most important pattern type is graphically displayed. In some implementations, the identifying of the determined pattern type for the at least one time period includes: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is distinctly different from other identifications of the plurality of identifications. In some implementations, the pattern types determined for at least one time period of a day include a plurality of pattern types for a plurality of time periods of a day. In some embodiments, the plurality of pattern types includes at least two of a hypoglycemic pattern, a hyperglycemic predominately occasional hypoglycemic pattern, a hyperglycemic pattern, or a no pattern. In some embodiments, if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern. In some embodiments, if the plurality of pattern types includes a hyperglycemia-dominant-low-blood-glucose pattern and does not include a hypoglycemia pattern, the identification of the most important pattern type includes an identification of a hyperglycemia-dominant-low-blood-glucose pattern. In some embodiments, if the plurality of pattern types includes a hyperglycemic pattern and does not include a hyperglycemic predominately low glycemic pattern or a hypoglycemic pattern, the identification of the most important pattern type includes identification of a hyperglycemic pattern.

In some implementations, the instructions further cause the one or more processors to: a display is output to the user interface that includes an identification of at least one time period of the day determined to have the most important pattern type. In some implementations, the display of the identification of the most important pattern type and the display of the identification of the at least one time period of the day determined to have the most important pattern type includes: a tag for identification of the most important pattern type and at least one tag of the day that is determined to have identification of at least one time period of the most important pattern type. In some embodiments, the color of the tag used for the identification of the most important pattern type is different from the color of the at least one tag used for the identification of the at least one time period determined to have the most important pattern type in the day.

In some implementations, the instructions further cause the one or more processors to: determining a change in at least one time period of a day; and outputting a display to the user interface including a statement regarding the change if the determined change is high. In some embodiments, the statement regarding the change includes: identification of behavior that may contribute to glucose changes.

In some implementations, the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to a user interface. In some embodiments, the very low threshold is between about 50mg/dL and about 58 mg/dL. In some embodiments, the very low threshold is about 54mg/dL.

In some implementations, the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface. In some embodiments, the statement regarding medication notes includes advice to adjust the medication. In some embodiments, the statement regarding medication notes includes advice regarding medications that contribute to low glucose levels.

In some implementations, the instructions further cause the one or more processors to: a display including statements related to lifestyle notes is output to a user interface. In some embodiments, the statement regarding lifestyle considerations includes a statement regarding at least one of missed meals, carbohydrates, activity levels, alcohol, and medications.

In some embodiments, the period of time is 14 days.

In many embodiments, a method for displaying information related to glucose levels in a subject, comprising the steps of: receiving an analyte level from a sensor control device; determining a pattern type for at least one time period of a day based on the hypoglycemic risk index and the hyperglycemic risk index for the at least one time period of the day; and displaying a user interface, the user interface comprising: at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device; a time range display comprising a graph including a target range time of a plurality of graph portions, wherein each graph portion of the plurality of graph portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graph portion, wherein the plurality of graph portions includes at least 4 graph portions; and a graph including a graph of analyte levels represented by the user's levels across a plurality of time periods of the day and identification of a determined pattern type for at least one time period.

In some embodiments, the at least one glucose indicator comprises a glucose average.

In some embodiments, the at least one glucose indicator comprises a glucose management indicator.

In some implementations, the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

In some embodiments, the plurality of graphics portions includes at least 5 graphics portions.

In some implementations, the plurality of graphical portions includes at least four graphical portions selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low threshold and the low threshold, a portion of the graph between the low threshold and the high threshold, a portion of the graph between the high threshold and the very high threshold, and a portion of the graph above the very high threshold.

In some embodiments, the target in-range time display further comprises: a description of the predefined analyte range associated with each graphical section.

In some implementations, the in-target-range time display further includes a value for each of the plurality of graphical portions that relates to an amount of time that the user's analyte level is within a predefined analyte range associated with the graphical portion within the time period. In some embodiments, the value is a percentage value.

In some implementations, the in-target-range time display further includes a combined value for at least two of the plurality of graphical portions that correlates to a sum of the amounts of time the user's analyte level is within a predefined analyte range associated with the at least two graphical portions over the period of time.

In some embodiments, the graph of time within the target range includes a histogram. In some embodiments, each graphical portion of the histogram is arranged in a vertical layout, wherein the graphical portion below the very low threshold is below the graphical portion between the very low threshold and the low threshold, the low threshold is below the graphical portion between the low threshold and the high threshold is below the graphical portion between the high threshold and the very high threshold, and the very high threshold is below the graphical portion above the very high threshold.

In some implementations, the identifying of the determined pattern type for the at least one time period includes: at least a partial outline of the time period in the graph.

In some implementations, the identifying of the determined pattern type for the at least one time period further includes: a flag of the pattern type is determined.

In some embodiments, the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately occasional hypoglycemic pattern, a hyperglycemic pattern, or a no pattern.

In some implementations, the graph includes a plurality of determined pattern types, and wherein the identification of a single pattern type is distinct from other identifications of the plurality of determined pattern types.

In some implementations, the instructions further cause the one or more processors to: a display of the identification of the most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for at least one time period of the day. In some implementations, the identification of the most important pattern type is graphically displayed. In some implementations, the identifying of the determined pattern type for the at least one time period includes: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is distinctly different from other identifications of the plurality of identifications. In some implementations, the pattern types determined for at least one time period of a day include a plurality of pattern types for a plurality of time periods of a day. In some embodiments, the plurality of pattern types includes at least two of a hypoglycemic pattern, a hyperglycemic predominately occasional hypoglycemic pattern, a hyperglycemic pattern, or a no pattern. In some embodiments, if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern. In some embodiments, if the plurality of pattern types includes a hyperglycemia-dominant-low-blood-glucose pattern and does not include a hypoglycemia pattern, the identification of the most important pattern type includes an identification of a hyperglycemia-dominant-low-blood-glucose pattern. In some embodiments, if the plurality of pattern types includes a hyperglycemic pattern and does not include a hyperglycemic predominately low glycemic pattern or a hypoglycemic pattern, the identification of the most important pattern type includes identification of a hyperglycemic pattern.

In some implementations, the instructions further cause the one or more processors to: a display is output to the user interface that includes an identification of at least one time period of the day determined to have the most important pattern type.

In some implementations, the display of the identification of the most important pattern type and the display of the identification of the at least one time period of the day determined to have the most important pattern type includes: a tag for identification of the most important pattern type and at least one tag of the day that is determined to have identification of at least one time period of the most important pattern type. In some embodiments, the color of the tag used for the identification of the most important pattern type is different from the color of the at least one tag used for the identification of the at least one time period determined to have the most important pattern type in the day.

In some implementations, the instructions further cause the one or more processors to: determining a change in at least one time period of a day; and outputting a display to the user interface including a statement regarding the change if the determined change is high. In some embodiments, the statement regarding the change includes: identification of behavior that may contribute to glucose changes.

In some implementations, the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to a user interface. In some embodiments, the very low threshold is between about 50mg/dL and about 58 mg/dL. In some embodiments, the very low threshold is about 54mg/dL.

In some implementations, the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface. In some embodiments, the statement regarding medication notes includes advice to adjust the medication. In some embodiments, the statement regarding medication notes includes advice regarding medications that contribute to low glucose levels.

In some implementations, the instructions further cause the one or more processors to: a display including statements related to lifestyle notes is output to a user interface. In some embodiments, the statement regarding lifestyle considerations includes a statement regarding at least one of missed meals, carbohydrates, activity levels, alcohol, and medications.

In some embodiments, the period of time is 14 days.

In many embodiments, an apparatus for displaying an index associated with a subject comprises: an input configured to receive drug administration data; a display configured to visually present information; and one or more processors coupled with the input, the display, and the memory, the memory storing instructions, a dose of medication received by the subject over a period of time, and a recommended dose of medication for the subject over a period of time, wherein the instructions, when executed by the one or more processors, cause the apparatus to: a graph is displayed that plots a plurality of doses of the drug taken by the subject at a plurality of times, wherein the graph includes an x-axis of time and a y-axis of a difference between the dose taken by the subject and the dose recommended for the subject.

In some embodiments, the plurality of drug doses includes at least one of a basal dose, a fixed meal dose, and a meal dose with a correction factor. In some embodiments, the fixed meal dose includes at least one of a fixed breakfast dose, a fixed lunch dose, and a fixed dinner dose. In some embodiments, the meal dose with the correction factor comprises at least one of a breakfast dose with the correction factor, a fixed lunch dose with the correction factor, and a fixed dinner dose with the correction factor.

In some embodiments, the difference between the dose taken by the subject and the recommended dose is in units.

In some systems, the input includes wireless communication circuitry.

In many embodiments, an apparatus for displaying an index associated with a subject, comprises: an input configured to receive measured analyte data and drug administration data; a display configured to visually present information; and one or more processors coupled with the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a time period, and a recommended dose of the drug by the subject over the time period, wherein the instructions, when executed by the one or more processors, cause the apparatus to: displaying a summary of the subject's treatment, including the dose administered over a period of time and an analyte indicator determined from the received measured analyte data; displaying a graph summarizing missed doses over the period of time; and displaying a graph summarizing the unauthorized doses, wherein the unauthorized doses comprise doses that the subject receives at a time at which there is a different amount than the recommended dose for the time.

In some embodiments, summarizing the pattern of missed doses includes: graphical representation of the percentage of missed doses for a plurality of dose types. In some embodiments, each percentage of the percentage of missed for a plurality of dose types is calculated as the percentage of the total number of doses of that dose type for the missed dose of that dose type over a period of time. In some embodiments, the plurality of dosage types includes at least one of a basal dosage, a breakfast dosage, a lunch dosage, and a dinner dosage.

In some embodiments, the graph summarizing missed doses is a bar graph.

In some embodiments, the graph summarizing the override dose is a bar graph.

In some embodiments, the input comprises a wireless communication circuit.

In many embodiments, an apparatus for displaying an index associated with a subject, comprises: an input configured to receive measured analyte data from a plurality of subjects, drug administration data from the plurality of subjects, and data related to an administration recommendation for the plurality of subjects; a display configured to visually present information; and one or more processors coupled with the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of each of the plurality of subjects, a dose of a drug received by each of the plurality of subjects over a period of time, and data related to a dosing recommendation for the plurality of subjects, the instructions, when executed by the one or more processors, cause the apparatus to: displaying a summary of analyte indicators for each of the plurality of subjects, wherein the analyte indicators include at least two of a time in the target range, a time below a low threshold, a time above a high threshold, a percentage of basal doses administered, and an average bolus dose administered per day; and displaying a summary of the information related to the dosing recommendation, wherein the information related to the dosing recommendation includes an indication of the dosing recommendation for a subject of the plurality of subjects that requires approval from the healthcare provider.

In some embodiments, the summary of analyte indicators includes at least three of time within the target range, time below the low threshold, time above the high threshold, percentage of basal dose administered, and average bolus dose administered per day.

In some embodiments, the low threshold is about 70mg/dL.

In some embodiments, the high threshold is about 180mg/dL.

In some embodiments, the indication of the dosing recommendation is an icon.

In some embodiments, the indication of the dosing recommendation is a statement indicating the number of dosing recommendations that require approval.

In some embodiments, the input comprises a wireless communication circuit.

In many embodiments, an apparatus for displaying treatment information related to a subject, comprises: an input configured to receive measured analyte data and drug administration data; a display configured to visually present information; and one or more processors coupled with the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a period of time, and a meal time of the subject, wherein the instructions, when executed by the one or more processors, cause the apparatus to: receiving an estimated dose parameter and an estimated meal dosing time range from a subject; determining a representative amount of each of a plurality of basal doses and a plurality of meal doses taken by the subject over a period of time based on the drug administration data; determining a representative meal dosing time range for the subject over the period of time based on the drug dosing data; determining a recommended dose for at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose; displaying the estimated dose parameters and estimated meal dosing time range received from the subject; displaying a representative amount of each of the plurality of basal doses and the plurality of meal doses and a representative meal dosing time range; and displaying the recommended dose for at least one of the base dose, breakfast dose, lunch dose, and dinner dose.

In some embodiments, the plurality of meal doses includes a plurality of breakfast doses, a plurality of lunch doses, and a plurality of dinner doses, and wherein the instructions, when executed by the one or more processors, cause the apparatus to: an average amount of each of the plurality of basal doses, the plurality of breakfast doses, the plurality of lunch doses, and the plurality of dinner doses is determined.

In some embodiments, the estimated dose parameters include an estimated amount of basal dose, breakfast dose, lunch dose, and dinner dose. In some embodiments, the estimated dose parameter further comprises estimated times at which the subject took the basal dose, breakfast dose, lunch dose, and dinner dose.

In some embodiments, the estimated meal dosing time range includes an estimated dosing start time and an estimated dosing end time for each of breakfast, lunch, and dinner.

In some embodiments, the representative amounts of each of the plurality of basal doses and the plurality of meal doses comprise: an average of each of the plurality of basal doses and the plurality of meal doses taken by the subject over a period of time.

In some embodiments, the representative amounts of each of the plurality of basal doses and the plurality of meal doses comprise: a pattern of each of a plurality of basal doses and a plurality of meal doses taken by a subject over a period of time.

In some implementations, the instructions, when executed by the one or more processors, further cause the apparatus to: determining a pre-meal correction factor and a post-meal correction factor based on the measured analyte data and the drug administration data; the pre-meal correction factor and the post-meal correction factor are displayed.

In some embodiments, the representative meal dosing time range is displayed adjacent to the estimated meal dosing time range.

In some embodiments, the representative amount of each of the plurality of basal doses and the plurality of meal doses is displayed adjacent to the estimated dose parameter.

In some implementations, the instructions, when executed by the one or more processors, further cause the apparatus to: determining a conservative value for at least one of the basal dose, the breakfast dose, the lunch dose, and the dinner dose, wherein the conservative value is lower than the corresponding determined representative amount for each of the plurality of basal doses and the plurality of dinner doses; and displaying the determined conservation value.

In many embodiments, the input includes wireless communication circuitry.

For any of the methods described herein, the methods may be performed on at least one processor of a remote device (e.g., server, phone/receiver), on a drug delivery device, or on a glucose monitoring device.

It should be noted that all features, elements, components, functions, and steps described with respect to any embodiment provided herein are intended to be freely combinable and replaceable with those from any other embodiment. If certain features, elements, components, functions, or steps are described with respect to only one embodiment, it should be understood that the features, elements, components, functions, or steps can be used with every other embodiment described herein unless expressly stated otherwise. Thus, at any time this section is used as a basis for reference and written support for introducing claims which combine features, elements, components, functions, and steps from different embodiments or replace features, elements, components, functions, and steps from one embodiment with features, elements, components, functions, and steps of another embodiment, even though the following description does not explicitly state such combinations or substitutions in particular cases. It is expressly recognized that making an explicit statement of each possible combination and substitution is overly cumbersome, especially in view of the tolerability of one of ordinary skill in the art to readily recognize each such combination and substitution.

To the extent that embodiments disclosed herein include or operate in association with memory, storage, and/or computer-readable media, the memory, storage, and/or computer-readable media are non-transitory. Thus, to the extent that memory, storage, and/or computer-readable medium is covered by one or more claims, the memory, storage, and/or computer-readable medium is merely non-transitory.

While the embodiments are susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. It should be understood, however, that the embodiments are not limited to the particular forms disclosed, but to the contrary, the embodiments are to cover all modifications, equivalents, and alternatives falling within the spirit of the disclosure. Furthermore, any feature, function, step or element of an embodiment can be recited in, or added to, the claims, and a negative limitation on the scope of the invention of the claims is defined by features, functions, steps or element that are not within that scope.

Clause of (b)

Exemplary embodiments are set forth in the numbered clauses below.

Clause 1. An apparatus for parameterizing a patient's drug administration practices to configure dose guidance settings, the apparatus comprising:

An input component configured to receive measured analyte data, meal data, and medication administration data;

a display component configured to visually present information; and

one or more processors coupled with the input, the display, and the memory, the memory storing instructions and time-related data characterizing an analyte of a patient during an analysis period, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform:

receiving patient dose regimen information for an analysis period;

estimating a measure of correspondence between the time-related data and patient dose regimen information; and determining the dose guidance information based on the consistency metric.

