JP2024505285A - Systems, devices and methods related to drug dose guidance - Google Patents

Abstract

Systems, devices and methods are provided for determining drug doses for a patient or user. Dose determination can take into account the patient's or user's recent and/or past analyte levels. Dose determination may also consider other information about the patient or user, such as physiological information, dietary information, activity and/or behavior. A number of different dose determination embodiments are presented for a wide variety of different aspects of systems or environments in which the embodiments can be practiced. Systems, apparatus and methods are provided for displaying information related to glucose values, including time in target range and graphs of analysis values including identification of pattern types for segments of the day.

Description

Related applications

This application is filed under U.S. Patent Application No. 63/237,769 filed on August 27, 2021, U.S. Patent Application No. 63/225,140 filed on July 23, 2021, and Claims priority to U.S. patent application Ser. . This application is also filed in U.S. Patent Application No. 29/817,852, filed on December 3, 2021; Related to and related to U.S. Patent Application No. 29/814,001, filed on November 2, 2021, and U.S. Patent Application No. 29/813,998, filed on November 2, 2021. the entire contents of which are hereby expressly incorporated by reference for all purposes. This application is also related to U.S. Patent Application No. 16/944,736, filed on July 31, 2020, which is related to U.S. Provisional Patent Application No. 62/944,736, filed on August 2, 2019. No. 882,249, U.S. Provisional Patent Application No. 62/979578, filed on February 21, 2020, and U.S. Provisional Patent Application No. 62/979,594, filed on February 21, 2020, We claim priority and benefit from U.S. Provisional Patent Application No. 62/979,618, filed on July 30, 2020, and U.S. Provisional Patent Application No. 63/058,799, filed on July 30, 2020, and the entire contents thereof are available for all purposes. is specifically incorporated herein by reference.

The subject matter described herein generally relates to systems, devices, and methods related to drug dose guidance, such as, for example, determining insulin doses to treat elevated glucose levels due to diabetes.

Detection and/or monitoring of analyte levels such as glucose, ketones, lactate, oxygen, hemoglobin A1C, etc. can be critical to the health of individuals with diabetes. Patients with diabetes can suffer complications including loss of consciousness, cardiovascular disease, retinopathy, neuropathy and nephropathy. People with diabetes generally need to monitor their glucose levels to ensure they are maintained within a clinically safe range, and can also use this information to It can also be determined whether and/or when insulin is needed to lower levels, or when additional glucose is needed to raise glucose levels in the body.

A growing body of clinical data shows a strong correlation between the frequency of glucose monitoring and glycemic control. However, despite this correlation, many individuals diagnosed with diabetes choose to monitor their glucose levels due to a combination of factors, including inconvenience, testing judgment, pain associated with glucose testing, and cost. Not monitoring as often as necessary.

For patients who rely on the administration of medications (e.g., insulin) to treat or manage diabetes, glucose information collected by the analyte monitoring system is automatically available and easily accessible when needed It would be desirable to have a system, device, or method that can provide drug dosage guidance in a manner that provides drug dosage guidance. It is further desirable that such systems, devices or methods consider the physiology, diet, activity and/or behavior of the user or patient being treated when providing such drug dosage guidance; Accuracy and reliability may be improved. Furthermore, in some situations it is also desirable for such systems, devices or methods to be able to automatically deliver selected drug doses.

For these and other reasons, a need exists for improved systems, methods and devices related to drug dosage guidance.

Provided herein are exemplary embodiments of systems, devices, and methods for providing drug dosage guidance and, in some embodiments, drug delivery. According to one aspect, many of the embodiments described herein include a dose guidance system (DGS) that includes a display device, a sensor control device, and a drug delivery device. The dose guidance system can include a dose guidance application (eg, software) that can determine and output dose guidance (eg, recommendations regarding dosage, correction, and titration) to the patient. Further, according to some embodiments, the dose guidance system can learn the patient's dosing strategy during a learning period during which the dose guidance system can estimate key dosing parameters. According to some embodiments, the dose guidance system can also provide guidance for titration and correction once the system is configured with the patient's current dosing strategy. The dose guidance system can also provide guidance for different dietary intake scenarios. For example, in some embodiments, the dose guidance system can provide dose guidance at or before the start of a meal or after the start of a meal. The dose guidance system can also provide dose guidance for added meals (e.g., desserts) or for "touch-up" dosing to address high postprandial glucose levels. . Exemplary systems and safety features of dose guidance systems are also described.

Many of the embodiments provided herein have improved software functionality or graphical user interfaces for use in analyte monitoring systems that are highly intuitive, user-friendly, and provide physiological information to users. Provide quick access to. More specifically, these embodiments allow users (or HCPs) to be sensitive to the user's physiological conditions, past dosing patterns, and other factors without having to go through the tedious task of examining large amounts of analyte data. Enables rapid determination of appropriate drug therapy based on relevant information. Additionally, the GUI and some of the GUI features allow users (and their caregivers) to better understand and improve their dosing patterns and subsequent hypoglycemic and hyperglycemic episodes. Similarly, many other embodiments provided herein provide improved software capabilities for dose guidance systems that enable safe titration strategies that minimize hypoglycemic episodes. the dosage guidance provided to the user, how to modify the dosage guidance depending on the timing of administration of the dose relative to the meal start time (e.g., before the start of the meal, at the start of the meal, or after the start of the meal); taking into account real-world events, post-prandial alarms based on predicted probabilities of occurrence rather than thresholds, etc., these are just a few examples.

Additionally, many of the embodiments described herein are improvements over traditional bolus calculators that require the HCP to configure numerous settings, which is a very time-consuming process. Alternatively, the system may require the patient to enter numerous settings, which is often confusing to the patient. Embodiments described herein require no or minimal input from the patient (e.g., typical dietary dosages) for both the patient and the HCP. Manual input is significantly reduced and the system is able to learn other necessary parameters related to the patient's insulin dosing habits. Additionally, many patients have poor adherence to suggested medication regimens. During the learning period, the system collects data regarding both glucose levels and insulin dosing and can assess the patient's level of adherence. In this way, the functionality of the system can solve problems related to HCP time investment and patient proficiency levels. Another advantage of the system is that traditional bolus calculators use fixed parameters that need to be set, whereas the present system automatically titrates (i.e., optimizes) dosing regimen parameters over time. ), meaning that dosing regimens can be optimized to reduce patterns of low or high glucose. This relieves HCPs from the time burden of having to perform the tasks of regularly reviewing patient glucose data and updating dosing parameters to address blood sugar issues. Another benefit is that if many patients frequently forget insulin doses, the system can detect and alert patients when they have forgotten a dose and provide a means for patients to safely administer it late. This can help lower the patient's overall glucose levels. Current standard practice is to wait at least two hours after the patient has started a meal before taking a correction dose to correct hyperglycemia, or to wait patiently until the next meal dose. It can be safely administered within two hours of starting a meal and can lower overall glucose levels while minimizing the additional risk of hypoglycemia. Other improvements and advantages are also provided. Various configurations of these devices are described in detail by way of exemplary embodiments only.

Other systems, devices, methods, features and advantages of the subject matter described herein will be apparent to those skilled in the art from consideration of the following figures and detailed description. It is intended that all such additional systems, devices, methods, features and advantages be included within this specification, be within the scope of the subject matter described herein, and be protected by the following claims. is intended. Features of the exemplary embodiments should not be construed as limitations on the scope of the claims unless such features are expressly recited in the claims.

Details of the subject matter described herein, both with respect to its structure and operation, may be apparent from a study of the accompanying drawings (like reference numerals refer to like parts in the drawings). The components in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the subject matter. Furthermore, all figures are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be shown schematically and not literally or precisely.
FIG. 2 is a block diagram of an exemplary embodiment of a dose guidance system. FIG. 2 is a block diagram of an exemplary embodiment of a dose guidance system. 1 is a schematic diagram illustrating an exemplary embodiment of a sensor control device; FIG. FIG. 2 is a block diagram illustrating an exemplary embodiment of a sensor control device. 1 is a schematic diagram illustrating an exemplary embodiment of a drug delivery device. FIG. 1 is a block diagram illustrating an exemplary embodiment of a drug delivery device. FIG. 1 is a schematic diagram illustrating an exemplary embodiment of a display device; FIG. 1 is a block diagram illustrating an exemplary embodiment of a display device. FIG. 1 is a block diagram illustrating an exemplary embodiment of a user interface device. FIG. FIG. 3 illustrates an example layout of a glucose pattern report. FIG. 3 illustrates an example layout of a glucose pattern report. This is a continuation of FIG. 6A-2-1. FIG. 3 illustrates an example layout of a glucose pattern report. This is a continuation of FIG. 6A-3-1. FIG. 3 illustrates an example layout of a glucose pattern report. This is a continuation of FIG. 6A-4-1. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 3 shows an exemplary glucose concentration profile. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a process flow of a portion of a dose guidance application directed to a learning method for estimating insulin dosing habits by a patient. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for parameterizing a patient's medication habits to configure dose guidance settings. 7B is a flow diagram illustrating additional optional elements for the method shown in FIG. 7B. FIG. 7B is a flow diagram illustrating additional optional elements for the method shown in FIG. 7B. FIG. 7B is a flow diagram illustrating additional optional elements for the method shown in FIG. 7B. FIG. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a process flow for operation with a dose guidance application to evaluate meal bolus titration for multiple injection (MDI) medication therapy. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a process flow for operation with a dose guidance application for glucose pattern analysis (GPA). FIG. 3 illustrates an exemplary embodiment of a graph showing information for determining hypoglycemia risk and other indicators for GPA. FIG. 2 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for evaluating meal bolus titration for MDI insulin dosing therapy. FIG. 2 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for evaluating meal bolus titration for MDI insulin dosing therapy. FIG. 2 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for evaluating meal bolus titration for MDI insulin dosing therapy. FIG. 2 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for evaluating meal bolus titration for MDI insulin dosing therapy. FIG. 2 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for evaluating meal bolus titration for MDI insulin dosing therapy. FIG. 3 illustrates an example report for review by an HCP. FIG. 3 illustrates an example report for review by an HCP. FIG. 2 illustrates an example adherence report. FIG. 3 illustrates an example plot associated with a clustering analysis of meal periods. FIG. 3 shows an exemplary plot of meal dose clustering and dose. FIG. 3 illustrates an example plot related to determining a pre-meal correction factor. FIG. 2 is a flow diagram illustrating an example embodiment of a process for facilitating access to electronic medical records by HCPs. FIG. 2 illustrates an example summary report for an HCP. FIG. 3 shows an exemplary plot showing patient adherence to a recommended dosing regimen. FIG. 3 illustrates an example summary report of a patient's treatment. FIG. 3 is a state transition diagram defining when an insulin dosing algorithm may be invoked during a guidance period. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for displaying dose guidance related to meal doses and correction doses. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for displaying a dose guidance screen. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for displaying multiple meal icons. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for displaying dose calculations. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for issuing a missed meal alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a missed meal alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a missed meal alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a missed meal alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a missed meal alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for issuing a correction dose alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a corrected dose alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a corrected dose alert. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for canceling a corrected dose alert. FIG. 3 shows IOB remaining from previous injection as a function of DIA of fast-acting insulin. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for providing dose guidance in response to analyte data. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for determining glucose pattern indicators. 21A and 21B are flow diagrams illustrating certain additional operations that may be performed in combination with one or more of the methods illustrated by FIGS. 21A and 21B. 21A and 21B are flow diagrams illustrating certain additional operations that may be performed in combination with one or more of the methods illustrated by FIGS. 21A and 21B. FIG. 3 is a flow diagram illustrating supplementary or alternative operations for glucose pattern display. FIG. 2 is an exemplary data flow diagram. FIG. 2 is an exemplary data flow diagram. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a delivery device's method for determining whether stored dose data is complete. FIG. 3 is a flow diagram illustrating an example embodiment of an application's method for determining whether received dose data is complete. FIG. 3 is a flow diagram illustrating an example embodiment of an application's method for determining whether received dose data is complete. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for tagging meals with recommended doses. FIG. 2 is a flow diagram illustrating an exemplary embodiment of a method for tagging meals with recommended doses.

Before describing the subject matter of the present invention in detail, it is to be understood that this disclosure is not limited to the particular embodiments described herein, as such may of course vary. sea bream. Additionally, the terminology used herein is for the purpose of describing particular embodiments only and is intended to be limiting, as the scope of the disclosure is limited only by the claims appended hereto. I hope you understand that this is not what I did.

Generally, embodiments of the present disclosure include systems, devices, and methods related to drug dose guidance. Dose guidance is based on a wide range of user-specific information and information categories, such as the user's current and past analyte levels, the user's current and past diet, the user's current and past physical activity, and the user's current and past medication history. may be based on a wide range of user-specific information and categories of information, such as , and other physiological information about the user. According to one aspect of an embodiment, the dosage guidance provided by the systems, devices, and methods of the present disclosure is based not only on individual information categories, but also on the predicted impact of such information categories on a user's future analyte levels. It can also be based on the influence of

Dose guidance functionality may be implemented as a dose guidance application (DGA) that includes software and/or firmware instructions stored in the memory of the computing device and executed by at least one processor or processing circuitry thereof. The computing device may be owned by a user or a healthcare professional (HCP), and the user or HCP may interface to the computing device via a user interface. According to some embodiments, the computing device may be a server or trusted computer system accessible via a network, and the dose guidance software communicates with the server or trusted computer system via the network. can be presented to a user in the form of an interactive web page via a browser running on a local display device (having a user interface). In this and other embodiments, the dose guidance software may run across multiple devices, or may run partially on the local display device's processing circuitry and partially on the server or It can be executed in the processing circuitry of a trusted computer system. When a DGA is described as performing an operation, such operation refers to computer memory (read-only memory) that, when executed by at least one processor of at least one computing device, causes the DGA to perform the described operation. It will be understood by those skilled in the art that instructions stored in the computer (including hard-coded instructions) are executed in accordance with instructions stored in the computer. In all cases, the operations may alternatively be performed by hardware physically embedded (e.g., dedicated circuitry) to perform the operations, as opposed to being performed by instructions stored in memory. can.

Additionally, as used herein, a DGA-implemented system can be referred to as a dose guidance system. A dose guidance system can be configured for the sole purpose of providing dose guidance, or it can be a multifunctional system of which dose guidance is only one aspect. For example, in some embodiments, the dose guidance system can also monitor the user's analyte levels. In some embodiments, the dose guidance system can also deliver medication to the user, such as with an injection or infusion device. In some embodiments, the dose guidance system is capable of both analyte monitoring and drug delivery.

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

Before describing dose guidance embodiments in detail, it is desirable to first describe an example dose guidance system in which a dose guidance application may be implemented.

Exemplary Embodiment of Dose Guidance System FIG. 1A is a block diagram illustrating an exemplary embodiment of a dose guidance system 100. In this embodiment, dose guidance system 100 is capable of providing dose guidance, monitoring one or more analytes, and delivering one or more drugs. This versatile example is used to illustrate the high degree of interoperability and performance provided by system 100. However, in embodiments described herein, the analyte monitoring component, the drug delivery component, or both can be omitted if desired.

Here, system 100 includes a sensor control device (SCD) 102 configured to collect analyte level information from a user, and a drug delivery device (MDD) 152 configured to deliver a medication to the user. a display device 120 configured to present information to a user and receive input or information from a user. The structure and function of each device is described in detail herein.

System 100 is configured for highly interconnected and highly flexible communication between devices. Each of the three devices 102, 120 and 152 connects to the network 190 either directly to each other (without going through an intermediate electronic device) or indirectly to each other (either through the cloud network 190 or through another device and then into the network 190. etc.). The two-way communication capability between devices and between the devices and network 190 is indicated by double-headed arrows in FIG. 1A. However, those skilled in the art will appreciate that any of the one or more devices (eg, SCD) are capable of unidirectional communications, such as, for example, broadcast, multicast, or advertisement communications. In all cases, whether bidirectional or unidirectional, the communication may be wired or wireless. The protocols that control communication on each route may be the same or different, and may be unique or standardized. For example, wireless communications between devices 102, 120, and 152 may be performed in accordance with Bluetooth (including Bluetooth Low Energy) standards, NFC (near field communication) standards, Wi-Fi (802.11x) standards, mobile telephony standards, etc. Can be done. All communications on the various paths may be encrypted, and each device in FIG. 1A may be configured to encrypt and decrypt those communications sent and received. In any case, the communication path in FIG. 1A can be direct (eg, Bluetooth or NFC) or indirect (eg, Wi-Fi, mobile telephony or other Internet protocols). Embodiments of system 100 need not have the ability to communicate across all of the paths shown in FIG. 1A.

In addition, although FIG. 1A shows a single display device 120, a single SCD 102, and a single MDD 152, those skilled in the art will appreciate that system 100 may include more than one of any of the aforementioned devices. will understand. By way of example only, system 100 may include a single SCD 102 in communication with multiple (eg, two, three, four, etc.) display devices 120 and/or multiple MDDs 152. Alternatively, system 100 may include multiple SCDs 102 in communication with a single display device 120 and/or a single MDD 152. Further, each of the plurality of devices can be the same or different device types. For example, system 100 can include multiple display devices 120, including smartphones, handheld receivers, and/or smart watches, each of which can communicate with SCD 102 and/or MDD 152, as well as each other. .

Analyte data is transmitted between each device in system 100 either autonomously (e.g., automatically transmitted on a schedule) or in response to a request for analyte data (e.g., transmitted from a first device to a second device). the analyte data from the second device to the first device). Other technologies for data communication, such as cloud network 190, may also be employed to accommodate more complex systems.

FIG. 1B is a block diagram illustrating 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 can communicate with each other and with display device 120-1, which aggregates information from SCD 102 and MDD 152, processes and displays the information at a desired location, and displays the information at a desired location. The cloud network 190 and/or the computer system 170 can act as a communications hub for transferring some or all of the information to the cloud network 190 and/or the computer system 170. Conversely, 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. Computer system 170 may be a personal computer, server terminal, laptop computer, tablet, or other suitable data processing device. Computer system 170 may include or present software for data management and analysis and communication with components within system 100. Computer system 170 can be used by a user or a medical professional to display and/or analyze analyte data measured by SCD 102. Additionally, although FIG. 1B depicts a single SCD 102, a single MDD 152, and two display devices 120-1 and 120-2, those skilled in the art will appreciate that the system 100 can include any of the aforementioned devices. It will be appreciated that a plurality may be included, and each plurality of devices may include the same or different types of devices.

With further reference to FIG. 1B, according to some embodiments, trusted computer system 180 may be connected to a manufacturer or distributor of a component of system 100, either physically or virtually via a secure connection. may be owned by the system 100 to perform authentication of the devices (e.g., devices 102, 120-n, 152), to securely store the user's data, and/or to store the user's measured data. It can be used as a server that provides a data analysis program (eg, accessible via a web browser) to perform analysis of analyte data and drug history. Trusted computer system 180 may also function as a data hub for routing and exchanging data between all devices communicating with system 180 via cloud network 190. In other words, all devices of system 100 that are capable of communicating with cloud network 190 (e.g., directly using an Internet connection or indirectly through other devices) are capable of communicating with cloud network 190 . It is also possible to communicate directly or indirectly with all other devices.

Display device 120-2 is shown communicating with cloud network 190. In this example, device 120-2 may be owned by another user who is authorized to access the analyte and drug data of the person wearing SCD 102. For example, the person who owns display device 120-2 may be, as one example, a parent of a child wearing SCD 102, or, as another example, a caregiver of an elderly patient wearing SCD 102. System 100 is configured to communicate analyte and drug data regarding the wearer via cloud network 190 (e.g., via trusted computer system 180) to another user authorized to access the data. be able to.

Exemplary Embodiments of Analyte Monitoring Devices The analyte monitoring functionality of dose guidance system 100 may be accomplished by including one or more devices capable of collecting, processing, and displaying a user's analyte data. can. Exemplary embodiments of such devices and methods of their use are described in WO 2018/152241 and US Patent Application Publication No. 2011/0213225, the entire contents of which are incorporated herein by reference for any purpose. is incorporated herein by reference.

Analyte monitoring can be performed in a number of different ways. A “continuous analyte monitoring” device (e.g., a “continuous glucose monitoring” device) continuously transmits data from a sensor control device to a display device, automatically, according to a schedule, whether prompted or not, for example. Can be sent to or repeatedly. A “flash analyte monitoring” device (e.g. a “flash glucose monitoring” device or simply a “flash” device) can be used, as another example, using NFC (near field communication) or RFID (Radio Frequency Identification) protocols. Data can be transferred from the sensor control device in response to a user-initiated data request (eg, scan) by the display device.

Analyte monitoring devices that utilize sensors configured to be placed partially or wholly within a user's body can be referred to as in vivo analyte monitoring devices. For example, an in vivo sensor may be configured within a user's body such that at least a portion of the sensor is in contact with body fluids (e.g., interstitial (ISF) fluids such as dermal fluid in the dermal layer or subcutaneous fluid below the dermal layer, blood, etc.). and the analyte concentration in that body fluid can be measured. In vivo sensors can use various types of sensing techniques (eg, chemical, electrochemical or optical). Some systems utilizing in vivo analyte sensors can also operate without the need for fingerstick calibration.

An "in vitro" device is one in which a sensor is brought into contact with a biological sample outside the body (or "ex vivo"). These devices typically include a port for receiving an analyte test strip carrying the user's bodily fluids, which can be analyzed to determine the user's blood sugar level. Other ex vivo devices seek to measure analyte levels in a user's body noninvasively, such as optical techniques that can measure analyte levels in a user's body without mechanically penetrating the user's body or skin. It is proposed to use . In vivo and ex vivo devices often include in vitro functionality (eg, in vivo display devices that also include test strip ports).

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

Embodiments described herein can be used in all types of in vivo, in vitro and ex vivo devices capable of monitoring analytes such as those mentioned above.

In many embodiments, sensor operation may be controlled by SCD 102. The sensor can be mechanically and communicatively coupled to the SCD 102, or simply communicatively coupled to the SCD 102 using wireless communication techniques. SCD 102 can include electronics and a power source to enable and control the analyte sensing performed by the sensor. In some embodiments, the sensor or SCD 102 can be self-powered so that no battery is required. SCD 102 may also include communication circuitry for communicating with another device (eg, a display device) that may or may not be local to the user's body. The SCD 102 can be present on the user's body (eg, attached to the user's skin, otherwise positioned, or carried in the user's clothing). SCD 102 can also be implanted within the user's body along with a sensor. The functionality of the SCD 102 includes a first component implanted within the body (e.g., a component that controls a sensor) and a second component residing on the body (e.g., a component that communicates with the first component and that also controls a computer or (a relay component that communicates with external devices such as smartphones). In other embodiments, the SCD 102 can be external to the body and configured to non-invasively measure the user's analyte levels. A sensor control device may also be a "sensor control unit", an "on-body electronics" device or unit, an "on-body" device or unit, an "in-body electronics" device or unit, to name a few, depending on the actual implementation or embodiment. ” device or unit, “in-body” device or unit, or “sensor data communication” device or unit.

In some embodiments, 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 levels to the user. could be. In this case, the dose guidance embodiments described herein can be implemented, in whole or in part, directly by the SCD 102. In many embodiments, the physical form factor of the SCD 102 may be minimized (e.g., to minimize appearance on the user's body) or the sensor control device may not be accessible to the user (e.g., to minimize its appearance on the user's body) or the sensor control device may be inaccessible to the user (e.g., Depending on whether the sensor is implanted in the sensor control device) or other factors, it may be desirable to have a display device available to the user to read the analyte levels and interface to the sensor control device.

FIG. 2A is a side view of an exemplary embodiment of SCD 102. SCD 102 can include a case or mount 103 (FIG. 2B) for sensor electronics, which can be electrically coupled to analyte sensor 101, here configured as an electrochemical sensor. According to some embodiments, the sensor 101 can be configured to reside partially within the user's body (e.g., through the outermost surface of the skin), where it is in fluid contact with the user's body fluids and the sensor 101 It can be used in conjunction with electronic equipment to measure analyte-related data of the user. An attachment structure 105, such as an adhesive patch, may be used to secure the case 103 to the user's skin. Sensor 101 may extend through attachment structure 105 and protrude away from case 103. Those skilled in the art will appreciate that other forms of attachment to the body and/or case 103 may be used in addition to or in place of adhesives and are fully within the scope of this disclosure. will understand.

SCD 102 can be applied to the body in any desired manner. For example, using an insertion device (not shown), sometimes referred to as an applicator, all or a portion of the analyte sensor 101 is positioned through the outer surface of the user's skin and into contact with the user's bodily fluids. can do. In doing so, the insertion device may also position the SCD 102 on the skin. In other embodiments, the insertion device first positions the sensor 101 and then inserts the associated electronics (e.g., wireless transmission circuitry and/or data processing circuitry, etc.) manually or with the aid of mechanical devices. The sensor 101 can then be coupled to the sensor 101 (for example, inserted into a mount). Examples of insertion devices include U.S. Patent Publication No. 2008/0009692, U.S. Patent Application No. 2011/0319729, U.S. Patent Application No. 2015/0018639, U.S. Patent Application No. 2015/0025345, and U.S. Pat. No. 0173661 and No. 2018/0235520, the entire contents of which are incorporated herein by reference for all purposes.

FIG. 2B is a block diagram illustrating an exemplary embodiment of SCD 102 with analyte sensor 101 and sensor electronics 104. Sensor electronics 104 may be implemented on one or more semiconductor chips (eg, an application specific integrated circuit (ASIC), a processor or controller, a memory, a programmable gate array, etc.). In the embodiment of FIG. 1B, sensor electronics 104 interfaces to sensor 101 in an analog manner and converts analog signals to and/or from digital form (e.g., using an A/D converter). ), an analog front end (AFE) 110 configured to convert (such as an oscillator and a phase-locked loop for providing a The communication circuit 116 includes a communication circuit 116.

SCD 102 may be implemented in a highly interconnected manner, with power supply 111 coupled to each component shown in FIG. Processing circuitry 112, memory 114, timing circuitry 115, and communication circuitry 116) may be communicatively coupled to all other such components via, for example, one or more communication connections or bus 118.

Processing circuitry 112 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a discrete chip or distributed between (and one of) a number of different chips. part). Processing circuitry 112 may include onboard memory. Processing circuitry 112 interfaces to communication circuitry 116 and performs analog-to-digital conversion, encoding and decoding, digital signal processing, and formatting data signals suitable for wireless or wired transmission (e.g., in-phase and quadrature). Other functions may be performed to facilitate conversion. Processing circuitry 112 may also interface to communications circuitry 116 to perform inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

Processing circuitry 112 may execute instructions stored in memory 114. These instructions may cause processing circuitry 112 to process raw analyte data (or preprocessed analyte data) to arrive at a final calculated analyte level. In some embodiments, instructions stored in memory 114, when executed, cause processing circuitry 112 to process raw analyte data to calculate calculated analyte levels, averaged within a predetermined time window. determining one or more of the calculated analyte level, the calculated rate of change for the analyte level within a predetermined time window, and/or whether the calculated analyte index exceeds a predetermined threshold condition. can be done. These instructions also cause processing circuitry 112 to read and operate on received transmissions, adjust the timing of timing circuitry 115, and process data or information received from other devices (e.g., calibration information received from display device 120, encryption information received from display device 120, etc.). communication circuitry 116 may also perform tasks to establish and maintain communication with display device 120, interpret voice commands from a user, transmit, etc. In embodiments where SCD 102 includes a user interface, the instructions may cause processing circuitry 112 to control the user interface, read user input from the user interface, display information on the user interface, format data for display, etc. You can make it happen. The functions described herein that are encoded as instructions can alternatively be implemented by SCD 102 using hardware or firmware designs that do not rely on the execution of stored software instructions to accomplish the functionality. .

Memory 114 may be shared by one or more of the various functional units residing within SCD 102 or distributed between two or more functional units (e.g., residing within different chips). (as a separate memory). Memory 114 may also be itself a separate chip. Memory 114 is non-transitory and can be volatile memory (eg, RAM, etc.) and/or non-volatile memory (eg, ROM, flash memory, F-RAM, etc.).

Communication circuitry 116 includes one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and/or other communication components) that perform functions for communication over their respective communication paths or links. circuit). Communication circuitry 116 may include or be coupled to one or more antennas for wireless communication.

Power source 111 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuitry may also be included to regulate battery charging, monitor power supply 111 usage, boost power, provide DC conversion, and the like.

Additionally, an optional temperature sensor (not shown) can collect readings or measurements of the temperature on the skin or of the sensor. These readings or measurements may be communicated (individually or as aggregated measurements over time) from the SCD 102 to another device (eg, display device 120). However, temperature readings or measurements may be taken on the SCD 102 or on the display to correct or compensate for the analyte measurements that are output to the user, instead of or in addition to actually outputting the temperature measurements to the user. It can be used in conjunction with software routines executed by device 120.

Exemplary Embodiments of Drug Delivery Devices The drug delivery functionality of dose guidance system 100 may be accomplished by including one or more drug delivery devices (MDDs) 152. MDD 152 can be any device configured to deliver a specific dose of a drug. The MDD 152 can also include a device, such as a pen cap, that transmits data regarding the dose to the DGA even if the device itself does not deliver the drug. The MDD 152 can be configured as a portable injection device (PID) that can deliver one dose per injection, such as a bolus. PIDs are essentially manually operated syringes in which the drug is either preloaded into the syringe or must be drawn from a container into the syringe before injection. However, in most embodiments, the PID includes electronics to interface to the user and effectuate delivery of the drug. PIDs are often referred to as medication pens, although they do not necessarily have a pen-like appearance. PIDs with user interface electronics are often referred to as smartpens. PIDs can be used to deliver a single dose and then discarded, or they can be durable and used repeatedly to deliver many doses over a period of one day, week, or month. Sometimes it is done. PIDs are commonly used by users who perform multiple injection therapy (MDI), which involves multiple injections per day.

MDDs can also include pumps and infusion sets. The infusion set includes a tube cannula that resides at least partially within the recipient's body. The tube cannula is in fluid communication with a pump that can repeatedly deliver the drug in small portions over time through the cannula and into the recipient's body. The infusion set can be applied to the recipient's body using an infusion set applicator, and the infusion set often remains implanted for a period of two to three days or longer. The pump device includes electronics to interface to the user and control the slow infusion of the drug. Both the PID and the pump can store drugs in drug reservoirs.

The MDD152 is a closed-loop system (e.g., an artificial pancreas system that requires no user intervention for operation), a semi-closed-loop system (e.g., an artificial pancreas system that requires little user intervention for operation, such as confirming a dose change) It can function as part of an open-loop system (with or without insulin loop system) or as part of an open-loop system. For example, a diabetic patient's analyte levels can be repeatedly and automatically monitored by the SCD 102, and that information can be used by the dose guidance embodiments described herein to monitor the diabetic patient's analyte levels. The appropriate drug intake for control can be automatically calculated or otherwise determined and then delivered to the diabetic patient's body. This calculation may be performed within MDD 152 or any other device of system 100, and the resulting determined intake amount may then be communicated to MCD 152.

In many embodiments, the dose guidance provided by embodiments described herein is based on the type of insulin (e.g., rapid-acting (RA), short-acting insulin, intermediate-acting insulin (e.g., NPH). insulin), long-acting (LA), very long-acting insulin, and mixed insulin), resulting in the same drug delivered by MDD152. Types of insulin include human insulin and synthetic insulin analogs. Insulin can also include premix formulations. However, the dose guidance embodiments and drug delivery capabilities of MDD152 described herein can also be applied to other non-insulin drugs. Such agents may include, but are not limited to, exenatide, exenatide extended release formulation, liraglutide, lixisenatide, semaglutide, pramlintide, metformin, SLGT1-i inhibitors, SLGT2-i inhibitors, and DPP4 inhibitors. . Dose guidance embodiments can also include combination therapy. Combination therapies can include, but are not limited to, insulin and glucagon-like peptide-1 receptor agonists (GLP-1RA), insulin and pramlintide.

To facilitate the description of the dose guidance embodiments herein, MDD 152 is often described in the form of a PID, specifically a smartpen. 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 illustrating an exemplary embodiment of a PID, specifically an MDD 152 configured as a smart pen. MDD 152 can include a case 154 for electronics, an injection motor, and a reservoir for medication (see FIG. 3B) from which medication can be delivered via needle 156. Case 154 can include a removable or removable cap or cover 157 that can be installed to cover needle 156 when not in use and then removed for injection. The MDD 152 can also be used as a single component (e.g., a touch screen for outputting information to a user and receiving input from the user) or in combination with multiple components (e.g., one or more buttons, switches, etc.). A user interface (UI) 158 may also be included, which may be implemented as a touch screen or display. MDD 152 may also include an actuator 159 that can be moved, depressed, touched, or otherwise actuated to initiate delivery of medication from an internal reservoir through needle 156 and into the recipient's body. According to some embodiments, cap 157 and actuator 159 may also include one or more safety features to prevent separation and/or actuation to reduce the risk of injection of harmful agents. Details of these safety features and the like are described in U.S. Patent Application Publication No. 2019/0343385 ('385 Publication), the entire contents of which are incorporated herein for all purposes.

FIG. 3B is a block diagram illustrating an exemplary embodiment of an MDD 152 having electronics 160 coupled to a power source 161 and an electric injection motor 162 also coupled to a 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. Reservoir 163 may be permanent or removable and replaced with another reservoir containing the same or a different drug. Electronics 160 may be implemented on one or more semiconductor chips (eg, an application specific integrated circuit (ASIC), a processor or controller, a memory, a programmable gate array, etc.). In the embodiment of FIG. 3B, electronic device 160 is configured to communicate wired and/or wirelessly with processing circuitry 164, memory 165, and one or more devices (such as display device 120) external to MDD 152. High-level functional units may be included including communication circuitry 166 as well as user interface electronics 168.

MDD 152 may be implemented in a highly interconnected manner, with power supply 161 coupled to each component shown in FIG. 3B to communicate or receive data, information, or commands from these components (e.g., Processing circuitry 164, memory 165, and communication circuitry 166) may be communicatively coupled to all other such components via, for example, one or more communication connections or bus 169.

Processing circuitry 164 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a discrete chip or distributed between (and one of) a number of different chips. ) can be dispersed. Processing circuitry 164 may include onboard memory. Processing circuitry 164 interfaces to communication circuitry 166 and performs analog-to-digital conversion, encoding and decoding, digital signal processing, and formatting data signals suitable for wireless or wired transmission (e.g., in-phase and quadrature). Other functions may be performed to facilitate conversion. Processing circuitry 164 may also interface to communications circuitry 166 to perform the inverse functions necessary to receive the wireless transmission and convert it into digital data or information.

Processing circuitry 164 may execute software instructions stored in memory 165. These instructions cause processing circuitry 164 to receive a selection or provision of a specified dose from a user (e.g., entered via user interface 158 or received from another device) and to deliver the specified dose. commands (eg, signals from actuator 159) to control motor 162 to deliver a specified dose. These instructions also cause processing circuitry 164 to read and operate on received transmissions, process data or information received from other devices (e.g., calibration information, encryption or authentication information received from display device 120, etc.). , performing tasks to establish and maintain communications with display device 120 , interpreting voice commands from a user, transmitting them to communication circuitry 166 , and so on. In embodiments where the MDD 152 includes a user interface 158, the instructions direct the processing circuitry 164 to control the user interface, read user input from the user interface (e.g., enter a drug dose for administration, or enter a recommended drug dose). inputting confirmations), displaying information on a user interface, formatting data for display, etc. The functions described herein that are coded as instructions can alternatively be implemented by MDD 152 using hardware or firmware designs that do not rely on the execution of stored software instructions to accomplish the functionality. .

Memory 165 can be shared by one or more of the various functional units residing within MDD 152 or distributed between two or more functional units (e.g., residing within different chips). (as a separate memory). Memory 165 may also be itself a separate chip. Memory 165 is non-transitory and can be volatile memory (eg, RAM, etc.) and/or non-volatile memory (eg, ROM, flash memory, F-RAM, etc.).

Communication circuitry 166 includes one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and/or other communication circuit). Communications circuitry 166 may include or be coupled to one or more antennas for wireless communications. Details of exemplary antennas can be found in the '385 publication, the entire contents of which are incorporated herein for all purposes.

Power source 161 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuitry may also be included to regulate battery charging, monitor power supply 161 usage, boost power, provide DC conversion, and the like.

MDD 152 may also include an integrated or attachable in vitro glucose meter, including an in vitro test strip port (not shown) for receiving an in vitro glucose test strip for performing in vitro blood glucose measurements.

A communication- enabled insulin pen and pen cap device is a type of MDD 152 that measures the amount of insulin injected by a patient and then transmits that data to a display device 120, such as a smartphone. In connected pens, the electronics and mechanical devices necessary to transmit data are built into the insulin pen. In a connected pen cap, the electronics and mechanical devices are housed in a "cap" that is attached to the insulin pen.

Connected MDD 152 is an important part of DGS 100. Traditionally, bolus calculator applications have required patients to manually enter medication information, limiting the usability of the application. Having the connected MDD 152 automatically send insulin delivery data to the DGA substantially improves the usability of the DGS 100.

Functions related to how and what information is communicated between the DGA and the connected MDD 152 can have a significant impact on the degree of usability of the DGS 100.

Current connected MDDs 152 may include circuitry that can broadcast a record of insulin doses as the doses are administered. Additionally, many designs may rebroadcast the recording until the receiving application confirms that it has received the dose recording. As seen in FIG. 22A, in data flow design 2300, MDD 152 can broadcast dosage information 2302 to an application, and the application can send an acknowledgment to MDD 152. This data flow design may work well for applications that use dose information for specific functions, such as dose logging functions.

However, this dataflow design may not adequately address the needs of software applications intended to provide insulin dosing guidance. In particular, dose calculations typically require knowledge of previous insulin doses within the time frame of insulin action (eg, typically about 4.5 hours for most fast-acting insulins). Dose calculators can track a metric commonly referred to as IOB (insulin on board). The IOB is typically subtracted from the calculated dose before being displayed to the user. When a user requests dose guidance, the application needs to calculate the insulin dose and then the patient's IOB. The problem with the dataflow design 2300 is that if the MDD 152 is not functional or the communication path is interrupted, the application may not receive information about recent doses and may then incorrectly calculate the user's IOB and User overdosing may occur.

In one embodiment, this risk can be mitigated by having the DGA display a prompt requesting the user to confirm that no doses other than those received by the DGA have occurred. If the DGA does not have recent dose records to proceed with the dose guidance calculation, the DGA may provide a means for the patient to manually enter the dose and time. In another embodiment, the DGA may provide the user with instructions to correct the communication blockage and a means to retry the dose guidance calculation. However, this method can be cumbersome and add user steps in the process of requesting dose guidance, which can seriously reduce the usability of the DGA.

In another embodiment, as seen in FIG. 22B, the MDD 152 may be designed to provide a way for the DGA to query the pen for the most recent dose record. In addition to, or instead of, data flow 2300 in which the DGA monitors the MDD 152 for alert conditions, in data flow 2320, after the user initiates (e.g., requests) dose guidance, the DGA requests dose information 2322. An inquiry may be sent to the MDD 152 at any time. The query may be for recent dose information or dose information from a particular time period, eg, since the last time insulin dose data was received, as specified in the communication protocol. The MDD 152 may send the requested dose information back to the DGA 2324 in response to the inquiry, and the DGA may send an acknowledgment 2326 back to the MDD 152. Once the DGA receives all recent dose records, the DGA can calculate and display IOB and dose guidance amounts to the user, as described elsewhere in this application.

Data flow design 2320 can handle situations where the communication channel is interrupted. However, to ensure that the IOB is accurate, the DGA must ensure that the DGA has received all recent dose information, including confirmation that no other doses were provided by MDD152 in the recent past or during the specified time period. Confirmation from the MDD 152 can be requested. Some scenarios in which this may occur include: (a) the MDD 152's battery dies or other temporary failure causes the MDD 152 to properly record delivered doses; or (b) in the case of a pen cap delivery device, the pen cap may not have fit onto the insulin pen.

In an exemplary embodiment, in a method 2340 as seen in FIG. 22C, in a first step 2342, MDD 152 may store data for doses administered during a period of time. Data may include the dose and time administered. The data may also include the amount of drug remaining in the drug delivery device, such as insulin. At step 2344, MDD 152 may determine whether the stored data includes all doses delivered during a period of time. To make this determination, MDD 152 may include self-test circuitry that may periodically ensure proper functionality and battery power. The self-test circuit may maintain a counter that increments every self-test cycle at regular intervals, for example every minute. When the MDD 152 is interrogated, the MDD 152 may check a self-test counter to ensure that the counter value is equal to the estimated counter value based on the current elapsed time, which counter value is Can be provided by a separate circuit. In another embodiment, the MDD 152 may send the counter value to the DGA as part of the interrogation, and the DGA compares the MDD's 152 counter value to an estimated counter value based on the elapsed time of the clock in the DGA. Value checks can be performed. DGS 100 may include some timing tolerance between the DGA clock and MDD clock.

If it is determined in step 2346 that the stored data includes all doses delivered during the period, the MDD may send the stored data to the application that sent the query. can.

If it is determined at step 2348 that the stored data does not include all doses delivered during a period of time, MDD 152 may create an indication of incomplete dose data. At step 2350, MDD 152 may send an indication of incomplete dose data to the application that sent the inquiry.

In an alternative embodiment, the DGA may include circuitry to determine whether the data transmitted from the MDD 152 includes all doses administered during a period of time. In the example method 2360, as shown in FIG. 22D, in step 2362, the DGA may interrogate the MDD 152, eg, an insulin pen, and receive a first self-test counter value. This first counter value may be the current counter value at the time of the inquiry. At a later point, in step 2364, the DGA may send an additional inquiry to the MDD 152 and receive a second self-test counter value. The additional inquiry may be in response to a request for dosage guidance from the user. The second self-test counter value may be the current counter value at the time of the additional inquiry. At step 2366, the DGA may calculate an estimate of the second self-test counter value. The estimate may be calculated based on the first counter value + (elapsed time between inquiry and additional inquiry/duration of self-test). The self-test period may be a period in which the self-test circuit of the MDD 152 is configured to increment the counter value by "1".

At step 2368, the DGA may compare the second counter value and the estimated counter value to determine whether the value is within an acceptable range. If the comparison of values (eg, difference) is within an acceptable range, at step 2370, the DGA may calculate insulin dose guidance that may be displayed to the user. If the comparison is not within an acceptable range, in step 2372, the DGA requests confirmation from the user that no other doses were delivered apart from the dose recorded by the DGA (e.g., received in the data transfer). can do. If the user confirms that no other doses were delivered, at step 2370 the DGA may calculate insulin dose guidance that may be displayed to the user. If the user does not confirm that no additional doses were delivered, the DGA will not calculate and display dose guidance and the system can recheck the counter value.

In another alternative embodiment, in a method 2380 as seen in FIG. 22E, the DGA can query the MDD 152 for dose data over a period of time at step 2382. At step 2384, the DGA may receive data from the MDD 152. The received data may include dose data and may include an indication of incomplete dose data. At step 2386, the DGA may determine whether an incomplete dose indication was received from the MDD 152. If an incomplete dose indication is not received, in step 2388 the DGA may calculate dose guidance based on the data sent from the MDD 152. If the DGA receives an incomplete dose indication, in step 2390 the DGA outputs a prompt requesting the user to confirm that the received dose data includes all doses administered over a period of time. Can be done. If it is determined in step 2392 that the DGA has received confirmation from the user that no other doses were delivered during the period of time, the DGA may calculate dose guidance in step 2388. If the DGA determines that it has not received confirmation from the user that no other doses were delivered during the period, the DGA may re-query the MDD 152 for dose data, similar to step 2382. .

For the DGS100, which includes a pen cap system as MDD152, if the cap is removed from the insulin pen for a period of time and then reattached, the system can detect that a dose has been delivered (more than one dose). It may also be possible to detect the cumulative dose (if delivery has taken place). However, the actual timing of these one or more doses may be unknown. In one embodiment, the pen cap includes a mechanism for detecting when attached to or removed from an insulin pen in addition to a mechanism for detecting current insulin remaining in the pen. Can be done. The pen cap controller system can save the date and time of the last instance when the pen cap was reattached and the insulin level was different than when the pen cap was last removed. The date and time of this error display may be sent to the DGA in response to the inquiry. The pen cap controller system may exclude storage of this error indicator in special cases where the pen cap is removed when the pen is empty (or nearly empty) and reinstalled when the pen is full. The DGA can process this indicator in the same manner as described for the self-test indicator.

Exemplary Embodiments of Display Devices Display device 120 may be configured to display information related to system 100 to a user and accept or receive input from a user also related to system 100. Display device 120 may display recently measured analyte levels to the user in any number of formats. The display device displays not only the user's past analyte levels, but also other indicators describing the user's analyte information (e.g., time in range, ambulatory glucose profile (AGP), hypoglycemia risk level). etc.) can also be displayed. Display device 120 can display past dose information and drug delivery information, such as time and date of administration. Display device 120 can display alarms, alerts or other notifications related to analyte levels and/or drug delivery.

Display device 120 is not only intended for use with system 100 (e.g., an electronic device designed and manufactured primarily to interface with an analyte sensor and/or drug delivery device), but also includes: Devices that are handheld or portable mobile communication devices (eg, smartphones or tablets), or multifunctional, general purpose computing devices such as laptops, personal computers, or other computing devices can also be used. Display device 120 may be configured as smart glasses or a mobile smart wearable electronics assembly such as a smart watch or wristband. Display devices and variations thereof may be referred to as "reading devices", "readers", "handheld electronic devices" (or handhelds), "portable data processing" devices or units, "information receivers", "receivers", to name a few. It may be referred to as a device or unit (or simply a receiver), a "relay" device or unit, or a "remote" device or unit.

FIG. 4A is a schematic diagram illustrating an exemplary embodiment of display device 120. Here, display device 120 includes a user interface 121 and a case 124 in which display device electronics 130 (FIG. 4B) are held. User interface 121 may be implemented as a single component (e.g., a touch screen capable of input and output) or multiple components (e.g., a display and one or more devices configured to receive user input). Can be done. 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 connected to the case. 124.

Display device 120 may include stored software (e.g., by the manufacturer or downloaded by the user in the form of one or more "apps" or other software packages) that interfaces to SCD 102, MDD 152, and/or the user. software). Additionally or alternatively, the user interface may be controlled by a web page displayed on a browser or other Internet interface software executable on display device 120.

FIG. 4B is a block diagram of an exemplary embodiment 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, jog dials, microphones, speakers, etc.), processing circuitry 131 , memory 125 , communication circuitry 126 configured to communicate with and/or from one or more other devices external to display device 120 , power supply 127 , and timing circuitry 128 (eg, oscillators and phase-locked loops for providing clocks or other timing to components of SCD 102). Each of the aforementioned components may be implemented as one or more different devices or integrated into a multifunctional device (e.g., processing circuitry 131, memory 125, and communication circuitry 126 on a single semiconductor chip). integration). Display device 120 may be implemented in a highly interconnected manner, with power supply 127 coupled to each of the components shown in FIG. 4B to communicate or receive data, information, or commands ( For example, user interface 121, processing circuitry 131, memory 125, communication circuitry 126, and timing circuitry 128) can communicate with all other such components, e.g., via one or more communication connections or bus 129. Can be combined. FIG. 4B is a simplified representation of typical hardware and functionality present within a display device, and those skilled in the art will appreciate that other hardware and functionality (e.g., codecs, drivers, glue logic) It will be readily recognized that this may also be included.

Processing circuitry 131 may include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which may be a discrete chip or distributed between (and one of) a number of different chips. ) can be dispersed. Processing circuitry 131 may include onboard memory. Processing circuitry 131 interfaces to communications circuitry 126 and performs analog-to-digital conversion, encoding and decoding, digital signal processing, and formatting data signals suitable for wireless or wired transmission (e.g., in-phase and quadrature). Other functions may be performed to facilitate conversion. Processing circuitry 131 may also interface to communications circuitry 126 and perform inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

Processing circuitry 131 may execute software instructions stored in memory 125. These instructions may cause processing circuitry 131 to process the raw analyte data (or preprocessed analyte data) to arrive at a corresponding analyte level suitable for display to a user. These instructions may cause processing circuitry 131 to read, process, and/or store dosing instructions from a user, and in turn, communicate dosing instructions to MDD 152 . These instructions direct processing circuitry 131 to configure system parameters (e.g., alarm thresholds, notification settings, display preferences, etc.), present current and historical analyte level information to the user, and configure current and historical analyte level information to the user. Present drug delivery information to the user, collect other non-analyte information from the user (e.g., information regarding meals consumed, activities performed, drugs administered, etc.), and provide notifications and alarms to the user. User interface software may be executed that is adapted to present an interactive group of graphical user interface screens to a user for presentation to a user. These instructions also cause processing circuitry 131 to make transmissions to communication circuitry 126 and cause processing circuitry 131 to read and act on received transmissions, read inputs from user interface 121 (e.g., determine the drug dose to be administered), displaying data or information on user interface 121; adjusting the timing of timing circuit 128; inputting data or information received from other devices (e.g., analysis received from SCD 102); data, calibration information, encryption or authentication information), perform tasks to establish and maintain communication with the SCD 102, interpret voice commands from the user, and so on. The functionality described herein that is encoded as instructions may instead be implemented by display device 120 using hardware or firmware designs that do not rely on the execution of stored software instructions to accomplish the functionality. Can be done.

Memory 125 may be shared by one or more of the various functional units present within display device 120, or may be distributed between two or more functional units (e.g., within different chips). (as a separate memory that exists). Memory 125 may also be itself a separate chip. Memory 125 is non-transitory and can be volatile memory (eg, RAM, etc.) and/or non-volatile memory (eg, ROM, flash memory, F-RAM, etc.).

Communication circuitry 126 includes one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and/or other communication components) that perform functions for communication over a respective communication path or link. circuit). Communications circuitry 126 may include or be coupled to one or more antennas for wireless communications.

Power source 127 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuitry may also be included to regulate battery charging, monitor power supply 127 usage, boost power, provide DC conversion, and the like.

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. Display device 120 may also include an integrated or attachable in vitro glucose meter, including an in vitro test strip port (not shown) for receiving an in vitro glucose test strip for performing in vitro blood glucose measurements. You can also do it.

Display device 120 can display measured analyte data received from SCD 102 and can also be configured to output alarms, alert notifications, glucose values, etc., which can be visually, audibly, tactilely or any combination thereof. In some embodiments, SCD 102 and/or MDD 152 may also be configured to output alarm or alert notifications in visual, audible, tactile form, or a combination thereof. Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011/0193704, the entire contents of which are incorporated herein by reference for all purposes. do.

Exemplary Embodiments Related to Dose Guidance The following exemplary embodiments relate to dose guidance functionality provided by the dose guidance system 100. Dose guidance functionality is implemented in many embodiments as a set of software instructions stored and/or executed on one or more electronic devices. This dose guidance functionality will be referred to herein as the Dose Guidance Application (DGA). In some embodiments, the DGA is stored, executed, and presented to the user on the same single electronic device. In other embodiments, the DGA can be stored and executed on one device and presented to the user on different electronic devices. For example, the DGA may be stored and executed on trusted computer system 180 and presented to a user via a web page displayed via an Internet browser running on display device 120. DGA may be a standalone application or may be incorporated in whole or in part into other software applications.

Accordingly, many different embodiments exist with respect to the number and types of electronic devices used to store, execute, and present the DGA to a user. For 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 illustrating an example of one embodiment of UID 200. In this embodiment, UID 200 includes a case 201 coupled with a user interface 202. User interface 202 can output information to and receive input or information from a user. In some embodiments, user interface 202 is a touch screen. As shown, 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, jog dials, microphones, etc.). , touchpad, softkeys, keyboard, etc.).

Many of the devices described herein can be implemented as a UID 200. For example, display device 120 is used as UID 200 in many embodiments. In some embodiments, 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.

The Dose Guidance System (DGS100) leverages glucose and insulin data to learn, deliver, and titrate insulin doses. DGS is an application that integrates with connected insulin pens and long-acting glucose sensors, e.g. Including mobile applications.

DGS 100 can perform three main tasks. First, the DGS 100 can learn the patient's insulin dose regimen (ie, the frequency and dosage at which insulin doses are administered) during a "learning period" of the DGS. Second, the DGS 100 can provide dose recommendations to the patient for mealtime dosing and postprandial corrections. Third, DGS 100 can titrate current dose settings to maximize glycemic control. These second and third tasks may be performed in parallel during the "guidance period" of the DGS.