Clause 2 the device of clause 1, wherein the memory holds further instructions for outputting the dose guidance information to the display.

Clause 3 the device of clause 1, wherein the memory holds further instructions for receiving patient dose regimen information from the input component.

Clause 4 the device of clause 1, wherein the memory holds further instructions for receiving the patient dose regimen information via a transmission from a remote data server.

Clause 5 the device of clause 1, wherein the patient dose regimen information comprises: typical fixed doses of medication taken at meals and typical times of day when breakfast is taken.

Clause 6 the device of clause 1, wherein the patient dose regimen information comprises: information defining the frequency of patient compliance with a planned dose or meal.

Clause 7 the device of clause 1, wherein the drug comprises insulin.

Clause 8 the apparatus of clause 1, wherein the instructions for estimating the consistency metric further comprise:

classifying each dose of the patient dose regimen in a drug class based on the time-related data;

grouping each of the doses in one of a set of dining groups;

generating a dose parameter for the patient at least in part by applying the data for each of the dining sets to the model; and

dose parameters are stored for configuring dose guidance settings.

Clause 9 the device of clause 1, wherein the memory holds further instructions for accumulating time-related data characterizing the analyte of the patient over a period of time.

Clause 10 the device of clause 1, wherein the memory holds further instructions for determining the dose guidance information at least in part by reducing the dosing recommendation based on detecting that fluctuations in the analyte in the time-related data exceed a low threshold.

Clause 11 the device of clause 10, wherein the dosing recommendation is for fixed dosing only.

Clause 12 the device of clause 1, wherein the memory holds further instructions for determining patient compliance with the patient dose regimen information based on the time-related data.

Clause 13 the device of clause 1, wherein the memory holds further instructions for determining whether to output the dose guidance parameter based on the consistency metric.

Clause 14 the device of clause 1, wherein the memory holds further instructions for outputting a dose guidance parameter comprising a predetermined dose recommendation if the compliance metric indicates an unreliable system configuration.

Clause 15 the device of clause 1, wherein the input comprises a wireless communication circuit.

Clause 16. A method for facilitating effective access by a healthcare provider (HCP) to an electronic case (EMR) of a patient generated by a dose guidance system while preserving patient privacy, the method comprising:

authenticating, by at least one processor of the portable display device, a session with the patient;

generating, by the at least one processor, an EMR identification code (ID) in response to receiving input from the patient during the session indicating a request to share EMR with the HCP;

Providing, by the at least one processor, the EMR ID to a remote server that controls access to the EMR; and

the EMR ID is output by the at least one processor to a display of the portable display device.

Clause 17 the method of clause 16, further comprising: prior to authentication, EMR is provided to a remote server.

Clause 18 the method of clause 16, further comprising: EMR is received from a dose guidance system.

Clause 19 the method of clause 16, further comprising: a determination is made as to whether the EMR does not satisfy a condition of compliance with patient input indicative of a dosage pattern for the tracked drug.

Clause 20 the method of clause 19, further comprising: upon determining that the EMR does not meet the consistency condition, the patient is provided with an option to provide EMR to the HCP.

Clause 21 the method of clause 19, wherein generating, providing, and outputting are conditioned on determining that the EMR does not satisfy a consistency condition.

Clause 22. The method of clause 20, wherein generating, providing, and outputting are conditioned on determining that the EMR does not satisfy the consistency condition.

Clause 23 the method of clause 16, further comprising: the patient is provided with the option of providing EMR to the HCP.

Clause 24. The method of clause 17, wherein the remote server creates a web page for displaying the EMR addressed at least in part by the EMR ID upon receiving the EMR ID.

Clause 25 the method of clause 16, wherein the EMR comprises: determining a dosing parameter of a drug to be administered to a patient at a time within a defined period of time, and determining a measure of compliance with patient supply dosing information for the drug.

Clause 26 the method of clause 25, wherein the drug is insulin.

Clause 27, a system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device, wherein the dose data includes an amount and time of a last drug dose administered;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

classifying the last drug dose administered as a dining dose or a correction dose; and

in response to determining that a period of time has elapsed since the last drug dose was administered, additional dose guidance is displayed.

Clause 28 the system of clause 27, wherein the time period is about 2 hours.

The system of clause 29, wherein the last drug dose administered is a meal dose, and wherein the dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the time period is not reset to the time at which the at least one additional drug dose was administered.

Clause 30 the system of clause 27, wherein the last drug dose administered is not the initial dose.

Clause 31 the system of clause 27, wherein the time of the last drug dose administered is a time stamp from the connected drug delivery device.

The system of clause 32, wherein the last drug dose administered is a meal dose, and wherein the dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the beginning of the time period is reset to the time at which the at least one additional drug dose was administered.

Clause 33 the system of clause 27, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject.

Clause 34 the system of clause 27, wherein the input comprises a wireless communication circuit.

Clause 35. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dose data of the subject from the drug delivery device, wherein the drug dose data includes an amount and time of a last drug dose administered;

classifying the last drug dose administered as a dining dose or a correction dose; in response to determining that a period of time has elapsed since the last drug dose was administered, a dose guide is displayed.

Clause 36 the method of clause 35, wherein the time period is about 2 hours.

Clause 37 the method of clause 35, wherein the last drug dose administered is a meal dose, wherein the drug dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the beginning of the time period is not reset to the time at which the at least one additional drug dose was administered.

Clause 38 the method of clause 35, wherein the last drug dose administered is not the initial dose.

Clause 39 the method of clause 35, wherein the time of the last drug dose administered is a time stamp from the connected drug delivery device.

Clause 40 the method of clause 35, wherein the last drug dose administered is a correction dose, wherein the drug dose data further comprises at least one additional drug dose administered after the last drug dose was administered, and wherein the beginning of the time period is reset to the time at which the at least one additional drug dose was administered.

Clause 41 a system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device, wherein the dose data comprises data related to a recent drug dose administered;

A display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether the last drug dose administered is the last drug dose administered; and

in response to determining that the recent drug dose administered is the recent drug dose administered, a screen including a dose guidance recommendation is displayed.

Clause 42 the system of clause 41, wherein the recent drug dose administered is determined to be the recent drug dose administered by confirmation from the user.

The system of clause 43, wherein the dose data further comprises data related to at least one drug dose administered from the self-setting time, and wherein the instructions further cause the one or more processors to: the screen is displayed in response to determining that at least one drug dose administered since the self-setting time has been categorized.

Clause 44 the system of clause 43, wherein the at least one drug dose related dose data administered since the reset time is automatically categorized.

Clause 45 the system of clause 44, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject, wherein the instructions further cause the one or more processors to: one or more wireless interrogation signals are transmitted to the drug delivery device to determine that the last dose administered has been received.

Clause 46 the system of clause 43, wherein the dose data related to the at least one drug dose administered since the self-settling time has been categorized by the user.

Clause 47 the system of clause 41, wherein the instructions further cause the one or more processors to: a prompt is displayed to allow the user to confirm that the information relating to the last drug dose administered is correct.

Clause 48 the system of clause 47, wherein the instructions further cause the one or more processors to: a screen including a dose guidance recommendation is displayed during a period of time from after user confirmation.

Clause 49 the system of clause 41, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject.

Clause 50 the system of clause 41, wherein the input is further configured to receive measured analyte data and a request for dose guidance, and wherein the instructions further cause the one or more processors to:

determining whether the glucose concentration at the time the request for dose guidance is received is below a low threshold; and

in response to determining that the glucose concentration at the time the request for the dose guidance is received is below a low threshold, a screen including a message is displayed to address the low glucose level prior to administration of the drug.

Clause 51 the system of clause 50, wherein the instructions further cause the one or more processors to:

in response to determining that the glucose concentration at the time the request for dose guidance is received is below the low threshold, no dose guidance recommendation is displayed.

The system of clause 52, wherein the input is further configured to receive the measured analyte data and the request for the dose guidance after a start time of a meal, and wherein the instructions further cause the one or more processors to:

determining whether the glucose concentration at the estimated meal start time is below a low threshold; and

in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, a screen including a message is displayed to address the low glucose level prior to administering the drug.

Clause 53 the system of clause 52, wherein the instructions further cause the one or more processors to:

in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, no dose-guiding recommendation is displayed.

Clause 54 the system of clause 41, wherein the input comprises a wireless communication circuit.

Clause 55. A method for providing dose guidance, the method comprising:

Receiving, by the electronic device, dose data of the user from the drug delivery device, wherein the dose data comprises data related to a recent drug dose administered;

determining whether the last drug dose administered is the last drug dose administered; in response to determining that the recent drug dose administered is the recent drug dose administered, a screen including a dose guidance recommendation is displayed.

Clause 56 the method of clause 55, wherein the recent drug dose administered is determined to be the recent drug dose administered by confirmation from the user.

Clause 57 the method of clause 55, wherein the administered recent drug dose is administered after the reset time, wherein the method further comprises the steps of:

it is determined whether the recent dose administered after the reset time is classified.

Clause 58 the method of clause 57, wherein the recent dose administered after the reset time is automatically categorized.

Clause 59 the method of clause 55, wherein the method further comprises the steps of:

one or more wireless interrogation signals are transmitted to the drug delivery device to determine that the last dose administered has been received.

Clause 60. The method of clause 57, wherein the recent dose administered after the reset time is categorized by the user.

Clause 61 the method of clause 60, further comprising the steps of: a prompt is displayed to allow the user to confirm that the information relating to the last drug dose administered is correct.

Clause 62. The method of clause 55, wherein the screen comprising the dose guidance recommendation is displayed only for a period of time beginning after the user confirms that the information related to the last drug dose administered was correct.

Clause 63. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from the drug delivery device, wherein the dose data comprises data related to at least one meal dose administered from a self-setting time;

a display configured to visually present a plurality of meal icons;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether at least one meal dose administered since the reset time has been categorized; and

in response to determining that at least one meal dose administered since the self-set time has been categorized, a screen including a plurality of meal icons is displayed.

Clause 64 the system of clause 63, wherein the instructions cause the one or more processors to: it is determined whether the at least one meal dose administered since the reset time has been categorized as one of breakfast, lunch or dinner.

Clause 65 the system of clause 63, wherein the plurality of meal icons includes a breakfast icon, a lunch icon, and a dinner icon, and wherein each of the breakfast icon, the lunch icon, and the dinner icon includes a first appearance and a second appearance, wherein the first appearance is associated with a first state in which one of the at least one meal doses administered since the reset time has been classified as corresponding to a meal type of the breakfast, lunch, or dinner icon; and the second appearance is associated with a second state in which one of the at least one meal doses administered since the reset time is not classified as a meal type corresponding to a breakfast, lunch or dinner icon.

Clause 66 the system of clause 65, wherein the first appearance comprises a shadow appearance.

Clause 67 the system of clause 65, wherein the second appearance comprises an unshaded appearance.

Clause 68 the system of clause 65, wherein the second appearance is brighter than the first appearance.

Clause 69 the system of clause 63, wherein the instructions further cause the one or more processors to:

at least one meal dose administered since the reset time is classified as breakfast, lunch or dinner.

Clause 70 the system of clause 63, wherein the instructions further cause the one or more processors to:

an input is received from the user, wherein the input includes classifying at least one meal dose administered from the reset time as a breakfast dose, a lunch dose, or a dinner dose.

Clause 71 the system of clause 63, wherein the reset time is determined based on at least one time range associated with at least one meal.

Clause 72 the system of clause 63, wherein the reset time is determined based on the time range associated with the dinner dose and the time range associated with the breakfast dose.

Clause 73 the system of clause 63, wherein the reset time is midnight.

Clause 74 the system of clause 63, wherein the reset time is about half the time between the end of the dinner dose time range and the beginning of the breakfast dose time range.

Clause 75 the system of clause 63, further comprising; a drug delivery device configured to deliver at least one dose of a drug to a subject.

Clause 76 the system of clause 63, wherein the input comprises a wireless communication circuit.

Clause 77. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dosage data of the user from the drug delivery device, wherein the dosage data comprises data related to at least one meal dose administered from a self-setting time;

determining whether at least one meal dose administered since the reset time has been categorized;

in response to determining that at least one meal dose administered since the self-set time has been categorized, a screen including a plurality of meal icons is displayed.

The method of clause 78, 77, wherein the determining step comprises: it is determined whether the at least one meal dose administered since the reset time has been categorized as one of breakfast, lunch or dinner.

Clause 79 the method of clause 77, wherein the plurality of meal icons includes a breakfast icon, a lunch icon, and a dinner icon, and wherein each of the breakfast icon, the lunch icon, and the dinner icon includes a first appearance and a second appearance, wherein the first appearance is associated with a first state in which one of the at least one meal doses administered since the reset time has been classified as corresponding to a meal type of the breakfast, lunch, or dinner icon; and the second appearance is associated with a second state in which one of the at least one meal doses administered since the reset time is not classified as a meal type corresponding to a breakfast, lunch or dinner icon.

Clause 80. The method of clause 79, wherein the first appearance comprises a shadow appearance.

Clause 81 the method of clause 79, wherein the second appearance comprises an unshaded appearance.

Clause 82. The method of clause 79, wherein the second appearance is brighter than the first appearance.

Clause 83. The method of clause 77, wherein the at least one meal dose administered since the reset time has been automatically categorized by one or more processors of the reader device.

Clause 84. The method of clause 77, wherein the at least one meal dose administered since the reset time has been categorized by the user.

Clause 85 the method of clause 77, wherein the reset time is determined based on at least one time range associated with at least one meal.

Clause 86. The method of clause 77, wherein the reset time is determined based on a time range associated with the dinner dose and a time range associated with the breakfast dose.

Clause 87. The method of clause 77, wherein the reset time is midnight.

Clause 88 the method of clause 77, wherein the reset time is about half the time between the end of the dinner dose time range and the beginning of the breakfast dose time range.

Clause 89, a system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether the missed dose alert is valid;

in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on the normal meal dose; and

in response to determining that the missed dose alert is valid, a dose guidance recommendation calculated based on the later meal dose is displayed.

The system of clause 89, wherein the instructions further cause the one or more processors to:

determining whether dose data received from the drug delivery device includes a dose administered during a period of a current time; and

in response to determining that the dose data received from the drug delivery device includes a dose administered within a time period of the current time, a dose guidance recommendation calculated based on the later meal dose is displayed.

Clause 91 the system of clause 90, wherein the instructions cause the one or more processors to:

In response to determining that the missed dose alert is valid and that the dose data received from the drug delivery device includes a dose administered within a time period of the current time, a dose guidance recommendation calculated based on the later meal dose is displayed.

Clause 92 the system of clause 90, wherein the time period is about 2 hours.

Clause 93 the system of clause 89, wherein if the current glucose level of the user is below the target glucose value, the normal meal dose calculation is based on the fixed insulin dose and the amount of active insulin associated with the meal.

Clause 94 the system of clause 89, wherein if the current glucose level of the user is above the target glucose value, each of the normal meal dose calculation and the later meal dose calculation is based on a fixed insulin dose associated with the meal, an amount of active insulin, a corrective adjustment, and a trend adjustment.

Clause 95 the system of clause 94, wherein the normal meal dose calculation is based on the current glucose level determined from the scanned glucose data.

Clause 96. The system of clause 94, wherein the late meal dose calculation is based on the current glucose level determined from the streaming glucose data.

Clause 97 the system of clause 96, wherein the later meal dose calculation is further based on trend adjustments determined from the streaming glucose data.

Clause 98 the system of clause 94, wherein the late meal dose calculation is based on the amount of active insulin calculated based on the time of the user's request for the dose guidance recommendation.

Clause 99 the system of clause 94, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject.

The system of clause 94, wherein the input comprises a wireless communication circuit.

Clause 101. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dosage data of a user from the drug delivery device;

determining whether the missed dose alert is valid; and

in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on the normal meal dose; and

in response to determining that the missed dose alert is valid, a dose guidance recommendation calculated based on the later meal dose is displayed.

Clause 102. The method of clause 101, further comprising the steps of:

determining whether the drug dose data includes data for a dose administered over a period of time;

In response to determining that the drug dose data does not include any data for doses administered over a period of time, a dose guidance recommendation calculated based on the later meal dose is displayed.

Clause 103. The method of clause 102, wherein the time period is about 2 hours.

Clause 104. The method of clause 101, wherein if the current glucose level of the user is below the target glucose value, the normal meal dose calculation is based on the fixed insulin dose and the amount of active insulin associated with the meal.

Clause 105. The method of clause 101, wherein if the user's current glucose level is above the target glucose value, each of the normal meal dose calculation and the later meal dose calculation is based on a fixed insulin dose, an amount of active insulin, a corrective adjustment, and a trend adjustment associated with the meal.

Clause 106 the method of clause 105, wherein the normal meal dose calculation is based on the current glucose level determined from the scanned glucose data.

Clause 107. The method of clause 105, wherein the later meal dose calculation is based on the current glucose level determined from the streaming glucose data.

Clause 108. The method of clause 107, wherein the later meal dose calculation is further based on trend adjustments determined from the streaming glucose data.

Clause 109. The method of clause 105, wherein the later meal dose calculation is based on the amount of active insulin calculated according to the time of the user's request for the dose guidance recommendation.

Clause 110 a system for providing a warning to a subject, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes;

determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; and

responsive to detection of a missed meal dose condition for a consecutive number of minutes and a determination that no insulin dose was recorded for about 45 minutes prior to the estimated meal start time, a warning interface associated with the missed meal dose is displayed.

Clause 111 the system of clause 110, wherein the consecutive minutes are about 5 minutes.

The system of clause 110, wherein the instructions further cause the one or more processors to:

determining whether a meal dose has been recorded within about 2 hours of the current time; and

in response to determining that the meal dose has been recorded within about 2 hours of the current time, a warning interface associated with the missed meal dose is displayed.

Clause 113 the system of clause 110, wherein the instructions further cause the one or more processors to:

determining whether to issue a correction dose alert; and

in response to determining that the correction dose alert has not been issued, an alert interface associated with the missed meal dose is displayed.

Clause 114 the system of clause 110, further comprising: a sensor control device configured to collect data indicative of an analyte level of a subject, the sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of the subject.

Clause 115 the system of clause 110, wherein the input comprises a wireless communication circuit.

Clause 116. A method for alerting a user to missing a meal dose, the method comprising:

Receiving, by the electronic device, streaming glucose data from the sensor control device;

determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes;

determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; and

responsive to detection of a missed meal dose condition for a consecutive number of minutes and a determination that no insulin dose was recorded for about 45 minutes prior to the estimated meal start time, a warning interface associated with the missed meal dose is displayed.

Clause 117 the method of clause 116, wherein the consecutive minutes is about 5 minutes.

Clause 118 the method of clause 116, further comprising the steps of:

whether a meal dose has been recorded within about 2 hours of the current time; and

in response to determining that the meal dose has been recorded within about 2 hours of the current time, a warning interface associated with the missed meal dose is displayed.

Clause 119 the method of clause 116, further comprising the steps of:

determining whether to issue a correction dose alert;

in response to determining that the correction dose alert has not been issued, an alert interface associated with the missed meal dose is displayed.

Clause 120 a system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert;

determining whether a missed meal dose condition has been detected within a few consecutive minutes after a missed meal dose warning has been issued;

responsive to determining that the missed meal dose condition is not detected within a consecutive number of minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

Clause 121 the system of clause 120, wherein the consecutive minutes are consecutive 15 minutes.

Clause 122 the system of clause 120, wherein missing a meal dose condition comprises determining that no insulin dose was administered during the period of estimated meal start time.

Clause 123 the system of clause 120, wherein the input comprises a wireless communication circuit.

Clause 124. A method for revoking a warning of a missed meal dose, the method comprising:

Receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert;

determining whether a missed meal dose condition has been detected within a few consecutive minutes after a missed meal dose warning has been issued;

responsive to determining that the missed meal dose condition is not detected within a consecutive number of minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

Clause 125 the method of clause 124, wherein the consecutive minutes are 15 consecutive minutes.

Clause 126 the method of clause 124, wherein missing a meal dose condition comprises determining that no insulin dose was administered during the period of estimated meal start time.

Clause 127. A system for managing alerts, the system comprising:

an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Issuing a missed meal dose alert at the current time;

determining whether an insulin dosage has been recorded within about 2 hours of the current time;

responsive to determining that the insulin dose has been recorded within about 2 hours of the current time, the missed meal dose alert is withdrawn.

The system of clause 128, wherein the input comprises a wireless communication circuit.

Clause 129. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device; issuing a missed meal dose alert at the current time;

determining whether an insulin dosage has been recorded within about 2 hours of the current time; and

responsive to determining that the insulin dose has been recorded within about 2 hours of the current time, the missed meal dose alert is withdrawn.

Clause 130. A system for managing alerts, the system comprising:

an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and

responsive to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, the missed meal dose alert is withdrawn.

Clause 131 the system of clause 130, wherein the input comprises a wireless communication circuit.

Clause 132. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and

responsive to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, the missed meal dose alert is withdrawn.

Clause 133 a system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

A display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert at the current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether the estimated meal start time is within about 2 hours of the current time; and

in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, the missed meal dose alert is withdrawn.

Clause 134 the system of clause 133, wherein the input comprises a wireless communication circuit.

Clause 135 a method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert at the current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether the estimated meal start time is within about 2 hours of the current time; and

in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, the missed meal dose alert is withdrawn.

Clause 136 a system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from an insulin delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether a correction dose warning has been issued at the current time;

determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and

in response to determining that a correction dose has been delivered and that no insulin dose has been administered to the subject within about 2 hours of the current time, a correction dose guide is displayed.

Clause 137 the system of clause 136, wherein the instructions further cause the one or more processors to:

determining whether the correction dose warning has been resolved; and

in response to determining that the correction dose warning has been resolved, a correction dose guidance is displayed.

Clause 138 the system of clause 136, wherein the input comprises a wireless communication circuit.

Clause 139. A method for recommending a correction dose, the method comprising:

Receiving, by the electronic device, insulin dosage data of the subject from the insulin delivery device;

determining whether a correction dose warning has been issued at the current time;

determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and

a correction dose guide is displayed and the correction dose is displayed,

wherein, in response to determining that a correction dose has been delivered and determining that no insulin dose has been administered to the subject within about 2 hours of the current time, a correction dose guidance is displayed.

The method of clause 139, further comprising the steps of: a determination is made as to whether the correction dose warning has been resolved, wherein the correction dose guidance is displayed only when the correction dose warning has been resolved.

Clause 141 a system for providing a warning to a subject, the system comprising:

an input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device;

a display configured to visually present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Determining at the present time whether a correction dose condition has been issued within a succession of minutes;

determining whether no insulin dosage has been recorded within about 2 hours of the current time; and

in response to determining that a correction dose condition has been issued within a few consecutive minutes and that no insulin dose has been recorded for about 2 hours of the current time, a warning interface associated with the correction dose warning is displayed.

Clause 142 the system of clause 141, wherein the consecutive minutes are about 5 minutes.

Clause 143 the system of clause 141, wherein the instructions further cause the one or more processors to:

determining whether to issue a missed meal dose alert;

responsive to determining that a missed dose alert has not been issued, displaying an alert interface related to correcting the dose alert; and

in response to determining that a missed dose alert is issued, an alert interface associated with correcting the dose alert is not displayed.

Clause 144 the system of clause 141, wherein the input comprises a wireless communication circuit.

Clause 145 a method for alerting a user about a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device;

Determining at the present time whether a correction dose condition has been issued within a succession of minutes;

determining that no insulin dosage has been recorded for about 2 hours of the current time; and

in response to determining that a correction dose condition has been issued within a few consecutive minutes and that no insulin dose has been recorded for about 2 hours of the current time, a warning interface associated with the correction dose warning is displayed.

Clause 146 the method of clause 145, wherein the consecutive minutes are about 5 minutes.

Clause 147 the method of clause 145, further comprising the steps of: it is determined whether a missed meal dose alert has not been issued before a warning interface associated with the corrected dose alert is displayed.

Clause 148 the method of clause 147, wherein the warning interface associated with the corrected dose warning is displayed only in response to determining that the missed meal dose warning has not been issued.

Clause 149 a system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Issuing a correction dose warning;

determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered;

in response to determining that the correction dose condition is not detected within a consecutive number of minutes after the correction dose has been issued, the correction dose warning is withdrawn.

Clause 150 the system of clause 149, wherein the consecutive minutes are 15 consecutive minutes.

Clause 151 the system of clause 149, wherein the input comprises a wireless communication circuit.

Clause 152. A method for overriding a warning of a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a correction dose warning;

determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered;

in response to determining that the correction dose condition is not detected within a consecutive number of minutes after the correction dose has been issued, the correction dose warning is withdrawn.

Clause 153. The method of clause 152, wherein the consecutive minutes are consecutive 15 minutes.

Clause 154 a system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

A display configured to present a warning;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a correction dose alert, wherein the correction dose alert is issued first at a first time;

determining whether a calculation of active Insulin (IOB) has changed since a first time; and

responsive to determining that the calculation of active Insulin (IOB) has changed since the first time, the corrective dose warning is withdrawn.

Clause 155 the system of clause 154, wherein the input comprises a wireless communication circuit.

Clause 156 a method for overriding a warning of a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a correction dose alert, wherein the correction dose alert is issued first at a first time;

determining whether a calculation of active Insulin (IOB) has changed since a first time; and

responsive to determining that the calculation of active Insulin (IOB) has changed since the first time, the corrective dose warning is withdrawn.

Clause 157 a system for managing alerts, the system comprising:

An input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a correction dose alert at the current time;

determining whether an insulin dosage has been recorded within a time period of the current time; and

in response to the insulin dosage having been recorded within the period of the current time, the corrective dosage warning is withdrawn.

Clause 158 the system of clause 157, wherein the time period is about 2 hours.

Clause 159 the system of clause 157, wherein the input comprises a wireless communication circuit.

Clause 160. A method for overriding a warning of a correction dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device;

issuing a correction dose alert at the current time;

determining whether an insulin dosage has been recorded within a time period of the current time; and

If it is determined that the insulin dosage has been recorded within the period of the current time, the correction dosage warning is withdrawn.

Clause 161 the method of clause 160, wherein the time period is about 2 hours.

Clause 162 a system for classifying a dose, the system comprising:

an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes a recent dose comprising an amount of insulin and a timestamp;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide includes a recommended dose;

determining whether a timestamp of the recent dose is within a time period of the requested time;

determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and

responsive to determining that the timestamp of the recent dose is within the time period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, the recent dose is classified as being associated with the meal type of the recent meal.

Clause 163 the system of clause 162, wherein the time period is less than or equal to about 20 minutes.

Clause 164 the system of clause 162, wherein the meal type is selected from the group consisting of: breakfast, lunch and dinner.

Clause 165 the system of clause 162, wherein the instructions further cause the one or more processors to:

determining whether a timestamp of the recent dose is within a meal dose time range determined for a meal type of the recent meal; and

responsive to determining that the timestamp of the recent dose is within the meal dose time range determined for the meal type of the recent meal, classifying the recent dose as being associated with the meal type of the recent meal.

Clause 166 the system of clause 162, wherein the instructions further cause the one or more processors to:

determining whether a recent dose was taken while the user was in a post-meal state; and

responsive to determining that the recent dose was taken while the user was not in the post-meal state, the recent dose is classified as being associated with a meal type of the recent meal.

Clause 167 the system of clause 166, wherein in the post-meal state, the previous dose administered within about 2 hours of the requested time has been associated with a meal type of the recent meal.

Clause 168 the system of clause 162, wherein the input comprises a wireless communication circuit.

Clause 169. A method for classifying a dose from an attached insulin delivery device, the method comprising:

providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide includes a recommended dose;

receiving, by the electronic device, insulin dosage data of the user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp;

determining whether a timestamp of the recent dose is within a time period of the requested time;

determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and

responsive to determining that the timestamp of the recent dose is within the time period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, the recent dose is classified as being associated with the meal type of the recent meal.

Clause 170 the method of clause 162, wherein the time period is less than or equal to about 20 minutes.

The method of clause 171, clause 162, wherein the meal type is selected from the group consisting of: breakfast, lunch and dinner.

Clause 172 the method of clause 162, further comprising the steps of: it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

Clause 173 the method of clause 162, further comprising the steps of: it is determined whether a recent dose was taken while the user was in a post-meal state.

Clause 174 the method of clause 173, wherein in the post-meal state, the previous dose administered within about 2 hours of the requested time has been associated with a meal type of the recent meal.

Clause 175 a system for classifying a dose, the system comprising:

an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes a recent dose comprising an amount of insulin and a timestamp;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

providing a dose recommendation guide at a first time;

determining whether a timestamp of the recent dose is within a time period of the first time;

Determining whether the amount of insulin of the recent dose is the same as the recommended dose for meal dose guidance; and

the recent dose is classified as a meal dose, a correction dose, or a fuzzy dose.

Clause 176 the system of clause 175, wherein the time period is less than or equal to about 20 minutes.

Clause 177. The system of clause 175, wherein the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

Clause 178 the system of clause 175, wherein the dose recommendation guidance is a dose recommendation guidance for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type.

The system of clause 178, wherein the instructions further cause the one or more processors to:

it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

The system of clause 180, wherein the instructions further cause the one or more processors to:

it is determined whether a recent dose was taken while the user was in a post-meal state.

Clause 181 the system of clause 175, wherein the recent dose is classified as ambiguous.

Clause 182 the system of clause 181, wherein the instructions further cause the one or more processors to:

the user is prompted to manually sort the recent doses.

The system of clause 183, wherein the instructions further cause the one or more processors to:

the user is prompted to manually categorize the near term dose by prompting the user to select a categorization from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose.

Clause 184 the system of clause 182, wherein the instructions further cause the one or more processors to:

the user is prompted to manually categorize the recent dose by prompting the user to select a categorization from the group consisting of snack dose, initial dose, and non-taken dose.

The system of clause 185, wherein the instructions further cause the one or more processors to:

the user is prompted to manually categorize the recent dosage by prompting the user to select a categorization that relates more to the last meal than expected.

Clause 186. The system of clause 181, wherein the recent dose classified as a fuzzy dose must be classified as a classification other than the fuzzy dose before providing the additional dose guidance recommendation.

Clause 187 the system of clause 175, wherein the input comprises a wireless communication circuit.

Clause 188 a method for classifying a dose from an attached insulin delivery device, the method comprising:

providing a dose recommendation guide at a first time;

receiving, by the electronic device, insulin dosage data of the user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp;

determining whether a timestamp of the recent dose is within a time period of the first time;

determining whether the amount of insulin of the recent dose is the same as the recommended dose for meal dose guidance; and

the recent dose is classified as a meal dose, a correction dose, or a fuzzy dose.

Clause 189 the method of clause 188, wherein the time period is less than or equal to about 20 minutes.

The method of clause 190, wherein the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

Clause 191 the method of clause 188, wherein the dose recommendation guidance is a dose recommendation guidance for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type.

Clause 192 the method of clause 191, further comprising the steps of: it is determined whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

Clause 193 the method of clause 191, further comprising the steps of: it is determined whether a recent dose was taken while the user was in a post-meal state.

Clause 194. The method of clause 188, wherein the recent dose is classified as ambiguous.

Clause 195 the method of clause 194, further comprising the steps of: the user is prompted to manually sort the recent doses.

The method of clause 196, wherein prompting the user to manually classify the recent dose comprises: the user is prompted to select a category from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose.

Clause 197 the method of clause 195, wherein prompting the user to manually classify the recent dose comprises: the user is prompted to select a category from the group consisting of snack dose, initial dose, and non-taken dose.

Clause 198 the method of clause 195, wherein prompting the user to manually classify the recent dose comprises: the user is prompted to select more categories related to the last meal than expected.

Clause 199. The method of clause 194, wherein the recent dose classified as a fuzzy dose must be classified as a classification other than the fuzzy dose before providing the additional dose guidance recommendation.