Similar to insulin data, various forms of continuous glucose data (scan, historical and streaming) can serve as input to perform all three functions described above. DGS 100 can receive glucose data by various means and in various formats, including scanning, historical and streaming. The scan data, including the latest glucose values and trend values, may be obtained on demand by the user. Historical glucose data may be generated by a component of the DGS that may generate and record glucose and trend values at regular intervals, such as every 15 minutes. Past historical data can be obtained by scanning by the user. Streaming data may include glucose and trend values that are generated and recorded at regular intervals, such as every minute, and automatically sent to the DGA. Similarly, insulin data can also be obtained from multiple sources. Insulin data may be manually logged or transferred from MDD 152, eg, an insulin pen. Glucose data and insulin data may be transferred through any known means, for example wireless communication technology such as Bluetooth or NFC.

During the "learning period," the DGA can determine the user's insulin dose regimen through clustering of insulin data by time of day and subsequent curve fitting when combined with glucose data. This learning portion of the DGA may require glucose and insulin data from the user for a period of time, for example about 14 days.

A guidance period, including providing dose recommendations and titrating titration, may begin after the learning period has been completed and the initial insulin dose regimen has been determined. During the guidance period, the DGS can provide mealtime dose recommendations upon user request. The user may request dose recommendations for meals that the DGS has determined that the user is currently being treated with insulin. Dose Calculation utilizes the form of a bolus calculator to generate dose recommendations that modify the learned fixed dose by accounting for pre-meal high glucose and residual insulin remaining in the bloodstream from previous injections. can be provided.

The DGS can also notify the user in case of a missed mealtime dose and recommend a revised dose. It is recommended that fast-acting insulin analogs for meal dosing be taken either at or just before a meal. Forgetting to take mealtime insulin doses is common, and treatment concordance is known to be a factor influencing glycemic control. To address these behavioral trends, DGS leverages streaming data, e.g., data sent at regular intervals, such as every minute, to detect periods of significant glycemic excursions without associated insulin doses. be able to. Such periods may represent instances where the user ate but did not take the prescribed insulin dose. In this case, a prompt will be displayed to notify the user. Once a forgotten meal event is confirmed by the user, a modified mealtime insulin dosing may be recommended by taking into account the timing discrepancy between meal initiation and insulin injection.

The DGS can also notify the user when it is appropriate to take a correction dose and recommend a dose to correct for high glucose between meals. Every minute, the DGS can process the streaming data to identify periods during which the user is exhibiting high glucose during meal administration. In this case, the DGS may display a prompt informing the user of the occurrence. The user can then request a dose recommendation from the DGS to correct for high glucose. Alternatively, the DGS may provide a dose recommendation within the notification, eliminating the need for the user to request it.

DGS also titrates the user's insulin dose regimen to lower the user's glucose levels while avoiding excessive amounts of time below a low threshold, e.g. 70 mg/dL, and thus a target range, e.g. 70-180 mg/dL. can maximize blood sugar levels. Once the user transitions into the "guidance period," the DGS can periodically analyze insulin and glucose data to titrate previously learned or titrated insulin dose regimen parameters.

Detecting MDI Dosing Strategies Turning now to the DGA aspect, more specifically, the DGA can use knowledge of the patient's dosing strategy and analyte levels to provide accurate dose guidance. Described herein are exemplary embodiments for automatic detection of a patient's medication strategy that can make DGA setup easy and quick. Detection of dosing strategies can be based on a number of characteristics of the monitored drug (eg, insulin) dose. For example, in this embodiment, a dose can be identified as basal or bolus based on the MDD 152 used to administer the dose. Some patients can have more than one MDD 152. For example, a patient can have one MDD for administering long-acting insulin (eg, basal doses) and another MDD for administering fast-acting insulin (eg, prandial doses). Also, the count of basal doses per dose (eg, number of doses) and timing can be used to classify the basal strategy as a "single" or "split" basal dosing strategy. For example, in a "split" basal dosing strategy, a daily basal dose of 20 U can be split into two 10 U doses, with one dose administered before bed and the other dose administered upon waking. Can be administered at times.

If consecutive bolus doses are administered one after the other, the system may attempt to distinguish between the original meal dose, an increase to the original meal dose, or a correction dose for high glucose between meals. When the DGA detects a small dose followed by a large dose (both of which occur near the start of a meal), the DGA will The doses can be grouped as a single meal dose. Then, if a dose occurs much later than a known meal and/or a dose (group) tagged as a meal dose, DGA will use the later dose as a correction dose for postprandial high glucose or when consumed. It can be tagged as a dose that increases the previous meal dose to account for the extra food. When a meal event is recognized based on the meal detector algorithm or a user-entered meal event, the DGA uses the amount of the previous medication event and the timing relative to this detected meal to determine if the previous medication was Helps clarify whether it is the first meal dose or a correction for high glucose between meals. It is envisaged that the correction dose will be smaller in size compared to the mealtime dose. Furthermore, if the time elapsed between the previous medication and the current meal event is long enough, it is reasonable to think that these two events will not be associated with the treatment of the same glucose excursion event, and the previous medication eliminates the possibility that this is the first of multiple medications for a given meal. Therefore, if the previous dose is sufficiently smaller than the meal dose recorded within this window in the last few days and far enough away from the current meal, the previous dose can be classified as a correction dose event. .

The DGA can be configured to use real-time meal detection algorithms and dosing times to identify doses added to basal as breakfast, lunch, and/or dinner bolus doses, and/or correction doses. can. The DGA can also be configured to use the number of daily bolus doses to identify dosing strategies as basal only, basal +1, basal +2, etc.

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

To enhance the safety profile of the onboarding DGA, the HCP can approve the learned insulin dosing parameters and subsequent titrations calculated by the DGA. Embodiments of the DGA include a number of ways of interaction between the HCP and the DGA so that the HCP is provided with relevant evidence to approve proposed dose learning and titrations and the workflow. Provided in a concise and informative way to improve.

For diabetic patients already on an insulin dosing regimen, HCPs can leverage existing reports that provide insight into the patient's glucose patterns to identify users who may benefit from dose guidance. . Embodiments of the DGA provide a learning period (eg, while using the DGS 100) during which patient medication strategies and trends can be categorized. If the combined insulin and glucose data further confirms that the user is a suitable candidate for DGA, e.g. a candidate for which DGA can learn a particular dosing strategy, the insulin learned during the learning period The dosing parameters can serve as initial conditions for dose guidance, which can be titrated as needed by the DGA. A HCP notification method for initialization and titration of DGA dose parameters may also be presented. This process assists both HCPs and users by streamlining DGA onboarding and titration, while also helping to ensure that DGA is used only by those for whom it is directed. If the DGA is unable to learn the patient's medication parameters, the DGA can indicate the patient's medication discrepancy, which the HCP can utilize to address the patient's medication discrepancy.

The first step in identifying potential users of DGA may include an initial analysis of the patient's glycemic control by glucose concentration profile. In order to make DGA accessible to as many users as possible, this process may be independent of the user's current method of glucose monitoring.

For diabetic patients currently using the SCD102, key metrics, glucose concentration profiles (e.g., ambulatory glucose profile (AGP)), different time A glucose pattern report is available that includes patterns identified in the bands and titration and lifestyle suggestions to improve if glucose is consistently outside the target range. This pattern can be identified using the GPA algorithm, as detailed elsewhere.

As mentioned above, for those who are not currently using a device or system (e.g., SCD 102) associated with an application that can generate a glucose pattern report 250, an HCP can generate a report 250 or similar. It can be proposed that the patient be monitored by another device or system so that the patient can be created. For example, patients may be masked to collect glucose data over a period of several days or weeks, so the user cannot access the measured glucose levels and therefore cannot modify his or her behavior during this time. The SCD 102 may be installed in a mode or a blind mode. From these data, a glucose pattern report can be generated. If a suggested insulin titration is included in the glucose pattern report, the glucose pattern report 250 may also include a suggestion that the patient is a suitable candidate for DGS 100 and may suggest a learning period for a drug dosing strategy. can.

During the learning period, MDD152 can be integrated into the glucose sensing system used in the initial screening to provide a more complete portrait of insulin-intensive diabetes management. The learning period may utilize algorithms such as those described elsewhere herein to detect the user's insulin dosing strategy. During the learning period, the DGA can be configured to allow the user to determine how to determine mealtime doses. For example, the DGA may be used to determine whether a user is determining a mealtime dose based on carbohydrate counting or an experience where the user learns appropriate dosing based on past meal or similar meal experience. whether the user administers a fixed amount of insulin at mealtime, or whether the user determines mealtime dosing based on premeal glucose values (fixed dosing, carbohydrate counting); Users consider residual insulin (IOB) from previous injections when determining dosage (fixed dosing, carbohydrate counting, or determined from empiric dosing) whether to change dosage (determined from fixed dosing, carbohydrate counting, or empiric dosing) or whether to consider other techniques. The DGA can also determine whether the user's mealtime dose is constant depending on meal type (eg, breakfast, lunch, and dinner) or whether the mealtime dose varies. A determination that the mealtime dose is changing may indicate that the user is basing the mealtime dose on a carbohydrate counting method. The DGA can also determine whether the user is adjusting the mealtime dose to account for premeal high glucose. In some embodiments, the DGA can also determine a target glucose level, and the user can adjust the mealtime dose or to correct. The DGA can also determine which meals are associated with insulin dosing. DGA can also determine patterns in which meal doses are forgotten. For example, the DGA can detect whether a user has failed to administer an insulin dose associated with a meal or time period at least twice, instead at least three times, during a period of time (e.g., one week or two weeks). .

In some embodiments, the DGS may also prompt the user to enter a typical meal dose and a typical time period during which the meal dose is typically administered. In some embodiments, the DGS may prompt the user to enter the amount of fast-acting insulin typically taken with each meal when the glucose level is at a particular level. For example, the DGS may prompt the user to enter the amount of fast-acting insulin that would normally be taken with each meal if the glucose level is approximately 120 mg/dL. For fast-acting insulin, the DGA can prompt the user for the start and end times of each of breakfast, lunch, and dinner. The fast-acting dose at the specified time period for each of the various meals can then be logged as the dose for that meal. In some embodiments, the fast-acting dose may be logged as the dose for that meal without any additional input from the user, e.g., to confirm that the dose is for a particular meal. The user may not be prompted after administering the dose. For example, the breakfast bolus time period may be from approximately 2:00 AM to approximately 11:00 AM. The lunch bolus time period may be from approximately 11:30 a.m. to approximately 2:00 p.m. The dinner bolus time period may be from approximately 5:00 PM to approximately 8:00 PM. In this exemplary embodiment, a fast-acting insulin dose administered at 12:30 PM may be automatically logged as a lunch dose without additional confirmation from the user.

In some embodiments, the DGS may also prompt the user for the amount of the typical basal dose to be administered and the time or time period during which the typical basal dose will be administered. The DGS can also prompt the user to confirm and verify all dosages and administration times and time periods before making final decisions.

The learning period can last any period of time sufficient to obtain the necessary information. In many embodiments, this period is at least two days, more preferably one week or longer (eg, 14 days), and may vary depending on how well the DGA is able to learn trends. Results can be compiled into summary reports for both users and physicians.

Glucose Pattern Report As seen in FIGS. 6A-1 through 6G-2, the glucose pattern report 250 can include various elements that can be arranged in different layouts. Those skilled in the art will appreciate that glucose pattern report 250 can be a graphical user interface output to a display of a computing device. Elements may include identification of the most important glucose pattern 278, dosing guidance 260, variability statement 286, lifestyle guidance 284, and excursion statement 288. The glucose pattern report 250 also identifies the time period 264 that the report covers, the amount of time the CGM sensor is active 266, the average number of scans or views per day 268, the glucose metrics or statistics section 270, and the target range. It may include a time within (TIR) section 272, a guidance for clinicians 276 section, and a glucose pattern 282 section. The clinician guidance 276 section may include medication guidance 260, variation statements 286, lifestyle guidance 284, and excursion statements 288.

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

The amount of time 266 that the CGM sensor is active may be reported in the glucose pattern report 250, for example, as a percentage. The amount of time 266 that the CGM sensor is active may be listed at the top of the report, eg, near the 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 at the bottom of the report, in the header or footer, or elsewhere in the report's layout. May be listed.

The average number of scans or views 268 per day during the time period 264 may also be included in the glucose pattern report 250. The average number of scans or views per day 268 may be listed at the top of the report, eg, near the amount of time 266 that the CGM sensor is active. Alternatively, the average number of scans or average views per day 268 may be listed at the top of the report, e.g., below the report name, or at the bottom of the report, in the header or footer, or elsewhere in the report's layout. may be listed.

Glucose indicators section 270 may also be included in glucose pattern report 250. Glucose indicator section 270 may include average glucose over time period 264. Glucose indicators section 270 may also include a glucose management indicator (GMI) for time period 264. The goals for each of the average glucose and GMI may optionally be listed next to the average glucose and GMI values, indicating how close or far the user was to achieving the goal for the time period 264. Make it easy to identify. The goals may be displayed in a different color (eg, gray text versus black for the actual calculated average glucose and GMI values) and may be displayed in a smaller font size.

Time in target range (TIR) section 272 may include a TIR graphical display 252 and a text component 274 that describes the amount of time in each different range. TIR graphical display 252 may be a bar graph, pie chart, histogram graph, or any other graphical display that shows relative amounts of time at different concentration ranges. TIR graphical display 252 may include at least three, alternatively at least four, alternatively at least five, alternatively at least six different concentration ranges. The graphical representation of each concentration range can reflect the time within the target range for that concentration range. For example, the relative area or relative height of the graphical representation of each concentration range may be proportional to or related to a determined proportion of time for that concentration range during time period 264.

The ranges are: less than a very low threshold 290 (e.g., less than about 54 mg/dL), a very low threshold 290 to a low threshold 291 (e.g., about 54 mg/dL to about 69 mg/dL), a low threshold 291 to a high threshold 292 ( for example, about 70 mg/dL to about 180 mg/dL), high threshold 292 to very high threshold 293 (e.g., about 181 mg/dL to about 250 mg/dL), and above very high threshold 293 (e.g., about 250 mg/dL). super).

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

Different density ranges of the TIR graphical display can be labeled with different colors. For example, a graphical representation below a very low threshold (graphical representation) may be colored dark red or maroon, and a graphical representation of the range between the very low threshold and the low threshold may be colored bright red. , the graphical representation of the range between the low and high thresholds can be colored green, the graphical representation of the range between the high and very high thresholds can be colored yellow, and the very high The graphical display of the range exceeding the threshold can be colored orange.

Text component 274 of TIR display 272 may include a label for each range. Ranges below a very low threshold may be labeled "very low", ranges between the very low threshold and the low threshold may be labeled "low", and the range between the low and high thresholds The range between may be labeled "Target," the range between a high threshold and a very high threshold may be labeled "High," and the range above a very high threshold may be labeled "Target." May be labeled as "very high." The text component 274 of the TIR display 272 may include numerical limits for the multiple range concentration ranges, or alternatively may include a description thereof. The different concentration ranges may be listed next to or in close proximity to the corresponding graphical element and/or the range label. For example, for a graphical element with time below the very low threshold of 290, the text may be "Very Low < 54" or "Less than 54 mg/dL", "Low 54-69" or "54-69 mg/dL", " "Goal 70-180" or "Goal 70-180 mg/dL", "High 181-250" or "181-250 mg/dL", and/or "Very high >250" or "Over 250 mg/dL" Can be done. Text component 274 may also include a numerical value, such as a percentage, of time spent in each range of concentration. Instead of or in addition to individual numerical values for each concentration range, text component 274 can include a combined numerical value of two or more ranges, such as a combined percentage. For example, the time value below the very low threshold 290 and the time value between the low threshold 291 and the very low threshold 290 may be reported as a combined single value. Further, the time value above the very high threshold 293 and the time value between the high threshold 292 and the very high threshold 293 may be reported as a combined single value. The numerical value or combination of numerical values can be placed next to or in close proximity to the graphical element and/or legend for each concentration range. If both individual and combined numbers are reported, the combined numbers may be visually different from the individual numbers. For example, combined numbers may be displayed in bold, italics, or a different color. The sum of the reported or combined numbers may be equal to 100 or a number other than 100. The text component 274 may also include a goal 275 (see, eg, FIGS. 6A-2 and 6A-3), eg, a percentage, for each concentration range. Alternatively, text component 274 may include a combined target of two or more intensity ranges. For example, a goal for a time below a very low threshold and a goal for a time between the low and very low thresholds may be reported as a single goal. Furthermore, a goal for a time above a very high threshold and a goal for a time between a high threshold and a very high threshold may be reported as a single goal. The goals and/or combined goals for each concentration range may be listed next to or in close proximity to the determined value of the time period 264 for each concentration range.

A section detailing guidance for the clinician, HCP, or patient 276 may also be included in the glucose pattern report 250. The clinician guidance 276 section may include a most important patterns section 278, a medication guidance section 260, and a lifestyle guidance section 284.

Most important patterns section 278 may identify the most important patterns for time period 264 as determined by algorithms including, but not limited to, the GPA algorithm described elsewhere in this application. The most important patterns section 278 identifies patterns including, but not limited to, "Lows," "Highs with Some Lows," and "Highs." be able to. The most important patterns section 278 can also identify the periods of the day during which the most important patterns occurred, such as nighttime, morning, afternoon, and/or evening. Each pattern and the period of day during which the identified pattern occurred may be identified with text, such as a sentence or phrase, and each may be identified with a tag 280. If multiple patterns are detected, including a "low value" pattern, the glucose pattern report 250 may identify the "low value" pattern in the most important patterns section 278 and allow the clinician to identify the "low value in part" pattern. Before addressing the associated "highs" or "highs", we can first be able to address these "lows" patterns. If multiple patterns are identified, the patterns identified in the most important patterns section 278 first identify a "low value" pattern, then a "high value with some low values" pattern, and then may be prioritized to identify "overpriced" patterns. However, additional patterns may be identified in the outline or boxes of the glucose pattern profile 256 even if not identified in the most important pattern statement 278. The pattern tags 280 in the most important patterns section 278 may be color coded to match the color of the outlines or boxes or partial boxes or brackets that highlight these sections of the glucose concentration profile 256 in the glucose pattern section 282. . For example, a tag 280 identifying an evening "low value" may be colored red (e.g., white text on a red background), and a box or partial box surrounding the evening period of the glucose concentration profile 256 may be colored red (e.g., white text on a red background) It may have a red line color and a color-coded red tag identifying low values may be placed at the top of the box. The time periods in which the most important patterns occur may be listed next to tag 280. The time periods may be displayed as text according to the order of the glucose concentration profile 256, eg, from left to right: "Nighttime," "Morning," "Afternoon," and "Evening." Alternatively, the time periods may be identified with tags (not shown), but with a different color than the pattern tags. For example, time period tags can be gray. If the pattern occurs in all four time periods of the day, the most important patterns section 278 may include two tags: "all day" and "night". If the pattern occurs during all time periods of the day except "nighttime," then the most important patterns section 278 may include a single tag that says "all day," or "morning," " It may include three tags: "afternoon" and "evening". If the pattern occurs in multiple time periods, single boxes or partial boxes may outline adjacent time periods with a single label (see, eg, FIGS. 6C-6E).

Medication guidance 260 may also be provided in glucose pattern report 250 if the patient's current therapy (eg, basal + RA insulin, basal only, basal + SU, etc.) is known. Medication guidance can be provided in the form of text recommendations. General advice regarding insulin dose titration may be provided based on the identified high and low glucose patterns highlighted in box 258 in glucose concentration profile 256. It should be noted that this general advice may have been determined without access to data regarding actually administered insulin doses. Recommendations can generally follow the rule of first mitigating any low patterns before mitigating high patterns. If the glucose pattern report includes a suggestion for insulin dose titration, the glucose pattern report 250 may also include a suggestion that the patient is a suitable candidate for DGS 100, and may also include a suggestion that the patient is a suitable candidate for DGS 100, and the can facilitate conversation between

Medication guidance section 260 may include different descriptions and/or observations regarding medications administered during time period 264. Medication guidance section 260 may include a question asking if medication is contributing to the low value. Alternatively, the medication guidance section 260 may include a statement that medications added to address highs may worsen lows. Alternatively, the drug guidance section 260 may include a statement that if a patient is starting or adjusting a medication to address a high level, to consider how that medication may precipitate a low level. can. Medication guidance section 260 may also include statements advising that the clinician and/or patient should consider different therapies to address glucose fluctuations. Medication guidance section 260 may also indicate to consider adjusting insulin for T1 patients. For T2 patients, the Medication Guidance section advises you to consider medication adjustments for T2 patients currently taking insulin or sulfonylureas, or for other T2 patients, medication adjustments or insulin or sulfonylureas. A statement may be included advising to consider starting non-drug medications. Medication guidance section 260 may also indicate consideration for starting insulin for other T2 patients.

Medication guidance section 260 may include, but is not limited to, one or more statements regarding the following topics:
- Does medication contribute to low levels?
- Medications added to address highs can make lows worse.

- When starting or adjusting medication to address high levels, consider how the medication may induce low levels.

- Consider different treatments to address glucose fluctuations.

- For T1 patients, consider adjusting insulin.

- For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication.

- For other T2 patients, consider starting insulin.

- For other T2 patients, consider adjusting medication or starting medications other than insulin or sulfonylureas.

Lifestyle guidance section 284 may include variation statements 286, excursion statements 288, and self-care guidance 262. If volatility for a period is determined to be low, a statement regarding volatility may not be included. In some embodiments, variation statement 286 may not be displayed if no pattern was detected during any of the time periods. In some embodiments, the variation statement 286 may be displayed if it is determined that there is high variation and that a pattern exists. If the variability for a period of time is determined to be high, the glucose pattern report 250 may include a variability statement 286. Variation statement 286 may indicate that low values are often associated with high glucose fluctuations. The fluctuation statement 286 may alternatively or additionally indicate that high values are often associated with high glucose fluctuations. Fluctuation statement 286 may also indicate that certain behaviors may contribute to high glucose fluctuations, in which case it may include a list of specific behaviors. Variability statement 286 may also indicate that certain behaviors may contribute to glucose fluctuations, in which case it may include a list of specific behaviors that contribute to glucose fluctuations.

Variation statements 286 may include, but are not limited to, one or more statements regarding the following topics:
- Low values are often associated with high glucose fluctuations.

- The following actions can contribute to glucose fluctuations:

- The following actions can contribute to high glucose fluctuations:

- High values are often associated with high glucose fluctuations.

Determination of variation is described elsewhere in this application. Alternative determinations for determining variation are described in WO 2014/145335 and WO 2014/106263, both of which are incorporated by reference in their entirety for all purposes. It is expressly incorporated by reference into the specification.

As seen in FIG. 6A-3, an excursion statement 288 may be included in the glucose pattern report 250 when an excursion is detected. An excursion may be an instance where a glucose level below a very low threshold is detected. A very low threshold is about 50 mg/dL to about 65 mg/dL, or about 50 mg/dL to about 60 mg/dL, or about 53 mg/dL, or about 54 mg/dL, or about 55 mg/dL, or otherwise in this application. It may be as described in the section. If an excursion is detected, the excursion statement 288 may suggest that the clinician discuss with the patient that hypoglycemia below a very low threshold (e.g., approximately You can refer to the Weekly Summary Report or Daily View Report, which can list excursions (below 54 mg/dL). Alternatively, the excursion statement 288 can indicate that hypoglycemia below a very low threshold 290 occurs occasionally and can refer to a weekly summary report or daily view report (e.g., "less than 54 mg/dL"). Occasional hypoglycemia (see daily view report). If one or more excursions below a very low threshold are detected, an excursion statement 288 may be included in the glucose pattern report 250. In some embodiments, an excursion statement may not be included in the glucose pattern report 250 if a low pattern is detected, even if one or more excursions below a very low threshold are detected. In some embodiments, an excursion statement 288 may be displayed if a "high value" pattern, a "high value with some low values" pattern, or no pattern is detected.

Self-care guidance 262 may also be included in glucose pattern report 250. Self-care guidance 262 may be displayed in the glucose pattern report 250 if the GPA algorithm identifies a pattern with high variability. Alternatively, the glucose concentration profile 256 may have such high fluctuation values that the logic behind the report is unable to make specific recommendations, and in this case, instead of the previous case, the user may The default is to consult the HCP regarding changes to the policy.

The self-care guidance 262 displayed in the glucose pattern report 250 may depend on the type of pattern detected, amount of variation, median glucose, presence or absence of hypoglycemia risk, and presence or absence of excursions. Self-care guidance 262 may include one or more statements regarding the following topics:
- Do you sometimes forget to eat or are your carbohydrates inconsistent?
- Does your activity level vary from day to day?
- Does your alcohol intake vary from day to day?
- Do you sometimes forget to take your medication?
- Are your meals or snacks frequently high in carbohydrates?
- Are meals or snacks sometimes high in carbohydrates?
Glucose pattern section 282 may include a glucose concentration profile 256 that displays glucose data over a 24 hour period. Glucose concentration profile 256 may be an exogenous glucose profile (AGP). Alternatively, the glucose pattern profile may display various data points as points or dots on a graph. These points or dots may or may not be connected by a line representing each day's analyte curve. Exemplary glucose concentration profiles 256 are shown in FIGS. 6B-1 through 6G-2. Glucose concentration profile 256 may be a graph of glucose data for time period 264 of report 250, and various data points on the graph may be color-coded to correspond to which concentration ranges glucose analyte levels fall. (not shown). This color coding may correspond to the color coding of TIR display 252. Boxes or partial boxes 295 surrounding different portions of the glucose concentration profile 256 highlight detected patterns (eg, "high values,""lowvalues," and "high values with some low values"). Various thresholds and boundaries for different concentration ranges may be highlighted in the glucose concentration profile 256, including the labels Very Low Threshold 290, Low Threshold 291, High Threshold 292, and Very High Threshold 293. or other highlighting (eg, text labels and/or horizontal lines). A target or target range between low threshold 291 and high threshold 292 may also be labeled in glucose concentration profile 256, and may be shaded or highlighted with a different color (eg, green), for example. The median glucose level for each time period may be highlighted with a solid line 294 and labeled as the median or 50%. The median glucose line 294 can change color depending on the median value of that portion of the line. For example, the median glucose line 294 may be colored dark maroon or dark red for median values below a very low threshold, and colored red for median values that fall between the very low threshold and the low threshold. may be colored green for the median value that falls between the low and high thresholds, and may be colored yellow for the median value that falls between the high and very high thresholds. , may be colored orange for median values that exceed a very high threshold. The four time periods are nighttime 296 (e.g., about 12:00 a.m. to about 8:00 a.m.), morning 297 (e.g., about 8:00 a.m. to about 12:00 p.m.), and afternoon 298 (e.g., about 12:00 p.m. to about 12:00 p.m.). 6 p.m.), and in the evening 299 (eg, from about 6 p.m. to about 12 a.m.).

For each time period, the algorithm can determine that one of three possible patterns exists, or that no pattern exists for that time period. Therefore, each of the four time periods has a pattern of (1) a low value of 281, (2) a high value of 283 with some low values, (3) a high value of 285, or (4) no harmful pattern detected. It can be determined that it has been assigned. Each identified pattern is labeled with a colored tag above the appropriate section of the graph, and time periods are outlined with boxes or partial boxes, which may also be color-coded. The "low" 281 pattern may be displayed in red (see, eg, FIGS. 6B-1-6B-2). "High values with some low values" 283 may be displayed as amber or maroon (see, eg, FIGS. 6E-1-6E-2). The "high value" 285 may be displayed in orange (see, eg, FIGS. 6C-1-6C-2 and FIGS. 6D-1-6D-2). As seen in FIGS. 6B-1-6B-2, if multiple patterns are detected, multiple boxes or multiple partial boxes 295 may be added to all of the patterns in the relevant section on the glucose concentration profile 256. can be displayed. In some embodiments, the "low value" pattern 281 may be prioritized and highlighted in a different color, such as red, compared to boxes and labels of other patterns. As seen in Figures 6C-1-6C-2, when the same pattern occurs in two or more adjacent time periods, a single box or partial box 295 covers all of the relevant time periods. You can outline it and put a single label on top of the box. If the same pattern occurs during all time periods of the day (night, morning, afternoon, and evening), as seen in Figures 6D-1 to 6D-2 and Figures 6E-1 to 6E-2 , a single box or partial box 295 can outline all time periods and give the box 295 a single label.

In some embodiments, the pattern may be determined according to the GPA algorithm discussed elsewhere herein. Multiple variables may be used to determine the output of the GPA algorithm and the content of the glucose pattern report 250. Variables include preferred pattern, additional patterns in combination with preferred pattern, variation (high or low), median glucose (e.g., >180 mg/dL or <180 mg/dL), moderate hypoglycemic risk (yes or no), and excursion less than 54 mg/dL (yes or no). The layout and text associated with each element may vary depending on the output of the GPA algorithm.

The specific guidance text displayed on any given report is determined by the three patterns described herein, the "Moderate Hypoglycemia Risk" pattern, which is determined according to the GPA algorithm described elsewhere herein. may be based on a combination of "hidden" patterns defined as , glucose excursions, and hyperglycemia defined by overall median glucose compared to a 180 mg/dL threshold. In some embodiments, the patterns and indicia described herein may be replaced with similar patterns and indicia. In some embodiments, the low pattern may be replaced by a calculation where the number of low events (eg, glucose less than 70 mg/dL) exceeds a threshold. For example, a threshold may require that the number of low events be at least 4 low events in 14 days. In some embodiments, the variability measure may be replaced with any other common glucose variability measure. In some embodiments, the overall hyperglycemia index may be defined as the average glucose value instead of the median value. In some embodiments, a "highs with some lows" pattern includes about 2 to 3 low events and about 4 or more high events (where the high event may be replaced by the calculation of glucose (which may be defined as >180 mg/dL). In this way, guidance can be driven by comparable patterns and indicators.

Table 1 below outlines an example mapping of guidance or lookup tables that may be provided in a glucose pattern report. Inputs include: (1) whether there is significant variation; (2) determination of low values, high values with some low values, high values, or no pattern; and (3) determination of hypoglycemia. whether there is a risk or not; and (4) whether the median glucose (G _med ) is greater than 180 mg/dL. Table 1 can define the output of guidance text based on the input. Outputs may include (1) medication guidance, (2) lifestyle statements and guidance, (3) low excursions, and (4) identification of most important patterns. In other embodiments, the lookup table may require fewer entries. In some embodiments, the table may exclude global hyperglycemia and corresponding guidance or additional inputs, for example. The various scenarios outlined in Table 1 are described in more detail below.

“Any” = at least one of the four TOD periods “None” = zero of the four TOD periods
G _med = median glucose concentration
Medication guidance
Statement A1: For T1 patients, consider adjusting insulin.

Statement A2: For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication.

Statement A3: For other T2 patients, adjust medication or consider starting medications other than insulin or sulfonylureas.

Statement A4: For other T2 patients, consider starting new medications such as insulin.

Statement A5: When initiating or adjusting medication to address high levels, consider how the medication may induce low levels.

Statement A6: Consider different treatments to address glucose fluctuations.

Statement A7: Does medication contribute to low levels?
Statement A8: Drugs added to address highs can make lows worse.

Lifestyle Statement and Guidance Statement B1: The following behaviors can contribute to high glucose fluctuations.

Statement B2: The following actions can contribute to glucose fluctuations.

Statement B3: Low values are often associated with high glucose fluctuations.

Statement B4: High values are often associated with high glucose fluctuations.

Statement B5: Are meals or snacks frequently high in carbohydrates?
Statement B6: Are meals or snacks sometimes high in carbohydrates?
Statement B7: Do you sometimes forget to take your medication?
Statement B8: Do you sometimes forget to eat or are your carbohydrates inconsistent?
Statement B9: Does the activity level vary from day to day?
Statement B10: Does the amount of alcohol consumed vary from day to day?
As seen in FIGS. 6A-1 through 6A-4, the layout of the glucose pattern report 250 can have, for example, a time period 264 and a time when the CGM is active 266, which are located at the top of the report, e.g. , may be displayed below the report title. In some embodiments, the average number of scans/views per day may also be displayed at the top of the report. The time period 264, the time the CGM is active 266, and the average number of scans/views per day 268 may be listed adjacent to each other, separated by a line or outlined with a box. Good too. Glucose indicators section 270 and TIR section 272 may be displayed side-by-side below a list of time periods 264, times CGM is active 266, and average number of scans/views per day 268. Glucose indicator section 270 may display an average glucose value 271a along with a target value 271b, and may display a GMI 273a along with a target value 273b. In some embodiments, the average glucose value with target value may be displayed above the display of GMI with target value. TIR display section 272 may be displayed on the left and glucose indicator section 270 may be displayed on the right. Alternatively, TIR display section 272 may be displayed on the right and glucose indicator section 270 may be displayed on the left. Glucose pattern report 250 may include a clinician guidance section 276 below glucose indicators section 270 and TIR display section 272. The clinician guidance section 276 may include the identification of a most important pattern 278 at the top of the section 276 and below the most important pattern 278 medication guidance 260 and lifestyle guidance 284 are listed side by side. It's okay. Within the lifestyle guidance section 284, if all three statements are reported, the variation statement 286 may be listed first, the self-care guidance 262 may be listed second, and the excursion statement 288 may be listed last. Alternatively, each of the three statements 286, 262, and 288 may be listed in a different order, for example, self-care guidance 262 may be listed first, middle, or last, and variable statement 286 may be listed first, middle, or last. It may be listed last, and excursion statement 288 may be listed first, in the middle, or last. A glucose pattern section 282, which may include a glucose concentration profile 256, may be displayed below guidance for the clinician 276. Thus, in one embodiment, glucose indicator section 270 and TIR display section 272 may be displayed in the top third of report 250, and guidance for clinicians 276 may be displayed in the middle third of report 250. The glucose pattern section 282 may be displayed in the bottom third of the report. Alternatively, in other embodiments, the glucose indicator section 270 and the TIR display section 272 may be displayed in either the top, middle, or bottom third of the report 250, and the clinician guidance 276 is , may be displayed in the top, middle, or bottom third of the report 250, and the glucose pattern section 282 may be displayed in the top, middle, or bottom third of the report.

In some embodiments, if no pattern is found and no excursion is detected during the time period 264, the guidance for the clinician 276 may include a statement that no deleterious glucose pattern was detected. . Additionally, the glucose concentration profile may not include boxes highlighting any time periods during the day.

In some embodiments, if no pattern was found, but at least one excursion was detected during time period 264, guidance for the clinician 276 is that no deleterious glucose pattern was detected. A most important pattern statement 278 may be included that states: Guidance for clinicians 276 may also suggest that clinicians discuss with patients the occasional occurrence of hypoglycemia below a very low threshold, and refer to additional reports, such as weekly summary reports. An excursion statement 288 can be included that can recommend to the clinician. The glucose concentration profile may not include boxes highlighting any time period during the day, but a dark red or maroon color below a very low threshold 290 corresponding to one or more detected excursions. It may contain several data points highlighted in .

In some embodiments, if a "low value" pattern 281 is detected over a period of time and low variation is detected, guidance 276 for the clinician may indicate that the "low value" pattern 281 is colored red. It can include a most important pattern statement 278 that can be highlighted with tags, and can also include tags for times of day during which "low value" patterns occur. If the "low value" pattern 281 occurs at all times of the day, the most important pattern statement 278 may include two tags: "all day" and "nighttime." As seen in FIGS. 6F-1 to 6F-2, if the "low value" pattern 281 occurs at all times of the day, the glucose concentration profile is a graph with a "low value" heading at the top. It may include a single box 295 that is highlighted entirely, for example a red box. Alternatively, as seen in FIGS. 6G-1-6G-2, if the "low value" pattern 281 occurs in at least two non-adjacent time periods, those time periods can be separated by separate boxes or partial boxes 295. Highlighted.

In some embodiments, if a "low value" pattern 281 is detected in at least one time period and high variability is detected, or if a "low value" pattern 281 and other patterns are detected, the clinician Guidance 276 for may include a top pattern statement 278 that may highlight a "low value" pattern 281 with a red colored tag 280, indicating the number of days in which the "low value" pattern occurred. Identification of times of day may also be included, which may be colored in a different color, such as gray. Medication guidance 260 may include statements for the clinician to consider when determining treatment for the "low value" pattern. This statement may include, "Does the medication contribute to the low?" and "Medications added to address the high may worsen the low." Not limited. Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. This statement could include "Low values are often associated with high glucose fluctuations" and "The following actions can contribute to glucose fluctuations (this may be followed by a list of actions)" However, it is not limited to these. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements 262 may include, "Do you sometimes forget to eat or do your carbohydrate intake vary?", "Does your activity level vary from day to day?" and "Does your alcohol intake vary from day to day?" , but not limited to. In the glucose concentration profile, time periods with a "low value" pattern are highlighted with a red box and labeled "low value" at the top, as seen for example in FIGS. 6G-1 to 6G-2. be able to. In some embodiments where adjacent time periods have the same pattern, a single box may outline adjacent time periods that have the same pattern (see, e.g., FIGS. 6F-1 to 6F-2). ). As seen in Figures 6G-1 to 6G-2, when different types of patterns are detected in time periods, a box is displayed that schematically indicates the relevant time period during the day and identifies the type of pattern. All patterns may be identified with appropriate heading labels. In some embodiments, if a "low value" pattern 281 is detected along with a "high value" 285 and/or a "high value with some low values" 283 pattern, the "low value" pattern 281 may be detected in conjunction with the other patterns. , may be highlighted in a different color, eg, red, compared to, eg, gray (see, eg, FIGS. 6B-1-6B-2 and FIGS. 6G-1-6G-2).

In some embodiments, if a "high value with some low values" pattern 283 and high fluctuation are detected, or a "high value with some low values" pattern 283 and a "high value" pattern 285 are detected. If detected, the guidance for the clinician 276 may include a top pattern statement 278 that may highlight the "high values with some low values" pattern 283 with a red colored tag 280. It can also include identification of the time of day in which the "high prices with some low values" pattern 283 occurred. In some embodiments, a "high value with some low values" pattern may be prioritized over a "high value" pattern as it is more important for the clinician to address first. Medication guidance 260 can include statements for a clinician to consider when determining treatment for a pattern of low values. This statement includes ``When starting or adjusting medication to address high levels, consider how that medication may induce low levels'' and ``Different treatments to address glucose fluctuations.'' This may include, but is not limited to, "taking into account". Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include, but are not limited to, "The following actions can contribute to high glucose excursions (which may be followed by a list of actions)". Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements included: ``Do you sometimes forget to take your medication?'' ``Do you sometimes forget to eat or eat carbohydrates inconsistently?'' ``Does your activity level vary from day to day?'' and ``Does your alcohol intake vary from day to day?'' may include, but are not limited to, "Is it different?" In the glucose concentration profile 256, time periods with the "high values with some low values" pattern 283 are marked with a dark red box or partial box with the heading "high values with some low values" at the top. 6E-1-6E-2), a time period with a "high value" pattern 285 is highlighted in a box 295, e.g. a gray box, with " Add a heading ``High Value''.

In some embodiments, if only a "high" pattern 285 is detected, with low variation, median glucose <180 mg/dL, no hypoglycemic risk, and no excursion detected, guidance for the clinician 276 may include a most important pattern statement 278 that identifies a "high price" pattern 285 and the time period in which the pattern was detected. To distinguish the "high value" pattern 285 from the "low value" pattern 281 and the "high value with some low values" pattern 283, each of the "high value" patterns 285 may be highlighted in orange and " The "low values" pattern 281 may be highlighted in red, and the "high values with some low values" pattern 283 may be highlighted when that particular pattern is detected, reported, and identified as the most important pattern. May be highlighted in dark red/purple. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." For patients, this may include, but is not limited to, adjusting medications or considering starting medications other than insulin or sulfonylureas. Glucose pattern report 250 may not include variation statement 286 because low variation was detected. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods with a "high value" pattern 285 may be highlighted with a box, for example an orange box. As seen in FIGS. 6D-1-6D-2, a single box 295 may delineate adjacent time periods if the adjacent time periods are all determined to have a "high" pattern 285. .

In some embodiments, if only a "high" pattern 285 is detected, with low variation, median glucose >180 mg/dL, no risk of hypoglycemia, and no excursion detected, guidance for the clinician 276 may include a "high value" pattern 285 and a most significant pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 can include statements for the clinician to consider when determining treatment for the "high" pattern. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." patients, this may include, but is not limited to, "considering starting insulin." Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods with "high" patterns 285 may be highlighted with boxes, e.g., orange boxes (e.g., FIGS. 6C-1-6C-2 and 6D-1-6D -2). A single box or partial box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 to 6C -2 and Figures 6D-1 to 6D-2).

In some embodiments, guidance for clinicians if only a "high" pattern is detected, the variation is small, the median glucose is arbitrary, there is a moderate hypoglycemic risk, and no excursion is detected. 276 may include a "high value" pattern 285 and a most important pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement can include, but is not limited to, "When initiating or adjusting medication to address high values, consider how that medication may induce low values." Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods with a "high value" pattern may be highlighted with an orange box (see, e.g., FIGS. 6C-1 to 6C-2 and FIGS. 6D-1 to 6D-2). ). If the adjacent time periods are all determined to have a "high value" pattern, a single box may outline the adjacent time periods (e.g., Figures 6C-1-6C-2 and 6D-1). ~See Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose <180 mg/dL, no risk of hypoglycemia, and no excursion detected, guidance for the clinician 276 may include a “high value” pattern 285 and a most important pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 can include statements for the clinician to consider when determining treatment for the "high" pattern. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." For patients, this may include, but is not limited to, adjusting medications or considering starting medications other than insulin or sulfonylureas. Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include "Do you sometimes forget to take your medications?" and "Do you sometimes forget to eat or eat carbohydrates inconsistently?" In the glucose concentration profile, time periods with "high value" patterns 285 may be highlighted with boxes 295, e.g., orange boxes (e.g., FIGS. (See 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose >180 mg/dL, no risk of hypoglycemia, and no excursion detected, guidance for the clinician 276 may include a most important pattern statement 278 that identifies the "high value" pattern and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." patients, this may include, but is not limited to, "considering starting insulin." Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Do you sometimes forget to take your medications?" and "Are your meals or snacks sometimes high in carbohydrates?" In the glucose concentration profile 256, time periods with a "high value" pattern may be highlighted with an orange box 295 (e.g., FIGS. 6C-1-6C-2 and FIGS. 6D-1-6D-2). ). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, the variability is high, the median glucose is arbitrary, there is no hypoglycemic risk, and no excursion is detected, guidance for the clinician 276 may include a "high value" pattern 285 and a most important pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 can include statements for the clinician to consider when determining treatment for the "high" pattern. This statement may include, but is not limited to, "When initiating or adjusting a medication to address a high value, consider the possibility that the medication may induce a low value." Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, "Do you sometimes forget to take your medications?" and "Are your meals or snacks sometimes high in carbohydrates?" In the glucose concentration profile 256, time periods having a "high value" pattern 285 may be highlighted with a box, e.g., an orange box (e.g., FIGS. (See 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose <180, no hypoglycemia risk, and low excursion, the guidance 276 for the clinician is: A top pattern statement 278 may be included that identifies a "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." For patients, this may include, but is not limited to, adjusting medications or considering starting medications other than insulin or sulfonylureas. Glucose pattern report 250 may not include variation statement 286 because low variation was detected. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are your meals or snacks sometimes high in carbohydrates?" Guidance for the clinician 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with a "high" pattern may be highlighted with an orange box, e.g. ~See Figure 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose >180, no risk of hypoglycemia, and low excursion, the guidance 276 for the clinician is: A top pattern statement 278 may be included that identifies a "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." patients, this may include, but is not limited to, "considering starting insulin." Glucose pattern report 250 may not include variation statement 286 because low variation was detected. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, "Are your meals or snacks frequently high in carbohydrates?" Guidance for the clinician 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with "high value" patterns 285 may be highlighted with boxes, e.g., orange boxes (e.g., FIGS. 6C-1-6C-2 and 6D-1-1). See Figure 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, the variation is small, the median glucose is arbitrary, there is a moderate hypoglycemic risk, and a low excursion is detected, the clinician's Guidance 276 may include a "high value" pattern 285 and a most important pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement can include, but is not limited to, "When initiating or adjusting medication to address high values, consider how that medication may induce low values." Glucose pattern report 250 may not include variation statement 286 because low variation was detected. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are meals or snacks frequently high in carbohydrates?" Guidance for clinicians 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods having a "high value" pattern 285 may be highlighted with a box, e.g., an orange box (e.g., FIGS. 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose <180, no hypoglycemia risk, and low excursions, the guidance 276 for the clinician is: A top pattern statement 278 may be included that identifies a "high price" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." For patients, this may include, but is not limited to, adjusting medications or considering starting medications other than insulin or sulfonylureas. Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Do you sometimes forget to take your medications?" and "Are your meals or snacks sometimes high in carbohydrates?" Guidance for clinicians 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods having a "high value" pattern 285 may be highlighted with a box, e.g., an orange box (e.g., FIGS. (See 6D-2). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose >180, no hypoglycemia risk, and low excursions, the guidance 276 for the clinician is to A top pattern statement 278 may be included that identifies a "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement includes: "For T1 patients, consider insulin adjustment," "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment," and "For T2 patients currently taking insulin or sulfonylureas, consider medication adjustment." patients, this may include, but is not limited to, "considering starting insulin." Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Do you sometimes forget to take your medications?" and "Are your meals or snacks sometimes high in carbohydrates?" Guidance for clinicians 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with "high value" patterns 285 may be highlighted with orange boxes (e.g., FIGS. 6C-1-6C-2 and FIGS. 6D-1-6D-2). ). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

In some embodiments, if only a "high" pattern 285 is detected, the variability is high, the median glucose is arbitrary, there is a moderate hypoglycemic risk, and a low excursion is detected, the clinician's Guidance 276 may include a "high value" pattern 285 and a most important pattern statement 278 that identifies the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time of day may be identified in text form. Medication guidance 260 may include statements for the clinician to consider when determining treatment for "high" pattern 285. This statement can include, but is not limited to, "When initiating or adjusting medication to address high values, consider how that medication may induce low values." Guidance for the clinician 276 may also include a variation statement 286 regarding the detected high variation. Statements may include "High values are often associated with high glucose fluctuations" and "The following actions can contribute to high glucose fluctuations (this may be followed by a list of actions)" , but not limited to. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Do you sometimes forget to take your medications?" and "Are your meals or snacks sometimes high in carbohydrates?" Guidance for the clinician 276 may also include excursion statements 288. This statement may include, but is not limited to, "Hypoglycemia below 54 mg/dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with "high value" patterns 285 may be highlighted with orange boxes (e.g., FIGS. 6C-1-6C-2 and FIGS. 6D-1-6D-2). ). A single box 295 may outline the adjacent time periods (e.g., FIGS. 6C-1 - 6C-2 and 6D -1 to Figure 6D-2).

As seen in FIGS. 6B-1-6B-2, if multiple types of patterns are detected, all patterns may be identified in the glucose concentration profile 256. In some embodiments, glucose concentration profile 256 may include up to three patterns for each patient. Boxes or partial boxes or brackets 295 that schematically depict various patterns may be prioritized. The priorities from beginning to end may be "low value," "high value with some low values," and "high value." The various patterns may be color coded based on priority. In some embodiments, the highest priority pattern among the detected patterns may be the only pattern highlighted in color. The remaining lower priority patterns can be colored in a different color, for example gray or black. For example, if at least one of each of the "low value" pattern 281, the "high value with some low values" pattern 283, and the "high value" pattern 285 is detected, the "low value" pattern 281 is colored red (partially The “high values with some low values” pattern 283 and the “high value” pattern 285 may be displayed on the glucose concentration profile 256 in gray. In other embodiments, when at least one of each of the "high values with some low values" 283 and "high values" 285 patterns are detected, the "high values with some low values" 283 are dark red or maroon. (in the form of a partial box 295 and label) and the "high" pattern 285 may be displayed in gray on the glucose concentration profile 256.

If a patient is found to have met all of the TIR targets for a time period, but a "low value" pattern is detected for at least one time period, the report will include a "low value" pattern in all appropriate sections. can still be identified, and this section includes a box or partial box or bracket 295 that schematically indicates a “low value” pattern on the glucose concentration profile 256, and a top pattern statement 278. In an alternative embodiment, if a patient is found to have met all of the TIR goals for a period of time, the report may not identify or highlight the pattern.

In embodiments where only a single pattern is detected, the single pattern may be displayed in color on the glucose concentration profile 256. For example, the "low values" pattern 281 may be displayed in red (in the form of partial boxes 295 and labels), and the "high values with some low values" pattern 283 may be displayed in dark red or maroon (in the form of partial boxes 295 and labels). The “high value” pattern 285 may be displayed in orange (in the form of a partial box 295 and label).

The manual setting of the learning method DGS 100 requires the HCP to take time, but there are cases where sufficient time is not available. Additionally, even if time for HCP can be secured, the setup may be complex and error-prone. To alleviate these issues, the DGA can include a patient parameter initialization (PI) module that requires no or minimal configuration. The PI module learns the patient's dosing strategy, which may include, for example, basal only, basal +1, basal +2, etc., and parameterizes the patient's dosing habits to configure the DGA dose guidance settings.

According to one 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, the DGS 100 can enter guidance mode and the patient can request dose guidance and receive notifications regarding administration. During the learning process that precedes the guidance mode, the DGA processes glucose and insulin data collected by the patient's SCD 102, UID 202, and/or other devices and determines dosing information based on the processed data. Can be done.

Dosing information can include, for example, dosing regimen, meal-dose type, dose parameters, and dosing range. The dosing regimen can include, for example, Basal + BF, Basal + LU, Basal + DI, Basal + BF/LU, Basal + BF/DI, Basal + LU/DI, and Basal +3, where BF is LU indicates "breakfast", LU indicates "lunch", and DI indicates "dinner". Additional regimens may also be included, eg, afternoon snack doses. Meal-dose types can include, for example, fixed meal doses or variable meal doses. Dosage parameters can include, for example, a nominal fixed dose or carbohydrate ratio for each meal, a preprandial correction factor (CF), and a postprandial CF. The dose range can include an estimate of the lowest dietary dose. CF is also known as insulin sensitivity factor. This is a ratio that reports how much 1 U of insulin lowers blood sugar in either the fasting or premeal state. DGA can have two CF values that take into account fasting and pre-prandial physiology, i.e. differences in insulin sensitivity between pre-prandial and post-prandial. The unit of CF is (mg/dL)/insulin unit.

For each of the dosing information types described above, the DGA can determine whether the cumulative data is sufficient or insufficient to determine the dosing information. In some embodiments, the patient's SCD 102 may be configured to operate for a predetermined period of time, such as 14 days. In these embodiments, the DGA determines that after the predetermined time period (or earlier if the sensor ceases operation before the end of the period) the available analyte and dosing data determine each of the dosing information described above. It is possible to determine whether it is sufficient to If sufficient, the DGA may execute the parameterization method 300 and enter a dose guidance mode. In an alternative embodiment, periodically during the training period (eg, once a day), the DGA may determine whether the data is sufficient to determine each of the dosing information described above. In either case, if the collected data is sufficient, the DGA can end the learning period, perform parameterization, and begin the guidance period. Otherwise, the DGA can continue the learning process.

Referring to FIG. 7A, the DGA may be configured to perform method 300 on appropriate computing devices, such as 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 organization. Generally speaking, the method 300 includes determining, by the DGA in step 302, each of the doses of drug received by the patient during the analysis period based on data characterizing the patient's analyte and the dose of drug received by the patient during the analysis period. , may include the step of classifying. The method 300 can further include grouping each of the doses into one of a set of mealtime groups at step 304. The method can further include generating dose parameters for the patient, at least in part, at step 306 by applying data for each of the mealtime groups to the model. The method can include storing the dose parameters in computer memory and configuring dose guidance settings at step 308. In embodiments described herein, the analyte can be glucose or can include an indicator of glucose levels in the patient, and the drug can be or can include insulin. can include. The dose guidance settings may be used by the DGA to develop dose guidance or provided for output to an interface device, such as a UID 200 or a health care worker's terminal. More detailed aspects of each operation in method 300 are described below. As used herein, "PI module" refers to a portion or portion of the DGA that performs the operations of method 300 and any ancillary operations. PI modules are not limited to any particular configuration and can include various arrangements of computer code.

In one aspect, the classification operation 302 can include classifying each dose of a drug (eg, insulin) as either a meal dose, a correction dose, and/or an ambiguous dose. If the DGA is unable to classify a drug dose as a dietary dose or a correction dose with a defined confidence, the DGA can classify this dose as an ambiguous dose and generate dose parameters for dose guidance. The drug dose can be omitted from use when

DGA can perform drug dose classification through a two-step sequence of operations, referred to herein as feature extraction and classification. Relating this to FIG. 7A, classification operation 302 may include creating a feature matrix that correlates a set of classification features to each of the doses. In some embodiments, the DGA receives a vector of insulin injection timestamps, a data file containing analyte measurements from the patient's SCD 102, and a book as input to a function that outputs a feature matrix for insulin dose classification. Results from the meal detection algorithm module may be configured as discussed elsewhere in the specification. The number of rows in the feature matrix can indicate the amount of injections, or equivalent drug administration events, during the relevant analysis period. Each row of the feature matrix can be or contain a feature vector for a single medication event. In embodiments for classifying insulin injections, each vector may include elements described below, referred to herein as classification features. The DGA can determine each of the elements of the feature vector based on a corresponding segment of glucose monitoring data over a time range relative to the insulin injection time, eg, -2.5 hours to 1.5 hours.