Clause 200. An apparatus for providing dose guidance in response to analyte data, the apparatus comprising:

an input configured to receive measured analyte data, meal data, and medication administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform:

receiving into a buffer time-dependent analyte data of the patient taken during the analysis period;

dividing the time-dependent analyte data into discrete time of day (TOD) time periods;

determining, by executing an algorithm, a recommended fixed dose of the drug for a respective one of the TOD periods based on the time-dependent analyte data of the patient over the analysis period and at least a portion of the defined dosing strategy; and

an indicator of the recommended fixed dose is stored in computer memory for output to at least one of a user or a drug administration device.

Clause 201 the device of clause 200, wherein the memory holds further instructions for determining the recommended fixed dose at least in part by:

classifying each of the drug doses in a drug class based on the time-related data;

grouping each of the doses in one of a set of dining groups;

determining a glucose pattern closest to the fitting time-dependent data; and

a glucose mode indicator is selected based on the glucose mode.

Clause 202 the apparatus of clause 200, wherein the memory holds further instructions for classifying the doses into categories including fixed basal doses, fixed breakfast doses, fixed lunch doses, fixed dinner doses for the corresponding TOD periods.

Clause 203 the device of clause 200, wherein the memory holds further instructions for determining that the time-dependent analyte data segment does not exceed any gap defining the threshold as a condition for determining the recommended fixed dose.

Clause 204 the device of clause 200, wherein the memory holds further instructions for determining that the time-dependent analyte data segment has an associated initial meal dose as a condition for determining the recommended fixed dose.

Clause 205 the device of clause 200, wherein the memory holds further instructions for determining that the time-dependent analyte data segment is associated with a basal fixed dose within a previous 24 hour period as a condition for determining the recommended fixed dose.

Clause 206 the apparatus of clause 200, wherein the memory holds further instructions for clearing the data for each TOD period in response to any one or more of: determining a recommended fixed dose, determining a pre-meal correction factor, determining a post-meal correction factor, or determining a manual dose adjustment.

Clause 207 the apparatus of clause 200, wherein the memory holds further instructions for determining a glucose pattern for each TOD period based on the associated valid data segments for the set previous days, wherein the recommended fixed dose is determined further based on the glucose pattern.

Clause 208 the device of clause 207, wherein the memory holds further instructions for determining that the associated valid data segment is available for the set previous day as a condition for determining the recommended fixed dose.

Clause 209 the apparatus of clause 207, wherein the memory holds further instructions for determining that the glucose mode is low based on the count of low alarms occurring during each TOD period.

Clause 210 the device of clause 207, wherein the memory holds further instructions for determining that the glucose mode is low based on the count of hypoglycemic instances in each TOD period.

Clause 211 the device of clause 207, wherein the memory holds further instructions for determining that the glucose mode is high based on the count of hyperglycemic instances in each TOD period.

Clause 212 the device of clause 207, wherein the memory holds further instructions for determining a pre-meal correction factor based on the time-dependent analyte data independent of determining the recommended fixed dose, and maintaining the pre-meal correction factor if both the pre-meal correction factor and the recommended fixed dose indicate an increase in dose.

Clause 213 the apparatus of clause 201, wherein the memory holds further instructions for determining the glucose mode condition based on the count of low alarms, the count of post-meal correction during each TOD period, and the glucose mode indicator.

The apparatus of clause 214, wherein the memory holds further instructions for determining that the glucose mode is low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA method indicates a hypoglycemic mode.

Clause 215 the device of clause 213, wherein the memory holds further instructions for determining that the glucose mode is high/low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the result of the GPA method indicates a risk in hypoglycemia or a high/low glucose mode.

Clause 216 the device of clause 213, wherein the memory holds further instructions for determining that the glucose mode is high if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the glucose mode indicator indicates no mode or a hyperglycemic mode.

Clause 217 the device of clause 200, wherein the input comprises a wireless communication circuit.

Clause 218, a drug delivery device comprising:

an input configured to receive a query for dose data for a time period, wherein the dose data includes an amount and a time of all doses delivered within the time period;

one or more processors coupled with the input and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Storing data of the dose administered during the period of time to create stored data;

determining whether the stored data includes all doses delivered during the time period; and

in response to determining that the stored data does not include all doses delivered during the time period, an indication of incomplete dose data is created.

The apparatus of clause 218, wherein the instructions further cause the one or more processors to: in response to a query for dose data, an indication of incomplete dose data is transmitted.

Clause 220 the device of clause 218, wherein the indication of incomplete dose data is a counter value.

Clause 221 the device of clause 218, wherein the indication of incomplete dose data is based on a counter value.

Clause 222 the device of clause 218, wherein the indication of incomplete dose data is based on a comparison of the counter value with an estimated counter value.

Clause 223 the device of clause 222, wherein the estimated counter value is calculated based on the previous counter value and an elapsed time since the previous counter value was received.

Clause 224 the device of clause 218, wherein the drug delivery device is a connected insulin pen, and wherein the connected insulin pen is configured to wirelessly transmit the dose data.

Clause 225 the device of clause 218, wherein the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit the dose data.

Clause 226 the device of clause 218, wherein the indication of incomplete dose data is based on detecting that the cap is not attached to the insulin pen for a different period of time.

The apparatus of clause 227, wherein detecting that the cap is not attached to the insulin pen for a different period of time comprises: it is determined that the insulin pen contains a first amount of insulin before the cap is unattached and that the insulin pen contains a second amount of insulin after the cap is reattached to the insulin pen, wherein the first amount is different than the second amount.

Clause 228 the device of clause 218, wherein the query for dose data from the time period is sent from an application providing the dose guidance.

Clause 229 the device of clause 218, wherein the input comprises a wireless communication circuit.

Clause 230. A method of transmitting data, the method comprising the steps of:

receiving a query for dose data over a period of time, wherein the dose data includes an amount and time of all doses delivered over the period of time;

Storing data of the dose administered during the period of time to create stored data;

determining whether the stored data includes all doses delivered during the time period; and

in response to determining that the stored data does not include all doses delivered during the time period, an indication of incomplete dose data is created.

Clause 231 the method of clause 230, further comprising the steps of: in response to a query for dose data, an indication of incomplete dose data is transmitted.

Clause 232. The method of clause 230, wherein the indication of incomplete dose data is a counter value.

Clause 233. The method of clause 230, wherein the indication of incomplete dose data is based on a counter value.

Clause 234 the method of clause 230, wherein the indication of incomplete dose data is based on a comparison of the counter value with an estimated counter value.

Clause 235 the method of clause 234, wherein estimating the counter value is calculated based on the previous counter value and an elapsed time since the previous counter value was received.

Clause 236 the method of clause 230, wherein the indication of incomplete dose data is based on detecting that the cap is not attached to the insulin pen for a different period of time.

The method of clause 237, wherein detecting that the cap is not attached to the insulin pen for a different period of time comprises: it is determined that the insulin pen contains a first amount of insulin before the cap is unattached and that the insulin pen contains a second amount of insulin after the cap is reattached to the insulin pen, wherein the first amount is different than the second amount.

Clause 238. The method of clause 230, wherein the query for dose data from the time period is sent from the application providing the dose guidance.

Clause 239. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data includes data related to at least one dose administered over a period of time;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and the memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

querying a drug delivery device for dose data, the dose data comprising data relating to at least one dose administered during the period of time;

Determining whether an indication of incomplete dose data is received from the drug delivery device;

in response to determining that an indication of incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received during the time period includes dose data for all doses administered during the time period; and

in response to determining that no indication of incomplete dose data was received, a dose guideline is calculated.

The system of clause 239, wherein the instructions further cause the one or more processors to:

the dose guidance is output on a display.

Clause 241 the system of clause 239, wherein the indication of incomplete dose data is based on a counter value.

Clause 242. The system of clause 239, wherein the indication of incomplete dose data is based on a comparison of the counter value with an estimated counter value.

Clause 243 the system of clause 242, wherein the estimated counter value is calculated based on the previous counter value and an elapsed time since the previous counter value was received.

Clause 244 the system of clause 239, wherein the drug delivery device is a connected insulin pen, and wherein the connected insulin pen is configured to wirelessly transmit the dose data.

The system of clause 245, wherein the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit dose data.

The system of clause 246, wherein the system further comprises a drug delivery device, and wherein the drug delivery device further comprises:

an input configured to receive a query for dose data, the dose data comprising data relating to at least one dose administered during the time period;

one or more processors coupled with the input and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

storing data of the dose administered during the period of time to create stored data;

determining whether the stored data includes all doses delivered during the time period;

creating an indication of incomplete dose data in response to determining that the stored data does not include all doses delivered during the time period; and

an indication of incomplete dose data is transmitted in response to a query for dose data and in response to a determination that the stored data does not include all doses delivered during the time period.

Clause 247 the system of clause 239, wherein the input includes wireless communication circuitry.

Clause 248 a method for providing dose guidance to a subject, the method comprising the steps of:

receiving, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data comprises data relating to at least one dose administered over a period of time;

querying a drug delivery device for dose data, the dose data comprising data relating to at least one dose administered during the period of time;

determining whether an indication of incomplete dose data is received from the drug delivery device;

in response to determining that an indication of incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received during the time period includes dose data for all doses administered during the time period; and

in response to determining that no indication of incomplete dose data was received, a dose guideline is calculated.

Clause 249. The method of clause 248, further comprising the steps of: the dose guidance is output on a display.

Clause 250. The method of clause 248, wherein the indication of incomplete dose data is based on a counter value.

Clause 251. The method of clause 248, wherein the indication of incomplete dose data is based on a comparison of the counter value with an estimated counter value.

Clause 252. The method of clause 251, wherein estimating the counter value is calculated based on the previous counter value and an elapsed time since the previous counter value was received.

Clause 253 the method of clause 248, wherein the drug delivery device is a connected insulin pen, wherein the connected insulin pen is configured to wirelessly transmit the dose data.

Clause 254 the method of clause 248, wherein the drug delivery device is an insulin pen and a connected pen cap, wherein the connected insulin pen is configured to wirelessly transmit the dose data.

Clause 255. A method for recommending a dose for a meal, the method comprising:

prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of a meal type;

associating the input tag with the amount of drug administered for the instance of the meal type and the post-meal analyte data set administered for the instance of the meal type;

determining whether a threshold number of instances associated with the meal type is met; and

If a threshold number of instances are met, a recommended medication dose for the meal type is determined.

Clause 256 the method of clause 255, wherein the recommended medication dose for the meal type is based at least in part on the amount of medication administered for the instance of the meal type and the post-meal analyte data set for the instance of the meal type.

Clause 257 the method of clause 255, wherein the recommended medication dose for the meal type is based at least in part on the plurality of medication amounts administered for the plurality of instances of the meal type and the plurality of post-meal analyte data sets for the plurality of instances of the meal type.

Clause 258 the method of clause 257, wherein the instance of the meal type is a first instance of the meal type, and wherein the plurality of instances of the meal type includes the first instance of the meal type.

Clause 259 the method of clause 255, further comprising the steps of: analyte levels are received from the sensor control device.

Clause 260 the method of clause 259, wherein the recommended medication dose for the meal type is based at least in part on the analyte data received from the sensor control device.

Clause 261 the method of clause 255, further comprising the steps of: the recommended medication dose for the meal type is visually output to the display.

Clause 262. The method of clause 255, further comprising the steps of: the user is prompted with an option to track the meal type.

Clause 263. The method of clause 262, wherein in response to the user selecting the option to track the meal type, the user is prompted to enter a tag associated with the meal type.

Clause 264 a system for determining a recommended medication dose, the system comprising:

one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of a meal type;

associating the input tag with the amount of drug administered for the instance of the meal type and the post-meal analyte data set administered for the instance of the meal type;

determining whether a threshold number of instances associated with the meal type is met; and

if a threshold number of instances are met, a recommended medication dose for the meal type is determined.

The system of clause 265, clause 264, wherein the recommended medication dose for the meal type is based at least in part on the amount of medication administered for the instance of the meal type and the post-meal analyte data set for the instance of the meal type.

The system of clause 266, wherein the recommended medication dose for the meal type is based at least in part on the plurality of medication amounts administered for the plurality of instances of the meal type and the plurality of post-meal analyte data sets for the plurality of instances of the meal type.

Clause 267 the system of clause 266, wherein the instance of the meal type is a first instance of the meal type, and wherein the plurality of instances of the meal type include the first instance of the meal type.

The system of clause 264, further comprising: a wireless communication circuit configured to receive data indicative of an analyte level from the sensor control device;

clause 269 the system of clause 268, wherein the recommended medication dose for the meal type is based at least in part on data received from the sensor control device indicating the analyte level.

The system of clause 264, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the recommended medication dose for the meal type is visually output to the display.

The system of clause 271, clause 264, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted with an option to track the meal type.

Clause 272 the system of clause 271, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted to enter a tag associated with the meal type only when the user has selected an option to track the meal type.

Clause 273 a system for intelligent meal annotation, the system comprising:

one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of a first meal type, wherein the first meal type is associated with one or more previously input tags,

determining whether a meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, and

the input tag is associated with a second meal type, wherein the second meal type is different from the first meal type.

Clause 274. The system of clause 273, wherein the meal type characteristics of the instance are based at least in part on a difference between a meal size associated with one or more previous input tags and a meal size of the input tag for the instance.

Clause 275. The system of clause 273, wherein the meal type characteristic of the instance is based at least in part on a difference between the amount of medication associated with one or more previous input tags and the amount of medication associated with the input tag for the instance.

The system of clause 276, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: if the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, the user is prompted with an option to create a second tag.

Clause 277 the system of clause 273, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the input tag is associated with the second meal type only when the user has selected the option to create the second tag.

The system of clause 278, clause 273, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: if the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold, the input tag for the instance is disassociated from the first meal type.

The system of clause 279, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the input tag for the instance is disassociated from the first meal type before the input tag is associated with the second meal type.

Clause 280 a method for recommending a dose for a meal, the method comprising:

prompting the user to enter a tag associated with the meal type;

receiving a first input tag for a first instance of a meal type;

associating the first input tag with a first amount of medication administered for a first instance of the meal type;

receiving a second input tag for a second instance of the meal type;

associating a second input tag with a second amount of the drug administered for a second instance of the meal type and a second post-meal analyte data set for the instance of the meal type; and

in response to determining that the difference between the second amount of medication and the first amount of medication is greater than a predetermined threshold difference, the user is prompted to enter a modification tag associated with the meal type.

Clause 281. The method of clause 280, wherein the modification ticket comprises a meal of a different size.

Clause 282. The method of clause 280, wherein the predetermined threshold difference is at least about 2 units.

Clause 283. The method of clause 280, wherein the prompting the user to enter a modification tag associated with the meal type occurs in real time.

Clause 284 the method of clause 280, wherein the user is prompted to enter a modification tag associated with the meal type within about 5 minutes or less of receiving the second input tag.

Clause 285. The method of clause 280, wherein the user is prompted to enter a modification tag associated with the meal type within about 2 minutes or less of receiving the second input tag.

Clause 286 the method of clause 280, further comprising the steps of:

associating the first input tag with a first post-meal analyte data set for a first instance of a meal type; and

a second input tag is associated with a second post-meal analyte data set for a second instance of the meal type.

Clause 287 a system for meal annotation, the system comprising:

one or more processors coupled with the memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

prompting the user to enter a tag associated with the meal type;

receiving a first input tag for a first instance of a meal type;

associating the first input tag with a first amount of medication administered for a first instance of the meal type;

receiving a second input tag for a second instance of the meal type;

associating a second input label with a second amount of medication administered for a second instance of the meal type; and

In response to determining that the difference between the second amount of medication and the first amount of medication is greater than a predetermined threshold difference, the user is prompted to enter a modification tag associated with the meal type.

Clause 288. The system of clause 287, wherein the modification tags include meals of different sizes.

Clause 289 the system of clause 287, wherein the predetermined threshold difference is at least about 2 units.

The system of clause 290, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted to enter a modification tag associated with the meal type, the prompting occurring in real-time.

The system of clause 291, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted to enter a modified tag associated with the meal type within about 5 minutes or less of receiving the second input tag.

Clause 292 the system of clause 287, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the user is prompted to enter a modified tag associated with the meal type within about 2 minutes or less of receiving the second input tag.