In embodiments, the classification characteristics may include the time of dosing of each administration, for example, the time of day when the insulin injection was recorded by MDD 152 or by the patient using UID 200.

Classification features may also be applied to temporally filtered analyte values, e.g., non-parametric smoothing filters such as Savitsky-Golay filters, low-pass filters, band-pass filters, locally estimated scatterplot smoothing, or other Can include filtered glucose values using a filter. In one aspect, the Savitsky-Golay filter may be of order 2 and frame length 7 with a sampling interval of 15 minutes.

The classification feature further includes the rate of change of the analyte value closest to the time of dosing, e.g., five analyte data points centered on the data point closest to the dosing (e.g., injection) time (e.g., a 15-minute sampling interval). can include the rate of change in analyte (e.g., glucose) value calculated by linear regression (using

The classification feature may further include a left-hand area under the curve (AUC) metric that represents the difference between the analyte value and the analyte value closest to the time of dosing, integrated over an interval prior to the time of dosing. For example, to obtain the left AUC index, the DGA collects all data points from the filtered analyte data within a time window (e.g., 2.5 hours), counts back from the injection time, and then Calculate 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) and multiply this difference by the duration of the time window to calculate the left AUC. The left AUC index can be calculated by calculating the increment.

The classification feature can further include a right-hand AUC index that indicates the difference between the analyte value and the analyte value closest to the time of dosing, integrated over an interval after the time of dosing. For example, the DGA collects all data points from the filtered analyte data within a time window (e.g., 1.5 hours), counts back from the injection time, and then calculates the average analyte of the collected data points. Determine the right-hand AUC index by calculating the difference between the value and the data point closest to the injection time (reference data point) and multiplying this difference by the duration of the time window to calculate the right-hand AUC increment. can be calculated.

Classification characteristics may further include elapsed time between dosing times. For example, for each injection time, the DGA can 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, there is no previous injection time available, so the DGA can calculate the elapsed time from the first SCG time data point to the current injection time. Additionally, as a further example, the DGA can calculate the elapsed time 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, there is no next injection time available, so the DGA can calculate the elapsed time from the current injection time to the last SCG time data point. For both backward and forward calculations, if the elapsed time is greater than a predetermined maximum value (eg, 12 hours), the DGA may set the elapsed time value equal to the maximum time.

Classification features can further include the probability that a meal will be started within a defined interval before the dosing time, e.g., the maximum probability of meal initiation within a time window (e.g., 1.5 hours) before the injection. . This probability can be calculated by the meal detection module described elsewhere herein.

The classification feature may further include a most probable interval of time since the most recent meal, eg, time since the maximum probability of meal initiation relative to the time of injection (eg, determined by the meal detection module).

The classification features may further include the probability that a meal will be initiated within a defined interval after the time of dosing, e.g., the maximum point of meal initiation probability within 2 hours after injection (as determined by the meal detection module). .

The classification features can further include a most probable interval until the next meal, e.g., the predicted elapsed time from the time of injection to the maximum point of meal initiation probability after the meal injection (e.g., determined by the meal detection module). .

As mentioned above, calculating some of the classification features includes estimating the time of each meal taken by the patient during the analysis period, and the method of estimating meal time is described in further detail below. Ru. Briefly, estimating the time of each meal may further include creating a feature matrix based on the time-correlated analyte data by means of a DGA, where the feature matrix includes the time of the analyte (e.g., glucose) A set of data features is correlated to each of the distinct regions classified as up, pre-down, and down. The set of analyte data features are maximum analyte change rate, maximum analyte acceleration, analyte value at the point of maximum analyte acceleration, region duration, region height, maximum deceleration, and average rate of change within the region. , and the time of maximum analyte acceleration. The step of estimating can further include the step of creating an estimated meal time based on the feature matrix using an algorithm described below.

More detailed aspects of the retrospective mealtime detection algorithm for use in method 300 or for use elsewhere are described in the following paragraphs. Thereafter, the description of other aspects of method 300 continues. The DGA can perform retrospective mealtime detection based on time-correlated analyte data by executing one or more code modules, such as a feature extraction module and a meal detection module. The feature extraction module, when executed by the DGA, provides the DGA with a feature matrix that receives the glucose time series as input and is passed to the retrospective meal detection module to detect glucose excursions in response to meal events. It can be output.

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

In the smoothing sub-operation, the DGA smoothes the analyte (e.g., glucose) time series using a Savitzky-Golay filter (order 2) and calculates the rate of change and acceleration at each analyte data point. can. The frame length parameter of the filter may be the number of data points collected in a first time interval (eg, 60 minutes), so the sampling is interval dependent. DGA calculates the rate of change by taking the average of the before and after differences in smoothed analyte values between a point of interest and a point that is a second interval (e.g., 15 minutes) before and after that point of interest. The second interval may be smaller than the first interval, for example equal to one quarter of the first interval. Similarly, DGA calculates the acceleration rate by averaging the difference in the rate of change of the analyte between the point of interest and a second interval (e.g., 15 minutes) before and after it. Can be done.

In the segmentation sub-operation, the DGA can segment the smoothed analyte trace into monotonically increasing regions (ie, rise) and decreasing regions (ie, fall). Each elevated region is considered a candidate for glucose excursion in response to a meal event.

In the extraction sub-operation, the DGA can extract features from the data. For example, it may include or include features from each ascending region (e.g., eight features), a preceding descending region (e.g., four features), and a next descending region (e.g., four features). 16 features can be extracted. Features that DGA can extract from elevated features may include, for example: 2) Maximum analyte acceleration; 3) Analyte value at the point of maximum analyte acceleration (reference point); 4) Duration of the rising region (from the reference point to the last point of the region). 5) area height (difference in smoothed analyte value between the last point and the reference point); 6) maximum deceleration (negative acceleration at maximum absolute value); 7) the average rate of change (height/duration) within the region, and 8) the time of the reference point data. By way of further example, the four features extracted from the preceding and subsequent descending regions may include: These are: 1) the height of the descending region, 2) the duration of the descending region, 3) the average rate of change of the region (height/duration), and 4) the maximum absolute value of the rate of change of glucose. The number of rows of 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 can receive a feature matrix as input and output a binary detection result for each elevated region. Such output can include a binary classification result and a probability value that each elevated region is an analyte (eg, glucose) excursion in response to a dietary event. The DGA can assign a probability value for each elevated region to its reference point. In some embodiments, for example, a pre-trained machine learning model for meal detection can be implemented using a random forest classifier by scikit learn (https://scikitlearn.org/stable/ modules/generated/sklearn.ensemble.RandomForestClassifier.html). The meal detection module is capable of detecting meal-induced postprandial glucose excursions based on a number of decision trees constructed and optimized during the training process. In alternative embodiments, the DGA may build pre-trained models based on alternative classification algorithms, such as gradient boosting, Ada boosting, artificial neural networks, linear discriminant analysis, and extratrees. Can be done.

Referring again to method 300 of FIG. 7A, classification operation 302 can receive a patient feature matrix as input and output a binary classification result for each relevant medication event (eg, for each insulin injection). For example, the DGA may output binary data "1" meaning a dietary dose and "0" meaning a non-meal dose. According to some embodiments, the classification operation 302 can use meal detection results, in which case meal detection can occur before insulin dose classification. As mentioned for retrospective mealtime detection, the classification operation 302 may include a pre-trained machine learning model, such as a model implemented using a random forest classifier by scikit learn (see above). Can be done. The machine learning model implemented by DGA can perform classification based on tree construction rules and thresholds for various features in each decision tree, which are optimized during the training process. Alternatively, the model can be trained with other machine learning algorithms including gradient boosting, Ada boosting, artificial neural networks, linear discriminant analysis, and extra trees. Once the DGA has successfully classified each dose, one can proceed to determine the dosing regimen and administration parameters.

At step 304, method 300 can include the DGA grouping each of the doses into one of a set of mealtime groups or clusters. For example, DGA can determine dosing strategies by clustering analysis of dosing (eg, injection) times of meal doses. The DGA can run a clustering module implemented using the K-means algorithm with the elbow method, which takes the injection times as input and determines the optimal number of clusters K (up to 3) for each injection time and the cluster Output the index. The optimal number of clusters K may be the number of food doses that the patient takes in a day. Using the cluster index of each injection, the DGA can classify the meal doses into K groups according to the cluster index.

The DGA can identify these groups as breakfast, lunch or dinner (B, L, D) as follows. For each group, the DGA can determine the typical time of day (TOD) by calculating the group's median TOD. Alternatively, the DGA may use other centroid metrics. If K=3, the DGA can associate breakfast to a group after the longest period between typical TODs for the group. Then the next group will have lunch and the last group will have dinner. For K=2, the DGA can estimate which groups are associated with breakfast, lunch or dinner using hypothetical rules regarding the time between each meal. For example, if two groups are more than 6 hours apart from each other, the DGA may identify the groups as breakfast and dinner. Otherwise, if the first group occurs before 10 a.m., the DGA may identify the group as breakfast and lunch, otherwise lunch and dinner. In an alternative embodiment, after the DGA identifies typical times for meal events, the user may be prompted to identify meals associated with each typical time. As a further example, in an alternative embodiment, the DGA can combine the two methods described herein by prompting the user for confirmation after estimating the meal relevance. Further alternative methods may include analysis of glucose data to identify meals and clustering of meal times to detect typical meal times. This may be useful for distinguishing meals when K=2, ie for identifying meals for which a dose has not been taken.

Once the doses are grouped into mealtime clusters, at step 306 the DGA may perform the step of generating dose parameters for the patient at least in part by applying data for each of the mealtime groups to the model. can. For example, for each meal group (B, L, D), the DGA may pair each set of corresponding preprandial glucose levels with a corresponding meal dosage. DGA approximates each group to a suitable model, e.g., a linear function with zero slope, a linear function with non-zero slope, a piecewise linear function connected at a point, or a combined piecewise model. can be fitted with a nonlinear function with smooth curvature around the connection point. Other models are also suitable.

DGA can perform model fitting and parameter estimation by minimizing the sum of squared residuals (SSR) of model parameters. The DGA can then use a search algorithm to find the optimal parameters that minimize the value of SSR. For linear models, DGA can perform fitting using the Nelder-Mead simplex method. For nonlinear models, DGA can use the Levenberg-Marquardt algorithm. That is, the DGA can use the Nelder-Mead simplex numerical optimization method for linear models and the Levenberg-Marquardt optimization method for nonlinear models. Other methods of fitting data to these models are also possible.

If the number of iterations during optimization exceeds the convergence criterion, the model will not fit, and the DGA can exclude the non-fitting model as a candidate model. Furthermore, the DGA may apply certain rules to minimize the uncertainty of the parameter estimates, e.g. by requiring at least three preprandial glucose data points greater than the estimated threshold glucose and the estimated correction factor. or by verifying the estimated fixed capacity by requiring at least three preprandial glucose data points less than the estimated threshold glucose, or by requiring the 95% confidence interval of the parameter intercept to be exclusive of zero. or by requiring that the 95% confidence interval of the slope of the model exclude zero.

Insufficient data may cause model fitting to fail, resulting in certain models being excluded as candidate models. DGA can evaluate each model with the Akaike Information Criterion (AIC) and select the model with the lowest AIC value as the preferred model for each dietary group.

Once the DGA selects a model for each mealtime cluster, it can then determine dose parameters, including, for example, a fixed dose insulin amount, a target glucose level, and a correction factor, based on the selected model for each mealtime cluster. can. The DGA may determine the target glucose level and correction factor as a single value for all groups, respectively, as described in more detail in the next paragraph. In an alternative embodiment, the DGA may determine target glucose levels and correction factors for each group separately and use the separately determined parameters for downstream dose guidance operations.

According to another aspect of the embodiment, the DGA can form combined data groups to obtain more accurate correction factors for the patient. For example, after fitting the dose data to various models for each meal group to select the best model and estimate a fixed insulin dose, the DGA subtracts the fixed dose insulin amount from the associated meal dose for each meal group. Can be done. The remaining non-zero values correspond to doses with correction doses. These non-zero values can then be combined from all three meal groups (B, L, D) to form a combined group. If a fixed dose insulin amount cannot be determined for a group, the DGA can exclude that group's data from the combined group. The system can then repeat the operation to find the best model for the combined group, or use the same model identified when analyzing the groups individually. By using this combined group approach, we assume that patients have the same (or constant) correction factor and target glucose for all meals, and the combined group has a larger sample size. can provide a more accurate fitting. DGA completed estimation of dose parameters after determining target glucose levels and correction factors based on the best fitting model. Then, at step 308, the DGA may store the dose parameters in computer memory and configure dose guidance settings.

In an additional aspect, the DGA determines whether the patient is potentially performing a carbohydrate count (e.g., taking into account carbohydrate consumption) by comparing the preferred model's AIC value to a threshold, such as 50, or 75, or 100. to determine whether the patient is changing their dietary intake (or not). If the AIC value is greater than the threshold, the DGA determines that the patient is doing a carbohydrate count and can ask the patient for confirmation via UID 200.

Alternate embodiments may omit one or more of the operations described above and require the patient or HCP to provide the information manually, or provide the information from another source, such as an EMR or other software program. It may be replaced by extracting . Nevertheless, method 300 should be useful for a variety of applications that do not have more information than SCDs and MDDs can provide.

Alternative Learning Methods In alternative embodiments, the DGS 100 (e.g., SCD 102, display device 120, or MDD 152) classifies and characterizes drug doses based on patient input and patient GPA analyzed over a learning period. Can be configured using automatic or semi-automatic learning methods. Alternative learning embodiments process regimen input from the user, recorded glucose measurements (glucose readings and associated date/time stamps) and insulin dosing information (dosage and associated date/time stamps). include. However, the inputs and algorithms may be optimized to reduce burden on patients and HCPs. For example, in the embodiments described herein, HCP involvement may not be required to initiate dose guidance. This is advantageous because often HCPs do not have time to set up previous systems for their patients. Additionally, this embodiment provides an additional safety mechanism compared to the common practice of having the patient or HCP directly set the dose guidance system (or more commonly referred to as a bolus calculator). Specifically, as connected insulin pens are becoming more common and have recently been used in research, many MDI patients forget to take meal doses, take them late, or miss the required amount. There is evidence of non-compliance with the medication regimen, such as taking less insulin than required. While the first two are likely due to inconvenience and forgetfulness, the third may result from fear of hypoglycemia combined with a lack of confidence in managing hypoglycemia.

A fundamental problem with common practice is that dose guidance systems may be set based on what the patient or HCP deems necessary based on the glycemic profile. However, the profile may be based on previous regimens with poor adherence. For example, the HCP may recommend that the patient increase the breakfast fixed insulin dose to 10-12 to alleviate post-breakfast high glucose, but this high glucose may require the patient to take 50% of the breakfast dose. The actual trigger was forgetting or underdosing, often by taking only 8 units. Hypoglycemia problems can arise if patients suddenly decide to increase their adherence. Therefore, it is important that the process of setting up dose guidance includes an analysis of past adherence before presenting dose guidance to the patient.

As previously discussed, the patient or HCP may enter all or only some of the key dose guidance parameters. As an example, we assume that the patient enters the following portions of the dose guidance parameters, but other examples are possible. Specifically, the system provides a means for entering the following parameters before providing dose guidance: typical fixed doses for breakfast, lunch and dinner; ) during a typical day.

The foregoing is an example of a compact parameter set that should be easy enough for most patients to understand and enter correctly. Complex parameters such as correction factors that many patients do not understand are avoided. Alternatively, the system may optionally allow patients to enter more parameters for patients who have more sophisticated dosing regimens and/or who understand the details of these regimens well enough to enter them correctly. You may also do so. In another embodiment, the patient's adherence is monitored, such as how often the patient forgets doses during certain times of the day, or how often the patient skips meals and therefore does not need to administer insulin. Information about the level can be entered. Another embodiment is a system that prompts the patient to manually categorize recorded doses. However, this type of system is likely to require a high degree of patient interaction due to the lack of readily available values, which may be undesirable for most patients.

During a learning mode to analyze patient adherence to a user-entered regimen, the system acquires glucose data and insulin dose information over a period of, for example, two weeks. If the system does not initiate dose guidance after this period, the system may allow the patient to begin a subsequent observation period.

Once the learning period is complete, the system processes the input regimen parameters and observed glucose and insulin dose information using analysis algorithms executed by the processor. This algorithm may include categorizing doses as described above. Alternatively, or additionally, the classification may be based on dosing information entered by the user, with a difference between the measured analyte (e.g., glucose) data and the dosing data provided by the user. Absolute and relative times of the day may be considered. The classification may alternatively or additionally be based on dynamic relationships between different data inputs, eg, rate of change of glucose. The system can perform classification using classification models that can be developed and trained using common machine learning techniques applied to clinical and simulated data.

Once the system processor categorizes the doses, it associates each dose with one of multiple meal events during the day. The clustering analysis described above is one method for performing the association. For example, the system may associate with breakfast one of the clusters whose central measurement (e.g., median, mean) for that time period is closest in time to the value entered by the user for breakfast; The next cluster could be associated with lunch and the next cluster with dinner.

Alternative clustering methods may include not only dose information but also glucose data in the algorithmic cluster determination to identify clusters and identify which doses are associated with which clusters. Additionally, the processor may further include data indicating absolute and relative times of day between glucose data and dose data in its cluster analysis. For example, a dose of 10 units in the morning may be associated with a lunch cluster because 10 units is a common dose for this cluster, not a dose in the breakfast cluster, if that dose is taken late enough in the morning. This is the case. Associations may also be based on dynamic relationships between different data inputs, such as rate of change of glucose. The determination is made by a classification model, which can be developed and trained using common machine learning techniques applied to clinical and simulated data.

Once the clustering process is complete, the system estimates the regimen parameters, as described above. The parameter estimation process may include determining a degree of confidence in the parameter estimates using standard numerical analysis techniques. In the following discussion, for the sake of brevity, degrees of confidence are sometimes described as confidence intervals, but there are other common measures of confidence that can be used. These parameters are referred to herein as "learned" regimen parameters, which include parameter values and associated confidence intervals.

Once the regimen is learned, the system can perform the following checks between the input parameters and the learned parameters. The system processor can check the confidence intervals (CI) of the learned meal dose parameters and typical dosing times by comparing them to maximum thresholds appropriate for each parameter. If the CI exceeds that threshold, the processor may flag the system configuration as questionable. For meal doses, suitable maximum CIs are ±30%, or 20%, or 50%, and for typical meal administration times, suitable maximum CIs are ±1.5 hours, 1 hour, 2 hours. It's time.

In another aspect, the system processor may compare the learned parameters to user-entered parameters, specifically breakfast, lunch, and dinner doses. If the input parameters and the learned parameters differ 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 entered typical breakfast dosing time to the learned meal dosing time of the temporally closest dose cluster. If these parameters differ significantly, the processor may flag the confidence interval (or some multiple of the confidence interval) as questionable.

Furthermore, the system can check whether additionally learned parameters are valid. For example, the largest gap between learned typical meal dosing times should occur before the learned typical meal dosing time to ensure that overnight periods are properly taken into account. . A system configuration can be flagged as questionable if the validity check fails. Some parameters do not need to be estimated with high confidence so that the system configuration is not flagged as suspicious. For target glucose (GT), pre-prandial CF and post-prandial CF, if the CI is within the appropriate maximum threshold for that parameter, the learned value is used for the dose guidance regimen, otherwise conservative default values are used. be done. Suitable maximum CI thresholds for GT and CF are ±2, 5, 10 mg/dL or ±5, 10, 20 mg/dL/unit, respectively. Suitable default values for GT, preprandial CF, postprandial CF are 120, 125, 130 mg/dL, or 40, 50 mg/dL/unit, or 60, 70 mg/dL/unit.

Additionally, the system can evaluate the acquired data for patient compliance with the entered regimen. For example, the system's processor can estimate the frequency of missed meals (or meals that have a small impact on postprandial glucose levels). This is useful for calculating subsequent indicators. This estimate can be calculated using a model developed using machine learning or other techniques from glucose and insulin dose data and dietary records - this model can be calculated using clinical, real-world or simulated data. can be trained using Generally, if insulin dosing is not administered during a time period during which a meal administration is expected and the blood glucose pattern during this time period does not indicate an increase in glucose, this is indicative of meal forgetting.

The system processor can estimate the frequency of missed meal administrations for each breakfast, lunch, and dinner. For example, frequency can be calculated as (number of missed periods minus number of periods tagged as missed meals) divided by (number of total periods minus number of periods tagged as forgotten meals).

In another aspect, the system determines, for each meal (breakfast, lunch, and dinner), the difference between the median dose and the entered dose, i.e., how much the dose compared to the patient-entered dose. It can be estimated whether there is a shortage or an excess. Prior to initiating dose guidance, the system processor may output dose adherence findings to a display device or equivalent device. Similarly, the system can report to the patient the estimated impact of nonadherence on glycemic control and A1c, and conversely, if the patient corrects the adherence problem, the system can report on average glucose or time in target range. Patients may be shown the possibility of improvement in glucose indicators such as A1c, or laboratory test values such as A1c. This information can be generated by the processor from a model developed by correlating each diabetes management measure with adherence measurements for a particular regimen. A simple model could find a correlation parameter that relates adherence measurements to blood glucose measurements, based on real or simulated population data, and relate it to A1c measurements. More sophisticated models for relating specific patient characteristics, such as tracked regimens and blood glucose profiles, to adherence measurements may also be developed and implemented by the system processor.

In summary of the foregoing, and as an additional example, DGS 100 or one or more of its components (e.g., SCD 102, display device 120, or MDD 152) for parameterizing a patient's medication habits to configure dose guidance settings. ) includes an input component configured to receive the measured analyte data, dietary data and medication data, and a display component configured to visually present the information; and one or more processors coupled thereto. The memory can retain instructions and time-correlated data characterizing the patient's analytes over an analysis period, which instructions, when executed by one or more processors, cause the apparatus to perform a method as shown in FIG. 7B. Run 1000. In one aspect, the drug may be or include insulin.

Method 1000 may include receiving, at 1002, patient dose regimen information for an analysis period by one or more processors. One or more processors may maintain patient dose regimen information in memory for processing. The method 1000 may further include, at 1004, evaluating a measure of consistency between the time-correlated data and patient dose regimen information. In one aspect, patient dose regimen information may be or include a typical fixed drug dose taken at mealtimes and a typical time of day that breakfast is eaten. As a further example, patient dose regimen information may be or include information that defines the frequency of patient compliance to scheduled doses or meals. In some embodiments, a patient dosage regimen may include information regarding the type, amount, and/or timing of dosing. In particular, information regarding dosing schedules for one or more drugs.

Measures of agreement may include any numerical measure for comparing agreement between data sets, such as mean and standard deviation, or interquartile range (IQR). Evaluation of the measure of concordance may include comparison to a fixed or variable threshold. A measure of concordance may also be, for example, a measure of dispersion between time-correlated data and expected data calculated based on patient dose regimen. Variable thresholds can be determined using machine learning or other algorithms. More specific examples are provided above and below herein.

The method 1000 can further include, at 1006, determining dose guidance information based on a measure of concordance. 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 it may be stored in memory for later use. In one aspect, the dose guidance information may be or include dose guidance information as exemplified herein above. Dose guidance may also be information that changes the type, amount, or timing of administration of a particular drug.

Method 1000 may include additional acts 1100, 1200 and/or 1300 shown in FIGS. 7C-7E. Additional operations may be performed by one or more processors of DGS 100 in any operable order, and the presence or absence of one or more operations shown in any figure is dependent on the corresponding other operations shown in that figure. It does not necessarily mean the presence or absence of an action. Instructions for performing method 1000 and/or any one or more of additional acts 1100, 1200, and 1300 may be maintained in memory for execution by one or more processors of the DGS.

As shown in FIG. 7C, operations 1100 of method 1000 may include outputting dose guidance information to a display or other output device at 1102. The method may further include, at 1104, receiving patient dose regimen information from an input component, such as via input from a touch screen of a display device. Alternatively or additionally, the method may include receiving patient dose regimen information at 1106 via transmission from a remote data server.

Method 1000 may further include an operation 1200 for evaluating a measure of consistency, as shown in FIG. 7D. Method 1000 may include classifying each dose of a patient dose regimen into a medication class based on time-correlated data at 1202. The method may include grouping each dose into one of a set of mealtime groups, eg, breakfast, lunch, and dinner, at 1204 based on time of day and/or other factors. Alternatively, the method may include grouping each dose by time period, eg, hourly analysis. The method may include generating dose parameters for the patient, at least in part, at 1206 by applying data for each group of meal periods to a model. In some embodiments, the model may be based on historical data from users or from population surveys. The method may further include, at 1208, storing dose parameters to configure dose guidance settings.

Method 1000 may include further acts 1300, as shown in FIG. 7E. The method 1000 can include accumulating time-correlated data characterizing the patient's analyte over a period of time, eg, 10 days, 14 days, or 21 days, before evaluating the measure of concordance, at 1302. The method includes determining the dose guidance information, at least in part, at 1304, by reducing the dosing recommendation based on detecting an analyte excursion above a lower threshold in the time-correlated data. obtain. Recommendations should be correlated to the dietary doses associated with the excursion.

The method can include, at 1306, determining patient adherence to patient medication regimen information based on the time-correlated data. The method may include determining, at 1308, whether to output the dose guidance parameters based on the measure of consistency. For example, if the measure of concordance indicates sufficient patient adherence, dose guidance information is provided as determined by the DGS. If adherence is slightly inconsistent, DGS provides dose guidance but provides a warning regarding adherence. If there is a discrepancy in adherence, the DGS will provide information to the patient or HCP about what to correct before providing guidance.

In one aspect, at 1310, the method may include outputting dose guidance parameters including a predetermined dose suggestion if the concordance measure indicates an unreliable system configuration, e.g., unreliable data. A predetermined dose suggestion may be retrieved from memory as a fallback if dose guidance is not available depending on the method in use.

Dose guidance may not be automatically initiated if the HCP's participating system configuration during the learning period is tagged as questionable. The DGS 100 can provide the patient with two options: (a) repeat the learning period by addressing the deficiencies noted in the learning/adherence report, or (b) review the results of the learning period at the next HCP visit. Check the output. For the second option, the HCP can configure the system and initiate dose guidance or advise the patient on how to address deficiencies noted in the report and repeat the learning period.

Many HCPs do not have the time to obtain a web portal account and set up a patient dosage guidance system. Therefore, the DGS 100 can provide a very efficient means to facilitate HCP assistance with this configuration. Once the learning period is complete and the system configuration is tagged as suspect, the DGA may display control functions that allow the patient to initiate the following processes that facilitate HCP configuration of the system: .

The next time the patient has contact with the HCP (eg, in person or via telemedicine), the patient can press a button or select an option to initiate this process. The DGA can then display a URL and/or code for the HCP to enter into a web browser. A code can be randomly generated by the DGA and associated with that patient. When the HCP enters a URL into an internet browser, the DGA can open a web page that provides UI controls for entering these codes. The code may be, for example, four characters alphanumeric or any other acceptable format. The DGA may treat this code as valid only for a limited time, such as 5 or 15 minutes. When the HCP enters the code, the DGA checks whether the code is valid and, if so, provides the HCP with access to the patient's dose guidance learning and configuration information. In an alternative embodiment, the HCP is further required to enter a medical license, and the system may only grant access to the HCP if the entered license matches the format requirements for a medical license. The HCP's browser may save the medical license number for subsequent interactions with this web page.

If the HCP has access, the DGA may display a GUI report 1330, an example of which is shown in FIGS. 9A-1 and 9A-2, that includes information 1332 entered by the patient into the DGS 100, and Provides information regarding observed and learned values 1334 of the DGS 100 determined during the learning period. The parameters listed in the report 1330 are dose amount 1342, dose time 1341 (times at which various doses are typically taken or will be taken), dose time range 1344 (start and end dose for each meal dose). time), and correction parameters 1345. Doses 1342 can include basal doses and each meal (breakfast, lunch and dinner) doses. Correction parameters 1345 may include target glucose (mg/dL), pre-meal correction factor (mg/dL/U), and post-meal correction factor (mg/dL/U). Patient input data 1332 includes dose 1342, time of administration 1341 (e.g., basal dose and times at which each meal (breakfast, lunch, and dinner) dose is typically taken), and time range of administration 1344 (e.g., when meal dose is start and end times) that define the time range typically consumed for each meal. Observed or learned values 1334 include the dose 1342 for the basal dose and each meal dose, the time of administration 1341 for the basal dose and each meal dose, the time range of administration 1344 (the observed start and end times of each meal time range). time), and correction parameters 1345 (target glucose, pre-meal correction factor and post-meal correction factor). Report 1330 may also provide information and/or warnings regarding adherence issues, for example, in observation notes 1336 for any or all of the listed parameters. The report may also provide conservative values 1340 for correction parameters 1345 or other parameters listed in the report 1330. For example, if adherence observations indicate underdosing, a warning message can be provided to the HCP indicating that it may be safer to set the initial medication regimen to a lower learning dose (e.g., a conservative value) 1340 . Report 1330 also provides a means for HCPs to manually enter medication regimen parameters or copy values into the report from other appropriate sections, for example by entering values in fields under Initial Therapy 1338 in Dose Guidance. It is also possible to provide means for doing so. The report may include a means for the HCP to approve this initial regimen. When the HCP selects the approval UI function 1337, the system can download this initial dosing regimen to the patient's device and begin dose guidance. The HCP can also decline the treatment suggested in the report 1330 by selecting the treatment refusal option 1339. If a medication regimen parameter is not completely entered or is out of range, an error message may be provided to the HCP. In another alternative embodiment, the control functionality of the DGA is the same process (i.e., allowing the user to enter dose guidance parameters) on the patient's mobile phone, except displaying a URL and code for the HCP to enter. interface). In yet another embodiment, instead of (or in addition to) displaying a URL and code, the system can provide a UI means for entering an email address and the patient can, for example, An email address can be entered, and the system can then send an email to this address with an embedded hyperlink that, when selected by the user receiving the email, A report web page containing patient data and information can be opened on the user's web browser.

In some embodiments, the DGA can provide a status summary of patients under the care of an HCP or treatment facility. The DGA may display a GUI report 1450, an example of which is shown in FIG. Provide information about the patient. Report 1450 includes subject identifier 1452, study type 1454, subject status 1456, pending approval request 1458, time in target range (TIR) 1460, time below low threshold 1462 (e.g., 70 mg/dL (TB70)), high Time above threshold 1464 (e.g., 180 mg/dL (TA180)), % basal dose taken 1466, mean number of bolus doses taken per day 1468, glucose capture (%) 1470, and subjects in the study. Records for multiple objects may be included, including a column or field for the number of days 1472. Filter options can be included in either field, allowing the HCP to reorder the records as desired. Report 1450 includes at least one or more of: time in target range, time below low threshold, time above high threshold, percentage of basal dose taken, and average bolus dose taken per day. , alternatively at least three, alternatively at least four, alternatively at least five, alternatively at least six.

The report 1450 may display a column that includes a subject identifier 1452, such as a subject number or a subject name.

Report 1450 may display a column including the type of study 1454 in which the subject is enrolled. The study may be an exploratory study or other type of study.

The report 1450 can display a column that includes the status 1456 of the object. The status can be either "pre-observation", "observation 1", "observation 2", "regimen approved", "regimen updated" or "study completed". If the status is "Regimen Approved" or "Regimen Updated," the date 1474 the regimen was approved or updated may be listed under the status. In some embodiments, the date 1474 may be grayed out or in a lighter font than the status.

Report 1450 may display a column 1458 regarding the status of the approval request. The approval request field 1458 displays an icon 1476 (for example, an orange icon with an "!" in the center) indicating that approval by the HCP is required if there is an outstanding request awaiting review and approval by the HCP. (triangle). The report can also include a statement 1478 indicating the number of subjects who have a medication regimen that requires approval by the HCP.

The report 1450 may display a column reporting the amount of time the subject's glucose level was within the target range (“time in target range” or “TIR”) 1460, as described elsewhere in this application. can. TIR can be calculated for the number of days the subject has been participating in the study and/or the number of days the subject has been on the current medication regimen.

The report 1450 can display a column that reports times 1462 when the subject's glucose level is below a low threshold, eg, 70 mg/dL (“time below 70 mg/dL” or “TB70”). The time below the low threshold can be calculated for the number of days the subject has been in the study and/or the number of days the subject has been on the current medication regimen. In some embodiments, the low threshold is about 60 mg/dL, alternatively about 65 mg/dL, alternatively about 70 mg/dL, alternatively about 75 mg/dL, alternatively about 80 mg/dL, alternatively about 85 mg/dL, alternatively about 90 mg/dL. It can be dL.

The report 1450 can display a column that reports 1464 times when the subject's glucose level exceeds a high threshold, such as 180 mg/dL (“times above 180 mg/dL” or “TA180”). The time above the high threshold can be calculated for the number of days the subject has been in the study and/or the number of days the subject has been on the current medication regimen. In some embodiments, the high threshold is about 170 mg/dL, alternatively about 175 mg/dL, alternatively about 180 mg/dL, alternatively about 185 mg/dL, alternatively about 190 mg/dL, alternatively about 195 mg/dL, alternatively about 200 mg/dL. It can be dL.

The report 1450 can display a column that reports the ingested basal dose 1466, for example, as a percentage. The % basal dose taken 1466 can be calculated for the number of days the subject has been in the study and/or the number of days the subject has been on the current medication regimen.

Report 1450 may display a column reporting the average number 1468 of bolus doses taken per day. The average number of bolus doses taken per day 1468 can be calculated for the number of days the subject has been participating in the study and/or the number of days the subject has been on the current medication regimen.

The report 1450 can display a column that reports the amount of glucose captured 1470, for example, as a percentage. Glucose capture rate 1470 is calculated from the sensor to the system during a set period of time, e.g., the number of days the subject was participating in the study and/or the number of days the subject was on the current medication regimen (e.g., before titration). can be calculated as a percentage of glucose readings received by

Report 1450 may display a column reporting the number of days 1472 the subject was enrolled in the study.

In some embodiments, the DGA can provide a report that summarizes approved treatments with statistical analysis of glucose levels and doses that differ from the approved treatments. The DGA can display a GUI report 1486, an example of which is shown in FIG. 9I, that provides information about approved treatments 1488 for a particular patient 1496, an AGP plot 1490, a missed dose graph 1492, and Provided along with a graph of user overrides 1494. The clinician 1498 can switch back to the complete list of his patients to view another patient's GUI report 1486.

Approved therapies 1488 include the date the therapy was approved, the type of dosing strategy (e.g., initial, titration, manual override), various insulin doses, and length of treatment period (e.g., in days). , average number of bolus doses per day, number of low glucose events, TIR (%), time below low threshold (e.g. TB70 (%)), time above high threshold (e.g. TA180) (%)), and a table including a column for glucose capture (%). Types of dosing strategies may include, but are not limited to, initialization, titration, and manual override. The various insulin dose types for which amounts are reported include, but are not limited to, basal doses; fixed doses for breakfast, lunch, and dinner; and doses with correction factors for breakfast, lunch, and dinner. The percentages in the table can be calculated for an indicated time period of data, such as the last two weeks of data, the period during which the subject was enrolled in the study, or the period during which the subject was on a particular dosing strategy. .

AGP graph 1490 shows an ambulatory glucose profile (AGP) graph showing hourly 5 glucose readings presented over a "typical" 24-hour day based on all days within the selected time frame. , 25th, 50th (median), 75th, and 95th percentiles are displayed. Alternatively, the AGP may represent other hourly percentiles of glucose readings, such as 10th percentile, 25th percentile, 50th percentile, etc., of glucose readings presented over a "typical" 24-hour day based on all days within the selected time frame. Percentile (median), 75th percentile, and 90th percentile can be displayed. The AGP graph may also include two horizontal lines that mark the boundaries of the target range. For example, a first line may correspond to the lower end of the target range (eg, 70 mg/dL) and a second line may correspond to the upper end of the target range (eg, 180 mg/dL). The first and second lines may also be color coded. Data points below the lower limit may be colored a different color than other data points, for example red, to highlight these data points. In this way, the AGP graph simply illustrates the amount of time spent within the target range (or the amount of readings that fall within the target range) and the amount of time spent outside the target range. Other exemplary AGP graphs include, for example, U.S. Patent Application Publication No. 2018/0235524, U.S. Patent Application Publication No. 2014/0188400, U.S. Patent Application Publication No. 2014/0350369, U.S. Patent Application No. Publication No. 2018/0226150, all of which are hereby incorporated by reference in their entirety for all purposes.

Forgotten doses graph 1492 graphs the percentage of missed doses out of the total number of doses received or administered over a period of time for each of the basal, breakfast, lunch, and dinner doses. This graph can easily indicate to the HCP if a subject forgets a large number of doses of a particular type and recommend corrective action. The graph percentages can be calculated for data for an indicated time period, such as the most recent two weeks of the period during which the subject was enrolled in the study or the period during which the subject was on a particular dosing strategy. The graph may be configurable such that the HCP can select the desired time window from a drop-down menu. Forgotten doses graph 1492 may be a bar graph with dose type (basal, breakfast, lunch, and dinner) on one axis and percent forgotten as a percentage of total dose on the other axis.

User override graph 1494 graphs the percentage of doses for which the subject did not take the recommended dose for each of the basal, breakfast, lunch, and dinner doses out of the total number of doses received or administered. become This graph can easily indicate to the HCP if a subject is ignoring the recommended dosage for a particular type of administration or diet and recommend corrective action. The graph percentages can be calculated for data for an indicated time period, such as the most recent two weeks of the period during which the subject was enrolled in the study or the period during which the subject was on a particular dosing strategy. The graph may be configurable such that the HCP can select the desired time window from a drop-down menu. The user override graph 1494 may be a bar graph with dose type (basal, breakfast, lunch, and dinner) on one axis and user override as a percentage of total dose on the other axis.

In some embodiments, the DGA can provide a graph showing a correlation between the recommended dose and the dose actually taken by the subject. The DGA can display a recommended dose vs. ingested dose GUI 1480, an example of which is presented in Figure 9H, which shows the recommended dose vs. ingested dose at various times during the day. Graph the dose ingested. As seen in GUI 1480, X-axis 1481 can be time in minutes, hours, days, or weeks. In some embodiments, the graph may be from 12:00 AM to 12:00 PM. Graphs may display data accumulated over a period of days, weeks, or months, and dosages may be graphed on the same graph. For example, a graph can include all doses administered during a period of time during which a subject is in a particular study period or following a particular dosing regimen.

The y-axis 1482 can be the difference between the ingested dose and the recommended dose. If the dose taken is the same as the recommended dose, the y-coordinate of that dose is zero and the x-coordinate is the time the dose was administered or ingested by the user. If the ingested dose is X1 units greater than the recommended dose, the y-coordinate of that dose will be X1. (For example, if the dose taken was 2 units more than the recommended dose, the y-coordinate of that dose would be 2). If the dose taken was X2 units less than the recommended dose, the y-coordinate of that dose would be -X2. (For example, if the dose taken was one unit less than the recommended dose, the y-coordinate of that dose would be -1). For example, if a fixed breakfast dose is taken at 7:50 a.m., the corresponding point 1483 on the graph will be (7:50 a.m., 0) if the dose taken is the same as the recommended dose. If the basal dose taken at 9 PM was 2 units more than the recommended basal dose, the corresponding point 1484 on the graph would be (9 PM, 2).

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

In some embodiments, a report 1350 showing details of regimen adherence may also be displayed, as seen in FIG. 9B. Report 1350 can be accessed by the patient, HCP, or other interested party. Adherence report 1350 can include a table 1352 that covers a period of time (eg, one week, or two weeks, or a study period) and lists different analyzes for each of the different types of doses 1351. Different types of doses 1351 can include basal doses, breakfast doses, lunch doses, evening doses, and correction (eg, postprandial correction) doses. Table 1352 provides dose counts of ingested doses 1354, missed doses 1356, requested guidance 1358, ingested doses and The delta mean of the guidance dose 1360 and the delta IQR of the ingested dose and the guidance dose 1362 can be listed.

Definitions and/or descriptions 1364 for each of these categories may be listed below table 1352. The dose count 1354 may be a simple count of the dose type over the relevant time period (eg, the most recent week). Missed doses 1356 may be reported as a percentage and can be calculated by (7-basal count dose)/7 for the most recent week's basal dose. Missed Dose 1356 can be calculated as (Number of Missed Dose Detections with No Associated Dose)/(Number of All Missed Dose Detections). Postprandial correction missed doses 1356 can be calculated as (postprandial correction alerts with no associated dose)/(all postprandial correction alerts). Requested guidance 1358 may be reported in units of insulin and may be calculated as (dose associated with guidance display)/(total dose). The delta mean (intake guidance) 1360 may be reported in units of insulin and can be calculated by calculating the mean difference for the dose associated with the guidance. Delta IQR (Intake Guidance) 1362 can be calculated by calculating the IQR for the dose associated with the guidance. Delta IQR has a standard meaning to those skilled in the art and refers to the difference between the third quartile value and the first quartile value.

Additional metrics 1366 and statistics related to these metrics may also be listed in the report 1350, including late dose frequency, postprandial frequency, automatic vs. manual dose classification, non-meal and non-postprandial corrections. Frequency, Snack Dose Frequency, Percentage of Dose Ingested per Guidance (this may be a dose temporally linked to the guidance, not just aggregated), and Dose Ingested per Alert Contains percentages.

FIG. 9C shows an example plot 1370 associated with clustering analysis to determine meal periods, which can optionally be displayed on the HCP. Graph 1372 plots the cumulative counts of all doses within the training period versus the time period in which the administration occurred (eg, ascending time period). User-entered mealtime range values 1374 may be shown in plot 1370, for example near the x-axis, if available, along with user-entered mealtime midpoints 1376. The rapidly increasing portion of the curve represents each mealtime cluster. In alternative embodiments, the graph may be plotted as a histogram or pie chart of counts. Learned typical mealtimes 1378 may be indicated by vertical lines within plot 1370. Line segment 1378 has a length equal to the amount of insulin injected. If there are multiple injections within 15 minutes, the length will be equal to the sum of all injection volumes. This is necessary because patients may inject multiple times per meal, e.g. the insulin pen the patient is using will have more than the full amount needed to cover the required dose. Less often, if the patient refills the pen (or gets a new full pen) and completes the required dose, or multiple times because some patients require insulin doses that exceed the pen's injection volume capacity. This is the case when insulin is required to be injected, or when some patients find it painful to inject a large amount of insulin in a single injection, so the drug is administered in multiple injections. The learned 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 example plot 1390 associated with meal dose clustering and dosage. Graph 1392 plots each dose for all doses within the training period against the TOD at which the dose was administered. Each dose is indicated by a dot 1394 to indicate a different dose during the same time period. The thickness of the bar associated with a time period can be varied corresponding to the number of doses to indicate when the same dose occurs during the same time period. As seen in the inset window, the dots 1394 and the thickness of the bars allow the viewer to distinguish between different doses taken at the same time of the day. Doses taken within short periods of time, eg, within about 15 minutes, on the same day may be added together (eg, 7.5+1.5=9 units). Additional doses may be indicated by lines 1396 of different colors or types. Other means may be adopted to account for this.

User interface controls may be provided to toggle between displaying results for all data and displaying only results for doses associated with preprandial glucose below a certain threshold, eg, 150 mg/dL. Alternatively, user interface controls may be provided to allow the user to set the threshold.

FIG. 9E shows an example plot 1400 associated with pre-meal correction factor determination. Graph 1402 plots dots 1404 for each insulin dose for all initial meal doses during the training period against the preprandial glucose associated with each dose for a particular meal. The curve of each model fit performed in the learning analysis is overlaid with the associated learned parameters. The model may include a P1 zero slope model (no 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 with 1408 and 1410). The DGA can provide four plots (one for each meal and one overall) as described above.

If the DGS is unable to fully learn the patient's medication regimen, instead of further enabling dose guidance, the DGA may provide a means to share a report on the learning progress with the HCP. Note also that this method can be used to share any type of report. A method 1420 for facilitating efficient access by an HCP to reports generated by DGS 100 while protecting the privacy of patient health data is described in FIG. 9F.

At step 1422, the session with the patient is authenticated by at least one processor of the portable display device. This authentication may be accomplished when the patient logs into the DGA, or by other known authentication methods. Most commonly, this functionality is fulfilled by authentication functionality provided by the patient's smartphone.

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

At step 1428, the at least one processor may provide the ID along with the data necessary to generate the report to a remote report access server that controls access to the report. In step 1430, the HCP may launch a standard web browser on the PC and appropriately enter the URL displayed by the DGA. The report access server can then cause the browser to display a screen that accepts the ID. The HCP can enter an ID, the browser can send this ID to the server, and if the ID matches the ID sent from the DGA and a period of time, say 20 minutes, has passed since the ID was sent from the DGA. The server can send a report to the browser if it is received before the elapsed time.

In another aspect, the method 1420 can also include determining whether the DGS does not meet a concordance condition between the patient's input regimen and the learned regimen. If it is determined that the EMR does not meet the concordance condition, the method may include providing the patient with an option to provide a learning results report to the HCP. In one embodiment, generating the report access ID may be conditional on a determination that the EMR does not meet a match condition.

In another aspect, the method can include a remote server creating a user interface, e.g., a web page, addressed at least in part by the ID to display the report. The report includes a determination of the dosing parameters of the drug administered to the patient at a time during the defined period, and a combination of this determination and the dosing of the drug provided by the patient, as seen, for example, in FIGS. 9A-1 to 9A-2. and a measure of consistency with the information.

An alternative embodiment includes a DGA that provides a user interface means for the patient to directly generate a report for display on the DGA, which report may be shown to the HCP. It is to be understood that each of these steps is optional and not necessarily required in the method.

During the machine learning period of post-learning titration , the insulin dose may not be fully characterized as during the guidance period. Therefore, the initial titration using data from the learning period may differ from the titration during the guidance period. Specifically, the DGS 100 may only titrate the fixed dose and not the CF during the learning period. Additionally, titration may only be performed if a low pattern is detected by the fixed dose titration algorithm. A detected low pattern may trigger a dose reduction in insulin administration prior to that period, as described in the Fixed Dose Titration section above. The GPA algorithm can be used to evaluate fixed meal periods. Bedtime can be defined as the earlier of 6 hours after the fixed dinner administration time or about 6 hours before the fixed breakfast time.

User Feedback During a Learning Period An exemplary embodiment of a method for obtaining user feedback during or after learning a DGA will now be described. During the initial learning phase with the DGA, the user may be prompted for feedback. User feedback can indicate to the user that the system is progressing. The DGA determines whether the dose administered, the analyte history, the patient's behavior or activity, the overall dosing strategy, the specific dose type, and whether the dose type or strategy determined by the DGA (e.g., learned by the system) is correct. The user may be prompted for feedback (e.g., input or confirmation) regarding any aspect of the dosage guidance, such as confirmation of the dose guidance.

During (or after) the learning period, the DGA may output prompts or other indications to the UID 200 requesting user feedback. This feedback may relate to dosing strategies, eg, strategies regarding the type of insulin action (eg, long-acting and/or short-acting or fast-acting). If feedback (or other decisions) indicates the use of a long-acting strategy, the DGA monitors the patient's basal dosing pattern during a first time period, e.g., the first 3 days, and administers each dose or Classification of dosing patterns into single dose or split dose types and/or dosing by time period (e.g., single dose in the morning, single dose in the evening, or split doses (e.g., both morning and evening) ) can be characterized. DGA can also determine trends in dosage (eg, median, mean) and associated dose variation values. From this information, the DGA can formulate expected basal doses. After the first time period, if the actual dose administered (e.g., automatically registered by the MDD 152 or entered by the user) is different than expected, the user may be prompted for feedback. can.

According to one aspect of an embodiment, a user may be prompted in many different situations. For example, the DGA can be configured to detect missed doses, such as when a user fails to administer a basal or bolus dose during a time period in which a previous basal or bolus dose was administered. If a missed dose is detected, the DGA can be configured to prompt the user for input as to whether a basal dose was administered during that time period. According to some embodiments, the DGA can be configured to detect differences in administration timing. For example, the DGA will determine if a user administers a basal dose at a different time than the previous basal dose was administered (e.g., a basal dose that would normally be administered in the morning is administered in the evening). , can be configured to detect this. If such a difference in administration timing is detected, the DGA can be configured to prompt the user for input as to whether the basal doses were administered at different time periods. In another aspect of this embodiment, the DGA can also be configured to detect when an extra dose has been administered. For example, the DGA can be configured to detect changes in the number of basal doses administered per day. In yet another aspect of the embodiment, the DGA is configured such that the dosing strategy on day 1 (e.g., one basal dose is administered) is the same as the dosing strategy on day 2 (e.g., two basal doses are administered). can be configured to detect whether the If a different dosing strategy is detected, the DGA can be configured to prompt the user for input as to whether the user has adopted the dosing strategy used on the second day as the new dosing strategy. In yet another aspect of the embodiment, the DGA can also be configured to detect whether different dosages have been administered. For example, the DGA can be configured to detect whether a first amount of a dose administered during a time period is different (less or greater) than a previous dose administered during a time period the previous day. . If a different dosage is detected, the DGA can be configured to prompt the user for input as to whether the user has changed the dosage.

The user's responses to these prompts either confirm that the DGA has identified the correct pattern (e.g., the user confirms that he forgot to take his morning basal dose but usually takes it); or provide an opportunity for the user to modify the pattern (eg, the user can notify the DGA to adjust the basal dose based on glucose before ingestion).

For rapid-acting insulin dosing strategies, in addition to the prompts described above, the DGA can include prompts for dose classification. Dose categories can include, but are not limited to, bolus, correction, split dose, bolus + correction, and bolus + carbohydrate count + correction classification.

The user may be prompted for administration of fast-acting insulin in a variety of situations. The DGA can be configured to detect whether a non-meal-related dose has been administered. For example, the DGA can be configured to determine whether a dose was ingested during a time period in which no meal was identified or detected. If the DGA detects ingestion of a dose and no meal is detected within the time period of administration (e.g., within approximately 1 hour of administration), the DGA determines the reason the dose was administered (e.g., because the person ate a meal). , lowered glucose, or finished administering a previous meal). The DGA can also be configured to detect when a meal dose does not match a previous meal dose associated with the same meal type. For example, a DGA may require that a bolus dose associated with a first meal type and administered during a time period not be the same as a previous bolus dose associated with a first meal type and administered during that time period the previous day. It can be configured to determine whether If such a difference in bolus doses is detected, the DGA can be configured to determine the reason for the different doses. For example, the DGA can be configured to determine a difference in preprandial glucose values associated with a bolus dose and a previous bolus dose and determine whether the detected difference is a correction. The DGA also provides input to the user about the reason for the difference in bolus doses (e.g., eating less/more and/or correcting for hyperglycemia and/or correcting for other factors). You can also request.

In addition to allowing the DGA to determine what type of fast-acting dose is being taken throughout the day, it can facilitate the timing of anticipating doses. After a learning period in which prompts are not provided, the DGA can provide these prompts to the user when the dose differs from the expected dose in order to improve the DGA's model of the user's dosing strategy.

In both long-acting and fast-acting cases, the DGA may aim to minimize the number of prompts as time passes and the user responds. You can focus on prompting frequently in the early stages and tapering off as repetitive patterns are observed.

Glucose Pattern Analysis and Meal Bolus Titration for MDI Insulin Dosing Therapy An exemplary embodiment of a method for determining meal bolus titration will now be described. Once the system learns (or is configured to) the patient's current dosing strategy, it can provide titration guidance for multiple injection (MDI) dosing regimens. For patients using fixed meal dosing, fixed doses (eg, breakfast, lunch, dinner, snacks, etc.) can be titrated. For patients performing carbohydrate counts, carbohydrate ratios can be adjusted for the same meal or at different times. For patients on empiric dosing, dose titration can be done with each meal. Titration guidance by DGA can provide recommendations for changing doses or carbohydrate ratios in specific directions. The amount of change can be changed at an appropriate rate such as 5%, 10%, 15%, etc., for example. Dosage guidance may also include initiation of dietary administration. For example, if a patient is on Basal+1 (e.g., a lunch dosing regimen) and breakfast shows a pattern of high values, the DGA can provide a recommendation for administering RA insulin at breakfast.