Clause 293 the system of clause 287, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: the first input tag is associated with a first post-meal analyte data set for a first instance of a meal type and the second input tag is associated with a second post-meal analyte data set for a second instance of the meal type.

Clause 294 an analyte monitoring system comprising:

a sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of a subject; and

a reader device comprising:

a wireless communication circuit configured to receive an analyte level from the sensor control device; and

one or more processors coupled to a memory, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:

determining a pattern type for at least one time period of a day based on the hypoglycemic risk index and the hyperglycemic risk index for the at least one time period of the day; and

outputting a user interface to a display, the user interface comprising:

At least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;

a time range display comprising a graph including a time range of a plurality of graph portions, wherein each graph portion of the plurality of graph portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graph portion, wherein the plurality of graph portions includes at least 4 graph portions;

a graph including a graph of analyte levels represented by a user's levels across a plurality of time periods of a day and identification of a determined pattern type for at least one time period.

Clause 295 the system of clause 294, wherein the at least one glucose indicator comprises a glucose average.

Clause 296 the system of clause 294, wherein the at least one glucose indicator comprises a glucose management indicator.

Clause 297 the system of clause 294, wherein the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

The system of clause 298, 294, wherein the plurality of graphic portions comprises at least 5 graphic portions;

Clause 299 the system of clause 294, wherein the plurality of graphical parts comprises at least four graphical parts selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low threshold and the low threshold, a portion of the graph between the low threshold and the high threshold, a portion of the graph between the high threshold and the very high threshold, and a portion of the graph above the very high threshold.

Clause 300 the system of clause 294, wherein the target-scoping time display further comprises: a description of the predefined analyte range associated with each graphical section.

Clause 301. The system of clause 294, wherein the target in-range time display further comprises a value for each of the plurality of graphical portions that correlates to an amount of time the user's analyte level is within a predefined analyte range associated with the graphical portion within the time period.

Clause 302 the system of clause 301, wherein the value is a percentage value.

Clause 303 the system of clause 294, wherein the target in-range time display further comprises a combined value for at least two of the plurality of graphical portions, the combined value related to a sum of the amounts of time the user's analyte level is within a predefined analyte range associated with the at least two graphical portions over the period of time.

Clause 304 the system of clause 294, wherein the graph of time within the target range comprises a histogram.

Clause 305 the system of clause 304, wherein each graphical portion of the histogram is arranged in a vertical layout, wherein the graphical portion below the very low threshold is below the graphical portion between the very low threshold and the low threshold, the low threshold is below the graphical portion between the low threshold and the high threshold, the high threshold is below the graphical portion between the high threshold and the very high threshold, and the very high threshold is below the graphical portion above the very high threshold.

Clause 306 the system of clause 294, wherein the identifying of the determined pattern type for the at least one time period comprises: at least a partial outline of the time period in the graph.

The system of clause 307, wherein the identifying of the determined pattern type for the at least one time period further comprises: a flag of the pattern type is determined.

Clause 308 the system of clause 294, wherein the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

Clause 309 the system of clause 294, wherein the graph includes a plurality of determined pattern types, and wherein the identification of a single pattern type is distinct from other identifications of the plurality of determined pattern types.

Clause 310, the system of clause 294, wherein the instructions further cause the one or more processors to: a display of the identification of the most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for at least one time period of the day.

Clause 311. The system of clause 310, wherein the identification of the most important pattern type is graphically displayed.

Clause 312 the system of clause 310, wherein the identifying of the determined pattern type for the at least one time period comprises: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is distinctly different from other identifications of the plurality of identifications.

Clause 313 the system of clause 310, wherein the pattern type determined for at least one time period of the day comprises a plurality of pattern types for a plurality of time periods of the day.

Clause 314 the system of clause 313, wherein the plurality of pattern types includes at least two of a hypoglycemic pattern, a hyperglycemic predominately occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

Clause 315 the system of clause 314, wherein if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern.

Clause 316 the system of clause 314, wherein if the plurality of pattern types includes a hyperglycemia-dominant-occasional hypoglycemic pattern and does not include a hypoglycemic pattern, the identification of the most important pattern type includes an identification of a hyperglycemia-dominant-occasional hypoglycemic pattern.

Clause 317. The system of clause 314, wherein if the plurality of pattern types includes a hyperglycemic pattern and does not include a hyperglycemic predominately low glycemic pattern or a low glycemic pattern, the identification of the most important pattern type includes an identification of a hyperglycemic pattern.

Clause 318 the system of clause 310, wherein the instructions further cause the one or more processors to: a display is output to the user interface that includes an identification of at least one time period of the day determined to have the most important pattern type.

The system of clause 319, wherein the display of the identification of the most important pattern type and the display of the identification of the at least one time period of the day determined to have the most important pattern type comprises: a tag for identification of the most important pattern type and at least one tag of the day that is determined to have identification of at least one time period of the most important pattern type.

Clause 320 the system of clause 319, wherein the color of the tag used for the identification of the most important pattern type is different from the color of the at least one tag used for the identification of the at least one time period determined to have the most important pattern type in the day.

Clause 321 the system of clause 294, wherein the instructions further cause the one or more processors to:

determining a change in at least one time period of a day;

if the determined change is high, a display including a statement regarding the change is output to the user interface.

Clause 322 the system of clause 321, wherein the statement regarding the change comprises: identification of behavior that may contribute to glucose changes.

The system of clause 323, wherein the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to a user interface.

Clause 324 the system of clause 323, wherein the very low threshold is between about 50mg/dL and about 58 mg/dL.

Clause 325 the system of clause 323, wherein the very low threshold is about 54mg/dL.

Clause 326 the system of clause 294, wherein the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface.

Clause 327 the system of clause 326, wherein the statement regarding medication attention includes a suggestion to adjust medication.

The system of clause 328, wherein the statement regarding medication notes comprises advice regarding medications that contribute to low glucose levels.

Clause 329 the system of clause 294, wherein the instructions further cause the one or more processors to: a display including statements related to lifestyle notes is output to a user interface.

Clause 330 the system of clause 329, wherein the statement regarding lifestyle notes includes statements regarding missing at least one of meals, carbohydrates, activity levels, alcohol, and medications.

Clause 331 the system of clause 294, wherein the time period is 14 days.

Clause 332. A method for displaying information related to glucose levels of a subject, comprising the steps of:

receiving an analyte level from a sensor control device;

determining a pattern type for at least one time period of a day based on the hypoglycemic risk index and the hyperglycemic risk index for the at least one time period of the day; and

Displaying a user interface, the user interface comprising:

at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;

a time range display comprising a graph including a time range of a plurality of graph portions, wherein each graph portion of the plurality of graph portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graph portion, wherein the plurality of graph portions includes at least 4 graph portions; and

a graph including a graph of analyte levels represented by a user's levels across a plurality of time periods of a day and identification of a determined pattern type for at least one time period.

Clause 333 the method of clause 332, wherein the at least one glucose indicator comprises a glucose average.

Clause 334 the method of clause 332, wherein the at least one glucose indicator comprises a glucose management indicator.

The method of clause 335, wherein the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

Clause 336. The method of clause 332, wherein the plurality of graphic portions comprises at least 5 graphic portions;

clause 337. The method of clause 332, wherein the plurality of graphical parts comprises at least four graphical parts selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low threshold and the low threshold, a portion of the graph between the low threshold and the high threshold, a portion of the graph between the high threshold and the very high threshold, and a portion of the graph above the very high threshold.

Clause 338 the method of clause 332, wherein the time display within the target range further comprises: a description of the predefined analyte range associated with each graphical section.

Clause 339 the method of clause 332, wherein the target in-range time display further comprises a value for each of the plurality of graphical parts related to an amount of time the user's analyte level is within a predefined analyte range associated with the graphical part within the time period.

Clause 340 the method of clause 339, wherein the value is a percentage value.

The method of clause 341, wherein the target in-range time display further comprises a combined value for at least two of the plurality of graphical portions, the combined value related to a sum of the amounts of time the user's analyte level is within a predefined analyte range associated with the at least two graphical portions over the period of time.

Clause 342. The method of clause 332, wherein the graph of time within the target range comprises a histogram.

The method of clause 343, wherein each graphical portion of the histogram is arranged in a vertical layout, wherein the graphical portion below the very low threshold is below the graphical portion between the very low threshold and the low threshold, the low threshold is below the graphical portion between the low threshold and the high threshold, the high threshold is below the graphical portion between the high threshold and the very high threshold, and the very high threshold is below the graphical portion above the very high threshold.

Clause 344 the method of clause 332, wherein the identifying of the determined pattern type for the at least one time period comprises: at least a partial outline of the time period in the graph.

Clause 345 the method of clause 344, wherein the identifying of the determined pattern type for the at least one time period further comprises: a flag of the pattern type is determined.

Clause 346. The method of clause 332, wherein the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

Clause 347 the method of clause 332, wherein the graph includes a plurality of determined pattern types, and wherein the identification of a single pattern type is distinct from other identifications of the plurality of determined pattern types.

The method of clause 348, wherein the instructions further cause the one or more processors to: a display of the identification of the most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for at least one time period of the day.

Clause 349 the method of clause 348, wherein the identification of the most important pattern type is graphically displayed.

Clause 350 the method of clause 348, wherein the identifying of the determined pattern type for the at least one time period comprises: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is distinctly different from other identifications of the plurality of identifications.

Clause 351. The method of clause 348, wherein the pattern type determined for the at least one time period of the day comprises a plurality of pattern types for a plurality of time periods of the day.

Clause 352 the method of clause 351, wherein the plurality of pattern types includes at least two of a hypoglycemic pattern, a hyperglycemic predominately rare hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

Clause 353 the method of clause 352, wherein if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern.

Clause 354. The method of clause 352, wherein if the plurality of pattern types includes a hyperglycemia-based occasional hypoglycemic pattern and does not include a hypoglycemic pattern, the identification of the most important pattern type includes an identification of a hyperglycemia-based occasional hypoglycemic pattern.

Clause 355 the method of clause 352, wherein if the plurality of pattern types includes a hyperglycemic pattern and does not include a hyperglycemic predominately occasional hypoglycemic pattern or hypoglycemic pattern, the identification of the most important pattern type includes an identification of a hyperglycemic pattern.

Clause 356. The method of clause 348, wherein the instructions further cause the one or more processors to: a display is output to the user interface that includes an identification of at least one time period of the day determined to have the most important pattern type.

Clause 357. The method of clause 348, wherein the displaying of the identification of the most important pattern type and the displaying of the identification of the at least one time period of the day determined to have the most important pattern type comprises: a tag for identification of the most important pattern type and at least one tag of the day that is determined to have identification of at least one time period of the most important pattern type.

Clause 358 the method of clause 357, wherein the color of the label used for the identification of the most important pattern type is different from the color of the at least one label used for the identification of the at least one time period determined to have the most important pattern type in the day.

Clause 359. The method of clause 332, wherein the instructions further cause the one or more processors to:

determining a change in at least one time period of a day;

if the determined change is high, a display including a statement regarding the change is output to the user interface.

Clause 360 the method of clause 359, wherein the statement regarding the change comprises: identification of behavior that may contribute to glucose changes.

Clause 361. The method of clause 332, wherein the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to a user interface.

Clause 362 the method of clause 361, wherein the very low threshold is between about 50mg/dL and about 58 mg/dL.

Clause 363 the method of clause 361, wherein the very low threshold is about 54mg/dL.

Clause 364. The method of clause 332, wherein the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface.

Clause 365. The method of clause 364, wherein the statement regarding medication attention comprises a suggestion to adjust the medication.

Clause 366. The method of clause 364, wherein the statement regarding medication attention comprises a recommendation regarding a medication contributing to a low glucose level.

The method of clause 367, wherein the instructions further cause the one or more processors to: a display including statements related to lifestyle notes is output to a user interface.

Clause 368 the method of clause 367, wherein the statement regarding lifestyle considerations includes a statement regarding at least one of missed meals, carbohydrates, activity levels, alcohol, and medication.

Clause 369. The method of clause 332, wherein the period of time is 14 days.

An apparatus for displaying an indicator associated with a subject, the apparatus comprising:

an input configured to receive drug administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and the memory, the memory storing instructions, a dose of medication received by the subject over a period of time, and a recommended dose of medication for the subject over a period of time, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

A graph is displayed that plots a plurality of doses of the drug taken by the subject at a plurality of times, wherein the graph includes an x-axis of time and a y-axis of a difference between the dose taken by the subject and the dose recommended for the subject.

The apparatus of clause 371, wherein the plurality of medication doses comprises at least one of a basal dose, a fixed meal dose, and a meal dose with a correction factor.

Clause 372 the device of clause 371, wherein the fixed dining dose comprises at least one of a fixed breakfast dose, a fixed lunch dose, and a fixed dinner dose.

Clause 373 the device of clause 371, wherein the dining doses with correction factors comprise at least one of breakfast doses with correction factors, fixed lunch doses with correction factors, and fixed dinner doses with correction factors.

Clause 374 the device of clause 370, wherein the difference between the dose taken by the subject and the recommended dose is in units.

Clause 375 the device of clause 370, wherein the input comprises a wireless communication circuit.

Clause 376. An apparatus for displaying an indicator related to a subject, the apparatus comprising:

An input configured to receive measured analyte data and drug administration data; a display configured to visually present information; and

one or more processors coupled with the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a period of time, and a recommended dose of the drug by the subject over the period of time, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

displaying a summary of the subject's treatment, including the dose administered over a period of time and an analyte indicator determined from the received measured analyte data;

displaying a graph summarizing missed doses over the period of time; and

a graph summarizing the override dose is displayed, wherein the override dose comprises a dose that the subject receives at a time at which there is a different amount than the recommended dose for that time.

The apparatus of clause 377, wherein summarizing the pattern of missed doses comprises: graphical representation of the percentage of missed doses for a plurality of dose types.

The apparatus of clause 378, 377, wherein each percentage of missed percentages for a plurality of dose types is calculated as a percentage of missed doses for that dose type over a period of time in the total number of doses for that dose type.

The apparatus of clause 379, wherein the plurality of dosage types includes at least one of a basal dosage, a breakfast dosage, a lunch dosage, and a dinner dosage.

Clause 380 the device of clause 376, wherein the graph summarizing the missed dose is a bar graph.

Clause 381 the device of clause 376, wherein the graph summarizing the unauthorized dose is a bar graph.

Clause 382 the device of clause 376, wherein the input comprises a wireless communication circuit.

An apparatus for displaying an indicator associated with a subject, the apparatus comprising:

an input configured to receive measured analyte data from a plurality of subjects, drug administration data from the plurality of subjects, and data related to an administration recommendation for the plurality of subjects;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of each of the plurality of subjects, a dose of a drug received by each of the plurality of subjects over a period of time, and data related to a dosing recommendation for the plurality of subjects, the instructions, when executed by the one or more processors, cause the apparatus to:

Displaying a summary of analyte indicators for each of the plurality of subjects, wherein the analyte indicators include at least two of a time in the target range, a time below a low threshold, a time above a high threshold, a percentage of basal doses administered, and an average bolus dose administered per day; and

a summary of information related to the dosing recommendation is displayed, wherein the information related to the dosing recommendation includes an indication of the dosing recommendation for a subject of the plurality of subjects that requires approval from the healthcare provider.

The apparatus of clause 384, wherein the summary of analyte indicators includes at least three of time within the target range, time below the low threshold, time above the high threshold, percentage of basal dose administered, and average bolus dose administered per day.

Clause 385 the apparatus of clause 383, wherein the low threshold is about 70mg/dL.

Clause 386 the apparatus of clause 383, wherein the high threshold is about 180mg/dL.

Clause 387 the device of clause 383, wherein the indication of the dosing recommendation is an icon.

Clause 388 the device of clause 383, wherein the indication of the dosing recommendation is a statement indicating the number of dosing recommendations that require approval.

The apparatus of clause 389, wherein the input comprises a wireless communication circuit.