The DGA may require defining administration categories such as time of day (TOD) period, meal type (eg, breakfast), and meal composition (eg, cereal with milk). For example, a dosing category can be a time period defined by the time period associated with meal insulin dosing for the time period. In a further example, the post-breakfast time period begins when a prandial insulin dose is taken between a defined time window, e.g. 5 a.m. to 10 a.m. (after) or when the next meal insulin dose is taken, whichever comes first. one or more postprandial glycemic responses to define whether the postprandial glycemic response is nominal or in need of modification, or to rank one postprandial glycemic pattern as more or less favorable than another. Indicators may be required. The likelihood of low glucose (LLG) index and median glucose can be used to quantify the degree of hypoglycemic risk and hyperglycemic risk, respectively.

U.S. Patent Application Publication No. 2018/0188400 ('400), the entire contents of which are incorporated herein by reference for all purposes, describes glucose pattern analysis (GPA) of embodiments of DGA. ) describes example implementations for deriving and determining risk indicators that can be used in This embodiment utilizes, among other things, central tendency (e.g., mean, median, etc.) and variability data from a multi-day period to develop a risk index that addresses the degree of hypoglycemia risk ("hypo risk"). Determine. This embodiment is summarized herein, and a more comprehensive description of the embodiment and variations thereof can be obtained with reference to the '400 publication.

Alternatives to the embodiments described in the '400 publication are described in U.S. Patent Application Publication No. 2014/0350369, the entire contents of which are also incorporated herein by reference for all purposes. shall be used for reference. For example, instead of using median and variation values, the method can employ any two statistical measures that define the distribution of data. As described in the '369 publication, statistical measures can be based on glucose target ranges (eg, G _LOW = 70 mg/dL and G _HIGH = 140 mg/dL). Common measurements related to target range are time in target range (TIR), time above target (t _AT ), and time below target (t _BT ). If the glucose data is modeled as a distribution (eg, a gamma distribution), t _AT and t _BT can be calculated for predefined thresholds G _LOW and G _HIGH . For a threshold, the algorithm can also define t _{BT_HYPO} , _above which the patient can be determined to be at high risk of hypoglycemia. For example, high risk of hypoglycemia can be defined as t _BT greater than 5% when G _LOW =70 mg/dL. Similarly, an index called t _{AT_HYPER} can be defined, and if t _AT is exceeded, the patient can be determined to be at high risk of hyperglycemia. The degree of risk of hypoglycemia and hyperglycemia can be adjusted by adjusting either _GLOW or _{tBT_HYPO} , _GHIGH or _{tAT_HYPER} , respectively. Any two of the three measures TIR, t _BT and t _AT can be used to define the control grid. These options (and others) can be used to determine the risk index of the DGA embodiments described herein.

The DGA embodiments described herein can operate based on quantitative evaluation of a user's analyte data during the TOD period. This quantitative evaluation can be performed in various ways. For example, embodiments described herein may evaluate multiple days of analyte data to determine one or more indicators that describe the associated risk that the analyte data represents for a corresponding TOD. can. These indicators can be used to classify analyte data during the TOD period into one of several patterns. For example, these patterns can indicate glucose behavior or trends that are common or generalized to that TOD. Any number of two or more patterns may be utilized depending on the DGA embodiment. For ease of reference, these patterns are referred to herein as glucose pattern types, and in the embodiments described herein, three glucose pattern types (e.g., low value patterns, high/low Although we refer to embodiments that utilize only two types, or more than two types (high value patterns), other embodiments may utilize only two types, or more than two types, which are described herein It can be different from what you have.

Taking a fixed meal dose as an example, once the DGA has learned the dosing strategy and the amount of the dose or carbohydrate ratio, it can start the titration evaluation, which can be "at night", "after breakfast", "after lunch" and "after dinner". For each of these categories, the DGA can map the two metrics mentioned above (LLG and median glucose) to four logical "pattern" variables according to the GPA method described below. FIG. 8A illustrates operation of an exemplary method 400 with DGA for evaluating meal bolus titration for multiple injection (MDI) medication therapy. The method 400 begins at 402 with the DGA performing at least one TOD by executing a glucose pattern analysis (GPA) algorithm that receives as input time-correlated analyte data originating from a sensor control device worn by a patient during an analysis period. Determining an analyte pattern type can be included. The method 400 can further include, at 404, the DGA selects an MDI dosing guidance value based on the analyte pattern type and the patient's prescribed dosing strategy for the analysis period by executing a recommendation algorithm. The method 400 can further include, at 406, storing the recommended course of action in computer memory for output by the DGA to at least one of the UID 200 or the MDD 152 that administers the drug to the patient. The UID 200 uses the recommended course of action to control a user interface, for example, by displaying a human readable representation of the guideline on a display or by creating an audio output expressing the guideline in human language. can do. MDD152 can use the guidelines to adjust or maintain the next relevant dose administration. Further details of method 400 are described below.

FIG. 8B is a flowchart illustrating an exemplary embodiment of a GPA method 410 that may be implemented as a GPA algorithm referenced at 402. The method 410 can be performed for an entire day (e.g., a 24-hour period) or for a day defined by blocks of time (e.g., three 8-hour periods) or user activities (e.g., eating, exercising, sleeping, etc.). It may be performed for a particular TOD period, which may be a part. In many embodiments, multiple TOD periods can correspond to eating (eg, after breakfast, after lunch, after dinner) and sleeping (eg, overnight). These TOD periods may correspond to fixed times of the day when activities would normally take place (e.g., from 5 a.m. to 10 a.m. after breakfast), and such time blocks are condition that the meal or activity was actually taken, as determined by automatic detection of the meal or activity, or user instructions indicating the same (e.g., using UID 200). It can be done.

The DGA may perform method 410 independently for each TOD period to obtain an individual pattern estimate for that period. At 412, the DGA can determine central tendency and variation values from the user's analyte data for a particular TOD period. The user's analyte data may be available from the user's own records or the user's medical professional's records, or the user's analyte data may be collected by the DGS 100, for example. The analyte data preferably spans multiple days (e.g., 2 days, 2 weeks, 1 month, etc.) so that there is sufficient data within the TOD period to make reliable determinations. . In other embodiments, the method can be performed in real time on limited data. DGA can use any type of central tendency measure that correlates to the central tendency of the data, including but not limited to the median or mean. Also, ranges of variation that span the entire data set (e.g., from minimum to maximum), ranges of variation that span most of the data but less than the entire data set (e.g., 90 to 90%) to reduce the significance of outliers. percentile to 10th percentile, 75th percentile to 25th percentile), or ranges of variation that target specific asymmetric ranges (e.g., low range variation, which refers to the lower value of the data, e.g. from the central tendency value to Any measure of variation may be used, including, but not limited to, the 25th percentile, the 10th percentile, or a range up to a minimum value. The selection of indicators representing central tendency and variation can vary depending on the implementation.

At 414, the DGA can evaluate a "hypo risk" indicator based on the central tendency value and the variability value. One such methodology for determining hypoglycemic risk is described with respect to FIG. 8C, which depicts an exemplary embodiment of a framework for determining hypoglycemic risk and other indicators. Figure 8C is intended to convey the framework to the reader; can be implemented electronically in a number of different ways, such as.

FIG. 8C illustrates central tendency versus variation (e.g., low range variation). Any number of two or more zones can be used. In this embodiment, the data pair may correspond to a target zone 425 or one of three hypo risk regions: low 426, moderate 428, or high 430. A first hypoglycemic risk function (eg, a curved or linear boundary), referred to as a moderate risk function 422 , distinguishes between a low region 426 and a moderate region 428 . A second hypoglycemia risk function, referred to as high risk function 424, distinguishes between a moderate range 428 and a high range 430. The central tendency and variability data pairs may be evaluated against or compared to zones to determine a hypoglycemia risk index for a corresponding TOD period.

Hypoglycemia risk functions 422 and 424 can be implemented explicitly in the DGA as mathematical functions (e.g., polynomials), or by defining each zone by included pairs, using lookup tables, if-else It can be implemented implicitly by a set of statements, threshold comparisons, etc. Hypoglycemia risk functions 422 and 424 can be preloaded into the DGA, downloaded from trusted computer system 480, or configured by another party, such as the HCP. When implemented in the DGA, hypoglycemia risk functions 422 and 424 can be treated as fixed or can be adjusted by the user or HCP. An exemplary methodology for determining a hypoglycemic risk function is described in the '400 publication.

At 416, the DGA can assess a hyperglycemic risk indicator ("hyper risk") based on the central tendency value. In this embodiment, hyperglycemia risk may be assessed by comparing the central tendency value for a particular TOD period to a central tendency target or threshold 432. The magnitude and/or sign of the difference in central tendency value from target 432 can identify the amount of hyperglycemic risk. For example, if the central tendency value is less than the target 432 (eg, a negative value), a low hyperglycemic risk may exist. Moderate hyperglycemic risk may exist if the central tendency value exceeds the target 432 (eg, a positive value) by less than a threshold (eg, 5 percent, 10 percent, etc.). High hyperglycemic risk may exist if the central tendency value exceeds the target 432 by a value greater than the threshold value. The use of three separate groupings for hyperglycemic risk (eg, low, medium, high) is one example, and any number of two or more groupings can be used.

In other embodiments, the DGA may assess a hyperglycemia risk indicator at 416 prior to the assessment of hypoglycemia risk at 414. Alternatively, in another embodiment, the assessment of hypoglycemia risk at 414 and the assessment of hyperglycemia risk at 416 can be performed in parallel at the same time.

Other indicators such as volatility risk can also be assessed. For example, a variation value less than the first variation threshold 434 may indicate a low variation risk, and a variation value greater than the first variation threshold 434 and less than the second variation threshold 436 may indicate a moderate risk of variation. A volatility risk may be indicative, and a volatility value greater than the second volatility threshold 436 may indicate a high volatility risk. Again, the use of three separate groupings for variable risk is an example. DGA can use any number of two or more groupings.

At step 418, the DGA may determine a pattern type for the TOD period based on the evaluated one or more risk indicators. In an exemplary embodiment, pattern determination can be evaluated using hypoglycemic risk indicators and hyperglycemic risk indicators. If the hypoglycemia risk index is high, the pattern can be set as a low value pattern (or "low" pattern). Otherwise, if the risk of hypoglycemia is moderate and the risk of hyperglycemia is high or moderate, the pattern is a high/low (or moderate) pattern (or "low with some high" or "low with some high"). Otherwise, if the risk of hyperglycemia is high or moderate and the risk of hypoglycemia is low, the pattern can be set as a high value pattern (or "high" pattern). . If both hyperglycemia risk and hypoglycemia risk are low, the identified pattern may be a No Problem (e.g., an "OK" message is displayed and printed) (or a "No pattern" ”).

Thus, method 410 is an example of a method for the DGA to output one of a plurality of pattern types every TOD period. The number of pattern types themselves may be different from those described in this embodiment (eg, low, high/low, high). Once the pattern type for the TOD period is determined, the DGA may store an indication of the pattern type in a memory location for use in determining titration recommendations. Referring again to FIG. 8A, once the DGA completes the GPA for each associated TOD period at 404, it can proceed to determine titration recommendations.

Recommended methods include pattern type (e.g., low, high/low, high), TOD duration, dosing strategy, adherence status to strategy (e.g., underdose or not), and whether sufficient data are available to make the assessment. You can branch out depending on other factors such as availability. The DGA will not make a titration recommendation until sufficient data is available for the corresponding TOD period, e.g. If the number of different days with more than a minimum portion (eg, 90%) is less than a threshold (eg, 5), the evaluation can be skipped and an error message generated.

8D-8H illustrate examples of branching recommendation algorithms or methods for determining dose titration recommendations based on the input information described above. Other branches may also be useful. FIG. 8D shows the TOD for which sufficient data are available and possible causes of the low value pattern type include one or more of the following: basal dose, meal dose, preprandial correction dose, or postprandial dose higher than the optimal amount. A recommended method branch 440 is shown. At 442, the DGA evaluates whether the pattern type for the night TOD period is low. If the pattern is low, at 444 the DGA reduces all relevant doses by the same amount, e.g., by 10%, including at least the basal dose and optionally one or more of a meal dose, a pre-meal correction dose, or a post-meal dose. Generate recommendations for. The titration recommendation rule for the low value pattern can include generating a recommendation to reduce the long-acting insulin dose or basal rate at 444 for the nighttime TOD period. At 446, if other TOD periods are in a low value pattern, the DGA can generate a recommendation at 448 to reduce the fixed meal dose for the relevant TOD period.

In this embodiment, if there is at least one low price pattern, no titration guidance is provided for the TOD period of the high price pattern. The idea here is to emphasize prevention of hypoglycemia and increase the dose only when the risk of hypoglycemia is low during all TOD periods. In addition, in some cases, if the TOD period has a high value pattern, this may be because the previous TOD period had a low pattern and the patient is overeating to compensate for this. Therefore, addressing low value patterns may itself help address subsequent high value patterns. At 449, if the pattern is not high, the process 440 waits or exits without generating a recommendation, or moves to high value pattern evaluation 450.

Therefore, for high/low patterns, the DGA does not generate titration guidance. If there is no TOD period for which titration guidance can be given and data for all time periods is sufficient, the DGA indicates that glucose fluctuations need to be addressed before further titration guidance can be given. Messages can be provided to the patient. The DGA may also provide a report to the patient's HCP to consider alternative medications or treatments that may address glucose fluctuations.

FIG. 8E illustrates operation by the DGA to generate titration recommendations for a high price pattern in the absence of a TOD period for a low price pattern. At 452, if the overnight period has a high value pattern and there are no other periods with moderate risk of hypoglycemia, the DGA increases the long-acting insulin dose or basal rate recommendation at 454. Can be done. At 456, if the overnight period has a high value pattern and there is at least one other non-dinner period that has a moderate risk of hypoglycemia, then at 458, the DGA determines whether any The meal insulin dose associated with the period of time can be reduced. At 460, if the overnight TOD period does not have a moderate risk of hypoglycemia and does not have an elevated pattern, then at 462 the DGA recommends increasing the amount of dietary insulin associated with the first TOD period that has an elevated pattern. can be generated. At 464, if the overnight period has a moderate risk of hypoglycemia and the only postprandial period with a high value pattern is dinner, then at 466 the DGA is used to increase the long-acting insulin dose or basal rate. can generate recommendations. If the overnight period has a moderate risk of hypoglycemia and the after-dinner period does not, then at 462 the DGA recommends increasing the meal insulin dose associated with the first TOD period with a high value pattern. can generate matters.

In alternative embodiments, the premeal glucose may be higher or lower than the target glucose (eg, 120 mg/dL). Glucose data for each meal that contributes to the calculation of hypoglycemic and hyperglycemic risk indicators can be modified to compensate for effects from previous meals or conditions that affect glucose that are not attributable to the current meal. The DGA can correct these data by subtracting an offset so that the resulting starting glucose is at the target level. Alternatively, the DGA can modify these data by a "trigonometric" function in which the difference between the meal start glucose and the target glucose is subtracted for the meal start time; It may be reduced linearly over a period of time (eg, 3 hours) or by another decay function.

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

According to another aspect of embodiments, the algorithm that generates meal bolus titration recommendations considers additional aspects such as, for example, missed meal doses, missed basal doses, postprandial corrections, and premeal corrections. , can be more complex. Algorithms to provide appropriate recommendations when these factors are present, while excluding some data, must still meet a data sufficiency threshold after excluding data to provide guidance. obtain.

For example, referring to FIG. 8F, if a high value pattern is detected at 461 and there are several days of forgotten meal doses, the days of forgotten meal doses are excluded and the GPA analysis 410 is repeated at 463. It can be done. Thereafter, if a high pattern is detected 465, the dose can be increased 467 based on patterns identified in other TODs, or further input or return can be awaited at 469. Alternatively, the system can evaluate only high value patterns using data that excludes days when meal doses were missed. An algorithm 470 with this branching pattern is shown in FIG. 8F. If the system detects a low value pattern at 473, the low value pattern algorithm 472 described in the next paragraph may be executed. If the system does not detect a high or low value pattern, it may return to block 469 for further input or return.

At 472, if a meal dose is missed, the DGA detects a pattern of low values during the TOD period, and if a meal dose is missed on any number of days during this TOD, the DGA makes a recommendation to reduce the dose. can be generated. Recommendations may include, for example, reducing the fixed dose or corrected dose portions.

Regarding forgotten basal doses, if the DGA detects a low value pattern 473 in the overnight TOD, then forgotten basal doses should not affect the dose titration logic. Similarly, if a pattern of low values is detected in the non-nighttime TOD, forgetting basal doses should not affect the dose titration logic.

If the DGA detects a pattern of high 461 in TOD using data that includes at least one day (or TOD) on which a basal dose is forgotten, then the Data can be excluded 463 and pattern analysis 410 repeated. Subsequent actions may depend on the particular TOD where the high price pattern was detected. For example, if the DGA detects a pattern of high values in the overnight TOD for data that includes at least one day in which a basal dose is forgotten, it may exclude data from any day in which a basal dose is forgotten and repeat the pattern analysis. Can be done. If a pattern of high values is detected in the nocturnal TOD when excluding days when basal doses are forgotten, the nocturnal TOD results can be used as a guide to adjust the basal dose and the basal dose can be increased. If a pattern of high values is detected in TOD at times other than nighttime, the pattern analysis can be repeated excluding days when basal administration is forgotten. If a pattern of high values is detected when excluding days when basal doses are missed, the dietary doses associated with TODs that have a pattern of high values can be analyzed and titrated as described herein. can. In either case, the logic flow 470 is as illustrated in FIG. 8F.

FIG. 8G shows an example logic flow 474 for formulating recommendations with postprandial corrections. If, after GPA 410, the DGA detects a low TOD pattern 479 for several days including postprandial correction, the following analysis can be used to perform correction or titration of the meal dose. At 475, if the DGA first detects a low value pattern 479, it can first test that sufficient data is available at 487 to exclude data for days without postprandial correction. If sufficient data is not available, the DGA may perform error recovery routines 489, such as displaying an error message. If sufficient data is available, the DGA may repeat pattern analysis 410. Subsequently, if the DGA detects a low value pattern, the postprandial correction dose may be decreased (ie, the correction factor may be increased) at 476, subject to pattern analysis results at other TODs. The meal dose can then be reduced at 477 if the DGA does not detect a low value pattern.

In all embodiments described herein, correction dose modification (eg, titration) in one direction can be accomplished by correction factor modification in the opposite direction. These two parameters have an inverse relationship, such that an increase in the correction dose can be achieved by decreasing the correction factor, and a decrease in the correction dose can be achieved by increasing the correction factor. Thus, in all embodiments described herein, the DGA can recommend or perform corrections, either by modifying the correction factor or by modifying the correction dose. Thus, to the extent that modification or titration of the correction factor is described herein, embodiments can be configured to achieve the same effect by inverse modification of the correction dose, and conversely, the correction To the extent that dose modification or titration is described herein, embodiments can be configured to achieve the same effect by inverse modification of the correction factor. Given this compatibility, both options are available for all embodiments described herein, although not described for all embodiments solely for ease of explanation.

Additionally or alternatively, starting from the original data set 491, at 478 the DGA can exclude days on which meal administrations were missed. After finding sufficient data at 487, if pattern analysis 410 of these data excluding the days on which the postprandial correction was made at 490 does not show a pattern of low values, the DGA recommends reducing the postprandial correction dose at 476 can do. In addition, the DGA has published U.S. Patent Application No. 16/944, published as U.S. Patent Application Publication No. 2021/0050085, the entire contents of which are hereby incorporated by reference for all purposes. The logic 510 of FIG. 10B of No. 736 may be implemented, such that a reduction in either the prandial insulin or preprandial correction portion of the dose guidance may be recommended. If the DGA does not detect a low value at 479 and does not detect a high glucose pattern at 492, it can wait for further input or return at 469. If the DGA detects a high price pattern at 492, process 480 may be performed at block 471 (FIG. 8H).

Referring to FIG. 8H, if the DGA detects a high value pattern 493 for TOD with several days including postprandial correction, the following steps are taken to develop recommendations for correction and titration of the meal dose. 480 can be implemented. At 481, the DGA includes data for days of missed doses and days of postprandial corrections, and pattern analysis 410 can be repeated. Thereafter, if the DGA detects an elevated pattern at 494, the postprandial correction dose can be increased (ie, the correction factor reduced) at 482, subject to pattern analysis results at other TODs. If no high value pattern is detected at 494, check for a low value pattern at 495 and return to 474 of FIG. 8G if a low value pattern is detected, or else wait for further input or return at 469. It's okay. Although not shown in FIG. 8H, after excluding any data in the GPA 410 and before running the GPA, the DGA tests for data sufficiency and performs an error recovery routine if insufficient data is available. may be executed.

Alternatively, or additionally, starting with the original data set at 493, a pattern analysis of the data excluding days on which meal administrations were missed at 483 is performed along either one of branches 2.1 or 2.2. If the value indicates a high price pattern, the DGA may continue with procedure 480 as follows. For branch 2.1, if the pattern analysis 410 of the day's data excluding postprandial correction at 484 (i.e., bolus-only data) does not show a high value pattern at 497, the DGA performs pattern analysis of other TODs. As a condition, a recommendation to increase the postprandial correction dose can be generated at 482. Otherwise, the DGA is shown in FIG. 10C of U.S. patent application Ser. A recommendation to increase either the mealtime insulin or the preprandial correction portion can be generated according to step 550 of .

For branch 2.2, if the pattern analysis of day-only data with postprandial corrections at 485 does not show a pattern of high values at 496, DGA has previously incorporated by reference the entire contents of U.S. Pat. Either the prandial insulin or the preprandial correction portion can be increased according to step 550 of FIG. If a high pattern is not detected at 496, the DGA may return to block 484.

If correction factor titration recommendations from different TODs are conflicting and the patient is currently using the same correction factor on all TODs, the DGA can increase the correction factor. Step 480 may first increase the meal dose if all three components of meal dose, pre-meal correction, and post-meal correction are not optimal. Preprandial corrections can be titrated up after the meal dose has been titrated. Postprandial corrections can be increased by titration after the meal dose and preprandial corrections are completed.

During subsequent analysis, the TOD for which the DGA generated an "increase correction factor" recommendation in the manner described above now becomes a "do not change correction factor" recommendation. Conversely, if a TOD for which a recommendation to "reduce the correction coefficient" is issued using the method described above, and a recommendation to "reduce the correction coefficient" is issued this time, different TODs have been optimized using different correction coefficients. It is considered highly possible.

Although FIGS. 8D-8H illustrate aspects of various recommendation algorithms 404 for use in method 400, it should be understood that these are exemplary. Various other algorithms may also be suitable.

Guidance Period Algorithm Description During the guidance period, the DGS100 (1) provides the user with insulin dose recommendations for meals and postprandial corrections, and (2) provides the user with insulin dose recommendations initially learned from the learning period to improve glycemic control. Dose settings can be titrated.

Insulin dose settings may need to be initialized before starting the guidance period. Initialization can be accomplished by manually entering initialized values if learning is successful or if learning is not successful. Once insulin dose settings are initialized, the user can receive insulin dose advice in multiple ways. A user can request a specific meal dose through the DGA. Alternatively, DGS 100 can detect and notify the user of a forgotten meal event. If the user confirms a missed meal dose event, the DGS 100 will configure the 'Late Meal Dose ” recommendations can be provided. Alternatively, the DGS 100 can detect and notify the user if the glucose between meal administrations is too high. If the user determines that the glucose between meal doses is too high, the DGS 100 may suggest a "postprandial correction dose" to bring the user's glucose to the target value or range before the next meal dose.

The frequency with which the subroutine for dose suggestions is processed may vary. Those subroutines associated with meal administration forgetting and detection of postprandial high glucose may be executed continuously, whereas those subroutines associated with dose calculation are executed only when requested by the user. It's okay. The implementation of when mealtime or postprandial dose suggestions may be invoked may be managed by the state transition diagram shown in FIG. 10. Periodically, the DGS 100 may titrate the fixed dose and correction factor values to improve the user's blood sugar levels. These titration algorithms may be processed daily and may operate independently of the dosing algorithms described above.

Customized Settings In some embodiments, a user can customize certain settings. For example, in some embodiments, a user may indicate that certain data should be ignored by the algorithm. Or if the user is sick or taking a new medication, the user may want to ignore certain periods of time. The DGS may include a "vacation mode," in which the user may have the ability to have the DGA ignore certain past or future days or time periods in which his or her behavior may be or have been anomalous. The DGS may be notified to do so. For example, if a user goes on a cruise or wants to exclude Labor Day weekend, the user can select a period of time that the DGS will ignore when determining titration.

In some embodiments, the user may also adjust the previously selected dosage and time period if there is a change in the user's routine. In some embodiments, if the time at which the user normally eats a meal changes, the user may enter a new time period for the meal so that the DGS can correctly record the meal dose.

In some embodiments, the user can also enter dosage adjustments as a result of new dosing. In some embodiments, this change may only be changed if the user's HCP advises the user to enter the change. For example, the HCP may have prescribed a new oral diabetes medication, which the DGS needs to take into account. For example, it may be necessary to enter a 10% increase in a glucose-elevating agent (eg, a steroid) or a 10% decrease in a glucose-lowering agent (eg, metformin). If such a change occurs, the DGS will immediately adjust the indicated dose and the DFA can continue to learn and adjust based on the user's glucose results after the dose change.

State Transition Diagram of Dosing Algorithm The DGS 100 can recommend two types of fast-acting insulin doses to the user: a meal dose and a correction dose. The meal dose is the dose required by the user to counteract the rise in glucose in response to a meal. The correction dose is the dose recommended by DGS 100 to correct high glucose during meal doses.

The state transition diagram determines when these meal dose and correction dose calculation functions are called. A state transition diagram for managing insulin dosing, also known as a dose guidance state machine (DGSM) 2000, is presented in FIG.

The current state of dose guidance is determined by two factors: the time since the dose was delivered and the recent past dose classification. Insulin administration time is defined by the timestamp that the system receives from the connected insulin pen. Classification relies on rules for defining insulin doses in other parts of the application that account for real-time dose classification.

Meal Dose State Machine 2002 - A standby state, shown as "Meal Dose Guidance Available State" 2004, may be defined as a state in which more than two hours have elapsed since the initial prandial insulin administration. In the Meal Dose Guidance Available state 2004, the user can receive meal dose guidance by querying the DGS 100 for dose recommendations. Once an insulin dose (including timestamp and amount) is received by the DGS 100 and correctly classified as an initial meal dose, it transitions to the "Postprandial Dose State" 2006, where the DGS 100 determines whether the insulin dose reported by the connected pen It remains for a certain period of time following the timestamp, for example 2 hours. In this "postprandial dose state" 2006, meal administration may not be recommended to the user. Additional doses received by the DGS 100 while in the postprandial dose state 2006 may also be considered meal doses and may not affect the time spent in this state.

Correction Dose State Machine 2010 - Criteria for triggering correction dose notifications are defined elsewhere in this application. When any non-prime dose is received and classified as a correction dose, a transition occurs from the Standby (correction-only dose guidance available) state 2014 to the correction-only dose state 2016, where correction-only dose guidance is available. Dose guidance may not be available. DGS 100 may remain in this state for a period of time, such as two hours, from the timestamp reported by the connected pen. While in the post-correction dosing state 2016, correction doses may not be recommended. If any non-prime insulin dose is recorded in the corrected dose state 2016, the timer for a period of time, e.g., two hours, is restarted with the new dose timestamp. Priming before administering an insulin dose removes air from the needle and cartridge, ensuring that the correct amount of insulin is fully administered. The prime dose is part of a safety test that injects a small amount of insulin (eg, 2 units) into the air and the user can watch to see the insulin coming out of the needle tip.

As seen in FIG. 11, in an example method 2020 beginning at step 2022, DGS 100 may receive or otherwise access insulin data for a subject (eg, from MDD 152). For example, the DGA may request delivery information from various sources including, but not limited to, the MDD 152, an MDD-related application, or an interface (e.g., an MDD application web server) that stores the latest insulin delivery information; The latest insulin delivery information can be checked by checking the memories of various applications for insulin delivery information.

At step 2024, DGS 100 may classify the most recent administered drug dose as one of a particular dose category. For example, the most recently administered dose can be a meal dose, a correction dose or a prime dose. The most recent dose administered may be automatically classified by the DGS 100 or manually by the user, as described elsewhere in this application. If the most recent dose is the prime dose, no further treatment is given. If the most recently administered dose was a meal dose or a correction dose, in step 2026, the DGS 100 enters a period during which additional drug dose recommendations may not be displayed. The time period may be about 1 hour to about 3 hours, alternatively about 1.5 hours to about 2.5 hours, alternatively about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours, or about 3 hours. obtain.

At step 2028, the DGS 100 may determine whether at least one additional drug dose has been administered, i.e., after the time at which the previous "most recent drug dose" was administered. If the at least one additional drug dose is administered, in step 2030, the DGS 100 may classify the at least one additional drug dose as either a meal dose or a correction dose. If at least one additional drug dose was a meal dose, no changes are made in step 2032 to the period during which additional drug dose recommendations may not be displayed. However, if the at least one additional drug dose was a correction dose, then in step 2034 the initiation of the period during which additional drug dose recommendations may not be displayed is resumed and the at least one additional drug dose is It can be set to start from the time of administration.

Interaction with the Meal Dose Calculation Algorithm User Interface The DGS 100 can display the Dose Guidance screen only if the Dose Guidance State Machine (DGSM) and Dose Classification State Machine DCSM are up to date. As seen in FIG. 12, in the example method 2040, beginning at step 2042, the DGS 100 may receive or otherwise access insulin data for the subject (eg, from the MDD 152). For example, the DGA may request delivery information from different sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface (e.g., an MDD application web server) that stores the latest insulin delivery information; The latest insulin delivery information can be checked by checking the memories of various applications for insulin delivery information. The DGA may record the dose when the DGA receives insulin dose information (eg, dose and time of administration) electronically.

At step 2044, the DGS 100 may determine whether the received data includes the most recent drug dose data administered, ie, whether the data is "current." To be up-to-date, the DGSM requires that (a) the most recently recorded insulin dose be confirmed by the user, and/or (b) all doses recorded since the reset time (t _reset ) , may need to be classified automatically by the system or manually by the user. Verification may be done manually, for example, the DGS 100 may prompt the user to confirm that the last dose recorded was indeed the last dose. Alternatively, verification may be performed by the DGA interrogating the connected pen to ensure that it has the latest dose information. In some embodiments, display device 120 may be configured to send requests for data (eg, queries) 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 embodiments, for example, display device 120 may be configured to communicate with an insulin pen according to a near field communication (NFC) protocol. In other embodiments, MDD 152 may autonomously transmit data to a reader device via a wired or wireless communication link. The MDD 152 may be configured to transmit on a schedule, based on a triggering event or condition, and/or when within wireless communication range of a reading device. In some embodiments, MDD 152 may be configured to communicate with display device 120 according to Bluetooth or Bluetooth Low Energy networking protocols. However, those skilled in the art will recognize that other wireless communication protocols may be implemented (eg, infrared, UHF, 802.11x, etc.). The DCSM may be reset with _{t_reset} . The reset time (t _reset ) may be set to midnight, or may be a different TOD depending on the timing parameters of the dosing regimen. It is to be understood that each of these conditions is optional and not necessarily required.

At step 2046, if it is determined that the DGS 100 has data related to the most recent drug dose administered, a screen may be displayed. This screen may be the home screen of the DGA containing dose guidance recommendations. This screen may only be displayed for a predetermined period of time after the user confirms the most recent dose. The predetermined period of time can 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 recording is recorded while the Home screen or any of its subordinate flow screens are active, the DGA will exit these screens and display a new user confirmation (if the dose is not automatically classified) and subsequent Flow and logic may be required so that the state machine is updated before the home screen is displayed again.

Dose Classification State Machine The DCSM 2000 has the information necessary to determine how to display the meal carousel icon in the DGA home screen and how the icon functions when selected. As seen in FIG. 13, in an example method 2050, beginning at step 2052, DGS 100 may receive (eg, from MDD 152) or otherwise access insulin data for the subject. For example, the DGA may request delivery information from different sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface (e.g., an MDD application web server) that stores the latest insulin delivery information; The latest insulin delivery information can be checked by checking the memory of various applications for insulin delivery information. Insulin dose data may relate to meal doses administered since the reset time.

At step 2054, DGS 100 may determine whether meal doses received since the reset time have been classified. Meal doses may be categorized as relating to a particular meal, ie breakfast, lunch or dinner. Meal doses may be automatically classified by the DGS 100 or manually by the user, as described with respect to other methods and embodiments described herein.

If it is determined in step 2056 that all meal doses received since the reset time have been classified, the DGS 100 may display a screen that includes multiple meal icons. The plurality of meal icons may include breakfast, lunch, and dinner icons. Each meal icon can have a first appearance or display associated with a first state and a second appearance or display associated with a second state. The first appearance or display can be associated with a first condition in which a meal dose administered since the reset time has been classified as a meal type corresponding to a breakfast, lunch, or dinner icon. The second appearance or display can be associated with a second condition in which meal doses administered since the reset time have not been classified as a meal type corresponding to the breakfast, lunch, or dinner icon. For example, a meal icon (one each for breakfast, lunch, or dinner) may be "shaded" (first state) if an associated meal record exists since that meal's t _reset . When selected, the shaded meal icon displays recorded dose information and may provide an option to display meal dose guidance calculations, if available. If there is no associated meal record within this time period (t _reset ) (second state), the icon is unshaded and, if selected, can display meal dose calculations if available. can. When the time has passed t _reset , all icons are reset and unshaded (second state) and meal dose calculations can be displayed.

The variable t _reset may be determined by regimen parameters, dinner dose time range and breakfast dose time range. If the dinner dose time range is the last meal dose time range before midnight, then t _reset is equal to midnight (which is the most likely case). Otherwise, t _reset should be set to the midpoint 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 is selected and Meal Dose Calculation is displayed, the DGA determines whether the normal Meal Dose Calculation should be used or if the Late Dose Calculations can be determined to be used. To determine which meal dose calculations are appropriate and obtain the associated calculation results, the DGA determines whether the missed dose alerts described elsewhere in this application are in effect. be able to.

The dose guidance displayed may be based on later meal dose calculations in the event of a missed meal dose alert. Furthermore, it is possible that no insulin doses were recorded in the past two hours. It is to be understood that each of these conditions is optional and not necessarily required. Note that the DGA may ensure that all alerts are cleared before dosage guidance is provided to avoid race conditions. For example, a dose may not have been received and the alert may not yet have been cleared until the user is prompted to connect or scan the MDD 152. If the above conditions are not met, the dose displayed by the DGA may be based on normal dietary dose calculations.

As seen in FIG. 14, in the example method 2060, beginning at step 2062, the DGS 100 may receive or otherwise access insulin data (eg, from the MDD 152) for the subject. For example, the DGA may request delivery information from different sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface (e.g., an MDD application web server) that stores the latest insulin delivery information; The latest insulin delivery information can be checked by checking the memories of various applications for insulin delivery information.

At step 2064, the DGA may determine whether insulin doses have been recorded over a period of time, such as two hours. As seen in step 2066, if an insulin dose has been recorded in the past two hours, the DGA may not display a dose recommendation.

If the insulin dose has not been recorded in the past two hours, in step 2068, the DGA may determine whether a missed dose alert is enabled. If a forget alert is enabled or has been issued, in step 2070, the DGA may display a dose recommendation based on a later meal dose calculation. If the missed dose alert is not enabled or has not been raised, in step 2072 the DGA may display a dose recommendation based on normal food dose calculations.

Regular meal dose calculations (i.e., not later meal dose calculations) may be based on recent glucose levels (e.g., scanned glucose or streaming glucose) and are represented by the logic and equations below. May be:
If G _prm ≦G _T
I _guide = I _fixed - IOB (Equation 1)
Else
I _guide = I _fixed - IOB + CorrectionAdj + TrendAdj (Equation 2)
During the ceremony,
G _prm = current scanned glucose value G _T = target glucose (regimen parameter lookup)
I _guide = Insulin dose calculation result I _fixed = Fixed insulin dose associated with the applicable breakfast, lunch or dinner (regimen parameter lookup)
IOB = insulin onboard (calculated by insulin onboard module)
CF _prm = pre-meal correction factor (regimen parameter lookup)
CorrectionAdj=(G _prm - G _T )/CF _prm
TrendAdj = trend adjustment value (based on Kudva, et al. table lookup), C _F = based on CF _prm .

For definitions of glucose trends and TrendAdj by CF _prm , see Kudva, et al., "Approach to Using Trend Arrows in the FreeStyle Libre Flash Glucose", the entire contents of which are hereby incorporated by reference for all purposes. Monitoring Systems in Adults.'' J Endocr Soc. 2018;2(12):1320-1337.

The I _guide calculation can be rounded to the nearest unit of insulin according to standard rounding rules. If the calculated dose is negative, the displayed dose may be set to zero. In some embodiments, CorrectionAdj may be negative if the preprandial glucose is less than the target glucose. Note that in alternative embodiments, the IOB value is only subtracted from the correction and trend adjustment values. For example, if there are no correction and trend adjustment values, Iguide=Ifixed. If there is an adjusted value, IOB is subtracted from it before being added to Ifixed. If the value obtained by subtracting IOB from the adjustment value is less than zero, Iguide=Ifixed.

As an additional safeguard against timeouts described above (see, e.g., step 2046 in FIG. 12), on a schedule or based on a triggering event or condition, and/or when the reading device enters wireless communication. The glucose data received by the DGS may be used to determine whether to calculate and present dietary dose recommendations to the user. In some embodiments, if the user's current glucose at the time of the meal dose calculation request is below a threshold, the DGS may not present the calculated meal dose suggestion to the user. Instead, the DGS may present an alert to the user regarding the current glucose level and suggest raising the current glucose value before administering the insulin dose. In some embodiments, dose guidance is not displayed if the current glucose value is determined to be below a threshold. This additional safety measure is to avoid hypoglycemia due to dosing insulin at too low glucose levels. This threshold may be configurable based on the user's tolerance to hypoglycemia.

IOB (insulin onboard) is a measure of active insulin remaining in a user's bloodstream after injection. IOB is subtracted from the currently calculated dose to account for active insulin from previous injections to avoid insulin-induced hypoglycemia. It is calculated by multiplying the previous dose by the number of minutes that represents the percentage of insulin remaining at that point after the injection. IOB has insulin units (U).

Meal dose calculation may be triggered by a user request for a meal dose in the DGA. The input data stream may include on-demand scan glucose, on-demand scan glucose trend data, on-demand IOB, and late dose checks. Input parameters may be a fixed dose of the requested meal, meal time CF, and target glucose. The output may be a suggestion for mealtime insulin dosing.

Calculating Late Meal Dose Both the normal meal dose and the late meal dose can be calculated when one selects a meal dose within the DGA. Presentation of late meal dose values may depend on the DGS 100 detecting meal administration forget events, as described elsewhere in this application.

Calculation of late meal doses, except that G _prm and TrendAdj may be calculated using continuous streaming glucose data, e.g., streaming glucose once per minute, rather than scanned glucose. It can be the same as the usual dietary dose calculations explained above. For later meal dose calculations, G _prm may be the glucose value at the estimated meal start time and TrendAdj may be determined using the glucose trend at the estimated meal start time. The estimated meal start time may be calculated based on once-per-minute streaming data and may be an output from a meal administration forget detection algorithm described elsewhere in this application. Furthermore, for calculation of a late meal dose, the IOB may be calculated according to the time at which the late meal dose was requested by the user, rather than the estimated meal start time. If the late meal dose calculation is less than the normal meal dose calculation, the DGS 100 may display a lower value.

As an additional safeguard against timeouts described above (see, e.g., step 2046 in FIG. 12), on a schedule or based on a triggering event or condition, and/or when the reading device enters wireless communication. The glucose data received by the DGS may be used to determine whether to calculate and present late meal dose recommendations to the user. In some embodiments, if the user's glucose at the scheduled meal time is below a threshold, the DGS may not present the calculated dose suggestion to the user. Alternatively, the DGS may present a warning to the user to correct for low glucose before dosing insulin in lieu of a dose recommendation. This additional safety measure is to avoid hypoglycemia due to dosing insulin at too low a glucose level. This threshold may be configurable based on the user's tolerance to hypoglycemia.

Late meal dose calculations may be triggered by the user responding to a missed meal notification, the user selects a meal, and scans for glucose. The input data stream may include an estimated meal start time, historical glucose at the estimated meal start time, and IOB at the requested late dose. Input parameters may be the required fixed dose for meal forgetting, mealtime CF, and target glucose. The output may be a suggested mealtime insulin dose for the estimated meal start time.

Dose Calculation Instruction Display When a user selects a displayed dose, a pop-up screen may display an explanation of how the dose was calculated. Dosages may be expressed for two components: I _fixed and I _guide -I _fixed . Further, explanatory text may be displayed according to Table 4.

Meal Tracking and Tagging In some embodiments, a user may be able to tag meals that resulted in fluctuating glucose levels, eg, glucose levels above or below a target range. In some embodiments, based on the tagged meal and associated postprandial glucose data from the multiple tagged meals, the DGS provides a new suggested dose specific to the tagged meal. can do.

In some embodiments, a user may eat the same pancake and egg breakfast at a local restaurant once a week. However, every time I eat a breakfast of pancakes and eggs, my postprandial glucose levels fall outside of the target range. In some embodiments, the DGS may notify the user that the meal dose did not return the user to the target value after the meal and prompt the user to tag or track this meal. The user can enter this meal description tag as a favorite.

Subsequently, after the user has eaten or consumed the same meal, the user can tag that meal and the DGS will display the administered meal dose associated with the tagged meal and the postprandial glucose associated with the meal. Datasets can be associated with this tag. Once the DGS has acquired a sufficient amount of data set, the DGS will determine the specific Dose recommendations can be determined. The DGS may also consider the user's current glucose level at the time the request for a dose recommendation is made in determining the dose recommendation for the tagged meal. The DGS may require data from at least 3 meals, alternatively at least 4 meals, or at least 5 meals before calculating the recommended dose for the tagged pancake and egg meal. The tag may be any food and/or beverage consumed at breakfast, lunch, dinner or snack. A dose recommendation may include multiple components, such as a base dose and a correction dose. The base dose is the user's standard or typical meal dose, and the correction dose can take into account the user's previous response to the same tagged meal. In some embodiments, the correction dose may take into account the user's current glucose level at the time the request for dose recommendation is made.

In an exemplary embodiment, as seen in FIG. 23A, in the method 2400, in a first step 2402, the DGS may prompt the user to enter a tag associated with the meal type. . For example, meal types can include specific foods and foods consumed by the user during a meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda." / Or it may be a description of the drink.

At step 2404, the DGS may receive the entered tag of the meal type instance. The user can enter the description tag for this meal as a favorite. Alternatively, if the meal type is already entered into the DGS, the user can select the tag from a list of favorite meal types, for example.

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

In some embodiments, after a user tags a meal, the DGS can detect that the administered drug dose is different from a drug dose previously administered while consuming the same meal. DGS indicates a significant difference in drug dosage, such as a 1 unit difference, or a 2 unit difference, or a 3 unit difference, or a 4 unit difference, or a 5 unit difference, or a difference of about 1 unit to about 5 units. , or a difference of about 2 units to about 5 units, or a difference of about 3 units to about 5 units, the DGS can prompt the user. In some embodiments, the DGS may prompt the user to create a new tag. The new tag may include instructions on the tag to account for different drug dosages. For example, the new tag can include the amount of food eaten, or another reason why the drug dosage is different (eg, exercised before the meal, etc.). In some embodiments, the DFS updates the user in real time, that is, within 5 minutes, or within 4 minutes, or within 3 minutes, or within 2 minutes, or within 1 minute, after the user tags the meal type. can be prompted.

In an exemplary embodiment, as seen in FIG. 23B, in method 2420, in a first step 2422, the DGS may prompt the user to enter a tag associated with the meal type. . For example, meal types can include specific foods and foods consumed by the user during a meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda." / Or it may be a description of the drink.

At step 2424, the DGS may receive a first input tag for an instance of a first meal type. The user can enter the description tag for this meal as a favorite. Alternatively, if the meal type is already entered into the DGS, the user can select the tag from a list of favorite meal types, for example.

At step 2426, the DGS may associate the first input tag with the amount of the first drug administered for the first meal type instance. In some embodiments, the first input tag can also be associated with a first postprandial analyte data set for an instance of a first meal type.

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

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

At step 2432, the DGS may determine whether the difference between the administered first dose and the second dose exceeds a threshold. If the difference exceeds a threshold, in step 2434, the DGS may prompt the user to enter a modification tag that may account for different doses.

In some embodiments, if the DGS has sufficient data to determine a recommended value, in step 2436, the DGS outputs a recommended dose for the meal type if the difference is less than a threshold. can do.

Forgotten Meal Dose Alert Both the Forgotten Meal Dose Calculator and the Forgotten Meal Alert function use a forgotten meal dose detection algorithm. The missed dose alert feature calls the missed dose detector every minute. When an alert is fired (which may also be referred to as an active alert or activated alert), it may be presented in a lock screen notification or in-app modal, and may be presented in a notification center or notification banner on display device 120. You can also do it. An alert may be dismissed under certain conditions, i.e., if it is determined that the alert should no longer be valid, the alert may become inactive or deactivated, or the display It may be removed from the notification center or notification banner of device 120.

A missed meal dose alert may be issued if the following conditions are met:
- A missed meal condition may have been detected for several consecutive minutes (eg, 5 minutes or 5 consecutive missed meal positive events) or a missed meal alert is currently being issued.

- No insulin dose was recorded within the past 2 hours.

- Insulin dose is not recorded within 45 minutes before the estimated meal start time.

- Estimated meal start time is within the past 2 hours.

- No correction dose alert has been issued.

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

As seen in FIG. 15, in the example method 2080, beginning at step 2082, the DGA may receive streaming glucose data from the sensor control device 102.

In step 2084, the DGA detects a meal administration forget condition for consecutive minutes (e.g., 5 minutes or 5 consecutive meal administration forget positive events) associated with a meal with an estimated meal start time. Whether a meal dose has been forgotten can be determined at the current time. Alternatively, the DGA can also determine whether a missed meal alert is currently being issued (not shown).

If the answer to step 2084 is affirmative, then in step 2086 the DGA may determine whether the insulin dose was recorded within a previous time period, such as within the past two hours.

If the answer to step 2086 is negative, then in step 2088 the DGA determines whether an insulin dose was recorded within a period (e.g., 30 minutes, or 45 minutes, or 60 minutes) prior to the estimated meal start time. can be determined.

If the answer to step 2088 is negative, then in step 2090 the DGA may determine whether the estimated meal start time is within the past two hours, ie, within two hours of the current time.

If the answer to step 2090 is affirmative, then in step 2092 the DGA can determine whether a correction dose alert is currently being issued. The DGA may be prevented from issuing a missed meal dose alert at the same time as issuing a correction dose alert.

If the answer to step 2092 is negative, then in step 2094 the DGA may display or issue a missed meal alert.

It is to be understood that each of the conditions mentioned in the example method above are optional and are not necessarily required for a missed meal alert to be issued.

If a missed meal alert condition is triggered, the DGS 100 may present a lock screen notification to notify the user of the missed meal alert. When the forgotten meal administration alert condition is canceled, the DGS 100 can delete the forgotten meal administration alert notification.

If a missed meal dose alert is currently in effect, the missed meal dose alert may be canceled if the following conditions are met:
- No conditions for forgetting to administer meals have been detected in the past 15 minutes.

- Or one or more of the following conditions are met:
- Insulin doses have been recorded in the past 2 hours.

- Or insulin dose was recorded within 45 minutes before the estimated time of meal start.

- Or the estimated meal start time is more than 2 hours earlier.

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

At step 2104, the DGS 100 may issue a missed meal alert. Conditions under which a missed meal alert may be issued are described elsewhere in this application.

At step 2106, the DGS 100 may determine whether a missed meal condition has been detected for consecutive minutes after the missed meal alert is issued. For example, DGS 100 may determine whether no meal administration forgetting conditions have been detected over the past 10 minutes, or over the past 15 minutes, or over the past 20 minutes.

If the answer to step 2106 is negative, then in step 2108 the DGS 100 can clear the missed meal alert.

If streaming glucose data is not available, for example, if a forget alert condition cannot be computed over the past 14 minutes and then at 15 minutes, a forget alert condition is not computed and fired, the DGS 100 will issue a missed meal dose alert. can be canceled.

As seen in FIG. 16B, in an example method 2110 beginning at step 2112, the DGA receives streaming glucose data from the sensor control device 102 and insulin dose data from the MDD 152 or other methods described above in other embodiments. can be received.

At step 2114, the DGS 100 may issue a missed meal alert. Conditions under which a missed meal alert may be issued are described elsewhere in this application.

At step 2116, DGS 100 may determine whether an insulin dose was recorded within a time period from the current time. This period can be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If the answer to step 2116 is affirmative, then in step 2108, DGS 100 can cancel the missed meal alert.

As seen in FIG. 16C, in the example method 2120, starting at step 2122, the DGA receives streaming glucose data from the sensor control device 102 and receives insulin from the MDD 152 or other methods described above in other embodiments. Dose data can be received.

At step 2124, the DGS 100 may issue a missed meal alert. The conditions under which a missed meal alert is issued are described elsewhere in this application.

At step 2126, the DGS 100 may determine whether an insulin dose was recorded within a period of time from the estimated meal start time. This period can be about 30 minutes, alternatively about 45 minutes, or alternatively about 60 minutes.

If the answer to step 2126 is affirmative, then in step 2128 the DGS 100 can cancel the missed meal alert.

As seen in FIG. 16D, in the example method 2130, beginning at step 2132, the DGA may receive streaming glucose data from the sensor control device 102.

At step 2134, the DGS 100 may issue a missed meal alert, where the missed meal alert is associated with a missed meal having an estimated start time. The conditions under which a missed meal alert is issued are described elsewhere in this application.

In step 2136, the DGS 100 can determine whether the estimated meal start time is within a period from the current time. This period can be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If the answer to step 2126 is negative, then in step 2128 the DGS 100 can clear the missed meal alert.

Meal Administration Forget Condition Detection The meal administration forget condition detection module of DGS 100 received streaming glucose data as input. The meal dose forget condition detection module calls once every minute (e.g., after each minute that streaming glucose is received) to estimate whether a meal dose has been forgotten and, if so, to determine the meal start time. It can be estimated. The meal administration forget condition detection module can also be called as needed to estimate the meal start time of a missed meal.

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

Algorithms describing real-time meal detection and missed meal administration detection are described elsewhere in this application.

Forgotten meal administration detection may be triggered by the DGA detecting a meal event. The input data stream may include detected meal events, estimated meal start times, and most recent insulin doses and timestamps. The output may be a dose and a tagged meal event without notification to the user.

Correction-Only Dose Calculations Within the DGA, postprandial correction-only insulin dose guidance is available if (1) the DGSM has correction-only guidance available and (2) a correction-only dose alert is issued. could be.

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

A correction dose calculation may be triggered by a user responding to a correction dose notification and scan from a correction dose detector. The input data stream may include scan glucose, scan trend arrows, and IOB from the most recent scan. Input parameters can be target glucose and postprandial CF. The output may be a proposed calculation of a corrected dose.

Correct Dose Alert When a Correct Dose Alert is fired (which may also be referred to as an active alert or an activated alert), it can be presented in a lock screen notification or in-app modal and displayed in the notification center or notifications on display device 120. It can also be displayed on a banner. An alert may be dismissed under certain conditions, i.e., if it is determined that the alert should no longer be valid, the alert may become inactive or deactivated, or the display It may be removed from the notification center or notification banner of device 120.

A corrected dose alert may be issued if the following conditions are met:
- A corrected dose condition has been issued for all of the past 5 minutes, or a corrected dose alert is currently issued.

- No insulin dose was recorded within the past 2 hours.

・A forgotten meal administration alert has not been issued (make sure that the correction dose alert and the forgotten meal administration alert do not overlap).

In some embodiments, a corrected 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 a corrective dose alert is issued. It is to be understood that each of these conditions is optional and not necessarily required.

As seen in FIG. 17, in an example method 2140 beginning at step 2142, the DGA receives streaming glucose data from the sensor control device 102 and insulin dose data from the MDD 152 or other methods described above in other embodiments. can be received.

In step 2144, the DGA may determine whether a correction dose condition has been issued for consecutive minutes (eg, 5 minutes or 5 consecutive missed meal administration positive events) at the current time. In some embodiments, a correction dose condition may be a condition indicating that the user requires a correction dose. Alternatively, the DGA can also determine whether a correction dose alert is currently being issued (not shown).

If the answer to step 2144 is affirmative, then in step 2146 the DGA may determine whether the insulin dose was recorded within a previous time period, such as within the past two hours.

If the answer to step 2146 is negative, then in step 2148 the DGA may display or issue a correction dose alert.

If a missed correction dose alert condition is triggered, the DGS 100 may present a lock screen notification notifying the user of the correction dose alert. If the corrected dose alert condition is cleared, DGS 100 may delete the corrected dose alert notification.

The DGS 100 may clear the corrected dose alert if a corrected dose alert is currently issued and any of the following conditions are met:
- No correction dose conditions have been detected in any of the past 15 minutes.

- Or, the IOB calculation has changed since the last time the corrected dose alert condition was calculated.

- Or an insulin dose was recorded within the past 2 hours.

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

At step 2154, DGS 100 may issue a corrected dose alert. The conditions under which a correction alert is issued are described elsewhere in this application.

At step 2156, DGS 100 may determine whether a corrected dose condition has been detected for consecutive minutes after the corrected dose alert is issued. For example, DGS 100 may determine whether a correction dose condition has not been detected over the past 10 minutes, or over the past 15 minutes, or over the past 20 minutes.

If the answer to step 2156 is negative, then in step 2158 the DGS 100 may clear the corrected dose alert.

If streaming glucose data is not available, for example, if a corrected missed dose alert condition cannot be calculated over the past 14 minutes, and then at 15 minutes, a corrected missed dose alert condition is not calculated and issued, the DGS 100: Correction dose alerts can be canceled.

As seen in FIG. 18B, in the example method 2160, beginning at step 2162, the DGA may receive streaming glucose data from the sensor control device 102.

At step 2164, DGS 100 may issue a corrected dose alert at a first time. The conditions under which a correction alert is issued are described elsewhere in this application.

At step 2166, the DGS 100 may determine whether the insulin onboard (IOB) calculation has changed since the first time.

If the answer to step 2166 is affirmative, then in step 2168 the DGS 100 may clear the corrected dose alert.

As seen in FIG. 18C, in the example method 2170, beginning at step 2172, the DGA may receive streaming glucose data from the sensor control device 102.

At step 2174, DGS 100 may issue a corrected dose alert. The conditions under which a correction alert is issued are described elsewhere in this application.

At step 2176, DGS 100 may determine whether an insulin dose was recorded within a time period from the current time. This period can be about 1.5 hours, alternatively about 2 hours, alternatively about 2.5 hours.

If the answer to step 2176 is affirmative, then in step 2178 the DGS 100 may clear the corrected dose alert.

In some embodiments, a user can customize the conditions under which correction dose alerts are presented to the user. In configuring the DGS, the user can set a minimum threshold for correction dose alerts so that the alert is displayed only if the correction dose exceeds the minimum dose. For example, a user may indicate that they wish to receive correction alerts only if the proposed correction dose is at least about 2 units. The minimum dosage can 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 a minimum correction dose prevents the user from being overloaded with alerts (alert fatigue) and allows the user to set a minimum correction dose for conditions that are more severe and require a larger correction dose. You will be able to concentrate only on correction. This allows the user to receive alerts only when the minimum unit amount is required.

Correction Dose Detector The correction dose condition detector can indicate whether the correction dose conditions are correct. The corrected dose condition detector can also provide a corrected dose calculation. The corrected dose calculation can also use streaming glucose data, eg, one minute of streaming glucose. Therefore, a corrected dose calculation may match or match a corrected dose alert condition that also uses streaming glucose.

An exemplary method for determining a correction dose is shown below in pseudocode.

corrected dose==FALSE, but
if t _dose >3 hours and ≦4 hours if G _corr >180mg/dL for the past 60 minutes continuously
if G _trend for the past 5 minutes continuously >0mg/dL/min if I _min -IOB≧0.5
then correction dose required==
True
end
end
end
end
or if _dose >4 hours if G _corr >180mg/dL for the past 60 minutes continuously
if G _trend for the past 5 minutes continuously >0mg/dL/min if I _min +TrendAdjPost-IOB≧0.5
then correction dose required==
True
end
end
end
end
where T _dose = time since last recorded insulin dose (unprimed if this attribute is available)
G _corr = current streaming glucose value (current meaning at initial display)
G _T = target glucose (regimen parameter lookup)
G _trend = current streaming glucose trend value (current meaning at initial display)
I _guide = Insulin dose calculation result I _min = Minimum corrected dose that the user would like to be notified of. This value will be configured by the user during mobile app onboarding.

In the above pseudocode, if correction dose is required == TRUE, the correction dose calculation can proceed as follows:
if t _dose >2 hours and <4 hours I _guide =CorrectionAdjPost-IOB
else if t _dose >4 hours I _guide =CorrectionAdjPost+TrendAdjPost-IOB
else
I _guide =0
end
During the ceremony,
IOB = insulin onboard during dose calculation CF _post = postprandial correction factor (regimen parameter lookup)
CorrectionAdjPost=(G _corr - G _T )/CF _post
TrendAdjPost=CF=Trend adjustment based on CF _post (Kudva table lookup).

Definitions of glucose trends and TrendAdj by CF _post are set forth in Tables 1 and 2, as adapted from Kudva, et al., the entire contents of which are previously incorporated by reference.

The I _guide calculation can be rounded to the nearest integer according to standard rounding rules.

if I _guide ≦I _min , then I _guide =0.

Determination of the correction dose may be performed continuously. The input data stream may include streaming glucose, streaming trend arrows, time since last classified meal dose, and time since last classified correction dose. Input parameters may include a minimum correction dose that may be configured by the user. The output may include a notification to the user.

Interaction of Correction Alerts and Missed Meal Alerts Alert processing for both correction alerts and missed meal administration alerts may be performed every minute using streaming glucose data. To ensure that the missed meal dose alert takes precedence (and thus prevents the correction dose alert from being raised), if the conditions are such that both alerts start firing, the missed meal dose alert process is , may be performed prior to correction dose alert processing.

Insulin Onboard (IOB) Management and Calculations To prevent dose recommendations based on outdated glucose data, the elapsed time between the most recent scan and dose request should not exceed 5 minutes. IOB can be calculated according to the 4.5 hour insulin action time of fast-acting insulin. The IOB value subtracted in Equations 1 and 2 is the % IOB at meal demand multiplied by the previous insulin dose. For example, according to the information in FIG. 19, a dose of 10 U administered at 12:00 has a remaining IOB of 4.7 U at 14:15. Further details are shown in FIG. The current IOB is the sum of each of these IOB portion calculations for each insulin dose less than 4.5 hours.

Duration of insulin action (DIA) is the estimated time that a given insulin injection produces a glucose-lowering effect. DGS100 assumes a DIA of 4.5 hours for fast-acting prandial insulin and 24 hours for long-acting basal insulin. If the DIA of an insulin dose is 4.5 hours, the IOB will be zero 4.5 hours after injection. The unit of DIA is time.

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

Real-Time Meal Detection In many embodiments, the DGA can be configured to detect forgotten meal administration using a real-time meal detection algorithm. Systems and processes are described herein for detecting missed meal administrations in real time and subsequently alerting patients. The process of detecting missed meal administrations can be performed periodically (eg, each time new glucose data is available to the system). Alternatively, the process may be performed whenever it is appropriate to provide a "missed dose" alert to the patient, or whenever an alert is activated. Users can enable or disable alerts if they become bothersome.

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

In one embodiment, each time a new glucose data point is received, the feature extraction module smooths the data by fitting the data within a time window and working backwards from the current data point using a quadratic function. configured to run. The time window may be approximately 60 minutes. The feature extraction module may be configured to store the fitted value at the center of the time window as the current smoothed data. The feature extraction module may also be configured to store the coefficients of the linear and quadratic terms of the fitted value centered on the time window as current glucose rate of change and acceleration values, respectively. In addition to being configured to store the fitted value at the center point, the feature extraction module may also be configured to store the fitted value at the current point for feature extraction. can. The feature extraction module compares the current smoothed glucose value with the previous smoothed glucose value (e.g., the smoothed glucose value immediately before the current smoothed glucose value) to determine the smoothed glucose value. The glucose data may be configured to determine whether the measured glucose data is rising or falling. The feature extraction module extracts a plurality of features and then applies the plurality of features to a meal detection method after the feature extraction module determines that the current smoothed glucose value is elevated in comparison with the previous smoothed glucose value. It can be configured to be passed to a module.

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 the current up segment and the previous down segment. Multiple features extracted from the current ascending segment include: 1) maximum acceleration, 2) time of maximum acceleration point, 3) glucose value at maximum acceleration point, 4) current time point (fitted value) and maximum Height calculated by the difference in glucose values between the acceleration point (reference point), 5) Duration of the current segment calculated by the elapsed time from the reference point to the current point, 6) Sustain height 7) Maximum increase in acceleration (the increase in acceleration at a given time is obtained by subtracting the acceleration at that time from the acceleration at the previous time) , and 8) the increment in area under the curve (average glucose value minus the reference point glucose value and the difference multiplied by the duration of the segment). The multiple features extracted from the previous descending segment include: 1) duration, 2) height, 3) average descending rate (height/duration), and 4) maximum descending rate (maximum absolute value of rate of change). and 5) maximum deceleration (maximum absolute value of acceleration). The feature extraction module may be configured to pass the extracted features to the meal dose module.

The meal detection module may be configured to receive a feature vector as input and output a binary detection result indicating whether the current ascending segment is a meal response glucose excursion. The meal detection module may also be configured to output a probability value along with the binary detection result. In one embodiment, the pre-trained machine learning model in the meal detection module uses a random forest classifier by scikit learn (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). It can be implemented using The meal detection module can be configured to detect the start of a meal based on tree construction rules and feature thresholds for each feature in each tree, which can be optimized during the training process. In one embodiment, pre-trained models can also be built based on alternative classification algorithms including gradient boosting, ADA boosting, artificial neural networks, linear discriminant analysis, and extratrees.

The meal detection model can also be configured to estimate the start time of a meal if a meal is detected. In one embodiment, the meal start time can be estimated as the point in time at which there is a maximum increase in glucose value acceleration, backwards from the point of detection within a time window size of approximately 1.25 hours. For example, if the algorithm detects a forgotten meal at 1:15 p.m., the model can work back to around 12 p.m. to determine the start time of the meal. The acceleration of the glucose value at each point can be calculated by fitting five data points centered on the data point of interest using a quadratic function, y=ax ² +bx+c. The fitted parameter "a" is the acceleration at the point of interest. The increase in acceleration of the glucose value at a certain point in time k may be defined as a(k+1)−a(k).

The meal detection model can also be configured to output a notification to the user about the missed dose to the UID 200 if no mealtime insulin medication is detected within a period before and after the estimated start of the meal. In one embodiment, if the estimated meal start is less than 2 hours, the notification may also indicate that the patient can still obtain meal dosage guidance and that medication will be delayed.

Details of other types of meal detection methods and algorithms are described in U.S. Patent Application Publication No. 2017/0185748 and PCT Application No. PCT/US2020/12134, the entire contents of which are herein incorporated by reference. shall be used for reference.

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

Real-Time Dose Classification To drive the dose guidance state diagram described elsewhere in this application and to track dose matches for dosing reporting, the algorithm uses input from connected pens. Doses can be categorized in real time.

Doses are automatically classified by an algorithm if certain criteria are met. A dose may be automatically classified only as a specific meal dose (breakfast, lunch or dinner) or a correction dose. Automated meal dose and correction dose classification has its own logic for classification.

Automatic real-time dose classification may be performed continuously. The input data stream may include insulin dose timestamps, insulin doses, and previous dose recommendations (meals or corrections). Input parameters may include insulin dose time window. The output may be a dose classified as meal or correction.

Meal Dose For automated meal dose classification, the following criteria may be used to determine whether a given dose can be automatically associated with a particular meal (breakfast, lunch or dinner). The logic is presented below in pseudocode:
If the insulin dose timestamp from the connected pen is within ≦20 minutes of the time the user requested the meal dose in the mobile app &&
・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 &&
- Not ingested in the postprandial state - Then the dose is classified as a dose associated with a meal requirement - Else The dose is classified as an ambiguous dose.

In one embodiment, all of the above conditions must be true for a dose to be automatically classified into a given meal. If any of the above is false, the dose may be marked as an ambiguous dose and the user may be required to manually classify the dose before using the app for subsequent dose guidance. required. This manual classification can be performed via entries in the DGA. In other embodiments, only one or more of these conditions may be met for doses to be automatically classified. It is to be understood that each of these conditions is optional and not necessarily required.

As seen in FIG. 20A, in the example method 2180, starting at step 2182, the DGA can provide dosage recommendation guidance for the meal requested by the user at the request time, and the meal has a meal type. However, the dosage recommendation guidance includes the recommended dosage.

At step 2184, the DGA may receive the user's insulin dose data from the connected insulin delivery device. Insulin dose data may include recent doses including insulin amount and timestamp.

At step 2186, the DGA may determine whether the timestamp of the most recent dose is within a period from the request time. This time period can be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

At step 2188, the DGA may determine whether the amount of insulin in the most recent dose is the same as the recommended dose in the dietary dose guidance recommendation.

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

At step 2192, the DGA may determine whether the most recent dose was taken while the user was in a postprandial state. If the recent dose was taken while the dose guidance state machine was in the postprandial dose state (i.e., the previous dose was associated with a meal and triggered a transition to the postprandial dose state), then the recent dose is May be associated with the same meal as the previous dose.

In step 2194, the DGA classifies the recent dose as associated with the meal type of the recent meal if the answers in steps 2186-2190 above are positive and the answer in step 2192 is negative. Can be done. If any of the above conditions are not met, the DGA may classify the recent dose as an ambiguous dose (see step 2196) and prompt the user to manually classify the recent dose.

As an example, if a user requests a lunch dose at 12:00 pm and receives a dose consistent with the automatic classification logic described above, it may be classified as a lunch dose. This classification may trigger a transition from the standby state to the postprandial dose state at times starting from the injection time. The algorithm may remain in the postprandial dose state for 2 hours. If another dose is received at 1 pm (while the user is in the postprandial dose state), it may be automatically labeled as the lunch dose. Of note, when in the postprandial dose state, the algorithm may not provide dose guidance to the user. The doses received during this period can be based solely on the user's discretion.

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

Correction Dose For automated correction dose classification, all of the following criteria must be true for a given dose to be automatically classified as a correction dose. The logic is presented below in pseudocode.

If the dose was delivered within ≦20 minutes of the corrected dose recommendation in the mobile app &&
・The dose is exactly the same as the recommended dose &&
- Else The dose is classified as an ambiguous dose.

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

As seen in FIG. 20B, in the example method 2200, starting at step 2202, the DGA may provide dose recommendation guidance for correction at a first time.

At step 2204, the DGA may receive the user's insulin dose data from the connected insulin delivery device. Insulin dose data may include recent doses including insulin amount and timestamp.

At step 2206, the DGA may determine whether the timestamp of the most recent dose is within a time period from the first time. This time period can be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

At step 2208, the DGA may determine whether the insulin amount of the most recent dose is the same as the recommended dose of the corrected dose guidance recommendation.

At step 2210, the DGA may classify the recent dose as a correction dose if the answers to steps 2206-2208 above are positive. If any of the above conditions are not met, the DGA may classify the recent dose as an ambiguous dose (see step 2212) and prompt the user to manually classify the recent dose.

Since a correction dose may only be recommended while the system is in the standby state, this classification may trigger a transition from the standby state to the corrected dose state at times starting from the injection time. There is. This classification can clear any outstanding missed meal administration or correction notifications presented to the user.

As seen in FIG. 20C, in the example method 2220, starting at step 2222, the DGA may provide dose recommendation guidance at a first time.

At step 2224, the DGA may receive the user's insulin dose data from the connected insulin delivery device. Insulin dose data may include recent doses including insulin amount and timestamp.

At step 2226, the DGA may determine whether the timestamp of the most recent dose is within a time period from the first time. This time period can be about 15 minutes, alternatively about 20 minutes, alternatively about 25 minutes, alternatively about 30 minutes.

At step 2228, the DGA may determine whether the amount of insulin in the most recent dose is the same as the recommended dose in the dose guidance recommendation.

At step 2230, the DGA may classify the recent dose as a meal dose or a correction dose. If any of the above conditions are not met, the DGA classifies the recent dose as an ambiguous dose (see step 2232) and prompts the user to manually classify the recent dose in step 2234. be able to.

If a dose is marked as an ambiguous dose, the user may be required to manually classify the dose before using the DGA for subsequent dose guidance. The user may be provided with the following classification options and asked to select one:
- Breakfast dose - Lunch dose - Dinner dose - Correction dose - Eating more than expected from previous meals - Snack dose - Priming dose - Untaken dose.

In some embodiments, the ambiguity within a certain amount of time from the time the user opened the DGA, such as about the past 4 hours, or about the past 4.5 hours, or about the past 5 hours, before the DGA provides dose guidance. It may be necessary to manually categorize all doses. Unclassified ambiguous doses for longer than this period may remain ambiguous. If this period, for example a 4.5 hour window, spans the previous day, the period may be shortened to only the time the user opens the DGA to 12:00:00 AM of the current day.

In some embodiments, the user identifies the ambiguous dose as one of "eaten more than expected from previous meal," "snack dose," "prime dose," or "missed dose." If manually classified, there may be no changes to the dose guidance state machine.

Titration Method A dose guidance system (DGS) 100 (e.g., an SCD 102, a display device, 120, or MDD 152) can be configured using automatic or semi-automatic learning methods that classify and characterize drug doses based on patient input and the patient's glucose pattern analysis (GPA) analyzed over a learning period.

Once the processor of the DGS 100 has learned the patient's current dosing strategy (or is configured with the patient's current dosing strategy), it can administer multiple injections ( MDI) can provide titration guidance for drug therapy. For patients using fixed meal dosing, the system can determine guidance information for titrating fixed doses (eg, breakfast, lunch, dinner, snacks, etc.). For patients who are counting carbohydrates, the carbohydrate ratio can be titrated at these same meals or at different times of the day. Patients using empiric dosing can titrate the dose on a meal basis. Titration guidance by DGA can provide recommendations to change the dose or carbohydrate ratio in a specific direction. The amount of change can be changed at an appropriate rate of change, such as 5%, 10%, 15%, etc., for example. Dose guidance can also include starting a meal dose. For example, if a patient is on a Basal+1 (e.g., lunch dose) regimen) and breakfast exhibits a high pattern, the DGA may provide a recommendation to administer RA insulin at breakfast.

As used herein, insulin dose refers to a fast-acting insulin dose unless the insulin dose is explicitly referred to as "long-acting" or "basal."

In one aspect of the method, several (eg, six) parameters of the DGS 100 provide information for titrating dosing regimen parameters over time while the DGS 100 is in dose guidance mode. The multiple parameters can include, for example, a fixed basal dose, a fixed breakfast dose, a fixed lunch dose, a fixed dinner dose, a fixed pre-meal correction factor, and a fixed post-meal correction factor.

At least one processor of the DGS 100, e.g., a processor of a display device coupled to the memory, the wireless interface to the sensor control device, and the display screen, provides dose guidance in response to the analyte data when executed by the processor. The method 1500 for providing may include instructions for causing the DGS 100 to perform a method 1500 for providing, which is illustrated in FIG. 21A. The method 1500 can include, at 1502, receiving time-correlated analyte data of a patient captured in a buffer over an analysis period. The method 1500 may further include, at 1504, dividing the time-correlated analyte data into discrete time-of-day (TOD) periods. The method further includes, at 1506, determining a corresponding one of the TOD periods based on at least a portion of the time-correlated analyte data and the patient's prescribed dosing strategy for the analysis period by executing the algorithm. may include determining a recommended fixed dose of the drug in the patient. The method may further include, at 1508, storing the recommended fixed dose indicator in computer memory for output to at least one of a user or a drug administration device.

The processor of DGS 100 may initiate a titration method to adjust the fixed dose based on parameters configured by an authorized user (eg, HCP) before the system enters dose guidance mode. The DGS can remain in dose guidance mode for a period of time, eg, 7 days, 14 days, or 21 days.

The DGS100 titrates the first four parameters using the Glucose Pattern Analysis (GPA) titration method as disclosed in U.S. Patent Application No. 16/944,736 and elsewhere in this application. (Fixed Dose Titration), where the time period is demarcated by a fixed meal dose time defined by a regimen initially set by an authorized user. In one aspect, a fixed pre-meal correction factor (CF _pre ) parameter and a fixed post-meal correction factor (CF _post ) may be calculated by the DGS 100 using independent titration methods. DGS 100 may run the titration routine periodically, for example once a day. For example, the DGS 100 can run three different titration routines each day; first it runs the fixed dose routine, then it runs the CF _pre routine, and then it runs the CF _post routine. The results of each routine can affect each other as described below for each routine. FIG. 21B shows the general operation of a method 1600 of using a glucose pattern indicator for correction factor or fixed dose titration. Further details of method 1600 are as described in US patent application Ser. No. 16/944,736.

The method 1600 can include classifying each of the drug doses into a medication class at 1602 based on time-correlated data. The method 1600 can further include grouping each of the doses into one of a set of mealtime groups at 1604. The method 1600 may further include, at 1606, determining a glucose pattern that best fits the time-correlated data, and, at 1608, selecting a glucose pattern indicator based on the glucose pattern. The glucose pattern indicator may be selected from the group consisting of, for example, "HIGH," "LOW," "HIGH/LOW," or "NO PATTERN."

If the certified HCP approves the titration, the DGS 100 can update the dosing regimen to include the titration and use the resulting updated regimen for subsequent dosage guidance. If the authorized HCP does not approve the titration within the next 24 hours, the DGS 100 can cancel the titration approval request and invoke the titration routine at the next scheduled titration. Thereafter, the DGS may issue a new titration approval request. If the certified HCP specifically refuses titration, the DGS will cancel the titration approval request and refrain from invoking the titration routine until a confirmation period of at least several days (e.g., 7 days) has elapsed, after which a new titration can issue a request for approval.

Fixed Dose Titration: DGS uses the GPA titration method to construct time-correlated analyte (e.g., glucose) data with analyte measurements taken at regular intervals over an analysis period, e.g., over a learning period. Measurements at 15 minute intervals can be analyzed. GPS can divide data into discrete time-of-day (TOD) periods. TOD periods can be defined according to typical meal dose time parameters defined by certified HCPs as part of an approved dosing regimen.

Fixed dose titration input: TOD period can be defined as, for example:
Post-Breakfast TOD Period (TODBF): The starting record is the first glucose record after the fixed breakfast administration time. The ending record is the last glucose record before the fixed lunch administration time.

TOD Period After Lunch (TODLU): The starting record is the first glucose record after the fixed lunch administration time. The ending record is the last glucose record before the fixed dinner administration time.

Post-dinner TOD period (TODDI): The starting record is the first glucose record after the fixed dinner administration time. The end record is the last glucose record before bedtime, and the DGS can define bedtime as 6 hours after the fixed dinner dose time or about 6 hours before the fixed breakfast dose time, whichever comes first. can.

Nighttime TOD Period - First Half (TODON1): The total nighttime period can be defined as the time between bedtime and fixed breakfast administration time. The starting record is the first glucose record after bedtime. The ending record is the last glucose record before the midpoint time of the entire night period.

Nighttime TOD Period - Second Half (TODON2): The starting record is the first glucose record following the midpoint of the entire nighttime period. The ending record is the last glucose record before the typical breakfast dosing period.

The DGS 100 may impose the following restrictions on user (patient) and authorized HCP entry of fixed meal administration times, for example: The time between the fixed breakfast administration time and the fixed lunch administration time must be greater than or equal to 3 hours. The time between the fixed lunch dosing time and the fixed dinner dosing time must be at least 3 hours, and the time between the fixed dinner dosing time and the fixed breakfast dosing time must be at least 9 hours. No.

Validity of Inputs Referring to FIG. 21C, a method 1500 for providing dose guidance in response to analyte data can include additional operations 1700 in any operable order or combination, any one of which One can be omitted if not needed or desired. Operation 1700 may include sorting the doses into classes including fixed basal doses, fixed breakfast doses, fixed lunch doses, fixed dinner doses, at 1702, for corresponding TOD periods. Fixed dose titration to determine the need to exclude specific TOD periods requires dose classification (eg, identification of initial meal dose). Fixed dose titration algorithms also require the identification of postprandial correction doses.

A glucose data segment (one of many that may contribute data associated with a TOD period) is only considered valid by the processor of the DGS 100 if certain conditions are met, for example: Can be:
Condition 1: The data segment satisfies the data sufficiency requirement that there are no gaps in the glucose data that are larger than a threshold, eg, gaps that are larger than two consecutive past glucose values. In a related aspect, the operation 1700 can include determining, at 1704, that there are no gaps in the segment of time-correlated analyte data that exceed a predefined threshold as a condition to use in determining the 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 certified HCP when confirming the initial meal dose regimen setting. For example, if the initial meal dose falls within a fixed breakfast dose range, that dose is related to the post-breakfast TOD for analysis. In a related aspect, operation 1700 determines, at 1706, that the segment of time-correlated analyte data has an associated initial dietary dose as a condition for the DGS processor to use in determining the recommended fixed dose. may include.

Condition 3: Data segment has basal dose within the previous day. For example, operation 1700 includes determining, at 1708, that the segment of time-correlated analyte data is associated with a basal fixed dose within the past 24 hours as a condition to use in determining the recommended fixed dose. obtain. Alternatively or additionally, in the afternoon basal regimen, if no basal dose was recorded, the processor may treat subsequent TOD periods: overnight, after breakfast, after lunch, after dinner as not valid. can. Similarly, in the morning basal regimen, if no basal dose is recorded, the subsequent TOD period can be treated as ineffective.

The following events should not affect the validity of a fixed dose titration data segment: a high or low alarm that occurs during the segment; a postprandial correction dose that occurs during the segment; or any other event that occurs during the segment. Insulin dose. If a data segment is not valid, the processor of DGS 100 may exclude the data segment from the data set for the TOD period.

In a related aspect, operation 1700 includes, at 1710, one or more of determining a recommended fixed dose, determining a pre-prandial correction factor, determining a post-prandial correction factor, or determining a manual dose adjustment. may include clearing data for each TOD period in response to the TOD period. For example, the DGS 100's processor determines when fixed dose titration guidance is issued, when preprandial CF titration guidance is issued, when postprandial CF titration guidance is issued, or when a manual dose adjustment is issued. All TOD data buffers can be cleared when either occurs, along with low alarms and postprandial correction dose counters.

Data Processing: The DGS 100 processor can invoke the fixed dose titration analysis once a day. Each day, the TOD data buffer, low alarm counter, and postprandial correction counter may be updated to include only the most recent 14 valid data segments for each TOD 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 can include additional operations 1800 in any operable order or combination, any one of which Can be omitted if not available or desired. Operation 1800 may include determining, at 1802, a glucose pattern for each TOD period based on relevant valid data segments for a set number of days in the past, and determining a recommended fixed dose further based on the glucose pattern. There is. Operation 1800 may further include determining, at 1804, that relevant valid data segments are available for a set number of days in the past as a condition for determining the recommended fixed dose. Operation 1800 may further include determining, at 1806, that the glucose pattern is low based on the count of low alarms that occur during each TOD period.

In a related aspect, the fixed dose may be titrated by the number of hyperglycemic or hypoglycemic instances the user exhibits within a particular TOD period during a given time period. For example, if the DGS records a number of instances of hypoglycemia above a predefined threshold within a week-long TOD period, the insulin dose associated with that TOD can be reduced as a potential remedy. A similar situation may occur to increase the insulin dose if the DGS records a number of instances of hyperglycemia above a predefined threshold within a week-long TOD period. Both the number of thresholds and the time period may be configurable such that the DGS is more or less responsive to instances of glucose dysregulation. The definition and calculation of hypoglycemic or hyperglycemic instances may be based on many different factors including, but not limited to, exceeding a certain threshold and the duration spent below the threshold.

In a related aspect, the processor of DGS 100 can integrate fixed dose and correction factor titration. Operation 1800 includes, at 1808, determining a preprandial correction factor based on the time-correlated analyte data, independent of the determination of the recommended fixed dose, and if both the preprandial correction factor and the recommended fixed dose indicate an increase in dose. , may further include maintaining a preprandial correction factor. Methods for fixed dose titration and preprandial correction factor titration are described above and below. Both titrations may be performed independently and in parallel by the DGS processor. Each module can be checked for data sufficiency and titrated simultaneously daily if appropriate. There may be situations where the fixed dose and preprandial CF are titrated on the same day. Several rules can be applied in situations where both fixed dose and preprandial correction factor titration recommend dose escalation. These rules include, for example, not reducing preprandial CF if fixed-dose titration outputs a low pattern at any time of the day, and fixed-dose titration suggesting any fixed-dose increase. This may include not reducing preprandial CF.

Post-prandial correction factor titration: The processor of the DGS 100 may recalculate the post-prandial correction factor using a predetermined scale factor each time the pre-prandial correction factor changes.

As a further example of how GPA can be used in data processing, the contents of the TOD data buffer may, under certain conditions, e.g., in a glucose pattern analysis for each TOD, have relevant valid data segments on at least seven different days. Results may be affected if needed and pp titration fixed dose titration guidance can only be provided if 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 that occurred during that TOD. As part of the fixed dose titration process, this counter may be checked against a threshold of 4 occurrences. If this threshold is reached or exceeded, the associated low TOD pattern is input to the titration mapping module, regardless of the results of the GPA module. As described above, operation 1800 may further include determining that the glucose pattern is low at 1806 based on the count of low alarms that occur during each TOD period.

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 one of which , can be omitted if not needed or desired. For each postprandial TOD, the DGS 100 may maintain another counter that counts the number of postprandial correction doses that occurred during that TOD. Operation 1900 may include determining a glucose pattern condition at 1902 based on the low alarm count, the postprandial correction count during each TOD period, and the glucose pattern indicator. As part of the fixed dose titration process, the DGS processor may check this counter against a threshold of 4 occurrences. If this threshold is reached or exceeded, depending on the results from the GPA module, the following patterns are input into the titration mapping module: If the GPA indicates a low pattern, a low value pattern is input. If the GPA indicates moderate hypoglycemic risk or a high/low pattern, the high/low pattern is entered; if the GPA shows no pattern or a high pattern, the high pattern is entered.

Consistent with the foregoing, and as an additional example, operation 1900 further provides that, at 1904, the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the result of the GPA analysis is low pattern. may include determining that the glucose pattern is low if the glucose pattern is low. At 1906, the operation determines if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the results of the GPA analysis indicate moderate hypoglycemic risk or a high/low pattern. , the glucose pattern may further include determining that the glucose pattern is high/low. At 1908, the operation further determines that the glucose pattern is high if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the glucose pattern indicator indicates no pattern or high pattern. This may include determining that.

The DGS processor may adjust the fixed dose regimen for basal, breakfast, lunch and/or dinner as follows if the titration module output indicates a dose increase or decrease of any corresponding TOD. The amounts are listed below:
Increase: titrated fixed dose = current fixed dose + Idelta, where Idelta varies depending on Grisk, 2U if Grisk ≦15 mg/dL; Grisk > 15 mg/dL and Grisk 4U if ≦30mg/dL; 6U if Grisk>30mg/dL and Grisk≦60mg/dL; 8U if Grisk>60mg/dL.

Decrease: Titrated Fixed Dose = Current Fixed Dose - 10% of Current Fixed Dose.

In a related aspect, the operation 1800 further determines, at 1808, a preprandial correction factor based on the time-correlated analyte data, independent of the determination of the recommended fixed dose, wherein both the preprandial correction factor and the recommended fixed dose are If indicating an increase, this may include maintaining the preprandial correction factor. The DGS processor can calculate pre-meal correction factors, such as described in US patent application Ser. No. 16/944,736.

Processing: The DGS processor can run the CFpre titration analysis once a day. Each day, the CFpretitration glucose data buffer can be updated to include the latest date of valid glucose pairs from the data segment. Two aspects of processing related to data segment buffers have different requirements: proceeding with the analysis and performing the analysis.

To proceed with the CFpre titration analysis for the day, the data buffer should contain at least a certain threshold number (e.g., 14 valid pairs) of pre-dose and post-dose glucose measurements that meet the following conditions: : Pairs are associated with a new initial meal dose, i.e. these pairs are not used in the approved previous CFpretitration condition and glucose pairs are associated with an initial meal with modified portion size. Associated with the dose, ie, the dose has an additional amount above the fixed dose, with the condition that the pre-dose glucose is greater than the target glucose defined by the approved regimen. To start the analysis, only the aforementioned conditions need to be met. The analysis itself can use all valid data pairs, regardless of whether these pairs meet the aforementioned conditions.

For glucose pairs where the pre-dose glucose value is less than the target glucose defined by the regimen, the pre-dose glucose should be set to the target glucose value before processing. This change need not be made persistently, but only before processing. In some embodiments, if the pre-dose glucose value is less than the target glucose defined by the regimen, the pre-dose glucose is not set to the target glucose value before treatment. This situation represents a scenario in which the user's preprandial glucose value is less than the pre-specified target glucose. Preprandial corrections to meal doses therefore reduce overall meal doses and are negative for avoiding hypoglycemia.

Glucose pairs can be clustered according to the meal class (breakfast, lunch or dinner) with which each glucose pair is associated. Post-dose mean glucose values are calculated for each meal class. The meal class mean value may be subtracted from each post-dose glucose value in the respective cluster. Regression can then be performed on the data from all corrections.

CFpretitration can be performed in two alternative modes: rapid mode and stable mode. Transitions between these two modes may be controlled by mode transition logic. Both may require performing a linear regression on the pre-dose versus post-dose glucose pairs obtained from the CFpre titration buffer. Linear regression can produce two results: a slope estimate and a corresponding p-value to determine whether the estimate is statistically significantly different from zero.

Rapid Mode: This mode may be used when the system first starts during the guidance learning period and is executed daily until the mode transition logic described below is met. In this mode, the CFpre titration output can be determined by the slope estimate only. For example, if slope > 0, then CFpre decreases; else, if slope ≦0, then CFpre increases.

Mode Transition Logic: During rapid mode, each approved CFpre titration may be stored in a buffer. This buffer may be checked each time a rapid mode CFpre titration is issued. If the last five titration changes vary continuously between two CFpre values, the titration scheme may transition to stable mode.

Stable Mode: This second mode can be executed daily after the mode transition logic is satisfied. In this mode, the same logic can be followed as in rapid mode, except for the additional criterion of changing the CFpre value only if p-value < 0.05.

CFpre titration calculation: If the CFpre titration analysis indicates a change approved by the certified HCP, the resulting CFpre value can be calculated as follows; if the CFpre value increases, CFpre = CFpre, old* 1.33; or if the CFpre value decreases, CFpre=CFpre, old/1.33. CFpre values should be reduced only if fixed dose titration analysis performed on the same day results in valid pattern findings for all TOD periods and none of the patterns are low patterns.

Various aspects of the present subject matter are described below as a discussion of and/or supplement to previously described embodiments, with emphasis here on the interrelationship and compatibility of the embodiments. In other words, emphasis is placed on the fact that each feature of the embodiments can be combined with each other feature, unless explicitly stated otherwise or unless logically impossible. . The embodiments described herein are reproduced and expanded upon in the following paragraphs without explicit reference to the figures.

Systems, devices and methods are provided for determining drug doses for a patient or user. Dose determination can take into account the patient's or user's recent and/or past analyte levels. Dose determination may also consider other information about the patient or user, such as physiological information, dietary information, activity and/or behavior. A number of different dose determination embodiments are presented for a wide variety of different aspects of systems or environments in which the embodiments can be practiced. Systems, devices and methods are provided for displaying information related to glucose levels, including time in target range, and graphs of analysis values including identification of pattern types for segments of the day.

Many systems describe a device for parameterizing a patient's medication habits to configure dose guidance settings, the device being configured to receive measured analyte data, dietary data and medication data. a display component configured to visually present information, coupled to the input, the display, and a memory for storing time-correlated data characterizing the patient's analyte over the instruction and analysis period. one or more processors, the instructions, when executed by the one or more processors, cause the apparatus to receive patient dose regimen information for an analysis period and to combine the time-correlated data with the patient dose regimen information. and determine dose guidance information based on the measure of agreement.

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

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

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

In some systems, patient dosage regimen information includes typical fixed dosages taken at meals and typical times of day when breakfast is eaten.

In some systems, patient dose regimen information includes information that defines frequency of patient compliance with scheduled doses or meals.

In some systems, the drug includes insulin.

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

In some systems, instructions for evaluating a measure of concordance include classifying each dose of a patient dose regimen into a medication class based on time-correlated data and assigning each dose to a set of mealtime groups. generating dose parameters for the patient, at least in part, by grouping data for each mealtime group into one of the groupings and applying the data for each mealtime group to the model; and storing the dose parameters and dose guidance settings. further comprising configuring.

In some systems, the memory further retains instructions for accumulating time-correlated data characterizing the patient's analyte over a period of time.

In some systems, the memory includes instructions for determining dose guidance information, at least in part, by reducing dosing recommendations based on detecting analyte excursions above a low threshold in the time-correlated data. holds even more. In some systems, dosing recommendations are only fixed dosing.

In some systems, the memory further retains instructions for determining patient adherence to patient dosage regimen information based on the time-correlated data.

In some systems, the memory further retains instructions for determining whether to output dose guidance parameters based on the measure of consistency.

In some systems, the memory further retains instructions for outputting dose guidance parameters including a predetermined dose suggestion if the consistency measure indicates an unreliable system configuration.

Many methods describe methods for facilitating efficient access by healthcare providers (HCPs) to patient electronic medical records (EMRs) generated by dose guidance systems while protecting patient privacy. be done. The method includes, by at least one processor of the portable display device, authenticating a session with the patient and in response to receiving input from the patient indicating a request to share the EMR with the HCP during the session. generating, by one processor, an EMR identification code (ID); and providing, by at least one processor, the EMR ID and data necessary to generate the report to a remote server that controls access to the report. and outputting the report by the at least one processor to a display of the portable display device in response to receiving the EMR ID. In some embodiments, a portable display device may be a device under user control. In some embodiments, the EMR ID may be displayed on a device other than a portable display device. For example, the EMR ID may be sent in a separate message to another device for security reasons.

In some methods, the method further includes providing the EMR to a remote server prior to the step of authenticating.

In some methods, the method further includes receiving an EMR from a dose guidance system.

In some methods, the method further includes determining whether the EMR does not meet a concordance condition with patient input indicative of a tracked drug dose pattern. In some methods, the method further includes providing the patient with an option to provide the EMR to the HCP upon determining that the EMR does not meet the concordance condition. In some methods, the steps of generating, providing, and outputting are conditional on determining that the EMR does not meet a match condition. In some methods, the steps of generating, providing, and outputting are conditional on determining that the EMR does not meet a match condition.

In some methods, the method further includes providing the patient with the option of providing the EMR to the HCP. In some methods, once the remote server receives the EMR ID, it creates a web page addressed at least in part by the EMR ID to display the EMR.

In some methods, the EMR includes determinations of drug dosing parameters administered to the patient from time to time during a defined period of time and a measure of consistency of the determinations with patient-provided drug dosing information. In some methods, the drug is insulin.

Many systems describe systems for providing dosage guidance to a subject. The system includes an input configured to receive dose data from a drug delivery device, the dose data including the most recent drug dose administered and a time of drug administration; one or more processors coupled to the input, the display, and a memory storing instructions, the instructions being executed by the one or more processors; causing the one or more processors to classify the most recent drug dose administered as a meal dose or a correction dose, and in response to determining that a period of time has elapsed from the time of the most recent drug dose administered; Display additional dosage guidance.

In some systems, this period is approximately 2 hours. Reference herein to a time period of about 2 hours includes time periods in excess of 1 hour. A time period of about 2 hours also includes a time period of up to about 6 hours, preferably less than 4 hours.

In some systems, the most recent drug dose administered is a meal dose, the dose data further includes at least one additional drug dose administered after the most recent drug dose was administered, and the time period is at least It does not reset to the time of administration of one additional drug dose.

In some systems, the most recent drug dose administered is not a prime dose.

In some systems, the time of the most recent drug dose administered is a timestamp from a connected drug delivery device.

In some systems, the most recent drug dose administered is a correction dose, the dose data further includes at least one additional drug dose administered after the most recent drug dose administered, and the beginning of the period is , reset to the administration time of the at least one additional drug dose administered.

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

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for providing dosage guidance. The method includes the steps of receiving, by an electronic device, drug dose data for a subject from a drug delivery device, the drug dose data including the most recent drug dose administered and the time of drug administration; classifying the most recent drug dose administered as a meal dose or a correction dose; and displaying dose guidance in response to determining that a period of time has elapsed since the time of the most recent drug dose administered. .

In some methods, this period is about 2 hours.

In some methods, the most recent drug dose administered is a meal dose, the drug dose data further includes at least one additional drug dose administered after the most recent drug dose was administered, and the drug dose data further includes at least one additional drug dose administered after the most recent drug dose was administered, is not reset at the time of administration of the at least one additional drug dose.

In some methods, the most recent drug dose administered is not a prime dose.

In some methods, the time of the most recent drug dose administered is a timestamp from a connected drug delivery device.

In some methods, the most recent drug dose administered is a correction dose, and the drug dose data further includes at least one additional drug dose administered after the most recent drug dose administered, and the drug dose data further includes at least one additional drug dose administered after the most recent drug dose administered; is reset to the administration time of the at least one additional drug dose administered.

Many systems describe systems for providing dosage guidance to a subject. The system includes an input configured to receive dose data from the drug delivery device, the dose data including data related to recent drug doses administered; one or more processors coupled to the input, the display, and a memory storing instructions, the instructions being executed by the one or more processors; causing the one or more processors to determine whether the most recent drug dose administered is the most recent drug dose administered; in response to the determination that the dose guidance recommendations are met.

In some systems, the most recent drug dose administered is determined to be the most recent drug dose administered upon confirmation from the user.

In some systems, the dose data further includes data related to at least one drug dose administered since the reset time, and the instructions further include data related to at least one drug dose administered since the reset time. A screen is displayed in response to determining that one drug dose has been classified. In some systems, dose data related to at least one drug dose administered since a reset time is automatically categorized. In some systems, the system further comprises a drug delivery device configured to deliver at least one dose of drug to the subject, and the instructions further cause the one or more processors to send one or more doses of the drug to the drug delivery device. The above wireless interrogation signal is transmitted to determine that the most recently administered dose has been received. In some systems, dose data related to at least one drug dose administered since a reset time has been categorized by a user.

In some systems, the instructions further cause the one or more processors to display a prompt for the user to confirm that information related to the most recently administered drug dose is correct. In some systems, the instructions further cause the one or more processors to display a screen containing the dose guidance recommendations for a period of time beginning after confirmation from the user.

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

In some systems, the input is further configured to receive the measured analyte data and the request for dose guidance, and the instructions further direct the one or more processors to: upon receipt of the request for dose guidance. a message for determining whether the glucose concentration is below the low threshold and, in response to determining that the glucose concentration is below the low threshold upon receipt of the request for dose guidance, to address the low glucose level prior to drug administration; Display a screen containing . In some systems, the instructions further cause the one or more processors not to display a dose guidance recommendation in response to determining that the glucose concentration at the time of receiving the request for dose guidance is below a low threshold.

In some systems, the input is further configured to receive the measured analyte data and the request for dose guidance after the meal start time, and the instructions are further configured to send the request to the one or more processors after the meal start time. for determining whether the glucose concentration at the estimated time of the meal is less than the low threshold, and responsive to the determination that the glucose concentration at the estimated time of the start of the meal is less than the low threshold; Display a screen containing a message. In some systems, the instructions further cause the one or more processors not to display dose guidance recommendations in response to determining that the glucose concentration at the estimated meal start time is below a low threshold.

In some systems, the input includes wireless communication circuitry.

In some methods, the input is further configured to receive the measured analyte data and the request for dose guidance, and instructions cause the one or more processors to determine the glucose concentration at the time of receiving the request for dose guidance. is below a low threshold, and in response to the determination that the glucose concentration upon receipt of the request for dose guidance is below the low threshold, includes a message for addressing the low glucose level prior to drug administration. Display the screen. In some methods, the instructions further cause the one or more processors not to display a dose guidance recommendation in response to determining that the glucose concentration at the time of receiving the request for dose guidance is less than a low threshold.

In some methods, the input is further configured to receive the measured analyte data and the request for dosage guidance after the meal start time, and the instructions are further configured to cause the one or more processors to for determining whether the glucose concentration at the estimated time of the meal is less than the low threshold, and responsive to the determination that the glucose concentration at the estimated time of the start of the meal is less than the low threshold; Display a screen containing a message. In some methods, the instructions further cause the one or more processors not to display a dose guidance recommendation in response to determining that the glucose concentration at the estimated meal start time is below a low threshold.

Many methods provide methods for providing dosage guidance. The method includes the steps of receiving, by an electronic device, dose data of a user from a drug delivery device, the dose data including data related to recent drug doses administered; determining whether the most recent drug dose is the most recent drug dose administered; and in response to determining that the most recent drug dose administered is the most recent drug dose administered; displaying a screen containing recommendations.

In some methods, the most recent drug dose administered is determined to be the most recent drug dose administered upon confirmation from the user.

In some methods, the most recent drug dose administered is administered after the reset time, and the method further includes determining whether the most recent dose administered after the reset time has been classified. In some methods, recent doses administered after the reset time are automatically classified.

In some methods, the method further includes transmitting one or more wireless interrogation signals to the drug delivery device to determine that the most recent dose administered has been received.

In some methods, the most recent doses administered after the reset time are categorized by the user.

In some methods, the method further includes displaying a prompt for the user to confirm that the information related to the most recent drug dose administered is correct.

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

Many systems describe systems for providing dosage guidance to a subject. The system includes an input configured to receive dose data from the drug delivery device, the dose data including data related to at least one meal dose administered since a reset time; a display configured to visually present a plurality of meal icons; and one or more processors coupled to the input, the display, and a memory storing instructions, the instructions being one or more. causes the one or more processors to determine whether at least one meal dose administered since the reset time has been classified; In response to the determination that the food has been classified, a screen including a plurality of meal icons is displayed.

In some systems, the instructions cause one or more processors to determine whether at least one meal dose administered since the reset time was classified as one of breakfast, lunch, or dinner.

In some systems, the plurality of meal icons include a breakfast icon, a lunch icon, and a dinner icon, and each of the breakfast, lunch, and dinner icons includes a first appearance and a second appearance. In some systems, the first appearance includes a first appearance in which one of the at least one meal dose administered since the reset time is classified as a meal type corresponding to a breakfast icon, a lunch icon, or a dinner icon. The second appearance is associated with a condition in which one of the at least one meal dose administered since the reset time is not classified as a meal type corresponding to a breakfast icon, lunch icon, or dinner icon. It is associated with state 2. In some systems, the first appearance includes a shaded appearance. In some systems, the second appearance includes an unshaded appearance. In some systems, the second appearance is brighter than the first presentation. The meal icon may be text or alphanumeric characters, or may be an image. The icon may also change shape between the first appearance and the second appearance.

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

In some systems, the instructions further cause the one or more processors to receive input from a user, the input determining whether the at least one meal dose administered since the reset time is a breakfast dose, a lunch dose, or a dinner dose. Includes classification as a 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 a dinner dose and a time range associated with a breakfast dose.

In some systems, the reset time is midnight.

In some systems, the reset time is approximately halfway 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 drug to the subject.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for providing dosage guidance. The method includes the step of receiving, by the electronic device, drug dose data of the user from the drug delivery device, the drug dose data including data related to at least one meal dose administered since a reset time. and determining whether the at least one meal dose administered since the reset time has been classified; and in response to the determination that the at least one meal dose administered since the reset time has been classified; displaying a screen including a plurality of meal icons.

In some methods, the determining step includes determining whether at least one meal dose administered since the reset time was classified as one of breakfast, lunch, or dinner.

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

In some methods, at least one meal dose administered since the reset time is automatically classified by one or more processors of the reading 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 a dinner dose and a time range associated with a breakfast dose.

In some methods, the reset time is midnight.

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

Many systems describe systems for providing dosage guidance to a subject. The system includes an input configured to receive dose data from a drug delivery device, a display configured to visually present dose guidance, an input, a display, and a memory for storing instructions. one or more processors coupled to the one or more processors, the instructions, when executed by the one or more processors, cause the one or more processors to determine whether a missed dose alert is in effect; displaying a dose guidance recommendation based on a normal meal dose calculation in response to a determination that the missed dose alert is not in effect, and based on a later meal dose calculation in response to a determination that the missed dose alert is in effect; Display dose guidance recommendations.

In some systems, the input includes wireless communication circuitry.

In some systems, the instructions further cause the one or more processors to determine whether the dose data received from the drug delivery device includes a dose administered within a time period from the current time; in response to determining that the dose data received from the current time includes doses administered within a time period from the current time, causing a dose guidance recommendation based on a later meal dose calculation to be displayed. In some systems, the instructions include determining that a missed dose alert is in effect and determining that the dose data received from the drug delivery device includes doses administered within a period from a current time. In response to the determination, dose guidance recommendations based on later meal dose calculations are displayed. In some systems, this period is approximately 2 hours.

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

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

In some systems, the regular meal dose calculation and the late meal dose calculation each include a fixed insulin dose associated with the meal, insulin onboard, if the user's current glucose level is above the target glucose value. based on volume, correction adjustments, and trend adjustments. In some systems, normal meal dose calculations are based on current glucose levels determined from scanned glucose data. In some systems, later meal dose calculations are based on current glucose levels determined from streaming glucose data. In some systems, later meal dose calculations are further based on trend adjustments determined from streaming glucose data. In some systems, later meal dose calculations are based on insulin onboard amounts that are calculated according to the time of request for dose guidance recommendations by the user.

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

Many methods describe methods for providing dosage guidance. The method includes, by an electronic device, receiving drug dose data for a user from a drug delivery device, determining whether a missed dose alert is in effect, and responsive to determining that the missed dose alert is not in effect. , displaying a dose guidance recommendation based on the usual meal dose calculation; and in response to determining that the missed dose alert is in effect, displaying a dose guidance recommendation based on the later meal dose calculation. including.

In some methods, the method includes determining whether the drug dose data includes data for doses administered within a period of time; and the step of determining whether the drug dose data includes data for doses administered within a period of time. and displaying a dose guidance recommendation based on the late meal dose calculation in response to the determination that there is no later meal dose. In some methods, this period is about 2 hours.

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

In some methods, each of the regular meal dose calculation and the late meal dose calculation includes a fixed insulin dose associated with the meal, insulin onboard, if the user's current glucose level is above the target glucose value. based on volume, correction adjustments and trend adjustments. In some methods, the calculation of a regular meal dose is based on current glucose levels determined from scanned glucose data. In some methods, later meal dose calculations are based on current glucose levels determined from streaming glucose data. In some methods, later meal dose calculations are further based on trend adjustments determined from streaming glucose data. In some methods, later meal dose calculations are based on insulin onboard amounts that are calculated according to the time of request for dose guidance recommendations by the user.

Many systems describe systems that provide alerts to subjects. The system is coupled to an input configured to receive streaming glucose data from a sensor control device, a display configured to present an alert, an input, a display, and a memory storing instructions. the one or more processors, the instructions, when executed by the one or more processors, cause the one or more processors to receive a meal for consecutive minutes having an estimated meal start time. By detecting a forgetting condition for meal administration, it is determined at the current time whether the meal dose has been forgotten, and whether the insulin dose has not been recorded within about 45 minutes before the estimated meal start time, and continuously. An alert interface related to the missed meal is displayed in response to detecting a missed meal condition for several minutes and determining that an insulin dose has not been recorded within approximately 45 minutes prior to the estimated meal start time.

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

In some systems, the instructions further cause the one or more processors to determine whether a meal dose has been recorded within about two hours of the current time, and wherein the meal dose has been recorded within about two hours of the current time. In response to the determination that the meal administration has occurred, an alert interface related to the forgotten meal administration is displayed.

In some systems, the instructions further cause the one or more processors to determine whether a correction dose alert has been issued, and in response to the determination that the correction dose alert has not been issued, the instructions may cause the missed meal administration to occur. Display the related alert interface.

In some systems, the system further comprises a sensor control device configured to collect data indicative of the analyte level of interest, the sensor control device including an analyte sensor, and at least one of the analyte sensors. The portion is configured to be in fluid contact with body fluids of the subject.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for alerting a user to a missed meal administration. The method includes the steps of: receiving, by an electronic device, streaming glucose data from a sensor control device; a step of determining whether the insulin dose has been forgotten based on the current time; a step of determining whether the insulin dose has not been recorded within about 45 minutes before the estimated meal start time; and a step of determining whether the insulin dose has been forgotten for several consecutive minutes. displaying an alert interface related to the missed meal administration in response to the detection and determination that an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time.

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

In some methods, the method includes determining whether the meal dose was recorded within about 2 hours of the current time, and responsive to determining that the meal dose was recorded within about 2 hours of the current time. and displaying an alert interface related to the missed meal administration.

In some methods, the method includes determining whether the meal dose was recorded within about 2 hours of the current time, and responsive to determining that the meal dose was recorded within about 2 hours of the current time. and displaying an alert interface related to the missed meal administration.