Clause 390, an apparatus for displaying treatment information related to a subject, the apparatus comprising:

an input configured to receive measured analyte data and drug administration data;

a display configured to visually present information; and

one or more processors coupled to the input, the display, and the memory, the memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a period of time, and a meal time of the subject, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

receiving an estimated dose parameter and an estimated meal dosing time range from a subject;

determining a representative amount of each of a plurality of basal doses and a plurality of meal doses taken by the subject over a period of time based on the drug administration data;

determining a representative meal dosing time range for the subject over the period of time based on the drug dosing data;

determining a recommended dose for at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose;

Displaying the estimated dose parameters and estimated meal dosing time range received from the subject;

displaying a representative amount of each of the plurality of basal doses and the plurality of meal doses and a representative meal dosing time range; and

recommended doses for at least one of the base dose, breakfast dose, lunch dose, and dinner dose are displayed.

Clause 391 the apparatus of clause 390, wherein the plurality of meal doses includes a plurality of breakfast doses, a plurality of lunch doses, and a plurality of dinner doses, and wherein the instructions, when executed by the one or more processors, cause the apparatus to: an average amount of each of the plurality of basal doses, the plurality of breakfast doses, the plurality of lunch doses, and the plurality of dinner doses is determined.

Clause 392 the device of clause 390, wherein the estimated dose parameters include an estimated amount of the base dose, breakfast dose, lunch dose, and dinner dose.

The apparatus of clause 393, wherein the estimated dose parameter further comprises an estimated time for the subject to take the base dose, the breakfast dose, the lunch dose, and the dinner dose.

Clause 394 the apparatus of clause 390, wherein the estimated meal dosing time range includes an estimated dosing start time and an estimated dosing end time for each of breakfast, lunch, and dinner.

The apparatus of clause 395, wherein the representative amounts of each of the plurality of basal doses and the plurality of dining doses comprise: an average of each of the plurality of basal doses and the plurality of meal doses taken by the subject over a period of time.

The apparatus of clause 396, wherein the representative amounts of each of the plurality of basal doses and the plurality of meal doses comprise: a pattern of each of a plurality of basal doses and a plurality of meal doses taken by a subject over a period of time.

The apparatus of clause 397, wherein the instructions, when executed by the one or more processors, further cause the apparatus to:

determining a pre-meal correction factor and a post-meal correction factor based on the measured analyte data and the drug administration data; and

the pre-meal correction factor and the post-meal correction factor are shown.

Clause 398 the device of clause 390, wherein the representative meal dosing time range is displayed adjacent to the estimated meal dosing time range.

Clause 399 the device of clause 390, wherein the representative amount of each of the plurality of base doses and the plurality of meal doses is displayed adjacent to the estimated dose parameter.

Clause 400, the apparatus of clause 390, wherein the instructions, when executed by the one or more processors, further cause the apparatus to:

Determining a conservative value for at least one of the basal dose, the breakfast dose, the lunch dose, and the dinner dose, wherein the conservative value is lower than the corresponding determined representative amount for each of the plurality of basal doses and the plurality of dinner doses; and

the determination of the conservation value is shown.

Clause 401 the device of clause 390, wherein the input comprises a wireless communication circuit.

Claims (229)

1. An apparatus for parameterizing drug administration practices of a patient to configure dose guidance settings, the apparatus comprising:

an input component configured to receive measured analyte data, meal data, and medication administration data;

a display component configured to visually present information; and

one or more processors coupled with the input, the display, and a memory storing instructions and time-related data characterizing an analyte of the patient over an analysis period, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform:

receiving patient dose regimen information for the analysis period;

estimating a measure of correspondence between the time-related data and the patient dose regimen information; and

Dose guidance information is determined based on the consistency metric.

2. A method for facilitating effective access by a healthcare provider (HCP) to a patient of an electronic case (EMR) generated by a dose guidance system while preserving patient privacy, the method comprising:

authenticating, by at least one processor of a portable display device, a session with the patient;

generating, by the at least one processor, an EMR identification code (ID) in response to receiving input from the patient during the session indicating a request to share the EMR with the HCP;

providing, by the at least one processor, the EMR ID to a remote server that controls access to the EMR; and

outputting, by the at least one processor, the EMR ID to a display of the portable display device.

3. The method of claim 2, further comprising: the EMR is provided to the remote server prior to the authentication.

4. The method of claim 2, further comprising: the EMR is received from the dose guidance system.

5. The method of claim 2, further comprising: a determination is made as to whether the EMR does not satisfy a consistency condition with patient input indicative of a dosage pattern for tracking the drug.

6. The method of claim 5, further comprising: upon determining that the EMR does not meet the consistency condition, providing the patient with an option to provide the EMR to the HCP.

7. The method of claim 5, wherein the generating, providing, and outputting are conditioned on a determination that the EMR does not satisfy the consistency condition.

8. The method of claim 6, wherein the generating, providing, and outputting are conditioned on a determination that the EMR does not satisfy the consistency condition.

9. The method of claim 2, further comprising: providing the patient with an option to provide the EMR to the HCP.

10. A method according to claim 3, wherein the remote server, upon receipt of the EMR ID, creates a web page addressed at least in part by the EMR ID for displaying the EMR.

11. The method of claim 2, wherein the EMR comprises: determining a dosing parameter of a drug administered to the patient at a time within a defined period of time, and determining a measure of compliance with patient-supplied dosing information for the drug.

12. The method of claim 11, wherein the drug is insulin.

13. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device, wherein the dose data includes an amount and time of a last drug dose administered;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

classifying the last drug dose administered as a dining dose or a correction dose; and

in response to determining that a period of time has elapsed since the time of administration of the most recent drug dose, additional dose guidance is displayed.

14. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dose data of the subject from the drug delivery device, wherein the drug dose data includes an amount and time of a last drug dose administered;

classifying the last drug dose administered as a dining dose or a correction dose;

in response to determining that a period of time has elapsed since the time of administration of the most recent drug dose, a dose guide is displayed.

15. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device, wherein the dose data comprises data related to a recent drug dose administered;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether the recent drug dose administered is the recent drug dose administered; and

in response to determining that the recent drug dose administered is the recent drug dose administered, a screen including a dose guidance recommendation is displayed.

16. The system of claim 15, wherein the recent drug dose administered is determined to be the recent drug dose administered by confirmation from a user.

17. The system of claim 15, wherein the dose data further comprises data related to at least one drug dose administered since a reset time, and wherein the instructions further cause the one or more processors to display the screen in response to determining that the at least one drug dose administered since the reset time has been categorized.

18. The system of claim 17, wherein the dose data relating to the at least one drug dose administered from the reset time is automatically categorized.

19. The system of claim 18, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject, wherein the instructions further cause the one or more processors to transmit one or more wireless interrogation signals to the drug delivery device to determine that a last dose administered has been received.

20. The system of claim 17, wherein the dose data related to the at least one drug dose administered since the reset time has been categorized by a user.

21. The system of claim 15, wherein the instructions further cause the one or more processors to display a prompt for a user to confirm that the information related to the last drug dose administered is correct.

22. The system of claim 21, wherein the instructions further cause the one or more processors to display the screen including the dose guidance recommendation for a period of time beginning after the user confirmation.

23. The system of claim 15, further comprising: a drug delivery device configured to deliver at least one dose of a drug to a subject.

24. The system of claim 15, wherein the input is further configured to receive measured analyte data and a request for dose guidance, and wherein the instructions further cause the one or more processors to:

determining whether the glucose concentration at the time the request for dose guidance is received is below a low threshold; and

in response to determining that the glucose concentration at the time the request for dose guidance is received is below the low threshold, a screen including a message is displayed to address a low glucose level prior to administration of the drug.

25. The system of claim 24, wherein the instructions further cause the one or more processors to:

in response to determining that the glucose concentration at the time the request for dose guidance is received is below the low threshold, the dose guidance recommendation is not displayed.

26. The system of claim 15, wherein the input is further configured to receive measured analyte data and a request for dose guidance after a start time of a meal, and wherein the instructions further cause the one or more processors to:

Determining whether the glucose concentration at the estimated meal start time is below a low threshold; and

in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, a screen including a message is displayed to address the low glucose level prior to administration of the drug.

27. The system of claim 26, wherein the instructions further cause the one or more processors to:

in response to determining that the glucose concentration at the estimated meal start time is below the low threshold, the dose guidance recommendation is not displayed.

28. The system of claim 15, wherein the input comprises a wireless communication circuit.

29. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, dose data of a user from the drug delivery device, wherein the dose data comprises data related to a recent drug dose administered;

determining whether the recent drug dose administered is the recent drug dose administered;

in response to determining that the recent drug dose administered is the recent drug dose administered, a screen including a dose guidance recommendation is displayed.

30. The method of claim 29, wherein the recent drug dose administered is determined to be the recent drug dose administered by confirmation from the user.

31. The method of claim 29, wherein the administered recent drug dose is administered after a reset time, the method further comprising the steps of:

determining whether the recent dose administered after the reset time is categorized.

32. The method of claim 31, wherein the recent dose administered after the reset time is automatically categorized.

33. The method of claim 29, wherein the method further comprises the steps of:

one or more wireless interrogation signals are transmitted to the drug delivery device to determine that the last dose administered has been received.

34. The method of claim 31, wherein the recent dose administered after the reset time is categorized by the user.

35. The method of claim 34, further comprising the step of: a prompt is displayed to cause the user to confirm that the information relating to the last drug dose administered is correct.

36. The method of claim 29, wherein the screen including the dose guidance recommendation is displayed only for a period of time beginning after the user confirms that the information related to the last drug dose administered was correct.

37. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device, wherein the dose data comprises data relating to at least one meal dose administered since a reset time;

a display configured to visually present a plurality of meal icons;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether the at least one meal dose administered since the reset time has been categorized; and

in response to determining that the at least one meal dose administered since the reset time has been categorized, a screen including the plurality of meal icons is displayed.

38. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dosage data of a user from the drug delivery device, wherein the dosage data comprises data related to at least one meal dose administered since a reset time;

determining whether the at least one meal dose administered since the reset time has been categorized;

In response to determining that the at least one meal dose administered since the reset time has been categorized, a screen including a plurality of meal icons is displayed.

39. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from a drug delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether the missed dose alert is valid;

in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on a normal meal dose; and

in response to determining that the missed dose alert is valid, a dose guidance recommendation calculated based on the later meal dose is displayed.

40. A method for providing dose guidance, the method comprising:

receiving, by the electronic device, drug dosage data of a user from the drug delivery device;

determining whether the missed dose alert is valid; and

in response to determining that the missed dose alert is invalid, displaying a dose guidance recommendation calculated based on a normal meal dose; and

In response to determining that the missed dose alert is valid, a dose guidance recommendation calculated based on the later meal dose is displayed.

41. A system for providing a warning to a subject, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes;

determining if no insulin dosage has been recorded within about 45 minutes prior to the estimated meal start time; and

responsive to detection of a missed meal dose condition within the consecutive minutes and a determination that the insulin dose was not recorded within about 45 minutes prior to the estimated meal start time, a warning interface associated with the missed meal dose is displayed.

42. The system of claim 41, wherein the consecutive minutes is about 5 minutes.

43. The system of claim 41, wherein the instructions further cause the one or more processors to:

determining whether a meal dose has been recorded within about 2 hours of the current time; and

in response to determining that the meal dose has been recorded within about 2 hours of the current time, displaying the alert interface related to the missed meal dose.

44. The system of claim 41, wherein the instructions further cause the one or more processors to:

determining whether to issue a correction dose alert; and

responsive to determining that the corrective dose alert has not been issued, a warning interface associated with the missed meal dose is displayed.

45. The system of claim 41, further comprising: a sensor control device configured to collect data indicative of an analyte level of a subject, the sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of the subject.

46. The system of claim 41, wherein the input comprises a wireless communication circuit.

47. A method for alerting a user to missed a meal dose, the method comprising:

Receiving, by the electronic device, streaming glucose data from the sensor control device;

determining at the current time whether to miss a meal dose associated with a meal having an estimated meal start time by detecting a missed meal dose condition for a consecutive number of minutes;

determining if no insulin dosage has been recorded within about 45 minutes prior to the estimated meal start time; and

responsive to detection of a missed meal dose condition within the consecutive minutes and a determination that the insulin dose was not recorded within about 45 minutes prior to the estimated meal start time, a warning interface associated with the missed meal dose is displayed.

48. The method of claim 47, wherein the consecutive minutes is about 5 minutes.

49. The method of claim 47, further comprising the steps of:

whether a meal dose has been recorded within about 2 hours of the current time; and

in response to determining that the meal dose has been recorded within about 2 hours of the current time, displaying the alert interface related to the missed meal dose.

50. The method of claim 47, further comprising the steps of:

determining whether to issue a correction dose alert;

In response to determining that the corrective dose alert has not been issued, displaying the alert interface in relation to the missed meal dose.

51. A system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert;

determining whether a missed meal dose condition has been detected within a few consecutive minutes after the missed meal dose alert has been issued;

responsive to determining that the missed meal dose condition is not detected within the consecutive minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

52. The system of claim 51, wherein the consecutive minutes are consecutive 15 minutes.

53. The system of claim 51, wherein the missed meal dose condition includes a determination that no insulin dose was administered within a period of estimated meal start time.

54. The system of claim 51, wherein the input comprises a wireless communication circuit.

55. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert;

determining whether a missed meal dose condition has been detected within a few consecutive minutes after the missed meal dose alert has been issued;

responsive to determining that the missed meal dose condition is not detected within the consecutive minutes after the missed meal dose alert has been issued, the missed meal dose alert is withdrawn.

56. The method of claim 55, wherein the consecutive minutes are 15 consecutive minutes.

57. The method of claim 55, wherein the missed meal dose condition includes determining that no insulin dose was administered for a period of estimated meal start time.

58. A system for managing alerts, the system comprising:

an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device;

a display configured to present a warning;

One or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert at the current time;

determining whether an insulin dosage has been recorded within about 2 hours of the current time;

in response to determining that the insulin dose has been recorded within about 2 hours of the current time, the missed meal dose alert is withdrawn.

59. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert at the current time;

determining whether an insulin dosage has been recorded within about 2 hours of the current time; and

in response to determining that the insulin dose has been recorded within about 2 hours of the current time, the missed meal dose alert is withdrawn.

60. A system for managing alerts, the system comprising:

an input configured to receive flow glucose data from the sensor control device and dose data from the drug delivery device;

A display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and

responsive to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, the missed meal dose alert is withdrawn.

61. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining whether an insulin dosage has been recorded within about 45 minutes of the estimated meal start time; and

responsive to determining that the insulin dose has been recorded within about 45 minutes of the estimated meal start time, the missed meal dose alert is withdrawn.

62. A system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a missed meal dose alert at a current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

determining if the estimated meal start time is within about 2 hours of the current time; and

in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, the missed meal dose alert is withdrawn.

63. A method for revoking a warning of a missed meal dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a missed meal dose alert at a current time, wherein the missed meal dose alert relates to a missed meal having an estimated start time;

Determining if the estimated meal start time is within about 2 hours of the current time; and

in response to determining that the estimated meal start time does not occur within about 2 hours of the current time, the missed meal dose alert is withdrawn.

64. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive dose data from an insulin delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining whether a correction dose warning has been issued at the current time;

determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and

in response to determining that the correction dose has been delivered and that the insulin dose has not been administered to the subject within about 2 hours of the current time, a correction dose guide is displayed.

65. A method for recommending a correction dose, the method comprising:

Receiving, by the electronic device, insulin dosage data of the subject from the insulin delivery device;

determining whether a correction dose warning has been issued at the current time;

determining whether an insulin dose has not been administered to the subject within about 2 hours of the current time; and

a correction dose guide is displayed and the correction dose is displayed,

wherein the correction dose guidance is displayed in response to determining that the correction dose has been delivered and that the insulin dose has not been administered to the subject within about 2 hours of the current time.

66. A system for providing a warning to a subject, the system comprising:

an input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device;

a display configured to visually present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

determining at the present time whether a correction dose condition has been issued within a succession of minutes;

determining if no insulin dosage has been recorded within about 2 hours of the current time; and

Responsive to determining that the correction dose condition has been issued within the consecutive minutes and that the insulin dose has not been recorded within about 2 hours of the current time, a warning interface associated with the correction dose warning is displayed.