In some methods, the method includes determining whether a correction dose alert has been issued and, in response to determining that a correction dose alert has not been issued, displaying an alert interface related to the missed meal administration. The method further includes the step of:

Many systems describe systems for managing alerts. The system is coupled to an input configured to receive streaming glucose data from a sensor control device, a display configured to present an alert, an input, a display, and a memory storing instructions. the one or more processors, the instructions, when executed by the one or more processors, cause the one or more processors to issue a missed meal alert, and after the missed meal alert is issued; determining whether a forgotten-to-administer meal condition was detected for consecutive minutes, and in response to the determination that the forgotten-to-administer condition was not detected for consecutive minutes after the forgotten-to-administer alert was issued; Cancels the meal administration alert.

In some systems, the consecutive minutes are 15 consecutive minutes.

In some systems, the missed meal administration condition includes determining that the insulin dose was not administered within a period of time from the estimated meal start time.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for canceling alerts for missed meal administrations. The method includes, by an electronic device, receiving streaming glucose data from a sensor control device, issuing a missed meal alert, and detecting a missed meal condition for consecutive minutes after the missed meal alert is issued. determining whether the missed meal alert has been detected; and resetting the missed meal alert in response to determining that the missed meal condition has not been detected for consecutive minutes after the missed meal alert has been issued. including.

In some methods, the consecutive minutes are 15 consecutive minutes.

In some methods, the missed meal administration condition includes determining that the insulin dose was not administered within a period of time from the estimated meal start time.

Many systems describe systems for managing alerts. The system includes: an input configured to receive streaming glucose data from a sensor control device and a display configured to receive dose data from a drug delivery device; and one or more processors coupled to the memory storing instructions, the instructions, when executed by the one or more processors, causing the one or more processors to receive a missed meal alert at a current time. is issued, it is determined whether the insulin dose was recorded within approximately 2 hours from the current time, and in response to the determination that the insulin dose was recorded within approximately 2 hours from the current time, the missed meal administration alert is canceled. let

Many methods describe methods for canceling alerts for missed meal administrations. The method includes, by an electronic device, receiving streaming glucose data from a sensor control device, issuing a missed meal dose alert at a current time, and determining whether an insulin dose was recorded within about 2 hours from the current time. and in response to determining that the insulin dose was recorded within about two hours of the current time, canceling the missed meal alert.

Many systems describe systems for managing alerts. The system includes: an input configured to receive streaming glucose data from a sensor control device and dose data from a drug delivery device; a display configured to present an alert; and one or more processors coupled to the memory for storing instructions, the instructions, when executed by the one or more processors, causing the one or more processors to receive meal forgetting information having an estimated start time. issue an alert related to forgotten meal administration, determine whether the insulin dose was recorded within about 45 minutes from the estimated meal start time, and determine that the insulin dose was recorded within about 45 minutes from the estimated meal start time. In response, the meal administration forgotten alert is canceled.

Many methods describe methods for canceling alerts for missed meal administrations. The method includes the steps of: receiving, by an electronic device, streaming glucose data from a sensor control device; and issuing a missed meal administration alert associated with a missed meal having an estimated start time; and within about 45 minutes of the estimated start time of the meal. The method includes determining whether the insulin dose was recorded and, in response to determining that the insulin dose was recorded within about 45 minutes of the estimated meal start time, canceling the missed meal alert.

Many systems describe systems for managing alerts. The system is coupled to an input configured to receive streaming glucose data from a sensor control device, a display configured to present an alert, an input, a display, and a memory storing instructions. the one or more processors, the instructions, when executed by the one or more processors, instruct the one or more processors to perform a meal administration related meal forgetting event having an estimated start time at the current time. An alert is issued, a determination is made as to whether the estimated meal start time is within approximately 2 hours from the current time, and in response to determination that the estimated meal start time does not occur within approximately 2 hours from the current time, the meal is activated. Cancel the missed dose alert.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for canceling alerts for missed meal administrations. The method includes the steps of: receiving, by an electronic device, streaming glucose data from a sensor control device; and issuing, at a current time, a missed meal administration alert associated with a missed meal having an estimated start time; a step of determining whether it is within about 2 hours from the current time; and a step of canceling the meal administration forgotten alert in response to the determination that the estimated meal start time has not occurred within about 2 hours from the current time. include.

Many systems describe systems for providing dosage guidance to a subject. The system includes an input configured to receive dose data from an insulin delivery device, a display configured to visually present dose guidance, an input, a display, and a memory for storing instructions. and one or more processors coupled to the one or more processors, the instructions, when executed by the one or more processors, determining whether a correction dose alert has been issued to the one or more processors at a current time. determine whether a dose of insulin has not been administered to the subject within approximately 2 hours from the current time, determine that a correction dose alert has been issued, and determine whether a dose of insulin has been administered to the subject within approximately 2 hours from the current time. In response to the determination that the dosage is not corrected, corrected dose guidance is displayed.

In some systems, the instructions further cause the one or more processors to determine whether the correction dose alert has been disabled, and in response to determining that the correction dose alert has been disabled, provide correction dose guidance. Display.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for recommending correction doses. The method includes the steps of: receiving, by an electronic device, insulin dose data for a subject from an insulin delivery device; determining whether a corrected dose alert has been issued at a current time; determining whether the dose has not been administered to the subject; and displaying corrected dose guidance, the corrected dose guidance including determining that the corrected dose alert has been issued and within approximately 2 hours of the current time. is displayed in response to a determination that the insulin dose has not been administered to the subject.

In some methods, the method further includes determining whether the corrected dose alert is disabled, and the corrected dose guidance is displayed only if the corrected dose alert is disabled.

Many systems describe systems for providing alerts to subjects. The system includes an input configured to receive streaming glucose data from a sensor control device and dose data from an insulin delivery device, a display configured to visually present an alert; one or more processors coupled to the display and a memory for storing instructions, the instructions, when executed by the one or more processors, causing the one or more processors to: at a current time; determine whether a corrected dose condition has been issued for consecutive minutes; determine whether an insulin dose has not been recorded within approximately 2 hours from the current time; and determine whether a corrected dose condition has been issued for consecutive minutes. and in response to the determination that an insulin dose has not been recorded or received within about two hours from the current time, an alert interface associated with a corrected dose alert is displayed.

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

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

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for alerting users regarding correction doses. The method includes the steps of, by an electronic device, receiving streaming glucose data from a sensor control device and receiving subject insulin dose data from an insulin delivery device; determining whether an insulin dose has not been recorded within approximately two hours from the current time; determining that a corrected dose condition has been issued for consecutive minutes; displaying an alert interface associated with a corrected dose alert in response to the determination that an insulin dose has not been recorded or received within about two hours.

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

In some methods, the method further includes determining whether a missed meal dose alert has been issued prior to displaying the alert interface for the corrected dose alert. In some methods, an alert interface for a corrected dose alert is displayed only in response to a determination that a missed meal dose alert has not been issued.

Many systems describe systems for managing alerts. The system is coupled to an input configured to receive streaming glucose data from a sensor control device, a display configured to present an alert, an input, a display, and a memory storing instructions. one or more processors, the instructions, when executed by the one or more processors, cause the one or more processors to issue a corrective dose alert; for several minutes to determine whether a corrected dose condition was detected, and for consecutive minutes after the corrected dose alert is issued, to clear the corrected dose alert in response to a determination that no corrected dose condition was detected. let

In some systems, the consecutive minutes are 15 consecutive minutes.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for canceling alerts for correction doses. The method includes, by an electronic device, receiving streaming glucose data from a sensor control device, issuing a corrected dose alert, and determining whether a corrected dose condition is detected for consecutive minutes after the corrected dose alert is issued. and clearing the corrected dose alert in response to determining that no corrected dose condition has been detected for consecutive minutes after the corrected dose alert is issued.

In some methods, the consecutive minutes are 15 consecutive minutes.

Many systems describe systems for managing alerts. The system is coupled to an input configured to receive streaming glucose data from a sensor control device, a display configured to present an alert, an input, a display, and a memory storing instructions. the one or more processors, the instructions, when executed by the one or more processors, cause the one or more processors to issue a first issued correction dose alert at a first time; determining whether the insulin on-board (IOB) calculation has changed since the first time; and in response to determining that the insulin on-board (IOB) calculation has changed since the first time; Disable dose alert.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for canceling alerts for correction doses. The method includes the steps of: receiving, by an electronic device, streaming glucose data from a sensor control device; issuing a first corrective dose alert at a first time; and calculating insulin on-board (IOB) at a first time. and in response to determining that the insulin onboard (IOB) calculation has been changed since the first time, clearing the corrected dose alert. .

Many systems describe systems for managing alerts. The system includes an input configured to receive streaming glucose data from a sensor control device and dose data from an insulin delivery device, a display configured to visually present dose guidance; one or more processors coupled to a display, a display, and a memory storing instructions, wherein the instructions, when executed by the one or more processors, are sent to the one or more processors at a current time. Causes a correction dose alert to be issued, determines whether an insulin dose was recorded within a period from the current time, and releases the correction dose alert in response to the determination that an insulin dose was recorded within a period from the current time. let

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for canceling alerts for correction doses. The method includes, by an electronic device, receiving streaming glucose data from a sensor control device, receiving subject insulin dose data from an insulin delivery device, and issuing a corrected dose alert at a current time and for a period of time from the current time. and, if it is determined that the insulin dose was recorded within a period from the current time, canceling the corrected dose alert.

In some methods, this period is about 2 hours.

Many systems describe systems for classifying doses. The system includes an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes an insulin amount and a recent dose including a timestamp. a display configured to visually present dosage guidance; and one or more processors coupled to the input, the display, and a memory storing instructions, the instructions being one or more. causes the one or more processors to provide dose recommendation guidance for a meal requested by a user at a request time, the meal has a meal type, and the dose recommendation guidance includes a recommended dose. , determining whether the timestamp of the recent dose is within a period from the request time; causing the amount of insulin in the recent dose to be determined to be the same as the recommended dose of the dietary dose guidance recommendation; In response to determining that the timestamp of the recent dose is within the period from the request time and determining that the amount of insulin in the recent dose is the same as the recommended dose of the dietary dose guidance recommendation, be classified as related to the meal type of the most recent meal.

In some systems, that period is about 20 minutes or less.

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 determine whether the timestamp of the recent dose is within a determined meal administration time range for the meal type of the recent meal; Responsive to determining that the timestamp of the recent dose is within the determined meal administration time range for the meal type of the recent meal, causing the recent dose to be classified as associated with the meal type of the recent meal.

In some systems, the instructions cause the one or more processors to determine whether the most recent dose was ingested while the user was in the postprandial state; In response to the determination that the most recent dose has occurred, the most recent dose is classified as associated with the meal type of the most recent meal.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for sorting doses from connected insulin delivery devices. The method includes the steps of providing dose recommendation guidance for a meal requested by a user at a request time, the meal having a meal type, and the dose recommendation guidance including a recommended dose; receiving insulin dose data of the user from the connected insulin delivery device, the insulin dose data including a recent dose including an insulin amount and a timestamp; and a time of the recent dose. determining whether the stamp is within a period from the request time; determining whether the amount of insulin for the recent dose is the same as the recommended dose of the dietary dose guidance recommendation; In response to a determination that the timestamp is within the period from the request time and a determination that the amount of insulin in the most recent dose is the same as the recommended dose in the Meal Dose Guidance recommendation, the most recent dose is added to the most recent meal. and classifying the food as related to the meal type.

In some methods, the period is about 20 minutes or less.

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

In some methods, the method further includes determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal.

In some methods, the method further includes determining whether the most recent dose was taken while the user was in a postprandial state. In some methods, in the postprandial state, previous doses administered within about 2 hours of the requested time are associated with the meal type of the most recent meal.

Many systems describe systems for classifying doses. The system includes an input configured to receive insulin dose data of a user from a connected insulin delivery device, wherein the insulin dose data includes an insulin amount and a recent dose including a timestamp. a display configured to visually present dosage guidance; and one or more processors coupled to the input, the display, and a memory storing instructions, the instructions being one or more. causes the one or more processors to provide dose recommendation guidance at a first time and determine whether a timestamp of a recent dose is within a period from the first time; determine whether the amount of insulin in the most recent dose is the same as the recommended dose in the dose recommendation guidance, and classify the most recent dose as a meal dose, correction dose, or ambiguous dose.

In some systems, that period is about 20 minutes or less.

In some systems, the dose recommendation guidance is corrected dose recommendation guidance, and recent doses are classified as corrected doses.

In some systems, the dose recommendation guidance is that of a meal, where the meal has a meal type and the recent dose is classified as a meal type dose. In some systems, the instructions further cause the one or more processors to determine whether the timestamp of the recent dose is within a determined meal administration time range for the meal type of the recent meal. In some systems, the instructions further cause the one or more processors to determine whether the most recent dose was taken while the user was in a postprandial state.

In some systems, recent doses are classified as ambiguous doses.

In some systems, the instructions further cause the one or more processors to prompt the user to manually categorize recent doses. In some systems, the instructions further determine the most recent dose by prompting the user to select a classification from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose. Prompt the user to manually categorize the . In some methods, the instructions further determine the recent dose by prompting the user to select a classification from the group consisting of a snack dose, a priming dose, and an unconsumed dose to the one or more processors. Prompt users to manually categorize. In some methods, the instructions further cause the one or more processors to manually calculate recent doses by prompting the user to select a category for eating more than expected from previous meals. Prompt the user to categorize by.

In some systems, recent doses classified as ambiguous doses must be classified as non-ambiguous doses before providing additional dose guidance recommendations.

In some systems, the input includes wireless communication circuitry.

Many methods describe methods for sorting doses from connected insulin delivery devices. The method includes the steps of: providing dose recommendation guidance at a first time; and receiving, by an electronic device, insulin dose data of a user from a connected insulin delivery device, the insulin dose data comprising the steps of: providing dose recommendation guidance at a first time; receiving a recent dose including a timestamp; determining whether the timestamp of the recent dose is within a time period from a first time; and classifying the most recent dose as a dietary dose, a correction dose, or an ambiguous dose.

In some methods, the period is about 20 minutes or less.

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

In some methods, the dose recommendation guidance is dose recommendation guidance for a meal, the meal has a meal type, and the recent dose is classified as a meal type dose. In some methods, the method further includes determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal. In some methods, the method further includes determining whether the most recent dose was taken while the user was in a postprandial state.

In some methods, recent doses are classified as ambiguous doses. In some methods, the method further includes prompting the user to manually categorize recent doses. In some methods, prompting the user to manually classify the recent dose comprises prompting the user to select a classification from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose. Including. In some methods, prompting the user to manually classify the recent dose comprises prompting the user to select a classification from the group consisting of snack dose, priming dose, and missed dose. Including. In some methods, prompting the user to manually categorize recent doses includes prompting the user to select a category related to eating more than expected from previous meals. include. In some methods, recent doses classified as ambiguous doses must be classified as non-ambiguous doses before providing additional dose guidance recommendations.

Many systems describe devices for providing dose guidance in response to analyte data. The apparatus includes: an input configured to receive measured analyte data, dietary data and medication data; a display configured to visually present the information; an input; a display; a memory for storing instructions; and one or more processors coupled to the one or more processors, the instructions, when executed by the one or more processors, causing the apparatus to perform a time-correlated analysis of the patient buffered over an analysis period. receiving at least a portion of the time-correlated analyte data and dividing the time-correlated analyte data into discrete time-of-day (TOD) periods; an indicator of the recommended fixed dose in the computer memory for determining a recommended fixed dose of the drug for a corresponding one of the TOD periods based on the dosing strategy and outputting to at least one of the user or the drug delivery device; Store it.

In some systems, the memory includes categorizing each of the drug doses into a medication class based at least in part on the time-correlated data, and categorizing each of the doses within a set of mealtime groups. and determining a glucose pattern that best fits the time-correlated data; and selecting a glucose pattern indicator based on the glucose pattern. There is.

In some systems, the memory further maintains instructions for classifying doses into classes including fixed basal doses, fixed breakfast doses, fixed lunch doses, and fixed dinner doses for corresponding TOD periods.

In some systems, the memory further retains instructions for determining that there are no gaps in the segment of time-correlated analyte data that exceed a predefined threshold as a condition for use in determining the recommended fixed dose. ing.

In some systems, the memory further retains instructions for determining that the segment of time-correlated analyte data has an associated initial dietary dose as a condition for use in determining the recommended fixed dose. ing.

In some systems, the memory includes instructions for determining that the segment of time-correlated analyte data is associated with a basal fixed dose within the past 24 hours as a condition for use in determining the recommended fixed dose. holds even more.

In some systems, the memory is responsive to 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. It further holds an instruction for clearing the data of each TOD period.

In some systems, the memory further retains instructions for determining a glucose pattern for each TOD period based on relevant valid data segments for a set number of past days to determine a recommended fixed dose. is further based on glucose patterns. In some systems, the memory further retains instructions for determining that relevant valid data segments are available for a set number of days in the past as a condition for determining the recommended fixed dose. In some systems, the memory further maintains instructions for determining that the glucose pattern is low based on the number of low alarm counts that occur during each TOD period. In some systems, the memory further maintains instructions for determining that the glucose pattern is low based on the count of hypoglycemic instances during each TOD period. In some systems, the memory further maintains instructions for determining that the glucose pattern is high based on the count of hyperglycemic instances during each TOD period. In some systems, memory determines a preprandial correction factor based on time-correlated analyte data, independent of the determination of the recommended fixed dose, and if both the preprandial correction factor and the recommended fixed dose indicate an increase in dose. , further holds instructions for maintaining the preprandial correction factor. In some embodiments, the memory further retains instructions for determining that the glucose pattern is low based on the count of hypoglycemic instances during each TOD period. In some embodiments, the memory further retains instructions for determining that the glucose pattern is high based on the count of hyperglycemic instances during each TOD period.

In some systems, the memory further retains instructions for determining a glucose pattern condition based on the low alarm count, the postprandial correction count, and the glucose pattern indicator during each TOD period. In some systems, the memory is configured to lower the glucose pattern if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the GPA method results indicate a low pattern. It further holds instructions for determining that. In some systems, the memory determines that the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the GPS method results in a moderate hypoglycemic risk or high/low pattern. further retains instructions for determining the glucose pattern to be high/low if the glucose pattern is high/low. In some systems, the memory detects the glucose pattern if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the glucose pattern indicator indicates no pattern or a high pattern. It further holds an instruction for determining that the value is high.

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

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

Many systems describe drug delivery devices. The device includes an input configured to receive a query for dose data for a period of time, the dose data including all doses and times of administration delivered during the period; one or more processors coupled to an input and a memory storing instructions, the instructions, when executed by the one or more processors, transmit stored data to the one or more processors; To create a In response to a determination that does not include all doses delivered, an incomplete dose data indication is generated.

In some systems, the instructions further cause the one or more processors to send an indication of incomplete dose data in response to the dose data query.

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

In some systems, indication of incomplete dose data is based on counter values.

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

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

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

In some systems, the indication of incomplete dose data is based on a detection that the pen cap has not been attached to the insulin pen for a different period of time. In some systems, the detection that the pen cap has not been attached to the insulin pen for a different period of time means that the insulin pen contained a first amount of insulin before the pen cap was not attached and the pen cap was not attached. including determining that the insulin pen contains a second amount of insulin after being reattached to the insulin pen, the first amount being different than the second amount.

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

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

In some embodiments, the method further includes transmitting an indication of incomplete dose data in response to the dose data query.

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 counter values.

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 embodiments, the estimated counter value is calculated based on the previous counter value and the elapsed time since the previous counter value was received.

In some embodiments, the indication of incomplete dose data is based on a detection that the pen cap has not been attached to the insulin pen for a different period of time. In some embodiments, the detection that the pen cap has not been attached to the insulin pen for a different period of time indicates that the insulin pen contained the first amount of insulin before the pen cap was not attached, and the pen cap contains a second amount of insulin after being reattached to the insulin pen, the first amount being different than the second amount.

In some embodiments, the query for dose data from the period is sent from an application that provides dose guidance.

In many embodiments, a system for providing dose guidance to a subject includes an input configured to receive dose data and an indication of incomplete dose data from a drug delivery device, wherein the dose data includes: an input comprising data related to at least one dose administered during a period of time; a display configured to visually present dose guidance; an input, a display, and a memory storing instructions; and one or more processors coupled to the one or more processors, wherein the instructions, when executed by the one or more processors, cause the one or more processors to receive data related to at least one dose administered during a time period. causing the drug delivery device to interrogate the drug delivery device for dose data including: to determine whether an incomplete dose data indication has been received from the drug delivery device; and in response to determining that an incomplete dose data instruction has been received; prompt for confirmation that the dose data received for the period includes dose data for all doses administered during the period, and respond to a determination that no indication of incomplete dose data has been received. to calculate dose guidance.

In some embodiments, the instructions further cause the one or more processors to output dose guidance on the display.

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

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 embodiments, the estimated counter value is calculated based on the previous counter value and the elapsed time since the previous counter value was received.

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

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

In some embodiments, the system further comprises a drug delivery device configured to receive a query for dose data including data related to at least one dose administered during a time period. and one or more processors coupled to the input and a memory storing instructions, the instructions being executed by the one or more processors. to create stored data, store data for doses administered during a period, determine whether the stored data includes all doses delivered during the period, and determine whether the stored data includes all doses delivered during the period; In response to a determination that the stored data does not include all doses delivered during the period, cause an incomplete dose data indication to be created, query the dose data, and confirm that the stored data does not include all doses delivered during the period. An indication of incomplete dose data is caused to be sent in response to a determination that does not include all doses that have been received.

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

In many embodiments, a method for providing dose guidance to a subject includes receiving dose data and an indication of incomplete dose data from a drug delivery device, the dose data being administered during a period of time. querying the drug delivery device for dose data including data related to at least one dose administered during a time period; and incomplete dose data. and in response to the determination that an indication of incomplete dose data has been received from the drug delivery device, the dose data received in the period is determined to have been administered during the period. The method includes prompting for confirmation of including dose data for all doses, and calculating dose guidance in response to determining that an indication of incomplete dose data has not been received.

In some embodiments, the method further includes outputting dose guidance on a display.

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

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 embodiments, the estimated counter value is calculated based on the previous counter value and the elapsed time since the previous counter value was received.

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

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

In many embodiments, a method for recommending a meal dose includes the steps of: prompting a user to enter a tag associated with a meal type; and receiving the entered tag for an instance of the meal type. and associating the entered tag with a drug amount administered for an instance of the meal type and a postprandial analyte dataset for the instance of the meal type, and a threshold number of instances associated with the meal type. and, if the threshold number of instances is met, determining a recommended drug dose for the meal type.

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

In some embodiments, the recommended drug dose for a meal type is at least equal to the plurality of drug amounts administered for the plurality of instances of the meal type and the plurality of postprandial analyte data sets for the plurality of instances of the meal type. Partly based. In some embodiments, the instance of a meal type is a first instance of a meal type, and the plurality of instances of a meal type include a first instance of a meal type.

In some embodiments, the method further includes receiving analysis value data from the sensor control device. In some embodiments, the meal-type recommended drug dose is based at least in part on analysis data received from the sensor control device.

In some embodiments, the method further includes visually outputting the meal-type recommended drug dose on a display.

In some embodiments, the method further includes prompting the user with an option to track meal types. In some embodiments, prompting the user to enter a tag associated with the meal type occurs in response to the user selecting an option to track meal type.

In many embodiments, a system for determining a recommended drug dose comprises one or more processors coupled to a memory for storing instructions, the instructions, when executed by the one or more processors. , cause the one or more processors to prompt a user to enter a tag associated with a meal type, to receive the entered tag for an instance of the meal type, and to cause the one or more processors to associate the amount of drug administered for the instance with the postprandial analyte data set of the instance of the meal type, determine whether a threshold number of instances associated with the meal type is met, and determine whether the threshold number of instances is met; If met, let the meal type determine the recommended drug dose.

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

In some embodiments, the recommended drug dose for a meal type depends at least in part on the plurality of drug amounts administered for the plurality of instances of the meal type and the plurality of postprandial analyte data sets for the plurality of instances of the meal type. Based on. In some embodiments, the instance of a meal type is a first instance of a meal type, and the plurality of instances of a meal type include a first instance of a meal type.

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

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to visually output a recommended meal-type drug dose on a display.

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to prompt the user with an option to track meal types. In some embodiments, the instructions, when executed by the one or more processors, cause the one or more processors to be associated with the meal type only if the user selects an option to track the meal type. Prompt the user to enter the tag.

In many embodiments, a system for tagging a healthy diet comprises one or more processors coupled to memory for storing instructions, the instructions, when executed by the one or more processors. , causing the one or more processors to prompt the user to enter tags associated with the meal type, and for instances of the first meal type associated with the one or more previously entered tags. a second meal type that is different from the first meal type; associated with meal type.

In some embodiments, the meal type characteristic of the instance is based at least in part on the difference between the meal size associated with one or more previously input tags and the meal size of the input tag of the instance. ing.

In some embodiments, the meal type characteristic of the instance is determined by the difference between the drug amount associated with one or more previously entered tags and the drug amount associated with the instance's entered tag. Based at least in part.

In some embodiments, 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; , prompts the user for the option of creating a second tag.

In some embodiments, the instructions, when executed by the one or more processors, cause the one or more processors to create an input tag only if the user selects an option to create a second tag. associated with a second meal type.

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to determine that the meal type characteristic of the first meal type instance has a meal type characteristic threshold. If it exceeds, the input tag of the instance is disassociated from the first meal type. In some embodiments, the instructions, when executed by the one or more processors, cause the one or more processors to select a first meal type before the input tag is associated with a second meal type. Disassociates the input tag from the instance.

In many embodiments, a method for recommending a dosage for a meal includes the steps of: prompting a user to enter a tag associated with a meal type; receiving a first input tag; associating the first input tag with a first amount of drug administered for a first instance of a meal type; receiving a second inputted tag for an instance of the second inputted tag for the second instance of the meal type; and in response to determining that the difference between the second drug amount and the first drug amount is greater than a predetermined threshold difference. prompting the user to enter the modified modification tag.

In some embodiments, the modification tag includes different sizes of meals.

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

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

In some embodiments, prompting the user to enter a modified tag associated with the meal type occurs within about 5 minutes of receiving the second entered tag.

In some embodiments, prompting the user to enter a modified tag associated with the meal type occurs within about two minutes of receiving the second entered tag.

In some embodiments, the method includes associating a first input tag with a first postprandial analyte dataset of a first instance of a meal type; and associating the second instance of the meal type with a second postprandial analyte data set.

In many embodiments, a system for tagging meals includes one or more processors coupled to memory for storing instructions, the instructions, when executed by the one or more processors, include: causing the one or more processors to prompt a user to input a tag associated with a meal type and receive a first input tag for a first instance of the meal type; a second entered tag for a second instance of the meal type; a second entered tag for a second instance of the meal type; associate the entered tag with a second drug amount administered for a second instance of the meal type, and the difference between the second drug amount and the first drug amount is a predetermined threshold difference. In response to the determination that the meal type is greater than the meal type, the user is prompted to enter a modification tag associated with the meal type.

In some embodiments, the modification tag includes different sizes of meals.

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

In some embodiments, the instructions, when executed by the one or more processors, further prompt the one or more processors to enter a modification tag associated with the meal type. be performed in real time.

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to: within about 5 minutes of receiving the second input tag; Prompt the user to enter the modification tag associated with the type.

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to: within about two minutes of receiving the second input tag; Prompt the user to enter the modification tag associated with the type.

In some embodiments, the instructions, when executed by the one or more processors, further cause the one or more processors to set the first input tag of the first instance of the meal type to the one or more processors. A first postprandial analyte data set is associated with the second input tag, and a second input tag is associated with a second postprandial analyte data set for a second instance of the meal type.

In many embodiments, the analyte monitoring system is a sensor control device that includes an analyte sensor, at least a portion of the analyte sensor being configured to be in fluid contact with a body fluid of interest. and a reading device, the reading device comprising a wireless communication circuit configured to receive the analyte level from the sensor control device and one or more processors coupled to a memory storing instructions; The memory, when executed by the one or more processors, provides the one or more processors with a pattern type for at least one time segment of the day, a hypoglycemic risk indicator and a high blood sugar risk index for at least one time segment of the day. determining the glucose risk index based on the determined glucose risk index and outputting a display on the user interface, the display including the determined at least one glucose index for a period of time and the plurality of glucose indexes for a period of time based on the analyte level received from the sensor control device; A time-in-target display that includes a graph of time-in-target graph portions, each graph portion of the plurality of graph portions containing a user's analyte within a defined analyte range associated with each graph portion. A plot of the user's analyte level across a time-in-target display and a horizontal display of multiple time segments of the day, where the level indicates an amount of time and the plurality of graph portions includes at least four graph portions. and a graph including an identification of the determined pattern type for at least one time segment.

In some embodiments, the at least one glucose indicator includes a glucose average value.

In some embodiments, the at least one glucose indicator includes a glucose management indicator.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes a target value corresponding to the at least one glucose indicator.

In some embodiments, the plurality of graph portions includes at least five graph portions.

In some embodiments, the plurality of graph portions include a graph portion below a very low threshold, a graph portion between a very low threshold and a low threshold, a graph portion between a low threshold and a high threshold, and a graph portion below a high threshold. and a graph portion above a very high threshold.

In some embodiments, the time-in-target range display further includes a description of the defined analyte range associated with each graph portion.

In some embodiments, the time-in-target range display indicates, for each graph portion of the plurality of graph portions, the amount of time the user's analyte level was within a defined analyte range associated with the graph portion during the time period. further including values related to the amount of. In some embodiments, the value is a percentage value.

In some embodiments, the time-in-target indication indicates, for at least two of the plurality of graph portions, that each defined analyte associated with the at least two graph portions is within the range during the time period. further including a total value relating to the total amount of time the user's analyte level was present.

In some embodiments, the time-in-target graph includes a histogram. In some embodiments, each graph portion of the histogram is arranged in a vertical layout, with the graph portion below the very low threshold being located below the graph portion between the very low threshold and the low threshold; This graph portion lies below the graph portion between the low and high thresholds, and this graph portion lies below the graph portion between the high and very high thresholds, and this graph portion lies below the graph portion between the high and very high thresholds. The part is located below the part of the graph that exceeds a very high threshold.

In some embodiments, identifying the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph. In some embodiments, the identification of the determined pattern type for the at least one time segment further includes a label of the determined pattern type.

In some embodiments, the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

In some embodiments, the graph includes a plurality of determined pattern types, and identification of a single pattern type is visually distinguished from other identifications of the plurality of determined pattern types.

In some embodiments, the instructions further cause the one or more processors to output an indication to the user interface that includes an identification of the most important pattern types, the most important pattern types being selected for at least one time segment of the day. This is one of the determined pattern types. In some embodiments, identification of the most important pattern types is displayed on a graph. In some embodiments, identifying the determined pattern type for the at least one time segment includes multiple identifications of the determined pattern type for each of the at least one time segment, including identifying the most important pattern type. is visually distinct from other identifications of the plurality of identifications. In some embodiments, the determined pattern type for at least one time segment of the day includes multiple pattern types for multiple time segments of the day. In some embodiments, the plurality of pattern types includes at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. In some embodiments, if the plurality of pattern types include low value patterns, identifying the most important pattern type includes identifying the low value pattern. In some embodiments, when the plurality of pattern types includes high value patterns with some low values and no low value patterns, identifying the most important pattern type includes high value patterns with some low values. including the identification of In some embodiments, when the plurality of pattern types includes high value patterns and does not include high value patterns with some low values, identifying the most important pattern type includes identifying the high value patterns.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes an identification of at least one time segment of the day determined to have the most significant pattern type. In some embodiments, the display of the identification of the most important pattern type and the identification of at least one time segment of the day determined to have the most important pattern type includes the tag for identification of the most important pattern type and the most important pattern type. At least one tag for identification of at least one time segment of the day determined to have a significant pattern type. In some embodiments, the tag for identification of the most important pattern type is a different color than the at least one tag for identification of the at least one time segment of the day determined to have the most important pattern type. It is.

In some embodiments, the instructions further cause the one or more processors to determine a variation for at least one time segment of the day, and if the determined variation is high, display a display including a statement related to the variation. Output to the user interface. In some embodiments, statements related to fluctuations include identification of behaviors that may contribute to glucose fluctuations.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements related to excursions below a very low threshold. In some embodiments, the very low threshold is about 50 mg/dL to about 58 mg/dL. In some embodiments, the very low threshold is about 54 mg/dL.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements regarding medication guidance. In some embodiments, the statement regarding medication guidance includes suggestions for adjusting medication. In some embodiments, statements regarding medication guidance include suggestions related to medications that contribute to low glucose levels.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements regarding lifestyle guidance. In some embodiments, the statements regarding lifestyle guidance include statements regarding at least one of dietary forgetfulness, carbohydrates, activity level, alcohol, and medication.

In some embodiments, the period is 14 days.

In many embodiments, a method for displaying information related to glucose levels in a subject includes the steps of: receiving an analyte level from a sensor control device; determining based on a hypoglycemia risk indicator and a hyperglycemia risk indicator for at least one time segment of the analyte level; and displaying a user interface of: a time-in-target display that includes a graph of at least one glucose metric determined for a period of time and a time-in-target display that includes a plurality of graph portions, each graph portion of the plurality of graph portions; A time-in-target range display showing the amount of time the user's analyte level is within a predefined analyte range associated with each graph portion, the plurality of graph portions including at least four graph portions; displaying a user interface that includes a plot of the user's analyte level over a horizontal display of a plurality of time segments of the method and a graph that includes an identification of the determined pattern type for the at least one time segment.

In some embodiments, the at least one glucose indicator includes a glucose average value.

In some embodiments, the at least one glucose indicator includes a glucose management indicator.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes a target value corresponding to the at least one glucose indicator.

In some embodiments, the plurality of graph portions includes at least five graph portions.

In some embodiments, the plurality of graph portions include a graph portion below a very low threshold, a graph portion between a very low threshold and a low threshold, a graph portion between a low threshold and a high threshold, and a graph portion below a high threshold. and a graph portion above a very high threshold.

In some embodiments, the time-in-target range display further includes a description of the defined analyte range associated with each graph portion.

In some embodiments, the time-in-target range display indicates, for each graph portion of the plurality of graph portions, the amount of time the user's analyte level was within a defined analyte range associated with the graph portion during the time period. further including values related to the amount of. In some embodiments, the value is a percentage value.

In some embodiments, the time-in-target-range display indicates, for at least two of the plurality of graph portions, that the time-in-target range display indicates that the user is within each defined analyte range associated with the at least two graph portions during a time period. further includes a total value relating to the total amount of time that the analyte level was present.

In some embodiments, the time-in-target graph includes a histogram. In some embodiments, each graph portion of the histogram is arranged in a vertical layout, with the graph portion below the very low threshold being located below the graph portion between the very low threshold and the low threshold; This graph portion lies below the graph portion between the low and high thresholds, and this graph portion lies below the graph portion between the high and very high thresholds, and this graph portion lies below the graph portion between the high and very high thresholds. The part is located below the part of the graph that exceeds a very high threshold.

In some embodiments, identifying the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph.

In some embodiments, the identification of the determined pattern type for the at least one time segment further includes a label of the determined pattern type.

In some embodiments, the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

In some embodiments, the graph includes a plurality of determined pattern types, and identification of a single pattern type is visually distinguished from other identifications of the plurality of determined pattern types.

In some embodiments, the instructions further cause the one or more processors to output an indication to the user interface that includes an identification of the most important pattern types, the most important pattern types being selected for at least one time segment of the day. This is one of the determined pattern types. In some embodiments, identification of the most important pattern types is displayed on a graph. In some embodiments, identifying the determined pattern type for the at least one time segment includes multiple identifications of the determined pattern type for each of the at least one time segment, including identifying the most important pattern type. is visually distinct from other identifications of the plurality of identifications. In some embodiments, the determined pattern type for at least one time segment of the day includes multiple pattern types for multiple time segments of the day. In some embodiments, the plurality of pattern types includes at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. In some embodiments, if the plurality of pattern types include low value patterns, identifying the most important pattern type includes identifying the low value pattern. In some embodiments, when the plurality of pattern types includes high value patterns with some low values and no low value patterns, identifying the most important pattern type includes high values with some low values. Including pattern identification. In some embodiments, when the plurality of pattern types includes high value patterns and does not include high value patterns with some low values, identifying the most important pattern type includes identifying the high value patterns.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes an identification of at least one time segment of the day determined to have the most significant pattern type.

In some embodiments, the display of the identification of the most important pattern type and the identification of at least one time segment of the day determined to have the most important pattern type includes the tag for identification of the most important pattern type and the most important pattern type. At least one tag for identification of at least one time segment of the day determined to have a significant pattern type. In some embodiments, the tag for identification of the most important pattern type is a different color than the at least one tag for identification of the at least one segment of the day determined to have the most important pattern type.

In some embodiments, the instructions further cause the one or more processors to determine a variation for at least one time segment of the day, and if the determined variation is high, display a display including a statement related to the variation. Output to the user interface. In some embodiments, statements related to fluctuations include identification of behaviors that may contribute to glucose fluctuations.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements related to excursions below a very low threshold. In some embodiments, the very low threshold is about 50 mg/dL to about 58 mg/dL. In some embodiments, the very low threshold is about 54 mg/dL.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements regarding medication guidance. In some embodiments, the statement regarding medication guidance includes suggestions for adjusting medication. In some embodiments, statements regarding medication guidance include suggestions related to medications that contribute to low glucose levels.

In some embodiments, the instructions further cause the one or more processors to output a display to the user interface that includes statements regarding lifestyle guidance.

In some embodiments, the statements regarding lifestyle guidance include statements regarding at least one of dietary forgetfulness, carbohydrates, activity level, alcohol, and medication.

In some embodiments, the period is 14 days.

In many embodiments, an apparatus for displaying indicators related to a subject includes an input configured to receive drug administration data, a display configured to visually present information, and an input configured to visually present the information. a unit, a display, and a memory for storing a drug dose coupled to the instructions and a memory for storing a dose of the drug taken by the subject over a period of time and a recommended dose of the drug for the subject over a period of time. or more processors, the instructions, when executed by the one or more processors, cause the device to display a graph plotting multiple drug doses taken multiple times by the subject, the graph plotting X and a Y-axis of the 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 a correction factor includes at least one of a fixed breakfast dose with a correction factor, a fixed lunch dose with a correction factor, and a fixed dinner dose with a correction factor.

In some embodiments, the difference between the dose taken by the subject and the recommended dose is in units.

In some embodiments, the input includes wireless communication circuitry.

In many embodiments, an apparatus for displaying indicators related to a subject includes an input configured to receive measured analyte data and drug administration data, and an input configured to visually present the information. a configured display, an input, a display, and a memory for storing instructions and time-correlated data characterizing an analyte of interest, a dose of drug taken by the subject over a period of time, and a recommended dose of drug for the subject over a period of time. and one or more processors coupled to the one or more processors, wherein the instructions, when executed by the one or more processors, cause the device to receive a dose administered during a period of time and a received measured analyte. Display a summary of the subject's treatment, including analyte indicators determined from the data; display a graphic summarizing doses missed during the period; Display a graphic summarizing the override doses, including doses that are different from .

In some embodiments, the missed dose summary graphic includes a graphical representation of the percentage of forgotten doses for multiple dose types. In some embodiments, each percentage of the forgotten percentage for a plurality of dose types is calculated as the percentage of forgotten doses of the dose type over the total number of administrations of the dose type during a period of time. In some embodiments, the multiple dose types include at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose.

In some embodiments, the graphic summarizing missed doses is a bar graph.

In some embodiments, the graphic summarizing the override doses is a bar graph.

In some embodiments, the input includes wireless communication circuitry.

In many embodiments, an apparatus for displaying metrics related to a subject includes measured analyte data from multiple subjects, drug administration data from multiple subjects, and medication recommendations for multiple subjects. an input configured to receive associated data, a display configured to visually present information, an input, a display, instructions and times for characterizing an analyte of each of the plurality of objects. one or more processors coupled to a memory storing data related to correlation data, drug doses taken by each of the plurality of subjects over a period of time, and medication recommendations for the plurality of subjects; When executed by the above processor, the device displays, for each of the plurality of subjects, time in target range, time below low threshold, time above high threshold, percentage of basal dose ingested, and intake per day. dosing recommendations, including displaying a summary of analyte indicators including at least two of the average bolus doses administered, and displaying dosing recommendations for a subject of the plurality of subjects that require approval from a health care provider; Display a summary of information related to an item.

In some embodiments, the summary of analyte metrics includes time in target range, time below low threshold, time above high threshold, percentage of basal dose taken, and average bolus dose taken per day. Including at least three of them.

In some embodiments, the low threshold is about 70 mg/dL.

In some embodiments, the high threshold is about 180 mg/dL.

In some embodiments, the display of medication recommendations is an icon.

In some embodiments, the indication of medication recommendations is a statement indicating the number of medication recommendations that require approval.

In some embodiments, the input includes wireless communication circuitry.

In many embodiments, an apparatus for displaying treatment information related to a subject includes an input configured to receive measured analyte data and drug administration data, and an input configured to visually present the information. a display configured to provide instructions and time-correlated data characterizing an analyte of each of the plurality of subjects, a dose of drug taken by each of the plurality of subjects over a period of time, and a display configured to include instructions and time-correlated data characterizing the analyte of each of the plurality of subjects; one or more processors coupled to a memory storing data related to a dosing recommendation, the instructions, when executed by the one or more processors, causing the device to receive estimated dose parameters and estimated meal administration. receiving a time range from the subject and determining, based on the drug administration data, a representative amount of each of the plurality of basal doses and the plurality of dietary doses ingested by the subject during a period of time, based on the drug administration data; determining the subject's representative meal administration time range during the period, determining a recommended dose of at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose; and determining the estimated dose parameters received from the subject and the estimated meal. display the dosing time range, representative amounts for each of the plurality of basal doses and the plurality of meal doses, and the representative meal dosing time range, and display the representative amounts of each of the basal, breakfast, lunch, and dinner doses. Display at least one recommended dose.

In some embodiments, the plurality of meal doses include a plurality of breakfast doses, a plurality of lunch doses and a plurality of dinner doses, and the instructions, when executed by the one or more processors, cause the device to receive a plurality of meal doses. The average amount of each of the basal dose, multiple breakfast doses, multiple lunch doses, and multiple dinner doses is determined.

In some embodiments, the estimated dose parameters include estimates of basal dose, breakfast dose, lunch dose, and dinner dose. In some embodiments, the estimated dose parameters further include estimated times that the subject will ingest the basal dose, breakfast dose, lunch dose, and dinner dose.

In some embodiments, the estimated meal administration time range includes an estimated start and end time for each of breakfast, lunch, and dinner.

In some embodiments, the representative amount of each of the plurality of basal doses and the plurality of dietary doses comprises an average value of each of the plurality of basal doses and the plurality of dietary doses ingested by the subject during a period of time. .

In some embodiments, the representative amount of each of the plurality of basal doses and the plurality of dietary doses comprises the modal value of each of the plurality of basal doses and the plurality of dietary doses ingested by the subject during a period of time. .

In some embodiments, the instructions, when executed by the one or more processors, further cause the device to determine a pre-prandial correction factor and a post-prandial correction factor based on the measured analyte data and the drug administration data. to display the before-meal correction coefficient and the after-meal correction coefficient.

In some embodiments, the representative meal administration time range is displayed adjacent to the estimated meal administration time range.

In some embodiments, representative amounts for each of the plurality of basal doses and the plurality of dietary doses are displayed adjacent to the estimated dose parameters.

In many embodiments, the instructions, when executed by the one or more processors, further cause the device to receive a plurality of basal doses and a plurality of basal doses for at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose. causing a conservative value to be determined that is lower than the corresponding determined representative amount of each of the plurality of dietary doses, and causing the determined conservative value to be displayed.

In many embodiments, the input includes wireless communication circuitry.

For any of the methods described herein, the method is performed on at least one processor of a remote device, e.g., a server, a telephone/receiver, a drug delivery device, or a glucose monitoring device. can do.

All features, elements, components, functions, and steps described with respect to any embodiment provided herein are freely combinable and replaceable with those of any other embodiments. It should be noted that this is intended. When a feature, element, component, function, or step is described with respect to only one embodiment, the feature, element, component, function, or step is not explicitly described elsewhere. It should be understood that all other embodiments described herein can be used. Accordingly, this paragraph may combine features, elements, components, functions, and steps of different embodiments or substitute features, elements, components, functions, and steps of one embodiment with those of another embodiment. It serves as the underlying basis and written support for the introduction of each claim at any time, even if the following description does not expressly state that such combinations or substitutions are possible in a particular instance. It is expressly stated that it would be an undue burden to explicitly list all possible combinations and permutations, especially given that the permissibility of such combinations and permutations is readily recognized by those skilled in the art. Is recognized.

To the extent that embodiments disclosed herein include or operate in conjunction with memory, storage, and/or computer-readable media, the memory, storage, and/or computer-readable media are non-transitory. It is gender. Therefore, to the extent that memory, storage, and/or computer-readable medium is covered by one or more claims, that memory, storage, and/or computer-readable medium is only non-transitory.

While the embodiments are susceptible to various modifications and alternative forms, specific examples thereof are shown in the drawings and will herein be described in detail. However, these embodiments are not limited to the particular forms disclosed; on the contrary, these embodiments cover all modifications, equivalents, and alternatives falling within the spirit of the disclosure. It should be understood that this covers the following: Furthermore, any feature, function, step, or element of an embodiment may be recited or added to a claim, and any feature, function, step, or element not within the scope of a claim may Negative limitations that define the inventive scope of are also possible.

Clauses Exemplary embodiments are described in the numbered clauses below.

Clause 1 A device for parameterizing a patient's medication habits to configure dose guidance settings, the device comprising:
an input component configured to receive measured analyte data, dietary data and medication data;
a display component configured to visually present information;
one or more processors coupled to an input, a display, and a memory storing instructions and time-correlated data characterizing the patient's analyte over an analysis period;
When the instructions are executed by the one or more processors, the apparatus:
receiving patient dose regimen information for the analysis period;
evaluating a measure of consistency between the time-correlated data and the patient dose regimen information;
determining dose guidance information based on the consistency measure;
Device.

Clause 2. The apparatus of Clause 1, wherein the memory further retains instructions for outputting the dose guidance information to the display.

Clause 3. The apparatus of Clause 1, wherein the memory further retains instructions for receiving the patient dosage regimen information from the input component.

Clause 4. The apparatus of Clause 1, wherein the memory further retains instructions for receiving the patient dosage regimen information via transmission from a remote data server.

Clause 5. The device of Clause 1, wherein the patient dosage regimen information includes a typical fixed dosage taken at mealtimes and a typical time of day at which breakfast is eaten.

Clause 6. The apparatus of Clause 1, wherein the patient dose regimen information includes information defining frequency of patient compliance to scheduled doses or meals.

Clause 7 The device according to Clause 1, wherein the medicament comprises insulin.

Clause 8 The said order for assessing the measure of consistency shall be
classifying each dose of the patient dose regimen into a medication class based on the time-correlated data;
grouping each of the doses into one of a set of mealtime groups;
generating dose parameters for the patient at least in part by applying data for each of the mealtime groups to a model;
2. The apparatus of clause 1, further comprising storing the dose parameters and configuring dose guidance settings.

Clause 9. The apparatus of Clause 1, wherein the memory further retains instructions for accumulating the time-correlated data characterizing the patient's analyte over a period of time.

Clause 10. Instructions for the memory to determine the dose guidance information, at least in part, by reducing dosage recommendations based on detecting excursions of the analyte above a low threshold in the time-correlated data. The device according to clause 1, further comprising:

Clause 11. The device of Clause 10, wherein said dosing recommendation is only a fixed dosing.

Clause 12. The apparatus of Clause 1, wherein the memory further retains instructions for determining patient adherence to the patient dosage regimen information based on the time-correlated data.

Clause 13. The apparatus of Clause 1, wherein the memory further retains instructions for determining whether to output a dose guidance parameter based on the consistency measure.

Clause 14. The apparatus of Clause 1, wherein the memory further retains instructions for outputting dose guidance parameters including a predetermined dose suggestion if the consistency measure indicates an unreliable system configuration.

Clause 15 The apparatus according to clause 1, wherein the input section comprises a wireless communication circuit.

Clause 16. A method for facilitating efficient access by a healthcare provider (HCP) to a patient's electronic medical record (EMR) generated by a dosage guidance system while protecting patient privacy, the method comprising:
authenticating the session with the patient by at least one processor of a portable display device;
generating, by the at least one processor, an EMR identification code (ID) in response to receiving input from the patient indicating a request to share the EMR with an HCP during the session;
providing, by the at least one processor, the EMR ID to a remote server that controls access to the EMR;
outputting the EMR ID by the at least one processor to a display of the portable display device.

Clause 17. The method of Clause 16, further comprising providing the EMR to the remote server before the step of authenticating.

Clause 18. The method of Clause 16, further comprising receiving the EMR from the dose guidance system.

Clause 19. The method of Clause 16, further comprising determining whether the EMR does not meet a concordance condition with patient input indicative of a tracked drug dose pattern.

Clause 20. The method of Clause 19, further comprising providing the patient with an option to provide the EMR to the HCP upon determining that the EMR does not meet the concordance condition.

Clause 21. The method of Clause 19, wherein the generating, providing, and outputting steps are conditioned on determining that the EMR does not satisfy the consistency condition.

Clause 22. The method of Clause 20, wherein the generating, providing, and outputting steps are conditioned on determining that the EMR does not meet the consistency condition.

Clause 23. The method of Clause 16, further comprising providing the patient with an option to provide the EMR to the HCP.

Clause 24. The method of Clause 17, wherein once the remote server receives the EMR ID, it creates a web page addressed at least in part by the EMR ID to display the EMR.

Clause 25. Clause 16, wherein said EMR includes determinations of dosing parameters for drugs administered to said patient from time to time during defined time periods, and a measure of consistency of said determinations with patient-provided drug dosing information. Method described.

Clause 26. The method of Clause 25, wherein the drug is insulin.

Clause 27 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data from a drug delivery device, the dose data including the most recent drug dose administered and the time of drug administration;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
When the instructions are executed by the one or more processors, the one or more processors:
causing the most recent drug dose administered to be classified as a meal dose or a correction dose;
displaying additional dose guidance in response to a determination that a certain period of time has elapsed since the time of the most recent drug dose administered;
system.

Clause 28. The system of Clause 27, wherein said period is about 2 hours.

Clause 29. The most recent drug dose administered is a meal dose, the dose data further comprises at least one additional drug dose administered after the most recent drug dose was administered, and the period of time 28. The system of clause 27, which does not reset at the time of administration of one additional drug dose.

Clause 30. The system of Clause 27, wherein the most recent drug dose administered is not a prime dose.

Clause 31. The system of Clause 27, wherein the time of the most recent drug dose administered is a timestamp from a connected drug delivery device.

Clause 32: the most recent drug dose administered is a correction dose, the dose data further comprises at least one additional drug dose administered after the most recent drug dose administered, and the beginning of the period is 28. The system of clause 27, wherein the system is reset to the time of administration of the administered at least one additional drug dose.

Clause 33. The system of Clause 27, further comprising a drug delivery device configured to deliver at least one dose of drug to the subject.

Clause 34 The system according to Clause 27, wherein the input section comprises a wireless communication circuit.

Clause 35 A method for providing dosage guidance, comprising:
receiving, by the electronic device, drug dose data for the subject from the drug delivery device, the drug dose data including the most recent drug dose administered and the time of drug administration;
classifying the most recently administered drug dose as a meal dose or a correction dose;
displaying dose guidance in response to determining that a one-point period has elapsed since the time of the most recent drug dose administered.

Clause 36 The method according to clause 35, wherein said period is about 2 hours.

Clause 37: the most recent drug dose administered is a meal dose, the drug dose data further comprises at least one additional drug dose administered after the most recent drug dose was administered, and the start of the period is 36. The method of clause 35, wherein , is not reset to the time of administration of the at least one additional drug dose.

Clause 38. The method of Clause 35, wherein the most recent drug dose administered is not a prime dose.

Clause 39. The method of Clause 35, wherein the time of the most recent drug dose administered is a timestamp from a connected drug delivery device.

Clause 40: said most recent drug dose administered is a correction dose, said drug dose data further comprising at least one additional drug dose administered after said most recent drug dose administered, and said drug dose data further comprises at least one additional drug dose administered after said most recent drug dose administered; 36. The method of clause 35, wherein: is reset to the time of administration of the administered at least one additional drug dose.

Clause 41 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data from the drug delivery device, the dose data including data related to recent drug doses administered;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
When the instructions are executed by the one or more processors, the one or more processors:
determining whether the most recent drug dose administered is the most recent drug dose administered;
in response to determining that the most recent drug dose administered is the most recent drug dose administered, displaying a screen containing dose guidance recommendations;
system.

Clause 42. The system of Clause 41, wherein the administered most recent drug dose is determined to be the most recent administered drug dose upon confirmation from a user.

Clause 43. The dose data further comprises data relating to at least one drug dose administered since the reset time, and the instructions further cause the one or more processors to instruct at least one drug dose administered since the reset time. 42. The system of clause 41, responsive to determining that one drug dose has been classified, causes the screen to be displayed.

Clause 44. The system of Clause 43, wherein the dose data related to at least one drug dose administered since the reset time is automatically categorized.

Clause 45 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 send one or more wireless interrogation signals to the drug delivery device. 45. The system of clause 44, wherein the system determines that the most recent dose administered has been received.

Clause 46. The system of Clause 43, wherein the dose data relating to at least one drug dose administered since the reset time is categorized by a user.

Clause 47. The system of Clause 41, wherein the instructions further cause the one or more processors to display a prompt for a user to confirm that information related to the most recently administered drug dose is correct. .

Clause 48. The system of Clause 47, wherein the instructions further cause the one or more processors to display a screen containing the dose guidance recommendations for a period of time beginning after confirmation from the user.

Clause 49. The system of Clause 41, further comprising a drug delivery device configured to deliver at least one dose of drug to the subject.

Clause 50 The input is further configured to receive a request for measured analyte data and dose guidance, and the instructions further cause the one or more processors to:
determining whether the glucose concentration at the time of receiving the request for dose guidance is less than a low threshold;
in response to determining that the glucose concentration is below a low threshold upon receipt of the request for dose guidance, displaying a screen containing a message for addressing low glucose levels prior to drug administration;
The system described in Article 41.

Clause 51 The instructions further cause the one or more processors to:
suppressing the display of the dose guidance recommendation in response to a determination that the glucose concentration upon receipt of the request for dose guidance is below a low threshold;
The system described in Article 50.

Clause 52: the input is further configured to receive a request for measured analyte data and dose guidance after a meal start time, and the instructions further cause the one or more processors to:
determining whether the glucose concentration at the estimated meal start time is less than a low threshold;
in response to a determination that the glucose concentration at the estimated meal start time is below a low threshold, displaying a screen containing a message for addressing low glucose levels prior to drug administration;
The system described in Article 41.

Clause 53 The instructions further cause the one or more processors to:
not displaying the dose guidance recommendation in response to a determination that the glucose concentration at the estimated start time of the meal is below a low threshold;
The system described in Article 52.

Clause 54 The system according to Clause 41, wherein the input section includes a wireless communication circuit.

Clause 55 A method for providing dosage guidance, comprising:
receiving, by the electronic device, dosage data of the user from the drug delivery device, the dosage data including data related to recent drug doses administered;
determining whether the most recent drug dose administered is the most recent drug dose administered;
displaying a screen containing dose guidance recommendations in response to determining that the most recent drug dose administered is the most recent drug dose administered.

Clause 56. The method of Clause 55, wherein the most recent drug dose administered is determined to be the most recent drug dose administered by confirmation from the user.

Clause 57 The most recent drug dose administered is administered after a reset time, and the method comprises:
further comprising determining whether recent doses administered after the reset time have been classified;
The method described in Article 55.

Clause 58. The method of Clause 57, wherein the most recent doses administered after the reset time are automatically categorized.

Clause 59. The method of Clause 55, further comprising transmitting one or more wireless interrogation signals to the drug delivery device to determine that the most recent dose administered has been received.

Clause 60. The method of Clause 57, wherein the recent doses administered after the reset time are categorized by user.

Clause 61. The method of Clause 60, further comprising displaying a prompt for the user to confirm that information related to the most recently administered drug dose is correct.

Clause 62. The method of Clause 55, wherein the screen containing the dose guidance recommendations is displayed only for a period of time beginning after the user confirms that information related to the most recently administered drug dose is correct.

Clause 63 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data from a drug delivery device, the dose data comprising 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 to the input, the display, and a memory storing instructions;
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 classified;
displaying a screen including the plurality of meal icons in response to a determination that the at least one meal dose administered after the reset time has been classified;
system.

Clause 64. The instructions cause the one or more processors to determine whether the at least one meal dose administered since the reset time has been classified as one of breakfast, lunch or dinner. 63. The system described in 63.

Clause 65 The plurality of meal icons include a breakfast icon, a lunch icon, and a dinner icon, each of the breakfast icon, the lunch icon, and the dinner icon including a first appearance and a second appearance, is associated with a first condition in which one of the at least one meal dose administered since the reset time is classified as a meal type corresponding to the breakfast icon, the lunch icon, or the dinner icon. and the second appearance is such that one of the at least one meal doses administered since the reset time is classified as a meal type corresponding to the breakfast icon, the lunch icon, or the dinner icon. 64. The system according to clause 63, wherein the system is associated with a second state in which the system does not

Clause 66. The system of Clause 65, wherein the first appearance includes a shaded appearance.

Clause 67. The system of Clause 65, wherein the second appearance includes an unshaded appearance.

Clause 68. The system of Clause 65, wherein the second appearance is brighter than the first presentation.

Clause 69 The instructions further cause the one or more processors to:
causing the at least one meal dose administered since the reset time to be classified as a breakfast dose, a lunch dose or a dinner dose;
The system described in Article 63.

Clause 70 The instructions further cause the one or more processors to:
receiving input from a user, the input comprising classifying the at least one meal dose administered since the reset time as a breakfast dose, a lunch dose or a dinner dose;
The system described in Article 63.

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 a time range associated with a dinner dose and a time range associated with a breakfast dose.

Clause 73 The system according to Clause 63, wherein the reset time is midnight.

Clause 74. The system of clause 63, wherein the reset time is a time approximately halfway between the end of a dinner dose time range and the beginning of a 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 drug to the subject.

Clause 76 The system according to Clause 63, wherein the input section comprises a wireless communication circuit.

Clause 77 A method for providing dosage guidance, comprising:
receiving, by the electronic device, drug dose data of the user from the drug delivery device, the drug dose data including 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 classified;
displaying a screen including a plurality of meal icons in response to a determination that the at least one meal dose administered since the reset time has been classified.

Clause 78. The method of clause 77, wherein the determining step comprises determining whether the at least one meal dose administered since the reset time has been classified as one of breakfast, lunch or dinner. Method.

Clause 79 The plurality of meal icons include a breakfast icon, a lunch icon, and a dinner icon, each of the breakfast icon, the lunch icon, and the dinner icon including a first appearance and a second appearance, is associated with a first condition in which one of the at least one meal dose administered since the reset time is classified as a meal type corresponding to the breakfast icon, the lunch icon, or the dinner icon. and the second appearance is such that one of the at least one meal doses administered since the reset time is classified as a meal type corresponding to the breakfast icon, the lunch icon, or the dinner icon. 78. The method of clause 77, wherein the method is associated with a second state in which the

Clause 80. The method of Clause 79, wherein the first appearance comprises a shaded 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 presentation.

Clause 83. The method of Clause 77, wherein the at least one meal dose administered since the reset time is automatically classified by one or more processors of a reading device.

Clause 84. The method of Clause 77, wherein the at least one meal dose administered since the reset time is categorized by 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 a dinner dose and a time range associated with a breakfast dose.

Clause 87 The method according to Clause 77, wherein the reset time is midnight.

Clause 88. The method of Clause 77, wherein the reset time is a time approximately halfway between the end of the dinner dose time range and the beginning of the breakfast dose time range.

Clause 89 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data from the drug delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determine whether the missed dose alert is effective;
in response to determining that the missed dose alert is not in effect, displaying a dose guidance recommendation based on the usual dietary dose calculation;
in response to determining that the missed dose alert is enabled, displaying a dose guidance recommendation based on a later meal dose calculation;
system.

Clause 90 The instructions further cause the one or more processors to:
determining whether the dose data received from the drug delivery device includes doses administered within a time period from a current time;
in response to determining that the dose data received from the drug delivery device includes doses administered within a time period from a current time, displaying a dose guidance recommendation based on the later meal dose calculation;
The system described in Article 89.

Clause 91: The instructions cause the one or more processors to:
the late meal dose in response to a determination that the missed dose alert is in effect and a determination that the dose data received from the drug delivery device includes doses administered within a period from the current time; display dosage guidance recommendations based on calculations of
The system described in Article 90.

Clause 92. The system of Clause 90, wherein the period of time is approximately 2 hours.

Clause 93. The method of Clause 89, wherein the calculation of the regular meal dose is based on a fixed insulin dose associated with the meal and an insulin onboard amount if the user's current glucose level is below the target glucose value. system.

Clause 94: If each of said regular meal dose calculation and said late meal dose calculation is such that said user's current glucose level is above a target glucose value, then a fixed insulin dose associated with a meal, an insulin onboard amount; , a correction adjustment and a trend adjustment.

Clause 95. The system of Clause 94, wherein the calculation of the normal meal dose is based on current glucose levels determined from scanned glucose data.

Clause 96. The system of Clause 94, wherein the later meal dose calculation is based on current glucose levels determined from streaming glucose data.

Clause 97. The system of Clause 96, wherein the late meal dose calculation is further based on a trend adjustment determined from streaming glucose data.

Clause 98. The system of Clause 94, wherein the calculation of the late meal dose is based on an insulin onboard amount calculated according to a time of request for the dose guidance recommendation by the user.

Clause 99. The system of Clause 94, further comprising a drug delivery device configured to deliver at least one dose of drug to the subject.

Clause 100 The system of Clause 94, wherein the input section comprises a wireless communication circuit.

Article 101 A method for providing dosage guidance, comprising:
receiving, by the electronic device, drug dosage data of the user from the drug delivery device;
determining whether a missed dose alert is enabled;
responsive to the determination that the missed dose alert is not in effect, displaying a dose guidance recommendation based on the usual dietary dose calculation;
and displaying dose guidance recommendations based on later meal dose calculations in response to determining that the missed dose alert is in effect.

Clause 102: determining whether the drug dose data includes data for doses administered within a period of time;
in response to a determination that the drug dose data does not include data for doses administered within a period of time, displaying the dose guidance recommendation based on the late meal dose calculation. Method described.

Clause 103 The method of Clause 102, wherein the period of time is about 2 hours.

Clause 104. The method of clause 101, wherein the calculation of the regular meal dose is based on a fixed insulin dose associated with a meal and an insulin onboard amount if the user's current glucose level is below a target glucose value. Method.

Clause 105: each of said normal meal dose calculation and said late meal dose calculation is a fixed insulin dose associated with a meal, an insulin onboard amount, if said user's current glucose level is above a target glucose value; , a corrective adjustment and a trend adjustment.

Clause 106. The method of Clause 105, wherein the calculation of the normal dietary dose is based on current glucose levels determined from scanned glucose data.

Clause 107. The method of Clause 105, wherein the later meal dose calculation is based on current glucose levels determined from streaming glucose data.

Clause 108. The method of Clause 107, wherein the late meal dose calculation is further based on a trend adjustment determined from streaming glucose data.

Clause 109. The method of Clause 105, wherein the calculation of the late meal dose is based on an insulin onboard amount calculated according to the time of request of the dose guidance recommendation by the user.

Article 110 A system for providing alerts to a subject,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determining whether the meal dose has been forgotten at the current time by detecting a condition for forgetting to administer the meal for several consecutive minutes in relation to a meal having an estimated meal start time;
determining whether an insulin dose has not been recorded within about 45 minutes before the estimated meal start time;
an alert interface related to missed meal administration in response to detecting the missed meal administration condition over consecutive minutes and determining that the insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; to display,
system.

Clause 111 The system of Clause 110, wherein the consecutive minutes are about 5 minutes.

Clause 112 The instructions further cause the one or more processors to:
determining whether a meal dose was recorded within about 2 hours from the current time;
in response to determining that the meal dose was recorded within about 2 hours from the current time, displaying an alert interface related to the missed meal administration;
The system described in Article 110.

Clause 113 The instructions further cause the one or more processors to:
determine whether a correction dose alert has been issued;
in response to the determination that the correction dose alert has not been issued, displaying an alert interface related to the missed meal administration;
The system described in Article 110.

Clause 114 further comprising a sensor control device configured to collect data indicative of an analyte level in a subject, the sensor control device including an analyte sensor, at least a portion of the analyte sensor being in contact with a body fluid of the subject. 111. The system of clause 110, wherein the system is configured for fluid contact.

Clause 115. The system of Clause 110, wherein the input section comprises a wireless communication circuit.

Clause 116 A method for alerting a user of forgotten meal administration, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
determining at the current time whether the meal dose has been forgotten by detecting a meal dose forgetting condition for several consecutive minutes in relation to a meal having an estimated meal start time;
determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time;
an alert interface related to missed meal administration in response to detecting the missed meal administration condition over consecutive minutes and determining that the insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; A method, including steps for displaying.

Clause 117. The method of clause 116, wherein the consecutive minutes are about 5 minutes.

Clause 118: determining whether a meal dose was recorded within about 2 hours from the current time;
117. The method of clause 116, further comprising displaying an alert interface related to the missed meal dose in response to determining that the meal dose was recorded within about 2 hours of the current time.

Clause 119: determining whether a correction dose alert has been issued;
117. The method of clause 116, further comprising displaying an alert interface related to the missed meal administration in response to determining that the correction dose alert has not been issued.

Article 120 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Issue an alert for forgetting to administer a meal,
determining whether a condition for forgetting to administer a meal is detected for several consecutive minutes after the forgetting to administer to a meal alert is issued;
Responsive to determining that the forgotten meal administration condition has not been detected for several consecutive minutes after the forgotten meal administration alert is issued, canceling the forgotten meal administration alert;
system.

Clause 121 The system according to Clause 120, wherein the consecutive minutes are 15 consecutive minutes.

Clause 122. The system of Clause 120, wherein the missed meal condition comprises determining that an insulin dose was not administered within an estimated meal start time.

Clause 123. The system of Clause 120, wherein the input section comprises a wireless communication circuit.

Article 124 A method for canceling an alert for forgetting to administer a meal, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a meal administration forgotten alert;
determining whether a missed meal condition is detected for consecutive minutes after the missed meal alert is issued;
resetting the missed meal alert in response to a determination that the missed meal condition is not detected for consecutive minutes after the missed meal alert is issued.

Clause 125. The method according to Clause 124, wherein the consecutive minutes are 15 consecutive minutes.

Clause 126. The method of Clause 124, wherein the missed meal condition comprises determining that an insulin dose was not administered within an estimated meal start time.

Article 127 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the drug delivery device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Issue an alert for forgetting to administer a meal at the current time,
determining whether an insulin dose was recorded within about 2 hours from the current time;
in response to determining that the insulin dose was recorded within about 2 hours from the current time, disabling the missed meal administration alert;
system.

Clause 128. The system of Clause 127, wherein the input section comprises a wireless communication circuit.

Article 129 A method for canceling an alert for forgetting to administer a meal, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a forgotten meal administration alert at the current time;
determining whether an insulin dose has been recorded within about two hours of the current time;
resetting the missed meal alert in response to determining that the insulin dose was recorded within about 2 hours of the current time.

Article 130 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the drug delivery device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
causing a missed meal administration alert related to the forgotten meal to be issued having an estimated start time;
determining whether the insulin dose was recorded within about 45 minutes from the estimated meal start time;
Responsive to determining that the insulin dose was recorded within about 45 minutes from the estimated meal start time, canceling the meal administration forget alert;
system.

Clause 131. The system of Clause 130, wherein the input section comprises a wireless communication circuit.

Article 132 A method for canceling an alert for forgetting to administer a meal, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a missed meal administration alert associated with the missed meal having an estimated start time;
determining whether the insulin dose was recorded within about 45 minutes of the estimated meal start time;
resetting the missed meal dose alert in response to determining that the insulin dose was recorded within about 45 minutes of the estimated meal start time.

Article 133 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
at the current time, causing a meal administration forgetting alert related to the meal forgetting having an estimated start time to be issued;
determining whether the estimated meal start time is within about 2 hours from the current time;
Responsive to determining that the estimated meal start time did not occur within about 2 hours from the current time, canceling the meal administration forgetting alert;
system.

Clause 134. The system of Clause 133, wherein the input section comprises a wireless communication circuit.

Article 135 A method for canceling an alert for forgetting to administer a meal, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a missed meal administration alert associated with the missed meal having an estimated start time at the current time;
determining whether the estimated meal start time is within about 2 hours from the current time;
resetting the missed meal alert in response to determining that the estimated meal start time did not occur within about two hours of the current time.

Article 136 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data from the insulin delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determine whether a correction dose alert has been issued at the current time;
determining whether an insulin dose has not been administered to the subject within about 2 hours from the current time;
displaying correction dose guidance in response to a determination that a correction dose alert has been issued and a determination that the insulin dose has not been administered to the subject within about 2 hours from the current time;
system.

Clause 137 The instructions further cause the one or more processors to:
determining whether the correction dose alert has been disabled;
displaying the corrected dose guidance in response to a determination that the corrected dose alert has been disabled;
The system described in Article 136.

Clause 138. The system of Clause 136, wherein the input section comprises wireless communication circuitry.

Article 139 A method for recommending a correction dose, comprising:
receiving, by the electronic device, insulin dose data for the subject from the insulin delivery device;
determining whether a correction dose alert has been issued at the current time;
determining whether an insulin dose has not been administered to the subject within about two hours from the current time;
displaying corrected dose guidance;
the corrected dose guidance is displayed in response to a determination that the corrected dose alert has been issued and a determination that the insulin dose has not been administered to the subject within about 2 hours from the current time;
Method.

Clause 140. The method of Clause 139, further comprising determining whether the corrected dose alert is disabled, and wherein the corrected dose guidance is displayed only if the corrected dose alert is disabled.

Article 141 A system for providing alerts to a target,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the insulin delivery device;
a display configured to visually present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determining whether a correction dose condition has been issued for consecutive minutes at the current time;
determining whether an insulin dose has not been recorded within about 2 hours from the current time;
an alert associated with a correction dose alert in response to a determination that the correction dose condition has been issued for consecutive minutes and a determination that the insulin dose has not been recorded within about two hours from the current time; display the interface,
system.

Clause 142. The system of Clause 141, wherein the consecutive minutes are about 5 minutes.

Clause 143 The instructions further cause the one or more processors to:
Determine whether a meal administration forgetting alert has been issued,
displaying the alert interface associated with the correction dose alert in response to a determination that a missed meal dose alert has not been issued;
in response to determining that the missed dose alert has been issued, not displaying the alert interface associated with the corrected dose alert;
The system described in Article 141.

Clause 144 The system according to Clause 141, wherein the input section comprises a wireless communication circuit.

Clause 145 A method for alerting a user regarding a correction dose, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device and receiving subject insulin dose data from the insulin delivery device;
determining whether a corrected dose condition has been issued for consecutive minutes at the current time;
determining whether an insulin dose has not been recorded within about two hours from the current time;
an alert interface associated with a correction dose alert in response to a determination that the correction dose condition has been issued for consecutive minutes and a determination that the insulin dose has not been recorded within about two hours from the current time; A method, including steps for displaying.

Clause 146. The method of Clause 145, wherein the consecutive minutes are about 5 minutes.

Clause 147. The method of clause 145, further comprising determining whether a missed meal dose alert has been issued prior to displaying the alert interface associated with the corrected dose alert.

Clause 148. The method of Clause 147, wherein the alert interface associated with the corrected dose alert is displayed only in response to a determination that the missed meal alert has not been issued.

Article 149 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Cause a correction dose alert to be issued,
for consecutive minutes after the correction dose alert is issued, determining whether a correction dose condition is detected;
Responsive to determining that the correction dose condition has not been detected for several consecutive minutes after the correction dose alert is issued, causing the correction dose alert to be canceled;
system.

Clause 150 The system according to Clause 149, wherein the consecutive minutes are 15 consecutive minutes.

Clause 151. The system of Clause 149, wherein the input section comprises a wireless communication circuit.

Article 152 A method for canceling an alert for a correction dose, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a correction dose alert;
determining whether a correction dose condition is detected for consecutive minutes after the correction dose alert is issued;
canceling the corrected dose alert in response to a determination that the corrected dose condition is not detected for consecutive minutes after the corrected dose alert is issued.

Clause 153 The method according to Clause 152, wherein the consecutive minutes are 15 consecutive minutes.

Article 154 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
causing a correction dose alert to be issued for the first time at a first time;
determining whether an insulin onboard (IOB) calculation has been changed since the first time;
in response to determining that the insulin onboard (IOB) calculation has changed since the first time, causing the corrected dose alert to be canceled;
system.

Clause 155. The system of Clause 154, wherein the input section comprises a wireless communication circuit.

Article 156 A method for canceling an alert for a correction dose, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a first corrective dose alert at a first time;
determining whether an insulin onboard (IOB) calculation has been changed since the first time;
resetting the corrected dose alert in response to determining that the insulin onboard (IOB) calculation has changed since the first time.

Article 157 A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the insulin delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
cause a correction dose alert to be issued at the current time;
determining whether an insulin dose was recorded within a period from the current time;
Responsive to determining that the insulin dose was recorded within a period from the current time, causing the corrected dose alert to be canceled;
system.

Clause 158. The system of Clause 157, wherein the period of time is approximately 2 hours.

Clause 159. The system of Clause 157, wherein the input section comprises a wireless communication circuit.

Article 160 A method for canceling an alert for a correction dose, comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device and receiving subject insulin dose data from the insulin delivery device;
issuing a corrected dose alert at the current time;
determining whether an insulin dose was recorded within a period from the current time;
and canceling the corrected dose alert if it is determined that the insulin dose was recorded within a time period from the current time.

Clause 161 The method of Clause 160, wherein said period of time is about 2 hours.

Article 162 A system for classifying doses,
an input configured to receive insulin dose data of a user from a connected insulin delivery device, the insulin dose data including a recent dose including an insulin amount and a timestamp;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
providing dose recommendation guidance for a meal requested by a user at a request time, the meal having a meal type, and the dose recommendation guidance including a recommended dose;
determining whether the timestamp of the most recent dose is within the requested time period;
determining whether the insulin amount of the most recent dose is the same as a recommended dose of a dietary dose guidance recommendation;
responsive to a determination that the timestamp of the recent dose is within the request time period and a determination that the insulin amount of the recent dose is the same as the recommended dose of the dietary dose guidance recommendation; and classifying the recent dose as related to the meal type of a recent meal;
system.

Clause 163. The system of Clause 162, wherein the period of time is about 20 minutes or less.

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 instructions further cause the one or more processors to:
determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal;
In response to determining that the timestamp of the most recent dose is within the determined meal administration time range for the meal type of the most recent meal, the most recent dose is added to the meal of the most recent meal. to be classified as related to the type;
The system described in Article 162.

Clause 166 The instructions further cause the one or more processors to:
determining whether the recent dose was taken while the user was in a postprandial state;
in response to determining that the recent dose was ingested while the user was not in the postprandial state, causing the recent dose to be classified as associated with the meal type of the recent meal;
The system described in Article 162.

Clause 167. The system of Clause 166, wherein in the postprandial state, a previous dose administered within about 2 hours of the request time is associated with the meal type of the most recent meal.

Clause 168. The system of Clause 162, wherein the input comprises wireless communication circuitry.

Clause 169 A method for classifying doses from a connected insulin delivery device, comprising:
providing dosage recommendation guidance for a meal requested by a user at a request time, wherein the meal has a meal type and the dosage recommendation guidance includes a recommended dosage;
receiving, by the electronic device, insulin dose data of a user from a connected insulin delivery device, the insulin dose data including recent doses including insulin amounts and timestamps;
determining whether the timestamp of the most recent dose is within the requested time period;
determining whether the insulin amount of the most recent dose is the same as a recommended dose of a dietary dose guidance recommendation;
responsive to a determination that the timestamp of the recent dose is within the request time period and a determination that the insulin amount of the recent dose is the same as the recommended dose of the dietary dose guidance recommendation; and classifying the recent dose as related to the meal type of a recent meal.

Clause 170. The method of Clause 169, wherein the period of time is about 20 minutes or less.

Clause 171. The method of Clause 169, wherein the meal type is selected from the group consisting of breakfast, lunch, and dinner.

Clause 172. The method of Clause 169, further comprising determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal.

Clause 173. The method of Clause 169, further comprising determining whether the recent dose was taken while the user was in a postprandial state.

Clause 174. The method of Clause 173, wherein in a postprandial state, a previous dose administered within about 2 hours of the request time is associated with the meal type of the most recent meal.

Article 175 A system for classifying doses,
an input configured to receive insulin dose data of a user from a connected insulin delivery device, the insulin dose data including a recent dose including an insulin amount and a timestamp;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
providing dose recommendation guidance at a first time;
determining whether the timestamp of the most recent dose is within the first time period;
determining whether the insulin amount of the most recent dose is the same as the recommended dose of the dose recommendation guidance;
causing said recent dose to be classified as a meal dose, a correction dose or an ambiguous dose;
system.

Clause 176. The system of Clause 175, wherein the period of time is about 20 minutes or less.

Clause 177. The system of Clause 175, wherein the dose recommendation guidance is a correction dose recommendation guidance and the recent dose is classified as a correction dose.

Clause 178. The system of Clause 175, wherein the dose recommendation guidance is a meal dose recommendation guidance, the meal has a meal type, and the recent dose is classified as a dose of the meal type.

Clause 179 The instructions further cause the one or more processors to:
determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal;
The system described in Article 178.

Clause 180 The instructions further cause the one or more processors to:
determining whether the recent dose was taken while the user was in a postprandial state;
The system described in Article 178.

Clause 181. The system of Clause 175, wherein said recent dose is classified as an ambiguous dose.

Clause 182 The instructions further cause the one or more processors to:
prompting the user to manually categorize the recent doses;
The system described in Article 181.

Clause 183 The instructions further cause the one or more processors to:
prompting the user to manually classify the recent dose by prompting the user to select a classification from the group consisting of breakfast dose, lunch dose, dinner dose and correction dose;
The system described in Article 182.

Clause 184 The instructions further cause the one or more processors to:
prompting the user to manually classify the recent dose by prompting the user to select a classification from the group consisting of a snack dose, a priming dose, and a missed dose;
The system described in Article 182.

Clause 185 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 category related to eating more than expected from previous meals;
The system described in Article 182.

Clause 186. The system of Clause 181, wherein said recent dose classified as an ambiguous dose must be classified as a non-ambiguous dose category before providing additional dose guidance recommendations.

Clause 187. The system of Clause 175, wherein the input section comprises a wireless communication circuit.

Clause 188 A method for classifying doses from a connected insulin delivery device, comprising:
providing dose recommendation guidance at a first time;
receiving, by an electronic device, user insulin dose data from the connected insulin delivery device, the insulin dose data including recent doses including insulin amounts and timestamps;
determining whether the timestamp of the most recent dose is within the first time period;
determining whether the insulin amount of the recent dose is the same as the recommended dose of the dose recommendation guidance; and classifying the recent dose as a dietary dose, a correction dose, or an ambiguous dose. Including, methods.

Clause 189. The method of Clause 188, wherein the period of time is about 20 minutes or less.

Clause 190. The method of Clause 188, wherein the dose recommendation guidance is a correction dose recommendation guidance and the recent dose is classified as a correction dose.

Clause 191. The method of Clause 188, wherein the dose recommendation guidance is a meal dose recommendation guidance, the meal has a meal type, and the recent dose is classified as a dose of the meal type.

Clause 192. The method of Clause 191, further comprising determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of a recent meal.

Clause 193. The method of Clause 191, further comprising determining whether the recent dose was taken while the user was in a postprandial state.

Clause 194. The method of Clause 188, wherein said recent dose is classified as an ambiguous dose.

Clause 195. The method of Clause 194, further comprising prompting the user to manually categorize the recent dose.

Clause 196: said step of prompting said user to manually classify said recent dose prompts said user to select a classification from the group consisting of breakfast dose, lunch dose, dinner dose and correction dose. The method according to clause 195, comprising:

Clause 197: said step of prompting said user to manually classify said recent dose comprises prompting said user to select a classification from the group consisting of snack dose, priming dose and missed dose; The method according to clause 195, including.

Clause 198: said step of prompting said user to manually categorize said recent dose comprises prompting said user to select a category related to eating more than expected from previous meals. , the method described in Article 195.

Clause 199. The method of Clause 194, wherein the recent dose classified as an ambiguous dose must be classified as a non-ambiguous dose before providing additional dose guidance recommendations.

Article 200 A device for providing dose guidance in response to analyte data, comprising:
an input configured to receive measured analyte data, dietary data and medication data;
a display configured to visually present information;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the device to:
receiving time-correlated analyte data of the patient captured in a buffer over an analysis period;
dividing the time-correlated analyte data into discrete time-of-day (TOD) periods;
by executing an algorithm to determine a recommended fixed dose of the drug in a corresponding one of the TOD periods based on at least a portion of the time-correlated analyte data and the patient's prescribed dosing strategy for the analysis period; let them decide;
storing said recommended fixed dose indicator in a computer memory for output to at least one of a user or a drug delivery device;
Device.

Clause 201: said memory stores said recommended fixed dose at least in part;
classifying each of the drug doses into a medication class based on the time-correlated analyte data;
grouping each of the doses into one of a set of mealtime groups;
determining a glucose pattern that best fits the time-correlated data;
further comprising instructions for determining by selecting a glucose pattern indicator based on the glucose pattern;
Apparatus according to Article 200.

Clause 202, wherein the memory further retains instructions for classifying doses into classes comprising fixed basal doses, fixed breakfast doses, fixed lunch doses, fixed dinner doses for the corresponding TOD period. equipment.

Clause 203: the memory further retains instructions for determining, as a condition for use in determining the recommended fixed dose, that there are no gaps in the segment of time-correlated analyte data that exceed a predefined threshold; 200.

Clause 204, wherein the memory further retains instructions for determining that the segment of time-correlated analyte data has an associated initial dietary dose as a condition for use in determining the recommended fixed dose. 200.

Clause 205: instructions for the memory to determine, as a condition for use in determining the recommended fixed dose, that the segment of time-correlated analyte data is associated with a basal fixed dose within the past 24 hours; The apparatus according to clause 200, further comprising:

Clause 206: the memory is responsive to one or more of determining the recommended fixed dose, determining a pre-prandial correction factor, determining a post-prandial correction factor, or determining a manual dose adjustment; 200. The apparatus of clause 200, further retaining instructions for clearing data for each TOD period.

Clause 207, wherein the memory further retains instructions for determining a glucose pattern for each TOD period based on the relevant valid data segment for a set number of past days, and determining the recommended fixed dose comprises: 200. The device of clause 200, further based on glucose patterns.

Clause 208: The memory further retains instructions for determining, as a condition for determining the recommended fixed dose, that the associated valid data segment is available for the past set number of days. equipment.

Clause 209. The apparatus of Clause 207, wherein the memory further retains instructions for determining that the glucose pattern is low based on a count of low alarms occurring during each TOD period.

Clause 210. The apparatus of Clause 207, wherein the memory further retains instructions for determining that the glucose pattern is low based on a count of hypoglycemic instances in each TOD period.

Clause 211. The apparatus of clause 207, wherein the memory further retains instructions for determining that the glucose pattern is high based on a count of hyperglycemic instances in each TOD period.

Clause 212, wherein the memory determines a preprandial correction factor based on the time-correlated analyte data, independent of the determination of the recommended fixed dose, wherein both the preprandial correction factor and the recommended fixed dose indicate an increase in dose. 208. The apparatus of clause 207, further retaining instructions for maintaining the pre-meal correction factor if the pre-meal correction factor.

Clause 213 The memory further retains instructions for determining a glucose pattern condition based on a low alarm count, a postprandial correction count during each TOD period, and the glucose pattern indicator. equipment.

Clause 214: said memory is configured to lower said glucose pattern if said low alarm count exceeds a first threshold, said postprandial correction count exceeds a second threshold, and GPA method results indicate a low pattern; 214. The apparatus of clause 213, further retaining instructions for determining that.

Clause 215: The memory is configured such that the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the GPS method results in a moderate hypoglycemic risk or high/low pattern. 214. The apparatus of clause 213, further comprising instructions for determining the glucose pattern to be high/low if the glucose pattern is high/low.

Clause 216: the memory stores the glucose if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the glucose pattern indicator indicates no pattern or a high value pattern. 214. The apparatus of clause 213, further comprising instructions for determining the pattern to be high.

Clause 217. The apparatus of Clause 200, wherein the input comprises wireless communication circuitry.

Article 218 A drug delivery device comprising:
an input configured to receive a query for dose data for a period of time, the dose data including all doses and times of administration delivered during the period;
one or more processors coupled to the input and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
storing data of doses administered during the time period to create stored data;
determining whether the stored data includes all doses delivered during the time period;
in response to a determination that the stored data does not include all doses delivered during the time period, causing an indication of incomplete dose data to be created;
Drug delivery device.

Clause 219. The device of clause 218, wherein the instructions further cause the one or more processors to send an indication of the incomplete dose data in response to the query for dose data.

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 a counter value and an estimated counter value.

Clause 223. The device of clause 222, wherein the estimated counter value is calculated based on a 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 the connected insulin pen is configured to wirelessly transmit dose data.

Clause 225. The device of Clause 218, wherein the drug delivery device is an insulin pen and a connected pen cap, and the connected pen cap is configured to wirelessly transmit dosage data.

Clause 226. The device of Clause 218, wherein the indication of incomplete dose data is based on a detection that the pen cap has not been attached to an insulin pen for a different period of time.

Clause 227: the detection that the pen cap has not been attached to the insulin pen during the different time periods is such that the insulin pen contains a first amount of insulin before the pen cap was not attached; 227. The device of clause 226, comprising determining that the pen cap contains a second amount of insulin after being reattached to the insulin pen, and the first amount is different than the second amount. .

Clause 228. The device of clause 218, wherein the query for the dose data from the time period is sent from an application that provides dose guidance.

Clause 229. The device of Clause 218, wherein the input comprises wireless communication circuitry.

Article 230 A method of transferring data, comprising:
receiving a query for dose data for a period of time, the dose data including all doses and times of administration delivered during the period;
storing data of doses administered during said time period to create stored data;
determining whether the stored data includes all doses delivered during the time period;
and creating an indication of incomplete dose data in response to a determination that the stored data does not include all doses delivered during the time period.

Clause 231. The method of clause 230, further comprising transmitting an indication of the incomplete dose data in response to the dose data query.

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 a counter value and an estimated counter value.

Clause 235. The method of clause 234, wherein the estimated counter value is calculated based on a 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 a detection that a pen cap has not been attached to an insulin pen for a different period of time.

Clause 237: the detection that the pen cap has not been attached to the insulin pen during the different time periods is such that the insulin pen contains a first amount of insulin before the pen cap was not attached; The method of clause 236, comprising determining that the pen cap contains a second amount of insulin after being reattached to the insulin pen, and the first amount is different than the second amount. .

Clause 238. The method of clause 230, wherein the query for the dose data from the time period is sent from an application that provides dose guidance.

Article 239 A system for providing dosage guidance to a subject, comprising:
an input configured to receive dose data and an indication of incomplete dose data from a drug delivery device, the dose data comprising data relating to at least one dose administered during a period of time; , an input section;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
interrogating the drug delivery device for the dose data including data related to at least one dose administered during the time period;
determining whether the incomplete dose data indication is received from the drug delivery device;
in response to determining that the indication of incomplete dose data has been received, prompting for confirmation that the dose data received during the period includes dose data for all doses administered during the period; output,
in response to determining that the indication of incomplete dose data has not been received, causing dose guidance to be calculated;
system.

Clause 240 The instructions further cause the one or more processors to:
causing the dose guidance to be output on the display;
The system described in Article 239.

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 a counter value and an estimated counter value.

Clause 243. The system of Clause 242, wherein the estimated counter value is calculated based on a 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 the connected insulin pen is configured to wirelessly transmit dose data.

Clause 245. The system of Clause 239, wherein the drug delivery device is an insulin pen and a connected pen cap, and the connected pen cap is configured to wirelessly transmit dosage data.

Clause 246 The system further comprises a drug delivery device, the drug delivery device comprising:
an input configured to receive a query for said dose data comprising data relating to at least one dose administered during said time period;
further comprising one or more processors coupled to the input and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
storing data of doses administered during the time period to create stored data;
determining whether the stored data includes all doses delivered during the time period;
generating an indication of the incomplete dose data in response to a determination that the stored data does not include all doses delivered during the time period;
in response to querying the dose data and determining that the stored data does not include all doses delivered during the time period, causing an indication of the incomplete dose data to be sent;
The system described in Article 239.

Clause 247. The system of Clause 239, wherein the input section comprises a wireless communication circuit.

Article 248 A method for providing dosage guidance to a subject, comprising:
receiving an indication of dose data and incomplete dose data from a drug delivery device, the dose data including data relating to at least one dose administered during a period of time;
interrogating the drug delivery device for the dose data including data related to the at least one dose administered during the time period;
determining whether the incomplete dose data indication is received from the drug delivery device;
in response to determining that the indication of incomplete dose data has been received, prompting for confirmation that the dose data received during the period includes dose data for all doses administered during the period; A step to output
and calculating dose guidance in response to determining that the indication of incomplete dose data has not been received.

Clause 249. The method of Clause 248, further comprising outputting said dose guidance 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 a counter value and an estimated counter value.

Clause 252. The method of Clause 251, wherein the estimated counter value is calculated based on a 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, and the connected insulin pen is configured to wirelessly transmit dose data.

Clause 254. The method of Clause 248, wherein the drug delivery device is an insulin pen and a connected pen cap, and the connected pen cap is configured to wirelessly transmit dosage data.

Article 255 A method for recommending dietary doses, comprising:
prompting the user to enter a tag associated with the meal type;
receiving a tag entered for an instance of the meal type;
associating the entered tag with an amount of drug administered for the instance of the meal type and a postprandial analyte data set for the instance of the meal type;
determining whether a threshold number of instances associated with the meal type is met;
determining a recommended drug dose for the meal type if the threshold number of instances is met.

Clause 256: 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 postprandial analyte data set for the instance of the meal type. The method described in Article 255.

Clause 257: the recommended drug dose for the meal type depends at least in part on a plurality of drug amounts administered for the plurality of instances of the meal type and a plurality of postprandial analyte data sets for the plurality of instances of the meal type. The method according to article 255, which is based on

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 receiving analyte data from the sensor control device.

Clause 260. The method of Clause 259, wherein the recommended drug 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 visually outputting the recommended drug dose for the meal type on a display.

Clause 262. The method of Clause 255, further comprising prompting the user with an option to track the meal type.

Clause 263. Clause 262, wherein the step of prompting the user to enter the tag associated with the meal type occurs in response to the user selecting the option to track the meal type. Method described.

Article 264 A system for determining recommended drug doses, comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receive a tag entered for an instance of the meal type;
associating the entered tag with an amount of drug administered for the instance of the meal type and a postprandial analyte data set for the instance of the meal type;
determining whether a threshold number of instances associated with the meal type is met;
if the threshold number of instances is met, causing a recommended drug dose for the meal type to be determined;
system.

Clause 265: 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 postprandial analyte data set for the instance of the meal type. The system described in Article 264.

Clause 266: the recommended drug dose for the meal type depends at least in part on a plurality of drug amounts administered for the plurality of instances of the meal type and a plurality of postprandial analyte data sets for the plurality of instances of the meal type. The system according to article 264, which is based on

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.

Clause 268. The system of Clause 264, further comprising a wireless communication circuit configured to receive data indicative of the analyte level from the sensor control device.

Clause 269. The system of Clause 268, wherein the recommended drug dose for the meal type is based at least in part on the data received from the sensor control device indicating the analyte level.

Clause 270. The method of clause 264, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to visually output the recommended drug dose for the meal type on a display. system.

Clause 271. The system of Clause 264, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to prompt the user with an option to track the meal type.

Clause 272: when the instructions are executed by the one or more processors, the one or more processors are associated with the meal type only if the user selects the option to track the meal type. 272. The system of clause 271, wherein the system prompts the user to enter the assigned tag.

Article 273 A system for tagging healthy meals, comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receiving a tag entered for an instance of a first meal type associated with one or more previously entered tags;
determining whether a meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold;
associating the input tag with a second meal type different from the first meal type;
system.

Clause 274: the meal type characteristic of the instance is at least partially dependent on the difference between the meal size associated with the one or more previously entered tags and the meal size of the entered tag of the instance; The system according to clause 273, which is based on

Clause 275: the meal type characteristic of the instance is dependent on the difference between the drug amount associated with the one or more previously entered tags and the drug amount associated with the entered tag of the instance. 274. A system according to clause 273, based at least in part.

Clause 276: when the instructions are executed by the one or more processors, the one or more processors further determine that the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold; 274. The system of clause 273, wherein if the user has an option to create the second tag, the system prompts the user with an option to create the second tag.

Clause 277: When the instructions are executed by the one or more processors, the one or more processors receive the input information only if the user selects the option to create the second tag. 274. The system of clause 273, wherein the system associates a tag with the second meal type.

Clause 278: the instructions, when executed by the one or more processors, further cause the one or more processors to determine that the meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold; 274. The system of clause 273, wherein if the input tag of the instance is disassociated from the first meal type.

Clause 279: when the instructions are executed by the one or more processors, the one or more processors are configured to configure the first meal type before the entered tag is associated with the second meal type. 279. The system of clause 278, causing the input tag of the instance to be disassociated from a type.

Article 280 A method for recommending dietary doses, 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 drug administered for the 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 drug amount administered for the second instance of the meal type and a second postprandial analyte data set for the instance of the meal type; step and
prompting the user to input a modification tag associated with the meal type in response to determining that the difference between the second drug amount and the first drug amount is greater than a predetermined threshold difference; A method, including steps for displaying a prompt.

Clause 281. The method of Clause 280, wherein the modification tag includes a different size of the meal.

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 prompting the user to enter the modification tag associated with the meal type occurs in real time.

Clause 284. Prompting the user to enter the modified tag associated with the meal type occurs within about 5 minutes of receiving the second entered tag. the method of.

Clause 285. Prompting the user to enter the modified tag associated with the meal type occurs within about 2 minutes of receiving the second entered tag. the method of.

Clause 286: associating the first input tag with a first postprandial analyte data set of the first instance of the meal type;
280. The method of clause 280, further comprising associating the second input tag with a second postprandial analyte data set of the second instance of the meal type.

Article 287 A system for tagging meals, comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receiving a first input tag for a first instance of the meal type;
associating the first entered tag with a first amount of drug administered for the 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 drug administered for the second instance of the meal type;
prompting the user to enter a modification tag associated with the meal type in response to determining that the difference between the second drug amount and the first drug amount is greater than a predetermined threshold difference; display,
system.

Clause 288. The system of Clause 287, wherein the modification tag includes a different size of the meal.

Clause 289. The system of Clause 287, wherein the predetermined threshold difference is at least about 2 units.

Clause 290: When the instructions are executed by the one or more processors, the one or more processors further prompt the user to enter the modification tag associated with the meal type. A system described in Article 287 that allows things to be done in real time.

Clause 291: When the instructions are executed by the one or more processors, the one or more processors further receive the meal within about 5 minutes of receiving the second input tag. 288. The system of clause 287, prompting the user to enter the modification tag associated with a type.

Clause 292: When the instructions are executed by the one or more processors, the one or more processors further receive the meal within about two minutes of receiving the second input tag. 288. The system of clause 287, prompting the user to enter the modification tag associated with a type.

Clause 293: When the instructions are executed by the one or more processors, the instructions further cause the one or more processors to assign the first input tag to the first input tag of the first instance of the meal type. 288. The system of clause 287, wherein the second entered tag is associated with a second postprandial analyte data set of the second instance of the meal type.

Article 294 An analyte monitoring system comprising:
a sensor control device comprising an analyte sensor, at least a portion of the analyte sensor being configured to be in fluid contact with a body fluid of a subject;
and a reading device.
The reading device is
a wireless communication circuit configured to receive analyte levels from the sensor control device;
one or more processors coupled to memory for storing instructions;
when executed by the one or more processors, the one or more processors:
determining a pattern type for at least one time segment of a day based on a hypoglycemia risk indicator and a hyperglycemia risk indicator for the at least one time segment of the day;
Output the display to the user interface,
The above display is
at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;
A time-in-target display that includes a graph of time-in-target graph portions including a plurality of graph portions, each graph portion of the plurality of graph portions being within a predefined analyte range associated with each graph portion. a time-in-target display, wherein the plurality of graph portions includes at least four graph portions;
a graph comprising a plot of the user's analyte levels over a horizontal representation of a plurality of time segments of a day and an identification of the pattern type determined for the at least one time segment;
Analyte monitoring system.

Clause 295. The system of Clause 294, wherein the at least one glucose indicator comprises a glucose average value.

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 output a display to the user interface that includes a target value corresponding to the at least one glucose indicator.

Clause 298. The system of Clause 294, wherein the plurality of graph portions includes at least five graph portions.

Article 299 The plurality of graph portions are graph portions below a very low threshold, graph portions between a very low threshold and a low threshold, graph portions between a low threshold and a high threshold, and a graph portion between a high threshold and a very high threshold. 295. The system of clause 294, comprising at least four graph portions selected from the group consisting of graph portions between a threshold and graph portions above a very high threshold.

Clause 300. The system of Clause 294, wherein the time-in-target range display further includes a description of the defined analyte range associated with each graph portion.

Clause 301: said time-in-target range indication is such that, for each graph portion of said plurality of graph portions, said user's analyte level was within said defined analyte range associated with said graph portion during said time period; 295. The system of clause 294, further comprising a value related to an amount of time.

Clause 302 The system of clause 301, wherein the value is a percentage value.

Clause 303: said time-in-target-range indication is for at least two of said plurality of graph portions within each said defined analyte range associated with said at least two graph portions during said time period; 295. The system of clause 294, further comprising a total value related to a total amount of time the user's analyte level was present.

Clause 304. The system of clause 294, wherein the graph of time in target range includes a histogram.

Clause 305 Each graph portion of said histogram is arranged in a vertical layout, the graph portion below a very low threshold is located below the graph portion between the very low threshold and the low threshold; is located below a graph portion between a low threshold and a high threshold, said graph portion is located below a graph portion between a high threshold and a very high threshold, said graph portion is very 305. The system of clause 304, located below a portion of the graph that exceeds a high threshold.

Clause 306. The system of clause 294, wherein the identification of the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph.

Clause 307. The system of clause 306, wherein the identification of the determined pattern type for the at least one time segment further comprises a label for the determined pattern type.

Clause 308. The system of Clause 294, wherein the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

Clause 309. The system of Clause 294, wherein the graph includes a plurality of determined pattern types, and identification of a single pattern type is visually distinct from other identifications of the plurality of determined pattern types.

Clause 310 The instructions further cause the one or more processors to output an indication to the user interface that includes an identification of a most important pattern type, the most important pattern type being a type of pattern for at least one time segment of the day. 295. The system of clause 294, wherein the system is one of the determined pattern types.

Clause 311. The system of clause 310, wherein identification of the most important pattern types is displayed on the graph.

Clause 312. The identification of the determined pattern type for the at least one time segment comprises a plurality of identifications of the determined pattern type for each of the at least one time segment, and the identification of the most important pattern type is , visually distinct 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 segment of the day includes a plurality of pattern types for a plurality of time segments of the day.

Clause 314. The system of Clause 313, wherein the plurality of pattern types includes at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

Clause 315. The system of Clause 314, wherein if the plurality of pattern types includes a low value pattern, identifying the most important pattern type includes identifying the low value pattern.

Clause 316 If the plurality of pattern types include a high value pattern with some low values and no low value pattern, the identification of the most important pattern type shall be based on the identification of the high value pattern with some low values. 314. A system according to clause 314, including identification.

Clause 317: Where the plurality of pattern types includes a high value pattern and does not include a high value pattern or a low value pattern with a portion of low values, the identification of the most important pattern type includes the identification of the high value pattern. The system described in 314.

Clause 318. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of at least one time segment of the day that was determined to have the most significant pattern type. 310. The system described in 310.

Clause 319 An indication of the identification of said most important pattern type and the identification of at least one time segment of said day determined to have said most important pattern type, 310. The system of clause 310, comprising at least one tag for identification of at least one time segment of the day determined to have a significant pattern type.

Clause 320: said tag for identification of said most important pattern type is a different color than said at least one tag for identification of said at least one time segment of said day determined to have said most important pattern type; The system according to Article 319, which is

Clause 321 The instructions further cause the one or more processors to:
determining a variation in at least one time segment of the day;
if the determined variation is high, causing the user interface to output a display including a statement related to the variation;
The system described in Article 294.

Clause 322. The system of clause 321, wherein the statement related to fluctuations includes identification of behaviors that may contribute to glucose fluctuations.

Clause 323. The system of Clause 294, wherein the instructions further cause the one or more processors to output a display to the user interface that includes a statement relating to excursions below a very low threshold.

Clause 324. The system of Clause 323, wherein the very low threshold is between about 50 mg/dL and about 58 mg/dL.

Clause 325. The system of Clause 323, wherein the very low threshold is about 54 mg/dL.

Clause 326. The system of Clause 294, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to medication guidance.

Clause 327. The system of Clause 326, wherein the statements related to medication guidance include suggestions for adjusting medication.

Clause 328. The system of clause 326, wherein the statements related to medication guidance include suggestions related to 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 output a display to the user interface that includes statements related to lifestyle guidance.

Clause 330. The system of Clause 329, wherein the lifestyle guidance related statements include statements related to at least one of dietary forgetfulness, carbohydrates, activity level, alcohol, and medication.

Clause 331 The system according to Clause 294, wherein said period is 14 days.

Clause 332 A method for displaying information related to a subject's glucose level, the method comprising:
receiving an analyte level from a sensor control device;
determining a pattern type for at least one time segment of a day based on a hypoglycemic risk indicator and a hyperglycemic risk indicator for the at least one time segment of the day;
a step of displaying a user interface,
at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;
A time-in-target display that includes a graph of time-in-target graph portions including a plurality of graph portions, each graph portion of the plurality of graph portions being within a predefined analyte range associated with each graph portion. a time-in-target display, wherein the plurality of graph portions includes at least four graph portions;
displaying a user interface comprising: a plot of the user's analyte levels over a horizontal display of a plurality of time segments of a day; and a graph comprising an identification of the determined pattern type for the at least one time segment. and the method.

Clause 333. The method of Clause 332, wherein the at least one glucose indicator comprises a glucose average value.

Clause 334. The method of Clause 332, wherein the at least one glucose indicator comprises a glucose management indicator.

Clause 335. The method of Clause 332, wherein the instructions further cause the one or more processors to output a display to the user interface that includes a target value corresponding to the at least one glucose indicator.

Clause 336. The method of Clause 332, wherein the plurality of graph portions includes at least five graph portions.

Clause 337 The plurality of graph portions are graph portions below a very low threshold, graph portions between a very low threshold and a low threshold, graph portions between a low threshold and a high threshold, and a graph portion between a high threshold and a very high threshold. 333. The method of clause 332, comprising at least four graph portions selected from the group consisting of graph portions between a threshold and graph portions above a very high threshold.

Clause 338. The method of Clause 332, wherein the time-in-target range display further includes a description of the defined analyte range associated with each graph portion.

Clause 339: said time-in-target-range indication is such that, for each graph portion of said plurality of graph portions, said user's analyte level was within said defined analyte range associated with said graph portion during said time period; 333. The method of clause 332, further comprising a value related to an amount of time.

Clause 340 The method of Clause 339, wherein the value is a percentage value.

Clause 341: said time-in-target-range indication is for at least two of said plurality of graph portions within each said defined analyte range associated with said at least two graph portions during said time period; 333. The method of clause 332, further comprising a total value related to a total amount of time the user's analyte level was present.

Clause 342. The method of Clause 332, wherein the graph of time in target range includes a histogram.

Clause 343: each graph portion of said histogram is arranged in a vertical layout, the graph portion below a very low threshold is located below the graph portion between the very low threshold and the low threshold; is located below a graph portion between a low threshold and a high threshold, said graph portion is located below a graph portion between a high threshold and a very high threshold, said graph portion is very 343. The method of clause 342, being located below a portion of the graph that exceeds a high threshold.

Clause 344. The method of clause 332, wherein identifying the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph.

Clause 345. The method of clause 344, wherein the identification of the determined pattern type for the at least one time segment further comprises a label for the determined pattern type.

Clause 346. The method of Clause 332, wherein the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

Clause 347. The method of Clause 332, wherein the graph includes a plurality of determined pattern types, and identification of a single pattern type is visually distinguished from other identifications of the plurality of determined pattern types.

Clause 348: The instructions further cause the one or more processors to output an indication to the user interface that includes an identification of a most important pattern type, the most important pattern type being the most important pattern type for at least one time segment of the day. 333. The method of clause 332, wherein the pattern type is one of the determined pattern types.

Clause 349. The method of Clause 348, wherein the identification of the most important pattern types is displayed on the graph.

Clause 350: the identification of the determined pattern type for the at least one time segment comprises a plurality of identifications of the determined pattern type for each of the at least one time segment, and the identification of the most important pattern type comprises: , visually distinct from other identifications of the plurality of identifications.