67. The system of claim 66, wherein the consecutive minutes is about 5 minutes.

68. The system of claim 66, wherein the instructions further cause the one or more processors to:

determining whether to issue a missed meal dose alert;

in response to determining that the missed dose alert has not been issued, displaying the alert interface in connection with the corrected dose alert; and

in response to determining that the missed dose alert is issued, the alert interface associated with the corrected dose alert is not displayed.

69. A method for alerting a user about a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device;

determining at the present time whether a correction dose condition has been issued within a succession of minutes;

determining that no insulin dosage has been recorded for about 2 hours of the current time; and

Responsive to determining that the correction dose condition has been issued within the consecutive minutes and that the insulin dose has not been recorded within about 2 hours of the current time, a warning interface associated with the correction dose warning is displayed.

70. The method of claim 69, wherein the consecutive minutes is about 5 minutes.

71. The method of claim 69, further comprising the steps of: it is determined whether a missed meal dose alert has not been issued prior to displaying the alert interface in relation to the corrected dose alert.

72. The method of claim 71, wherein the alert interface associated with the corrected dose alert is displayed only in response to determining that the missed meal dose alert has not been issued.

73. A system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Issuing a correction dose warning;

determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered;

responsive to determining that the correction dose condition is not detected within the consecutive minutes after the correction dose has been issued, the correction dose alert is withdrawn.

74. The system of claim 73, wherein the consecutive minutes are consecutive 15 minutes.

75. The system of claim 73, wherein the input comprises a wireless communication circuit.

76. A method for overriding a warning of a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a correction dose warning;

determining whether a correction dose condition has been detected within a few consecutive minutes after the correction dose has been delivered;

responsive to determining that the correction dose condition is not detected within the consecutive minutes after the correction dose has been issued, the correction dose alert is withdrawn.

77. The method of claim 76, wherein the consecutive minutes is 15 consecutive minutes.

78. A system for managing alerts, the system comprising:

An input configured to receive streaming glucose data from the sensor control device;

a display configured to present a warning;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a correction dose alert, wherein the correction dose alert is issued first at a first time;

determining whether a calculation of active Insulin (IOB) has changed since the first time; and

responsive to determining that the calculation of the active Insulin (IOB) has changed since the first time, the corrective dose warning is withdrawn.

79. A method for overriding a warning of a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device;

issuing a correction dose alert, wherein the correction dose alert is issued first at a first time;

determining whether a calculation of active Insulin (IOB) has changed since the first time; and

responsive to determining that the calculation of the active Insulin (IOB) has changed since the first time, the corrective dose warning is withdrawn.

80. A system for managing alerts, the system comprising:

an input configured to receive streaming glucose data from the sensor control device and dose data from the insulin delivery device;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

issuing a correction dose alert at the current time;

determining whether an insulin dosage has been recorded within a time period of the current time; and

in response to the insulin dosage having been recorded within the period of the current time, the corrective dosage warning is withdrawn.

81. The system of claim 80, wherein the period of time is about 2 hours.

82. The system of claim 80, wherein the input comprises a wireless communication circuit.

83. A method for overriding a warning of a corrected dose, the method comprising:

receiving, by the electronic device, streaming glucose data from the sensor control device and insulin dosage data of the subject from the insulin delivery device;

Issuing a correction dose alert at the current time;

determining whether an insulin dosage has been recorded within a time period of the current time; and

the correction dose warning is withdrawn if it is determined that the insulin dose has been recorded within the period of the current time.

84. The method of claim 83, wherein the period of time is about 2 hours.

85. A system for classifying a dose, the system comprising:

an input configured to receive insulin dosage data of a user from a connected insulin delivery device, wherein the insulin dosage data comprises a recent dosage comprising an amount of insulin and a time stamp;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide comprises a recommended dose;

determining whether the timestamp of the recent dose is within a time period of the requested time;

Determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and

in response to determining that the timestamp of the recent dose is within a period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, classifying the recent dose as being associated with the meal type of the recent meal.

86. The system of claim 85, wherein the period of time is less than or equal to about 20 minutes.

87. The system of claim 85, wherein the meal type is selected from the group consisting of: breakfast, lunch and dinner.

88. The system of claim 85, wherein the instructions further cause the one or more processors to:

determining whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal; and

in response to determining that the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal, classifying the recent dose as being associated with the meal type of the recent meal.

89. The system of claim 85, wherein the instructions further cause the one or more processors to:

determining whether the recent dose was taken while the user was in a post-meal state; and

in response to determining that the recent dose was taken while the user was not in the post-meal state, classifying the recent dose as being associated with the meal type of the recent meal.

90. The system of claim 89, wherein in the post-meal state, a previous dose administered within about 2 hours of the requested time has been associated with the meal type of the recent meal.

91. The system of claim 89, wherein the input includes a wireless communication circuit.

92. A method for classifying a dose from a connected insulin delivery device, the method comprising:

providing a dose recommendation guide to a meal requested by a user at a requested time, wherein the meal is of a meal type and the dose recommendation guide comprises a recommended dose;

receiving, by an electronic device, insulin dosage data of a user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp;

Determining whether the timestamp of the recent dose is within a time period of the requested time;

determining whether the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide; and

in response to determining that the timestamp of the recent dose is within a period of the request time and that the amount of insulin of the recent dose is the same as the recommended dose recommended by the meal dose guide, classifying the recent dose as being associated with the meal type of the recent meal.

93. The method of claim 85, wherein the period of time is less than or equal to about 20 minutes.

94. The method of claim 85, wherein the meal type is selected from the group consisting of: breakfast, lunch and dinner.

95. The method of claim 85, further comprising the step of: determining whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

96. The method of claim 85, further comprising the step of: determining if the recent dose was taken while the user was in a post-meal state.

97. The method of claim 96, wherein in the post-meal state, a previous dose administered within about 2 hours of the requested time has been associated with the meal type of the recent meal.

98. A system for classifying a dose, the system comprising:

an input configured to receive insulin dosage data of a user from a connected insulin delivery device, wherein the insulin dosage data comprises a recent dosage comprising an amount of insulin and a time stamp;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

providing a dose recommendation guide at a first time;

determining whether the timestamp of the recent dose is within a period of time of the first time;

determining whether the amount of insulin of the recent dose is the same as a recommended dose of the dose recommendation guide; and

the recent dose is classified as a meal dose, a correction dose, or a fuzzy dose.

99. The system of claim 98, wherein the period of time is less than or equal to about 20 minutes.

100. The system of claim 98, wherein the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

101. The system of claim 98, wherein the dose recommendation guideline is a dose recommendation guideline for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type.

102. The system of claim 101, wherein the instructions further cause the one or more processors to:

determining whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

103. The system of claim 101, wherein the instructions further cause the one or more processors to:

determining if the recent dose was taken while the user was in a post-meal state.

104. The system of claim 98, wherein the recent dose is classified as ambiguous.

105. The system of claim 98, wherein the input comprises a wireless communication circuit.

106. The system of claim 104, wherein the instructions further cause the one or more processors to:

prompting the user to manually categorize the recent dose.

107. The system of claim 106, wherein the instructions further cause the one or more processors to:

prompting the user to manually categorize the recent dose by prompting the user to select a categorization from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose.

108. The system of claim 106, wherein the instructions further cause the one or more processors to:

the user is prompted to manually categorize the recent dose by prompting the user to select a categorization from the group consisting of snack dose, initial dose, and non-taken dose.

109. The system of claim 106, wherein the instructions further cause the one or more processors to:

the user is prompted to manually categorize the recent dose by prompting the user to select a categorization relating to more last meals than expected.

110. The system of claim 104, wherein the recent dose classified as a fuzzy dose must be classified as a classification other than a fuzzy dose before additional dose-directed recommendations are provided.

111. A method for classifying a dose from a connected insulin delivery device, the method comprising:

providing a dose recommendation guide at a first time;

receiving, by an electronic device, insulin dosage data of a user from the connected insulin delivery device, wherein the insulin dosage data includes a recent dosage comprising an amount of insulin and a timestamp;

determining whether the timestamp of the recent dose is within a period of time of the first time;

determining whether the amount of insulin of the recent dose is the same as a recommended dose of the dose recommendation guide; and

the recent dose is classified as a meal dose, a correction dose, or a fuzzy dose.

112. The method of claim 111, wherein the period of time is less than or equal to about 20 minutes.

113. The method of claim 111, wherein the dose recommendation guidance is a corrected dose recommendation guidance, and wherein the recent dose is classified as a corrected dose.

114. The method of claim 111, wherein the dose recommendation guideline is a dose recommendation guideline for a meal, wherein the meal is of a meal type, and wherein the recent dose is classified as a dose for the meal type.

115. The method of claim 114, further comprising the steps of: determining whether the timestamp of the recent dose is within a meal dose time range determined for the meal type of the recent meal.

116. The method of claim 114, further comprising the steps of: determining if the recent dose was taken while the user was in a post-meal state.

117. The method of claim 111, wherein the recent dose is classified as ambiguous.

118. The method of claim 117, further comprising the step of: prompting the user to manually categorize the recent dose.

119. The method of claim 118, wherein prompting the user to manually categorize the recent dose comprises: prompting the user to select a category from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose.

120. The method of claim 118, wherein prompting the user to manually categorize the recent dose comprises: the user is prompted to select a category from the group consisting of snack dose, initial dose, and non-taken dose.

121. The method of claim 118, wherein prompting the user to manually categorize the recent dose comprises: the user is prompted to select a category that relates to more than expected last meal.

122. The method of claim 117, wherein the recent dose classified as a fuzzy dose must be classified as a classification other than a fuzzy dose before additional dose-directed recommendations are provided.

123. A device for providing dose guidance in response to analyte data, the device comprising:

an input configured to receive measured analyte data, meal data, and medication administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform:

Receiving into a buffer time-dependent analyte data of the patient taken during the analysis period;

dividing the time-dependent analyte data into discrete time of day (TOD) time periods;

determining, by executing an algorithm, a recommended fixed dose of the drug for a respective one of the TOD periods based on the time-dependent analyte data of the patient over the analysis period and at least a portion of a defined dosing strategy; and

the indicator of the recommended fixed dose is stored in computer memory for output to at least one of a user or a drug administration device.

124. The apparatus of claim 123 wherein the memory holds further instructions for determining the recommended fixed dose at least in part by:

classifying each of the drug doses in a drug class based on the time-related data;

grouping each of the doses in one group of a set of dining groups;

determining a glucose pattern closest to the fitting time-related data; and

a glucose mode indicator is selected based on the glucose mode.

125. The apparatus of claim 123, wherein the memory holds further instructions for classifying the doses into categories comprising a fixed basal dose, a fixed breakfast dose, a fixed lunch dose, a fixed dinner dose for a corresponding TOD period.

126. The apparatus of claim 123, wherein the memory holds further instructions for determining that the time-dependent analyte data segment does not exceed any gap defining a threshold as a condition for determining the recommended fixed dose.

127. The device of claim 123 wherein the memory holds further instructions for determining that the time-dependent analyte data segment has an associated initial meal dose as a condition for determining the recommended fixed dose.

128. The apparatus of claim 123, wherein the memory holds further instructions for determining that the time-dependent analyte data segment is associated with a basal fixed dose within a previous 24 hour period as a condition for determining the recommended fixed dose.

129. The apparatus of claim 123, wherein the memory holds further instructions for clearing data for each TOD period in response to any one or more of: determining the recommended fixed dose, determining a pre-meal correction factor, determining a post-meal correction factor, or determining a manual dose adjustment.

130. The apparatus of claim 123, wherein the memory holds further instructions for determining a glucose pattern for each TOD period based on the associated valid data segments for a set previous day, wherein the recommended fixed dose is determined further based on the glucose pattern.

131. The apparatus of claim 130, wherein the memory holds further instructions for determining that the associated valid data segment is available for the set previous day as a condition for determining the recommended fixed dose.

132. The apparatus of claim 130, wherein the memory holds further instructions for determining that the glucose mode is low based on a count of low alarms occurring in each TOD period.

133. The apparatus of claim 130, wherein the memory holds further instructions for determining that the glucose mode is low based on a count of hypoglycemic instances in each TOD period.

134. The apparatus of claim 130, wherein the memory holds further instructions for determining that the glucose mode is high based on a count of hyperglycemic instances in each TOD period.

135. The apparatus of claim 130 wherein the memory holds further instructions for determining a pre-meal correction factor based on the time-dependent analyte data independent of determining the recommended fixed dose, and maintaining the pre-meal correction factor if both the pre-meal correction factor and the recommended fixed dose indicate an increase in dose.

136. The apparatus of claim 124, wherein the memory holds further instructions for determining a glucose mode condition based on a count of low alarms, a count of post-meal corrections within each TOD period, and the glucose mode indicator.

137. The apparatus of claim 136, wherein the memory holds further instructions for determining that the glucose mode is low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and a result of a GPA method indicates a hypoglycemic mode.

138. The apparatus of claim 136, wherein the memory holds further instructions for determining that the glucose mode is high/low if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and a result of a GPA method indicates a risk in hypoglycemia or a high/low glucose mode.

139. The apparatus of claim 136, wherein the memory holds further instructions for determining that the glucose mode is high if the count of low alarms exceeds a first threshold, the count of post-meal corrections exceeds a second threshold, and the glucose mode indicator indicates no mode or a hyperglycemic mode.

140. The device of claim 123 wherein the input comprises a wireless communication circuit.

141. A drug delivery device comprising:

an input configured to receive a query for dose data for a time period, wherein the dose data includes an amount and a time of all doses delivered within the time period;

one or more processors coupled with the input and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

storing data of the dose administered during the period of time to create stored data; determining whether the stored data includes all doses delivered during the time period; and

in response to determining that the stored data does not include all doses delivered during the time period, an indication of incomplete dose data is created.

142. A method of transmitting data, the method comprising the steps of:

receiving a query for dose data over a period of time, wherein the dose data includes an amount and a time of all doses delivered over the period of time;

storing data of the dose administered during the period of time to create stored data;

determining whether the stored data includes all doses delivered during the time period; and

in response to determining that the stored data does not include all doses delivered during the time period, an indication of incomplete dose data is created.

143. A system for providing dose guidance to a subject, the system comprising:

an input configured to receive, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data comprises data relating to at least one dose administered over a period of time;

a display configured to visually present a dose guidance;

one or more processors coupled with the input, the display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Querying the drug delivery device for the dose data, the dose data comprising data relating to at least one dose administered over the period of time;

determining whether an indication of the incomplete dose data is received from the drug delivery device;

in response to determining that an indication of the incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received over the time period includes dose data for all doses administered over the time period; and

in response to determining that an indication of the incomplete dose data has not been received, a dose guide is calculated.

144. A method for providing dose guidance to a subject, the method comprising the steps of:

receiving, from a drug delivery device, dose data and an indication of incomplete dose data, wherein the dose data comprises data relating to at least one dose administered over a period of time;

querying the drug delivery device for the dose data, the dose data comprising data relating to at least one dose administered over the period of time;

determining whether an indication of the incomplete dose data is received from the drug delivery device;

In response to determining that an indication of the incomplete dose data is received, outputting a prompt seeking the following confirmation: the dose data received over the time period includes dose data for all doses administered over the time period; and

in response to determining that an indication of the incomplete dose data has not been received, a dose guide is calculated.

145. A method for recommending a dose for a meal, the method comprising:

prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of the meal type;

associating the input tag with an amount of medication administered for the instance of the meal type and a post-meal analyte data set administered for the instance of the meal type;

determining whether a threshold number of instances associated with the meal type is met; and

if the threshold number of instances is met, a recommended medication dose for the meal type is determined.

146. A system for determining a recommended medication dose, the system comprising:

one or more processors coupled with a memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

Prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of the meal type;

associating the input tag with an amount of medication administered for the instance of the meal type and a post-meal analyte data set administered for the instance of the meal type;

determining whether a threshold number of instances associated with the meal type is met; and

if the threshold number of instances is met, determining the recommended medication dose for the meal type.

147. A system for intelligent meal annotation, the system comprising:

one or more processors coupled with a memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

prompting the user to enter a tag associated with the meal type;

receiving an input tag for an instance of a first meal type, wherein the first meal type is associated with one or more previously input tags,

determining whether a meal type characteristic of an instance of the first meal type exceeds a meal type characteristic threshold, and

the input tag is associated with a second meal type, wherein the second meal type is different from the first meal type.

148. A method for recommending a dose for a meal, the method comprising:

prompting the user to enter a tag associated with the meal type;

receiving a first input tag for a first instance of the meal type;

associating the first input tag with a first amount of medication administered for a first instance of the meal type;

receiving a second input tag for a second instance of the meal type;

associating the second input tag with a second amount of medication administered for a second instance of the meal type and a second post-meal analyte data set for the instance of the meal type; and

in response to determining that the difference between the second quantity of medication and the first quantity of medication is greater than a predetermined threshold difference, the user is prompted to enter a modification tag associated with the meal type.

149. A system for meal annotation, the system comprising:

one or more processors coupled with a memory for storing instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

prompting the user to enter a tag associated with the meal type;

Receiving a first input tag for a first instance of the meal type;

associating the first input tag with a first amount of medication administered for a first instance of the meal type;

receiving a second input tag for a second instance of the meal type;

associating the second input label with a second amount of medication administered for a second instance of the meal type; and

in response to determining that the difference between the second quantity of medication and the first quantity of medication is greater than a predetermined threshold difference, the user is prompted to enter a modification tag associated with the meal type.

150. An analyte monitoring system, comprising:

a sensor control device comprising an analyte sensor, wherein at least a portion of the analyte sensor is configured to be in fluid contact with a bodily fluid of a subject; and

a reader device comprising:

a wireless communication circuit configured to receive an analyte level from the sensor control device; and

one or more processors coupled to a memory, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:

Determining a pattern type for at least one time period of a day based on a hypoglycemic risk indicator and a hyperglycemic risk indicator for the at least one time period of the day; and

outputting a user interface to a display, the user interface comprising:

at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;

a target in-range time display comprising a graph of target in-range times comprising a plurality of graphical portions, wherein each graphical portion of the plurality of graphical portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graphical portion, wherein the plurality of graphical portions comprises at least 4 graphical portions;

a graph comprising a graph of analyte levels represented by the user's levels across a plurality of time periods of a day and an identification of a determined pattern type for the at least one time period.

151. The system of claim 150, wherein the at least one glucose indicator comprises a glucose average.

152. The system of claim 150, wherein the at least one glucose indicator comprises a glucose management indicator.

153. The system of claim 150, wherein the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

154. The system of claim 150, wherein the plurality of graphics portions includes at least 5 graphics portions.

155. The system of claim 150, wherein the plurality of graphical portions comprises at least four graphical portions selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low and low thresholds, a portion of the graph between the low and high thresholds, a portion of the graph between the high and very high thresholds, and a portion of the graph above the very high threshold.

156. The system of claim 150, wherein the target in-range time display further comprises: a description of the predefined analyte range associated with each graphical portion.

157. The system of claim 150, wherein the target in-range time display further comprises a value for each of the plurality of graphical portions that relates to an amount of time the user's analyte level is within the predefined analyte range associated with the graphical portion over the period of time.

158. The system of claim 157, wherein the value is a percentage value.

159. The system of claim 150, wherein the target in-range time display further comprises a combined value for at least two of the plurality of graphical portions, the combined value related to a sum of amounts of time the user's analyte level is within the predefined analyte range associated with the at least two graphical portions over the period of time.

160. The system of claim 150, wherein the graph of time within the target range includes a histogram.

161. The system of claim 160, wherein each graphical portion of the histogram is arranged in a vertical layout, wherein graphical portions below a very low threshold are below graphical portions between the very low threshold and a low threshold, the low threshold is below graphical portions between the low threshold and a high threshold is below graphical portions between the high threshold and a very high threshold, and the very high threshold is below graphical portions above the very high threshold.

162. The system of claim 150, wherein the identification of the determined pattern type for the at least one time period comprises: at least a partial outline of the time period on the graph.

163. The system of claim 162, wherein the identification of the determined pattern type for the at least one time period further comprises: the flag that determines the pattern type.

164. The system of claim 150, wherein the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

165. The system of claim 150, wherein the graph includes a plurality of determined pattern types, and wherein identification of a single pattern type is distinctly different from other identifications of the plurality of determined pattern types.

166. The system of claim 150, wherein the instructions further cause the one or more processors to: a display of an identification of a most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for the at least one time period of the day.

167. The system of claim 166, wherein the identification of the most important pattern type is displayed on the graphic.

168. The system of claim 166, wherein the identification of the determined pattern type for the at least one time period comprises: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is significantly different from other identifications of the plurality of identifications.

169. The system of claim 166, wherein the pattern type determined for the at least one time period of a day comprises a plurality of pattern types for a plurality of time periods of the day.

170. The system of claim 169, wherein the plurality of pattern types include at least two of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

171. The system of claim 170, wherein if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern.

172. The system of claim 170, wherein if the plurality of pattern types includes a hyperglycemia-dominant-low-blood-glucose pattern and does not include a hypoglycemia pattern, the identification of the most important pattern type includes an identification of the hyperglycemia-dominant-low-blood-glucose pattern.

173. The system of claim 170, wherein the identification of the most important pattern type comprises an identification of the hyperglycemic pattern if the plurality of pattern types includes a hyperglycemic pattern and does not include a hyperglycemic predominately low glycemic pattern or a low glycemic pattern.

174. The system of claim 166, wherein the instructions further cause the one or more processors to: a display is output to the user interface including an identification of at least one time period of the day determined to have the most important pattern type.

175. The system of claim 166, wherein the display of the identification of the most important pattern type and the display of the identification of the at least one time period determined to have the most important pattern type during the day comprises: a tag for identification of the most important pattern type and at least one tag of the day determined to have identification of the at least one time period of the most important pattern type.

176. The system of claim 175, wherein a color of a label used for identification of the most important pattern type is different from a color of the at least one label used for identification of the at least one time period determined to have the most important pattern type in the day.

177. The system of claim 150, wherein the instructions further cause the one or more processors to:

determining a change in at least one time period of a day;

If the determined change is high, a display including a statement regarding the change is output to the user interface.

178. The system of claim 177, wherein the statement regarding the change comprises: identification of behavior that may contribute to glucose changes.

179. The system of claim 150, wherein the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to the user interface.

180. The system of claim 179, wherein the very low threshold is between about 50mg/dL and about 58 mg/dL.

181. The system of claim 179, wherein the very low threshold is about 54mg/dL.

182. The system of claim 150, wherein the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface.

183. The system of claim 182, wherein the statement regarding the medication notice includes a suggestion to adjust medication.

184. The system of claim 182, wherein the statement regarding the medication notice includes advice regarding medications that facilitate low glucose levels.

185. The system of claim 150, wherein the instructions further cause the one or more processors to: a display including a statement regarding lifestyle notes is output to the user interface.

186. The system of claim 185, wherein the statement regarding the lifestyle notice includes a statement regarding at least one of missed meals, carbohydrates, activity levels, alcohol, and medications.

187. The system of claim 150, wherein the period of time is 14 days.

188. A method for displaying information related to glucose levels in a subject, comprising the steps of:

receiving an analyte level from a sensor control device;

determining a pattern type for at least one time period of a day based on a hypoglycemic risk indicator and a hyperglycemic risk indicator for the at least one time period of the day; and

displaying a user interface, the user interface comprising:

at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;

a target in-range time display comprising a graph of target in-range times comprising a plurality of graphical portions, wherein each graphical portion of the plurality of graphical portions indicates an amount of time that an analyte level of a user is within a predefined analyte range associated with each graphical portion, wherein the plurality of graphical portions comprises at least 4 graphical portions; and

A graph comprising a graph of analyte levels represented by the user's levels across a plurality of time periods of a day and an identification of a determined pattern type for the at least one time period.

189. The method of claim 188, wherein the at least one glucose indicator comprises a glucose average.

190. The method of claim 188, wherein the at least one glucose indicator comprises a glucose management indicator.

191. The method of claim 188, wherein the instructions further cause the one or more processors to: a display including a target value corresponding to the at least one glucose indicator is output to the user interface.

192. The method of claim 188, wherein the plurality of graphics portions includes at least 5 graphics portions.

193. The method of claim 188, wherein the plurality of graphical portions comprises at least four graphical portions selected from the group consisting of: a portion of the graph below a very low threshold, a portion of the graph between the very low and low thresholds, a portion of the graph between the low and high thresholds, a portion of the graph between the high and very high thresholds, and a portion of the graph above the very high threshold.

194. The method of claim 188, wherein the target in-range time display further comprises: a description of the predefined analyte range associated with each graphical portion.

195. The method of claim 188, wherein the target in-range time display further comprises, for each of the plurality of graphical portions, a value related to an amount of time the user's analyte level is within the predefined analyte range associated with the graphical portion over the period of time.

196. The method of claim 195, wherein the value is a percentage value.

197. The method of claim 188, wherein the target in-range time display further comprises a combined value for at least two of the plurality of graphical portions, the combined value related to a sum of amounts of time the user's analyte level is within a predefined analyte range associated with the at least two graphical portions over the period of time.

198. The method of claim 188, wherein the graph of time within the target range includes a histogram.

199. The method of claim 199, wherein each graphical portion of the histogram is arranged in a vertical layout in which graphical portions below a very low threshold are below graphical portions between the very low threshold and a low threshold, the low threshold is below graphical portions between the low threshold and a high threshold is below graphical portions between the high threshold and a very high threshold, and the very high threshold is below graphical portions above the very high threshold.

200. The method of claim 188, wherein the identifying of the determined pattern type for the at least one time period comprises: at least a partial outline of the time period on the graph.

201. The method of claim 200, wherein the identifying of the determined pattern type for the at least one time period further comprises: the flag that determines the pattern type.

202. The method of claim 188, wherein the pattern type is at least one of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

203. The method of claim 188, wherein the graph includes a plurality of determined pattern types, and wherein identification of a single pattern type is substantially different from other identifications of the plurality of determined pattern types.

204. The method of claim 188, wherein the instructions further cause the one or more processors to: a display of an identification of a most important pattern type is output to the user interface, wherein the most important pattern type is one of the pattern types determined for the at least one time period of the day.

205. The method of claim 204, wherein the identification of the most important pattern type is displayed on the graphic.

206. The method of claim 204, wherein the identification of the determined pattern type for the at least one time period comprises: a plurality of identifications of pattern types are determined for each of the at least one time period, and wherein the identification of the most important pattern type is significantly different from other identifications of the plurality of identifications.

207. The method of claim 204, wherein the pattern type determined for the at least one time period of a day comprises a plurality of pattern types for a plurality of time periods of the day.

208. The method of claim 207, wherein the plurality of pattern types includes at least two of a hypoglycemic pattern, a hyperglycemic predominately-occasional hypoglycemic pattern, a hyperglycemic pattern, or a non-pattern.

209. The method of claim 208, wherein if the plurality of pattern types includes a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hypoglycemic pattern.

210. The method of claim 208, wherein if the plurality of pattern types includes a hyperglycemia-dominant-low-glycemic pattern and does not include a hypoglycemic pattern, the identification of the most important pattern type includes an identification of the hyperglycemia-dominant-low-glycemic pattern.

211. The method of claim 208, wherein the identification of the most important pattern type comprises an identification of the hyperglycemic pattern if the plurality of pattern types include hyperglycemic patterns and do not include hyperglycemic dominant occasional hypoglycemic patterns or hypoglycemic patterns.

212. The method of claim 204, wherein the instructions further cause the one or more processors to: a display is output to the user interface including an identification of at least one time period of the day determined to have the most important pattern type.

213. The method of claim 204, wherein the displaying of the identification of the most important pattern type and the displaying of the identification of the at least one time period determined to have the most important pattern type in the day comprises: a tag for identification of the most important pattern type and at least one tag of the day determined to have identification of the at least one time period of the most important pattern type.

214. The method of claim 213, wherein a color of the tag for identification of the most important pattern type is different from a color of the at least one tag for identification of the at least one time period determined to have the most important pattern type during a day.

215. The method of claim 188, wherein the instructions further cause the one or more processors to:

determining a change in at least one time period of a day;

if the determined change is high, a display including a statement regarding the change is output to the user interface.

216. The method of claim 215, wherein the statement regarding the change comprises: identification of behavior that may contribute to glucose changes.

217. The method of claim 188, wherein the instructions further cause the one or more processors to: a display including a statement regarding fluctuations below a very low threshold is output to the user interface.

218. The method of claim 217, wherein the very low threshold is between about 50mg/dL and about 58 mg/dL.

219. The method of claim 217, wherein the very low threshold is about 54mg/dL.

220. The method of claim 188, wherein the instructions further cause the one or more processors to: a display including a statement regarding medication notes is output to the user interface.

221. The method of claim 220, wherein the statement regarding the medication notice includes a suggestion to adjust medication.

222. The method of claim 220, wherein the statement regarding the medication notice includes advice regarding medications that facilitate low glucose levels.

223. The method of claim 188, wherein the instructions further cause the one or more processors to: a display including a statement regarding lifestyle notes is output to the user interface.

224. The method of claim 223, wherein the statement regarding the lifestyle notice includes a statement regarding at least one of missed meals, carbohydrates, activity levels, alcohol, and medications.

225. The method of claim 188, wherein the period of time is 14 days.

226. An apparatus for displaying an indicator associated with a subject, the apparatus comprising:

an input configured to receive drug administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and a memory storing instructions, a dose of medication received by the subject over a period of time, and a recommended dose of the medication by the subject over the period of time, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

A graph is displayed that plots a plurality of doses of medication taken by the subject at a plurality of times, wherein the graph includes an x-axis of time and a y-axis of a difference between the dose taken by the subject and a dose recommended for the subject.

227. An apparatus for displaying an indicator associated with a subject, the apparatus comprising:

an input configured to receive measured analyte data and drug administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and a memory, the memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a period of time, and a recommended dose of the drug by the subject over the period of time, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

displaying a summary of the subject's treatment, including the dose administered over a period of time and an analyte indicator determined from the received measured analyte data;

displaying a graph summarizing missed doses over the period of time; and

A graph summarizing the unauthorized doses is displayed, wherein the unauthorized doses include doses that the subject receives at a time at which there is a different amount than the recommended dose for the time.

228. An apparatus for displaying an indicator associated with a subject, the apparatus comprising:

an input configured to receive measured analyte data from a plurality of subjects, drug administration data from the plurality of subjects, and data related to an administration recommendation for the plurality of subjects;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and a memory storing instructions, time-related data characterizing an analyte of each of the plurality of subjects, a dose of a drug received by each of the plurality of subjects over a period of time, and data related to a dosing recommendation for the plurality of subjects, the instructions, when executed by the one or more processors, cause the apparatus to:

displaying a summary of analyte indicators for each of the plurality of subjects, wherein the analyte indicators include at least two of a time within a target range, a time below a low threshold, a time above a high threshold, a percentage of basal doses administered, and an average bolus dose administered per day; and

A summary of information related to the dosing recommendation is displayed, wherein the information related to the dosing recommendation includes an indication of the dosing recommendation for a subject of the plurality of subjects that requires approval from a healthcare provider.

229. An apparatus for displaying treatment information related to a subject, the apparatus comprising:

an input configured to receive measured analyte data and drug administration data;

a display configured to visually present information; and

one or more processors coupled with the input, the display, and a memory storing instructions, time-related data characterizing an analyte of the subject, a dose of a drug received by the subject over a period of time, and a meal time of the subject, wherein the instructions, when executed by the one or more processors, cause the apparatus to:

receiving an estimated dose parameter and an estimated meal dosing time range from the subject;

determining a representative amount of each of a plurality of basal doses and a plurality of meal doses taken by the subject over a period of time based on the drug administration data;

Determining a representative meal dosing time range for the subject within the interval based on the drug dosing data;

determining a recommended dose for at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose;

displaying the estimated dose parameters and estimated meal dosing time range received from the subject;

displaying the representative amount for each of a plurality of basal doses and a plurality of meal doses and the representative meal dosing time range; and

displaying the recommended dose for at least one of the basal dose, the breakfast dose, the lunch dose, and the dinner dose.