Clause 351. The method of clause 348, wherein the pattern type determined for at least one time segment of the day includes a plurality of pattern types for a plurality of time segments of the day.

Clause 352. The method of clause 351, wherein the plurality of pattern types includes at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern.

Clause 353. The method of Clause 352, wherein if the plurality of pattern types include low value patterns, identifying the most important pattern type includes identifying the low value patterns.

Clause 354 If the plurality of pattern types include a high value pattern with some low values and no low value pattern, the identification of the most important pattern type is based on the identification of the high value pattern with some low values. A method according to clause 352, including identifying.

Clause 355: Where the plurality of pattern types includes a high value pattern and does not include a high value pattern or a low value pattern with a portion of low values, the identification of the most important pattern type includes the identification of the high value pattern. 352.

Clause 356. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of at least one time segment of the day that was determined to have the most significant pattern type. 348.

Clause 357: an indication of the identification of said most important pattern type and the identification of at least one time segment of said day determined to have said most important pattern type; 349. The method of clause 348, comprising at least one tag for identification of at least one time segment of the day determined to have a significant pattern type.

Clause 358: said tag for identification of said most important pattern type is of a different color than said at least one tag for identification of said at least one time segment of said day determined to have said most important pattern type; The method described in Article 357.

Clause 359 The instructions further cause the one or more processors to:
determining a variation in at least one time segment of the day;
if the determined variation is high, causing the user interface to output a display including a statement related to the variation;
The method described in Article 332.

Clause 360. The method of Clause 359, wherein the statement relating to fluctuations includes identifying behaviors that may contribute to glucose fluctuations.

Clause 361. The method of Clause 332, wherein the instructions further cause the one or more processors to output a display to the user interface that includes a statement relating to excursions below a very low threshold.

Clause 362. The method of Clause 361, wherein the very low threshold is about 50 mg/dL to about 58 mg/dL.

Clause 363. The method of Clause 361, wherein the very low threshold is about 54 mg/dL.

Clause 364. The method of Clause 332, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to medication guidance.

Clause 365. The method of Clause 364, wherein the statement related to medication guidance includes suggestions for adjusting medication.

Clause 366. The method of Clause 364, wherein the statements related to medication guidance include suggestions related to medications that contribute to low glucose levels.

Clause 367. The method of Clause 332, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to lifestyle guidance.

Clause 368. The method of Clause 367, wherein the lifestyle guidance related statements include statements related to at least one of dietary forgetfulness, carbohydrates, activity levels, alcohol, and medication.

Clause 369 The method according to Clause 332, wherein said period is 14 days.

Article 370 A device for displaying indicators related to an object, comprising:
an input configured to receive drug administration data;
a display configured to visually present information;
one or more processors coupled to the input, the display, and a memory storing instructions and a dose of the drug taken by the subject over a period of time and a recommended dose of the drug for the subject over the period of time; Equipped with
The instructions, when executed by the one or more processors, cause the device to:
displaying a graph plotting multiple drug doses taken by the subject multiple times, the graph having an x-axis of time and a y-axis of the difference between the dose taken by the subject and the dose recommended for the subject; including
Device.

Clause 371. The apparatus of Clause 370, wherein the plurality of drug 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 meal dose comprises at least one of a fixed breakfast dose, a fixed lunch dose, and a fixed dinner dose.

Clause 373. The apparatus of Clause 371, wherein the meal dose with a correction factor comprises at least one of a fixed breakfast dose with a correction factor, a fixed lunch dose with a correction factor, and a fixed dinner dose with a correction factor.

Clause 374. The apparatus of Clause 370, wherein the difference between the dose ingested by the subject and the recommended dose is in units.

Clause 375. The apparatus of Clause 370, wherein the input comprises wireless communication circuitry.

Article 376 A device for displaying indicators related to an object, comprising:
an input configured to receive measured analyte data and drug administration data;
a display configured to visually present information;
the input, the display, and a memory storing instructions and time-correlated data characterizing the analyte of the subject, the dose of drug taken by the subject over a period of time, and the recommended drug dose of the subject over a period of time; one or more processors coupled to;
The instructions, when executed by the one or more processors, cause the device to:
displaying a summary of the subject's treatment including doses administered over a period of time and an analyte index determined from the measured analyte data received;
displaying a graphic summarizing doses missed during the period;
displaying a graphic summarizing override doses taken by the subject at a certain time, including doses that are different from the recommended dose at that time;
Device.

Clause 377. The apparatus of Clause 376, wherein the missed dose summary graphic includes a graphical representation of a percentage of forgotten doses for a plurality of dose types.

Clause 378. The apparatus of Clause 377, wherein each proportion of the forgotten proportion for the plurality of dose types is calculated as a proportion of forgotten doses of the dose type to the total number of doses administered of the dose type during a period of time.

Clause 379. The device of Clause 377, wherein the plurality of dose types comprises at least one of a basal dose, a breakfast dose, a lunch dose, and a dinner dose.

Clause 380. The device of Clause 376, wherein the graphic summarizing missed doses is a bar graph.

Clause 381. The apparatus of Clause 376, wherein the graphic summarizing the override doses is a bar graph.

Clause 382. The apparatus of Clause 376, wherein the input comprises wireless communication circuitry.

Article 383 A device for displaying indicators related to an object,
an input configured to receive measured analyte data from a plurality of subjects, drug administration data from a plurality of subjects, and data related to dosing recommendations for the plurality of subjects;
a display configured to visually present information;
the input, the display, instructions and time-correlated data characterizing the analyte of each of the plurality of subjects, the dosage of drug taken by each of the plurality of subjects over a period of time and medication recommendations for the plurality of subjects; one or more processors coupled to a memory for storing data related to the matter;
When executed by the one or more processors, the apparatus includes:
at least two of the following: time in target range, time below low threshold, time above high threshold, percentage of basal dose taken, and average bolus dose taken per day for each of the plurality of subjects. Display a summary of analyte indicators including
displaying a summary of information related to medication recommendations, including displaying medication recommendations for a subject of the plurality of subjects that require approval from a health care provider;
Device.

Clause 384 The summary of said analyte indicators comprises at least three of the following: time in target range, time below low threshold, time above high threshold, percentage of basal dose taken and average bolus dose taken per day. A device according to clause 383, comprising:

Clause 385. The device of Clause 383, wherein the low threshold is about 70 mg/dL.

Clause 386. The device of Clause 383, wherein the high threshold is about 180 mg/dL.

Clause 387. The device of Clause 383, wherein the display of medication recommendations is an icon.

Clause 388. The apparatus of Clause 383, wherein the indication of medication recommendations is a statement indicating the number of medication recommendations that require approval.

Clause 389. The apparatus of Clause 383, wherein the input section comprises a wireless communication circuit.

Article 390 A device for displaying treatment information related to a subject, comprising:
an input configured to receive measured analyte data and drug administration data;
a display configured to visually present information;
one coupled to the input, the display, and a memory storing instructions and time-correlated data characterizing the subject's analyte, the dose of medication taken by the subject over a period of time, and the subject's meal times; Equipped with a processor of
The instructions, when executed by the one or more processors, cause the device to:
receiving estimated dose parameters and estimated meal administration time ranges from the subject;
determining a representative amount of each of a plurality of basal doses and a plurality of dietary doses ingested by the subject during a period of time based on the drug administration data;
determining a typical meal administration time range for the subject during the period based on the drug administration data;
determining a recommended dose of 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 administration time ranges received from the subject;
displaying the representative amount and the representative meal administration time range of each of the plurality of basal doses and the plurality of meal doses;
displaying the recommended dose of at least one of the basal dose, the breakfast dose, the lunch dose and the dinner dose;
Device.

Clause 391: the plurality of meal doses includes a plurality of breakfast doses, a plurality of lunch doses and a plurality of dinner doses, and the instructions, when executed by the one or more processors, cause the device to include the plurality of meal doses. 390. The device of clause 390, wherein the device determines an average amount of each of the basal dose, the plurality of breakfast doses, the plurality of lunch doses, and the plurality of dinner doses.

Clause 392. The apparatus of Clause 390, wherein the estimated dose parameters include estimates of basal dose, breakfast dose, lunch dose, and dinner dose.

Clause 393. The apparatus of Clause 392, wherein the estimated dose parameters further include an estimated time for the subject to ingest the basal dose, the breakfast dose, the lunch dose, and the dinner dose.

Clause 394. The apparatus of clause 390, wherein the estimated meal administration time range includes an estimated start and end time of administration for each of breakfast, lunch, and dinner.

Clause 395: the representative amount of each of the plurality of basal doses and the plurality of dietary doses is the average value of each of the plurality of basal doses and the plurality of dietary doses ingested by the subject during a period of time; Apparatus according to clause 390, including.

Clause 396: the representative amount of each of the plurality of basal doses and the plurality of dietary doses includes the mode of each of the plurality of basal doses and the plurality of dietary doses ingested by the subject during a period of time. , Article 390.

Clause 397 When the instructions are executed by the one or more processors, the apparatus further:
determining a pre-meal correction factor and a post-meal correction factor based on the measured analyte data and the drug administration data;
displaying the before-meal correction coefficient and the after-meal correction coefficient;
Apparatus according to article 390.

Clause 398. The apparatus of Clause 390, wherein the representative meal administration time range is displayed adjacent to the estimated meal administration time range.

Clause 399. The apparatus of Clause 390, wherein the representative amount of each of the plurality of basal doses and the plurality of dietary doses is displayed adjacent to the estimated dose parameter.

Clause 400: When the instructions are executed by the one or more processors, the apparatus further:
determining a conservative value for at least one of the basal dose, breakfast dose, lunch dose and dinner dose that is lower than the corresponding determined representative amount of each of the plurality of basal doses and the plurality of meal doses; ,
displaying the determined conservative value;
Apparatus according to article 390.

Clause 401. The apparatus of Clause 390, wherein the input section comprises a wireless communication circuit.

Claims (229)

An apparatus for parameterizing a patient's medication habits to configure dose guidance settings, the apparatus comprising:
an input component configured to receive measured analyte data, dietary data and medication data;
a display component configured to visually present information;
one or more processors coupled to an input, a display, and a memory for storing instructions and time-correlated data characterizing the patient's analyte over an analysis period;
When the instructions are executed by the one or more processors, the apparatus:
receiving patient dose regimen information for the analysis period;
evaluating a measure of consistency between the time-correlated data and the patient dose regimen information;
determining dose guidance information based on the consistency measure;
Device. A method for facilitating efficient access by a healthcare provider (HCP) to a patient's electronic medical record (EMR) generated by a dosage guidance system while protecting patient privacy, the method comprising:
authenticating the session with the patient by at least one processor of a portable display device;
generating an EMR identification code (ID) by the at least one processor in response to receiving input from the patient indicating a request to share the EMR with an HCP during the session;
providing, by the at least one processor, the EMR ID to a remote server that controls access to the EMR;
outputting the EMR ID by the at least one processor to a display of the portable display device. 3. The method of claim 2, further comprising providing the EMR to the remote server before the step of authenticating. 3. The method of claim 2, further comprising receiving the EMR from the dose guidance system. 3. The method of claim 2, further comprising determining whether the EMR does not meet a concordance condition with patient input indicative of a tracked drug dose pattern. 6. The method of claim 5, further comprising providing the patient with an option to provide the EMR to the HCP upon determining that the EMR does not meet the concordance condition. 6. The method of claim 5, wherein the steps of generating, providing, and outputting are conditional on determining that the EMR does not meet the consistency condition. 7. The method of claim 6, wherein the steps of generating, providing, and outputting are conditional on determining that the EMR does not meet the consistency condition. 3. The method of claim 2, further comprising providing the patient with an option to provide the EMR to the HCP. 4. The method of claim 3, wherein once the remote server receives the EMR ID, it creates a web page addressed at least in part by the EMR ID to display the EMR. 3. The EMR includes determinations of drug dosing parameters administered to the patient from time to time during defined time periods and a measure of consistency of the determinations with patient-provided drug dosing information. the method of. 12. The method of claim 11, wherein the drug is insulin. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data from a drug delivery device, the dose data including the most recent drug dose administered and the time of drug administration;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
When the instructions are executed by the one or more processors, the one or more processors:
causing the most recent drug dose administered to be classified as a meal dose or a correction dose;
displaying additional dose guidance in response to a determination that a certain period of time has elapsed since the time of the most recent drug dose administered;
system. A method for providing dosage guidance, the method comprising:
receiving, by the electronic device, drug dose data for the subject from the drug delivery device, the drug dose data including the most recent drug dose administered and the time of drug administration;
classifying the most recently administered drug dose as a meal dose or a correction dose;
displaying dose guidance in response to a determination that a one-point period has elapsed since the time of the most recent drug dose administered. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data from a drug delivery device, the dose data including data related to recent drug doses administered;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
When the instructions are executed by the one or more processors, the one or more processors:
determining whether the most recent drug dose administered is the most recent drug dose administered;
in response to determining that the most recent drug dose administered is the most recent drug dose administered, displaying a screen containing dose guidance recommendations;
system. 16. The system of claim 15, wherein the most recent administered drug dose is determined to be the most recent administered drug dose upon confirmation from a user. The dose data further includes data relating to at least one drug dose administered since the reset time, and the instructions further direct the one or more processors to administer at least one drug dose administered since the reset time. 16. The system of claim 15, wherein the system displays a screen in response to determining that the drug dose has been classified. 18. The system of claim 17, wherein the dose data related to at least one drug dose administered since the reset time is automatically categorized. further comprising a drug delivery device configured to deliver at least one dose of drug to a subject, the instructions further transmitting to the one or more processors one or more wireless interrogation signals to the drug delivery device; 19. The system of claim 18, wherein the system determines that the most recent dose administered has been received. 18. The system of claim 17, wherein the dose data related to at least one drug dose administered since the reset time is categorized by a user. 16. 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 information related to the most recently administered drug dose is correct. 22. The system of claim 21, wherein the instructions further cause the one or more processors to display a screen containing the dose guidance recommendations for a period of time beginning after confirmation from the user. 16. The system of claim 15, further comprising a drug delivery device configured to deliver at least one dose of drug to the subject. The input is further configured to receive measured analyte data and a request for dose guidance, and the instructions further cause the one or more processors to:
determining whether the glucose concentration at the time of receiving the request for dose guidance is less than a low threshold;
responsive to determining that the glucose concentration is below a low threshold upon receipt of the request for dose guidance, displaying a screen containing a message for addressing low glucose levels prior to drug administration;
16. The system of claim 15. The instructions further cause the one or more processors to:
suppressing the display of the dose guidance recommendation in response to a determination that the glucose concentration upon receipt of the request for dose guidance is below a low threshold;
25. The system of claim 24. the input is further configured to receive a request for measured analyte data and dose guidance after a meal start time, and the instructions further cause the one or more processors to:
determining whether the glucose concentration at the estimated meal start time is less than a low threshold;
in response to a determination that the glucose concentration at the estimated start time of the meal is below a low threshold, displaying a screen containing a message for addressing low glucose levels prior to drug administration;
16. The system of claim 15. The instructions further cause the one or more processors to:
not displaying the dose guidance recommendation in response to a determination that the glucose concentration at the estimated start time of the meal is below a low threshold;
27. The system of claim 26. 16. The system of claim 15, wherein the input comprises wireless communication circuitry. A method for providing dosage guidance, the method comprising:
receiving, by the electronic device, dosage data of the user from the drug delivery device, the dosage data including data related to recent drug doses administered;
determining whether the most recent drug dose administered is the most recent drug dose administered;
displaying a screen containing dose guidance recommendations in response to determining that the most recent drug dose administered is the most recent drug dose administered. 30. The method of claim 29, wherein the most recent drug dose administered is determined to be the most recent drug dose administered by confirmation from the user. the most recently administered drug dose is administered after a reset time, the method comprising:
further comprising determining whether recent doses administered after the reset time have been classified;
30. The method of claim 29. 32. The method of claim 31, wherein the most recent doses administered after the reset time are automatically categorized. 30. The method of claim 29, further comprising transmitting one or more wireless interrogation signals to the drug delivery device to determine that the most recent dose administered has been received. 32. The method of claim 31, wherein the recent doses administered after the reset time are categorized by user. 35. The method of claim 34, further comprising displaying a prompt for the user to confirm that information related to the most recently administered drug dose is correct. 30. The method of claim 29, wherein the screen containing the dose guidance recommendations is displayed only for a period of time beginning after the user confirms that information related to the most recently administered drug dose is correct. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data from the drug delivery device, the dose data comprising 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 to the input, the display, and a memory storing instructions;
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 classified;
displaying a screen including the plurality of meal icons in response to a determination that the at least one meal dose administered after the reset time has been classified;
system. A method for providing dosage guidance, the method comprising:
receiving, by the electronic device, drug dose data of the user from the drug delivery device, the drug dose data including 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 classified;
displaying a screen including a plurality of meal icons in response to a determination that the at least one meal dose administered since the reset time has been classified. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data from the drug delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determine whether the missed dose alert is effective;
in response to determining that the missed dose alert is not in effect, displaying a dose guidance recommendation based on the usual dietary dose calculation;
in response to determining that the missed dose alert is enabled, displaying a dose guidance recommendation based on a later meal dose calculation;
system. A method for providing dosage guidance, the method comprising:
receiving, by the electronic device, drug dosage data of the user from the drug delivery device;
determining whether a missed dose alert is enabled;
responsive to the determination that the missed dose alert is not in effect, displaying a dose guidance recommendation based on the usual dietary dose calculation;
and, in response to determining that a missed dose alert is in effect, displaying a dose guidance recommendation based on a later meal dose calculation. A system for providing alerts to a target,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determining at the current time whether the meal dose has been forgotten by detecting a condition for forgetting to administer the meal for several consecutive minutes in relation to a meal having an estimated meal start time;
determining whether an insulin dose has not been recorded within about 45 minutes before the estimated meal start time;
an alert interface related to missed meal administration in response to detecting the missed meal administration condition over consecutive minutes and determining that the insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; to display,
system. 42. The system of claim 41, wherein the consecutive minutes are about 5 minutes. The instructions further cause the one or more processors to:
determining whether a meal dose was recorded within about 2 hours from the current time;
in response to determining that the meal dose was recorded within about 2 hours from the current time, displaying an alert interface related to the missed meal administration;
42. The system of claim 41. The instructions further cause the one or more processors to:
determine whether a correction dose alert has been issued;
displaying an alert interface related to the missed meal administration in response to the determination that the correction dose alert has not been issued;
42. The system of claim 41. further comprising a sensor control device configured to collect data indicative of an analyte level in the subject, the sensor control device including an analyte sensor, at least a portion of the analyte sensor being in fluid contact with a bodily fluid of the subject. 42. The system of claim 41, configured to. 42. The system of claim 41, wherein the input comprises wireless communication circuitry. A method for alerting a user to forgetting to administer a meal, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
determining at the current time whether the meal dose has been forgotten by detecting a meal dose forgetting condition for several consecutive minutes in relation to a meal having an estimated meal start time;
determining whether an insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time;
an alert interface related to missed meal administration in response to detecting the missed meal administration condition over consecutive minutes and determining that the insulin dose has not been recorded within about 45 minutes prior to the estimated meal start time; A method, including steps for displaying. 48. The method of claim 47, wherein the consecutive minutes are about 5 minutes. determining whether a meal dose was recorded within about two hours from the current time;
48. The method of claim 47, further comprising displaying an alert interface related to the missed meal administration in response to determining that the meal dose was recorded within about 2 hours of the current time. determining whether a correction dose alert has been issued;
48. The method of claim 47, further comprising displaying an alert interface related to the missed meal administration in response to determining that the corrective dose alert has not been issued. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Issue an alert for forgetting to administer a meal,
determining whether a condition for forgetting to administer a meal is detected for several consecutive minutes after the forgetting to administer to a meal alert is issued;
Responsive to determining that the forgotten meal administration condition has not been detected for several consecutive minutes after the forgotten meal administration alert is issued, canceling the forgotten meal administration alert;
system. 52. The system of claim 51, wherein the consecutive minutes are 15 consecutive minutes. 52. The system of claim 51, wherein the meal administration forget condition includes a determination that an insulin dose was not administered within an estimated meal start time. 52. The system of claim 51, wherein the input comprises wireless communication circuitry. A method for canceling an alert for forgetting to administer a meal, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a meal administration forgotten alert;
determining whether a missed meal condition is detected for consecutive minutes after the missed meal alert is issued;
resetting the missed meal alert in response to a determination that the missed meal condition is not detected for consecutive minutes after the missed meal alert is issued. 56. The method of claim 55, wherein the consecutive minutes are 15 consecutive minutes. 56. The method of claim 55, wherein the meal administration forget condition comprises determining that an insulin dose was not administered within an estimated meal start time. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the drug delivery device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Issue an alert for forgetting to administer a meal at the current time,
determining whether an insulin dose was recorded within about 2 hours from the current time;
in response to determining that the insulin dose was recorded within about 2 hours from the current time, canceling the missed meal administration alert;
system. A method for canceling an alert for forgetting to administer a meal, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a forgotten meal administration alert at the current time;
determining whether an insulin dose has been recorded within about two hours of the current time;
resetting the missed meal alert in response to determining that the dose of insulin was recorded within about 2 hours of the current time. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the drug delivery device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
causing a missed meal administration alert related to the forgotten meal to be issued having an estimated start time;
determining whether the insulin dose was recorded within about 45 minutes from the estimated meal start time;
Responsive to determining that the insulin dose was recorded within about 45 minutes from the estimated meal start time, canceling the meal administration forget alert;
system. A method for canceling an alert for forgetting to administer a meal, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a missed meal administration alert associated with the missed meal having an estimated start time;
determining whether the insulin dose was recorded within about 45 minutes of the estimated meal start time;
resetting the missed meal dose alert in response to determining that the insulin dose was recorded within about 45 minutes of the estimated meal start time. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
at the current time, causing a meal administration forgetting alert related to the meal forgetting having an estimated start time to be issued;
determining whether the estimated meal start time is within about 2 hours from the current time;
Responsive to determining that the estimated meal start time did not occur within about 2 hours from the current time, canceling the meal administration forgetting alert;
system. A method for canceling an alert for forgetting to administer a meal, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a missed meal administration alert associated with the missed meal having an estimated start time at the current time;
determining whether the estimated meal start time is within about 2 hours from the current time;
resetting the missed meal alert in response to determining that the estimated meal start time did not occur within about two hours of the current time. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data from the insulin delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determine whether a correction dose alert has been issued at the current time;
determining whether an insulin dose has not been administered to the subject within about 2 hours from the current time;
displaying correction dose guidance in response to a determination that a correction dose alert has been issued and a determination that the insulin dose has not been administered to the subject within about two hours from the current time;
system. A method for recommending a correction dose, the method comprising:
receiving, by the electronic device, insulin dose data for the subject from the insulin delivery device;
determining whether a correction dose alert has been issued at the current time;
determining whether an insulin dose has not been administered to the subject within about two hours from the current time;
displaying corrected dose guidance;
the corrected dose guidance is displayed in response to a determination that the corrected dose alert has been issued and a determination that the insulin dose has not been administered to the subject within about 2 hours from the current time;
Method. A system for providing alerts to a target,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the insulin delivery device;
a display configured to visually present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
determining whether a correction dose condition has been issued for consecutive minutes at the current time;
determining whether an insulin dose has not been recorded within about 2 hours from the current time;
an alert associated with a correction dose alert in response to a determination that the correction dose condition has been issued for consecutive minutes and a determination that the insulin dose has not been recorded within about two hours from the current time; display the interface,
system. 67. The system of claim 66, wherein the consecutive minutes are about 5 minutes. The instructions further cause the one or more processors to:
Determine whether a meal administration forgetting alert has been issued,
displaying the alert interface associated with the correction dose alert in response to a determination that the missed meal dose alert has not been issued;
in response to determining that the missed dose alert has been issued, not displaying the alert interface associated with the corrected dose alert;
67. The system of claim 66. A method for alerting a user regarding a correction dose, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device and receiving subject insulin dose data from the insulin delivery device;
determining whether a corrected dose condition has been issued for consecutive minutes at the current time;
determining whether an insulin dose has not been recorded within about two hours from the current time;
an alert interface associated with a correction dose alert in response to a determination that the correction dose condition has been issued for consecutive minutes and a determination that the insulin dose has not been recorded within about two hours from the current time; A method, including steps for displaying. 70. The method of claim 69, wherein the consecutive minutes are about 5 minutes. 70. The method of claim 69, further comprising determining whether a missed meal dose alert has been issued prior to displaying the alert interface associated with 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 a determination that the missed meal alert has not been issued. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
Cause a correction dose alert to be issued,
determining whether a correction dose condition is detected for consecutive minutes after the correction dose alert is issued;
Responsive to determining that the correction dose condition has not been detected for several consecutive minutes after the correction dose alert is issued, causing the correction dose alert to be canceled;
system. 74. The system of claim 73, wherein the consecutive minutes are 15 consecutive minutes. 74. The system of claim 73, wherein the input comprises wireless communication circuitry. A method for canceling an alert for a correction dose, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a correction dose alert;
determining whether a correction dose condition is detected for consecutive minutes after the correction dose alert is issued;
canceling the corrected dose alert in response to a determination that the corrected dose condition is not detected for consecutive minutes after the corrected dose alert is issued. 77. The method of claim 76, wherein the consecutive minutes are 15 consecutive minutes. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device;
a display configured to present an alert;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
causing a correction dose alert to be issued for the first time at a first time;
determining whether an insulin onboard (IOB) calculation has been changed since the first time;
in response to determining that the insulin onboard (IOB) calculation has changed since the first time, causing the corrected dose alert to be cleared;
system. A method for canceling an alert for a correction dose, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device;
issuing a first corrective dose alert at a first time;
determining whether the insulin onboard (IOB) calculation has been changed since the first time;
resetting the corrected dose alert in response to determining that the insulin onboard (IOB) calculation has changed since the first time. A system for managing alerts,
an input configured to receive streaming glucose data from the sensor control device and receive dose data from the insulin delivery device;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
cause a correction dose alert to be issued at the current time;
determining whether an insulin dose was recorded within a period from the current time;
Responsive to determining that the insulin dose was recorded within a period from the current time, causing the corrected dose alert to be canceled;
system. 81. The system of claim 80, wherein the time period is approximately 2 hours. 81. The system of claim 80, wherein the input comprises wireless communication circuitry. A method for canceling an alert for a correction dose, the method comprising:
receiving, by the electronic device, streaming glucose data from the sensor control device and receiving subject insulin dose data from the insulin delivery device;
issuing a corrected dose alert at the current time;
determining whether an insulin dose was recorded within a period from the current time;
and canceling the corrected dose alert if it is determined that the insulin dose was recorded within a period from the current time. 84. The method of claim 83, wherein the time period is about 2 hours. A system for classifying doses, the system comprising:
an input configured to receive insulin dose data of a user from a connected insulin delivery device, the insulin dose data including a recent dose including an insulin amount and a timestamp;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
providing dose recommendation guidance for a meal requested by a user at a request time, the meal having a meal type, and the dose recommendation guidance including a recommended dose;
determining whether the timestamp of the most recent dose is within the requested time period;
determining whether the insulin amount of the most recent dose is the same as a recommended dose of a dietary dose guidance recommendation;
responsive to a determination that the timestamp of the recent dose is within the request time period and a determination that the insulin amount of the recent dose is the same as the recommended dose of the dietary dose guidance recommendation; and classifying the recent dose as related to the meal type of a recent meal;
system. 86. The system of claim 85, wherein the time period is about 20 minutes or less. 86. The system of claim 85, wherein the meal type is selected from the group consisting of breakfast, lunch, and dinner. The instructions further cause the one or more processors to:
determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal;
In response to determining that the timestamp of the most recent dose is within the determined meal administration time range for the meal type of the most recent meal, the most recent dose is added to the meal of the most recent meal. to be classified as related to the type;
86. The system of claim 85. The instructions further cause the one or more processors to:
determining whether the recent dose was taken while the user was in a postprandial state;
in response to determining that the recent dose was ingested while the user was not in the postprandial state, causing the recent dose to be classified as associated with the meal type of the recent meal;
86. The system of claim 85. 90. The system of claim 89, wherein in the postprandial state, a previous dose administered within about 2 hours of the request time is associated with the meal type of the recent meal. 90. The system of claim 89, wherein the input comprises wireless communication circuitry. A method for classifying doses from a connected insulin delivery device, the method comprising:
providing dosage recommendation guidance for a meal requested by a user at a request time, wherein the meal has a meal type and the dosage recommendation guidance includes a recommended dosage;
receiving, by an electronic device, user insulin dose data from the connected insulin delivery device, the insulin dose data including recent doses including insulin amounts and timestamps;
determining whether the timestamp of the most recent dose is within the requested time period;
determining whether the insulin amount of the most recent dose is the same as a recommended dose of a dietary dose guidance recommendation;
responsive to a determination that the timestamp of the recent dose is within the request time period and a determination that the insulin amount of the recent dose is the same as the recommended dose of the dietary dose guidance recommendation; and classifying the recent dose as related to the meal type of a recent meal. 93. The method of claim 92, wherein the period of time is about 20 minutes or less. 93. The method of claim 92, wherein the meal type is selected from the group consisting of breakfast, lunch, and dinner. 93. The method of claim 92, further comprising determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal. 93. The method of claim 92, further comprising determining whether the recent dose was taken while the user was in a postprandial state. 97. The method of claim 96, wherein in a postprandial state, a previous dose administered within about 2 hours of the request time is associated with the meal type of the most recent meal. A system for classifying doses, the system comprising:
an input configured to receive insulin dose data of a user from a connected insulin delivery device, the insulin dose data including a recent dose including an insulin amount and a timestamp;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
providing dose recommendation guidance at a first time;
determining whether the timestamp of the most recent dose is within the first time period;
determining whether the insulin amount of the most recent dose is the same as the recommended dose of the dose recommendation guidance;
causing said recent dose to be classified as a meal dose, a correction dose or an ambiguous dose;
system. 99. The system of claim 98, wherein the time period is about 20 minutes or less. 99. The system of claim 98, wherein the dose recommendation guidance is a corrected dose recommendation guidance and the most recent dose is classified as a corrected dose. 99. The system of claim 98, wherein the dose recommendation guidance is meal dose recommendation guidance, the meal has a meal type, and the recent dose is classified as a dose of the meal type. The instructions further cause the one or more processors to:
determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of the most recent meal;
102. The system of claim 101. The instructions further cause the one or more processors to:
determining whether the recent dose was taken while the user was in a postprandial state;
102. The system of claim 101. 99. The system of claim 98, wherein the recent dose is classified as an ambiguous dose. 99. The system of claim 98, wherein the input comprises wireless communication circuitry. The instructions further cause the one or more processors to:
prompting the user to manually categorize the recent doses;
105. The system of claim 104. The instructions further cause the one or more processors to:
prompting the user to manually classify the recent dose by prompting the user to select a classification from the group consisting of breakfast dose, lunch dose, dinner dose and correction dose;
107. The system of claim 106. The instructions further cause the one or more processors to:
prompting the user to manually classify the recent dose by prompting the user to select a classification from the group consisting of a snack dose, a priming dose, and a missed dose;
107. The system of claim 106. 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 category related to eating more than expected from previous meals;
107. The system of claim 106. 105. The system of claim 104, wherein the recent dose classified as an ambiguous dose must be classified as a non-ambiguous dose category before providing additional dose guidance recommendations. A method for classifying doses from a connected insulin delivery device, the method comprising:
providing dose recommendation guidance at a first time;
receiving, by an electronic device, user insulin dose data from the connected insulin delivery device, the insulin dose data including recent doses including insulin amounts and timestamps;
determining whether the timestamp of the most recent dose is within the first time period;
determining whether the insulin amount of the recent dose is the same as the recommended dose of the dose recommendation guidance; and classifying the recent dose as a dietary dose, a correction dose, or an ambiguous dose. Including, methods. 112. The method of claim 111, wherein the period of time is about 20 minutes or less. 112. The method of claim 111, wherein the dose recommendation guidance is a corrected dose recommendation guidance and the most recent dose is classified as a corrected dose. 112. The method of claim 111, wherein the dose recommendation guidance is meal dose recommendation guidance, the meal has a meal type, and the recent dose is classified as a dose of the meal type. 115. The method of claim 114, further comprising determining whether the timestamp of the most recent dose is within a determined meal administration time range for the meal type of most recent meal. 115. The method of claim 114, further comprising determining whether the recent dose was taken while the user was in a postprandial state. 112. The method of claim 111, wherein the recent dose is classified as an ambiguous dose. 118. The method of claim 117, further comprising prompting the user to manually categorize the recent dose. The step of prompting the user to manually categorize the recent dose includes prompting the user to select a category from the group consisting of breakfast dose, lunch dose, dinner dose, and correction dose. 119. The method of claim 118. The step of prompting the user to manually categorize the recent dose includes prompting the user to select a category from the group consisting of snack dose, priming dose, and missed dose. 120. The method of claim 118. The step of prompting the user to manually categorize the recent dose comprises prompting the user to select a category related to eating more than expected from a previous meal. The method according to paragraph 118. 120. The method of claim 117, wherein the recent dose classified as an ambiguous dose must be classified as a non-ambiguous dose category before providing additional dose guidance recommendations. An apparatus for providing dose guidance in response to analyte data, the apparatus comprising:
an input configured to receive measured analyte data, dietary data and medication data;
a display configured to visually present information;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the device to:
receiving time-correlated analyte data of the patient captured in a buffer over an analysis period;
dividing the time-correlated analyte data into discrete time-of-day (TOD) periods;
by executing an algorithm to determine a recommended fixed dose of the drug in a corresponding one of the TOD periods based on at least a portion of the time-correlated analyte data and the patient's prescribed dosing strategy for the analysis period; let them decide;
storing said recommended fixed dose indicator in a computer memory for output to at least one of a user or a drug delivery device;
Device. the memory at least partially stores the recommended fixed dose;
classifying each of the drug doses into a medication class based on the time-correlated analyte data;
grouping each of the doses into one of a set of mealtime groups;
determining a glucose pattern that best fits the time-correlated data;
further comprising instructions for determining by selecting a glucose pattern indicator based on the glucose pattern;
124. The apparatus of claim 123. 124. The memory further retains instructions for classifying doses into classes comprising fixed basal doses, fixed breakfast doses, fixed lunch doses, fixed dinner doses for corresponding TOD periods. Device. the memory further retains instructions for determining, as a condition for use in determining the recommended fixed dose, that there are no gaps in the segment of time-correlated analyte data that exceed a predefined threshold; 124. The apparatus of claim 123. the memory further retains instructions for determining that the segment of time-correlated analyte data has an associated initial dietary dose as a condition for use in determining the recommended fixed dose; 124. The apparatus of claim 123. The memory further retains instructions for determining, as a condition for use in determining the recommended fixed dose, that the segment of time-correlated analyte data is associated with a basal fixed dose within the past 24 hours. 124. The apparatus of claim 123. The memory is configured to determine each TOD in response to 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. 124. The apparatus of claim 123, further retaining instructions for clearing data for a period. The memory further retains instructions for determining a glucose pattern for each TOD period based on a set number of past days of relevant valid data segments, and determining the recommended fixed dose is configured to determine the glucose pattern based on the glucose pattern. 124. The apparatus of claim 123, further based on. 131. The method of claim 130, wherein the memory further retains instructions for determining that the associated valid data segment is available for the past set number of days as a condition for determining the recommended fixed dose. Device. 131. The apparatus of claim 130, wherein the memory further retains instructions for determining that the glucose pattern is low based on a count of low alarms occurring during each TOD period. 131. The apparatus of claim 130, wherein the memory further retains instructions for determining that the glucose pattern is low based on a count of hypoglycemic instances in each TOD period. 131. The apparatus of claim 130, wherein the memory further retains instructions for determining that the glucose pattern is high based on a count of hyperglycemic instances in each TOD period. when the memory determines a preprandial correction factor based on the time-correlated analyte data independently of the determination of the recommended fixed dose, and where both the preprandial correction factor and the recommended fixed dose indicate an increase in dose; 131. The apparatus of claim 130, further comprising instructions for maintaining the preprandial correction factor. 125. The memory further retains instructions for determining a glucose pattern condition based on a low alarm count, a postprandial correction count during each TOD period, and the glucose pattern indicator. Device. The memory determines the glucose pattern as low if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the GPA method result indicates a low pattern. 137. The apparatus of claim 136, further comprising instructions for determining. The memory is configured such that the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the GPS method result indicates moderate hypoglycemic risk or a high/low pattern. 137. The apparatus of claim 136, further comprising instructions for determining the glucose pattern as high/low if the glucose pattern is high/low. The memory is configured to determine the glucose pattern if the low alarm count exceeds a first threshold, the postprandial correction count exceeds a second threshold, and the glucose pattern indicator indicates no pattern or a high value pattern. 137. The apparatus of claim 136, further comprising instructions for determining a high. 124. The apparatus of claim 123, wherein the input comprises wireless communication circuitry. A drug delivery device comprising:
an input configured to receive a query for dose data for a period of time, the dose data including all doses and times of administration delivered during the period;
one or more processors coupled to the input and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
storing data of doses administered during the time period to create stored data;
determining whether the stored data includes all doses delivered during the time period;
in response to a determination that the stored data does not include all doses delivered during the time period, causing an indication of incomplete dose data to be created;
Drug delivery device. A method of transferring data, the method comprising:
receiving a query for dose data for a period of time, the dose data including all doses and times of administration delivered during the period;
storing data of doses administered during said time period to create stored data;
determining whether the stored data includes all doses delivered during the time period;
and creating an indication of incomplete dose data in response to a determination that the stored data does not include all doses delivered during the time period. A system for providing dosage guidance to a subject, the system comprising:
an input configured to receive dose data and an indication of incomplete dose data from a drug delivery device, the dose data including data relating to at least one dose administered during a period of time; , an input section;
a display configured to visually present dosage guidance;
one or more processors coupled to the input, the display, and a memory storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
interrogating the drug delivery device for the dose data including data related to at least one dose administered during the time period;
determining whether the incomplete dose data indication is received from the drug delivery device;
in response to determining that the indication of incomplete dose data has been received, prompting for confirmation that the dose data received during the period includes dose data for all doses administered during the period; output,
in response to determining that the indication of incomplete dose data has not been received, causing dose guidance to be calculated;
system. A method for providing dosage guidance to a subject, the method comprising:
receiving an indication of dose data and incomplete dose data from a drug delivery device, the dose data including data relating to at least one dose administered during a period of time;
interrogating the drug delivery device for the dose data including data related to the at least one dose administered during the time period;
determining whether the incomplete dose data indication is received from the drug delivery device;
in response to determining that the indication of incomplete dose data has been received, prompting for confirmation that the dose data received during the period includes dose data for all doses administered during the period; A step to output
and calculating dose guidance in response to determining that the indication of incomplete dose data has not been received. A method for recommending a dietary dose, the method comprising:
prompting the user to enter a tag associated with the meal type;
receiving a tag entered for an instance of the meal type;
associating the entered tag with an amount of drug administered for the instance of the meal type and a postprandial analyte data set for the instance of the meal type;
determining whether a threshold number of instances associated with the meal type is met;
determining a recommended drug dose for the meal type if the threshold number of instances is met. A system for determining recommended drug doses, the system comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receive a tag entered for an instance of the meal type;
associating the entered tag with an amount of drug administered for the instance of the meal type and a postprandial analyte data set for the instance of the meal type;
determining whether a threshold number of instances associated with the meal type is met;
if the threshold number of instances is met, causing a recommended drug dose for the meal type to be determined;
system. A system for tagging healthy meals, the system comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receiving a tag entered for an instance of a first meal type associated with one or more previously entered tags;
determining whether a meal type characteristic of the instance of the first meal type exceeds a meal type characteristic threshold;
associating the input tag with a second meal type different from the first meal type;
system. A method for recommending a dietary dose, 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 entered tag with a first amount of drug administered for the 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 drug amount administered for the second instance of the meal type and a second postprandial analyte data set for the instance of the meal type. step and
prompting the user to input a modification tag associated with the meal type in response to determining that the difference between the second drug amount and the first drug amount is greater than a predetermined threshold difference; A method, including steps for displaying a prompt. A system for tagging meals, the system comprising:
one or more processors coupled to memory for storing instructions;
The instructions, when executed by the one or more processors, cause the one or more processors to:
prompt the user to enter the tag associated with the meal type;
receiving a first input tag for a first instance of the meal type;
associating the first entered tag with a first amount of drug administered for the 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 drug administered for the second instance of the meal type;
prompting the user to enter a modification tag associated with the meal type in response to determining that the difference between the second drug amount and the first drug amount is greater than a predetermined threshold difference; display,
system. An analyte monitoring system comprising:
a sensor control device comprising an analyte sensor, at least a portion of the analyte sensor being configured to be in fluid contact with a body fluid of a subject;
and a reading device.
The reading device is
a wireless communication circuit configured to receive analyte levels from the sensor control device;
one or more processors coupled to memory for storing instructions;
when executed by the one or more processors, the one or more processors:
determining a pattern type for at least one time segment of a day based on a hypoglycemia risk indicator and a hyperglycemia risk indicator for the at least one time segment of the day;
Output the display to the user interface,
The above display is
at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;
A time-in-target display that includes a graph of time-in-target graph portions including a plurality of graph portions, each graph portion of the plurality of graph portions being within a predefined analyte range associated with each graph portion. a time-in-target display, wherein the plurality of graph portions includes at least four graph portions;
a graph comprising a plot of the user's analyte levels over a horizontal representation of a plurality of time segments of a day and an identification of the pattern type determined for the at least one time segment;
Analyte monitoring system. 151. The system of claim 150, wherein the at least one glucose indicator comprises a glucose average. 151. The system of claim 150, wherein the at least one glucose indicator comprises a glucose management indicator. 151. The system of claim 150, wherein the instructions further cause the one or more processors to output a display to the user interface that includes a target value corresponding to the at least one glucose indicator. 151. The system of claim 150, wherein the plurality of graph portions includes at least five graph portions. The plurality of graph portions include a graph portion below a very low threshold, a graph portion between a very low threshold and a low threshold, a graph portion between a low threshold and a high threshold, and a graph portion between a high threshold and a very high threshold. 151. The system of claim 150, comprising at least four graph sections selected from the group consisting of graph sections between and above a very high threshold. 151. The system of claim 150, wherein the time in target range display further includes a description of the defined analyte range associated with each graph portion. The time-in-target range display is configured to indicate, for each graph portion of the plurality of graph portions, the amount of time that the user's analyte level was within the defined analyte range associated with the graph portion during the time period. 151. The system of claim 150, further comprising a value related to a quantity. 158. The system of claim 157, wherein the value is a percentage value. The time-in-target-range indication indicates, for at least two graph portions of the plurality of graph portions, that the user is within each of the defined analyte ranges associated with the at least two graph portions during the time period. 151. The system of claim 150, further comprising a sum value related to the sum of the amount of time the analyte level was present. 151. The system of claim 150, wherein the graph of the time in target range includes a histogram. Each graph portion of said histogram is arranged in a vertical layout, a graph portion below a very low threshold is located below a graph portion between a very low threshold and a low threshold; and a high threshold, said graphical portion is located below a graphical portion between a high threshold and a very high threshold, and said graphical portion is located below a graphical portion between a high threshold and a very high threshold. 161. The system of claim 160, wherein the system is located below a portion of the graph that exceeds. 151. The system of claim 150, wherein identifying the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph. 163. The system of claim 162, wherein the identification of the determined pattern type for the at least one time segment further includes a label for the determined pattern type. 151. The system of claim 150, wherein the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. 151. The system of claim 150, wherein the graph includes a plurality of determined pattern types, and identifications of a single pattern type are visually distinguished from other identifications of the plurality of determined pattern types. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of a most important pattern type, the most important pattern type determined for at least one time segment of the day. 151. The system of claim 150, wherein the system is one of the pattern types. 167. The system of claim 166, wherein identification of the most important pattern types is displayed on the graph. The identification of the determined pattern type for the at least one time segment includes a plurality of identifications of the determined pattern type for each of the at least one time segment, and the identification of the most important pattern type comprises the 167. The system of claim 166, wherein the system is visually distinct from other identifications of the plurality of identifications. 167. The system of claim 166, wherein the pattern type determined for at least one time segment of the day includes multiple pattern types for multiple time segments of the day. 170. The system of claim 169, wherein the plurality of pattern types include at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. 171. The system of claim 170, if the plurality of pattern types include low value patterns, identifying the most important pattern type includes identifying the low value patterns. If the plurality of pattern types include a high value pattern with some of the low values and no low value pattern, the identification of the most important pattern type includes identification of the high value pattern with some of the low values. 171. The system of claim 170, comprising: 170. If the plurality of pattern types include a high value pattern and do not include a high value pattern or a low value pattern with a portion of low values, identifying the most important pattern type includes identifying the high value pattern. System described. 166. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of the at least one time segment of the day that was determined to have the most significant pattern type. System described. an indication of the identification of the most important pattern type and the identification of the at least one time segment of the day determined to have the most important pattern type; 167. The system of claim 166, comprising at least one tag for identification of at least one time segment of the day determined to have a type. the tag for identification of the most important pattern type is of a different color than the at least one tag for identification of the at least one time segment of the day determined to have the most important pattern type; 176. The system of claim 175. The instructions further cause the one or more processors to:
determining a variation in at least one time segment of the day;
if the determined variation is high, causing the user interface to output a display including a statement related to the variation;
151. The system of claim 150. 178. The system of claim 177, wherein the statement related to fluctuations includes identification of behaviors that may contribute to glucose fluctuations. 151. The system of claim 150, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to excursions below a very low threshold. 180. The system of claim 179, wherein the very low threshold is between about 50 mg/dL and about 58 mg/dL. 180. The system of claim 179, wherein the very low threshold is about 54 mg/dL. 151. The system of claim 150, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to medication guidance. 183. The system of claim 182, wherein the statements related to medication guidance include suggestions for adjusting medication. 183. The system of claim 182, wherein the statements related to medication guidance include suggestions related to medications that contribute to low glucose levels. 151. The system of claim 150, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to lifestyle guidance. 186. The system of claim 185, wherein the lifestyle guidance related statements include statements related to at least one of dietary forgetfulness, carbohydrates, activity level, alcohol, and medication. 151. The system of claim 150, wherein the time period is 14 days. A method for displaying information related to glucose levels in a subject, the method comprising:
receiving an analyte level from a sensor control device;
determining a pattern type for at least one time segment of a day based on a hypoglycemic risk indicator and a hyperglycemic risk indicator for the at least one time segment of the day;
a step of displaying a user interface,
at least one glucose indicator determined for a period of time based on the analyte level received from the sensor control device;
A time-in-target display that includes a graph of time-in-target graph portions including a plurality of graph portions, each graph portion of the plurality of graph portions being within a predefined analyte range associated with each graph portion. a time-in-target display, wherein the plurality of graph portions includes at least four graph portions;
displaying a user interface comprising: a plot of the user's analyte levels over a horizontal display of a plurality of time segments of a day; and a graph comprising an identification of the determined pattern type for the at least one time segment. and the method. 189. The method of claim 188, wherein the at least one glucose indicator comprises a glucose average. 189. The method of claim 188, wherein the at least one glucose indicator comprises a glucose management indicator. 189. The method of claim 188, wherein the instructions further cause the one or more processors to output a display to the user interface that includes a target value corresponding to the at least one glucose indicator. 189. The method of claim 188, wherein the plurality of graph portions includes at least five graph portions. The plurality of graph portions include a graph portion below a very low threshold, a graph portion between a very low threshold and a low threshold, a graph portion between a low threshold and a high threshold, and a graph portion between a high threshold and a very high threshold. 189. The method of claim 188, comprising at least four graph sections selected from the group consisting of graph sections between and above a very high threshold. 189. The method of claim 188, wherein the time-in-target range display further includes a description of the defined analyte range associated with each graph portion. The time-in-target range display is configured to indicate, for each graph portion of the plurality of graph portions, the amount of time that the user's analyte level was within the defined analyte range associated with the graph portion during the time period. 189. The method of claim 188, further comprising a value related to a quantity. 196. The method of claim 195, wherein the value is a percentage value. The time-in-target-range indication indicates, for at least two graph portions of the plurality of graph portions, that the user is within each of the defined analyte ranges associated with the at least two graph portions during the time period. 189. The method of claim 188, further comprising a total value relating to the total amount of time the analyte level was present. 189. The method of claim 188, wherein the graph of the time in target range includes a histogram. Each graph portion of said histogram is arranged in a vertical layout, a graph portion below a very low threshold is located below a graph portion between a very low threshold and a low threshold; and a high threshold, said graphical portion is located below a graphical portion between a high threshold and a very high threshold, and said graphical portion is located below a graphical portion between a high threshold and a very high threshold. 200. The method of claim 198, wherein the method is located below a portion of the graph that exceeds. 189. The method of claim 188, wherein identifying the determined pattern type for the at least one time segment includes at least a partial outline of the time segment on the graph. 201. The method of claim 200, wherein identifying the determined pattern type for the at least one time segment further includes a label for the determined pattern type. 189. The method of claim 188, wherein the pattern type is at least one of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. 189. The method of claim 188, wherein the graph includes a plurality of determined pattern types, and identifications of a single pattern type are visually distinguished from other identifications of the plurality of determined pattern types. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of a most important pattern type, the most important pattern type determined for at least one time segment of the day. 189. The method of claim 188, wherein the pattern type is one of the pattern types. 205. The method of claim 204, wherein identification of the most important pattern types is displayed on the graph. The identification of the determined pattern type for the at least one time segment includes a plurality of identifications of the determined pattern type for each of the at least one time segment, and the identification of the most important pattern type comprises the 205. The method of claim 204, wherein the plurality of identifications is visually distinguished from other identifications. 205. The method of claim 204, wherein the pattern type determined for at least one time segment of the day includes multiple pattern types for multiple time segments of the day. 208. The method of claim 207, wherein the plurality of pattern types include at least two of a low value pattern, a high value pattern with a portion of low values, a high value pattern, or no pattern. 209. The method of claim 208, if the plurality of pattern types include low value patterns, identifying the most important pattern type includes identifying the low value patterns. If the plurality of pattern types include a high value pattern with some of the low values and no low value pattern, the identification of the most important pattern type includes identification of the high value pattern with some of the low values. 209. The method of claim 208, comprising: 208. If the plurality of pattern types include a high value pattern and do not include a high value pattern or a low value pattern with a portion of low values, identifying the most important pattern type includes identifying the high value pattern. Method described. 204. The instructions further cause the one or more processors to output a display to the user interface that includes an identification of at least one time segment of the day that was determined to have the most significant pattern type. Method described. an indication of the identification of the most important pattern type and the identification of the at least one time segment of the day determined to have the most important pattern type; 205. The method of claim 204, comprising at least one tag for identification of at least one time segment of the day determined to have a type. the tag for identification of the most important pattern type is of a different color than the at least one tag for identification of the at least one time segment of the day determined to have the most important pattern type; 214. The method of claim 213. The instructions further cause the one or more processors to:
determining a variation in at least one time segment of the day;
if the determined variation is high, causing the user interface to output a display including a statement related to the variation;
189. The method of claim 188. 216. The method of claim 215, wherein the statement related to fluctuations includes identification of behaviors that may contribute to glucose fluctuations. 189. The method of claim 188, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to excursions below a very low threshold. 218. The method of claim 217, wherein the very low threshold is about 50 mg/dL to about 58 mg/dL. 218. The method of claim 217, wherein the very low threshold is about 54 mg/dL. 189. The method of claim 188, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to medication guidance. 221. The method of claim 220, wherein the statements related to medication guidance include suggestions for adjusting medication. 221. The method of claim 220, wherein the statements related to medication guidance include suggestions related to medications that contribute to low glucose levels. 189. The method of claim 188, wherein the instructions further cause the one or more processors to output a display to the user interface that includes statements related to lifestyle guidance. 224. The method of claim 223, wherein the lifestyle guidance related statements include statements related to at least one of dietary forgetfulness, carbohydrates, activity level, alcohol, and medication. 189. The method of claim 188, wherein the period of time is 14 days. A device for displaying indicators related to an object, the device comprising:
an input configured to receive drug administration data;
a display configured to visually present information;
one or more processors coupled to the input, the display, and a memory storing instructions and a dose of the drug taken by the subject over a period of time and a recommended dose of the drug for the subject over the period of time; Equipped with
The instructions, when executed by the one or more processors, cause the device to:
displaying a graph plotting multiple drug doses taken by the subject multiple times, the graph having an x-axis of time and a y-axis of the difference between the dose taken by the subject and the dose recommended for the subject; including
Device. A device for displaying indicators related to an object, the device comprising:
an input configured to receive measured analyte data and drug administration data;
a display configured to visually present information;
the input, the display, and a memory storing instructions and time-correlated data characterizing the analyte of the subject, the dose of drug taken by the subject over a period of time, and the recommended drug dose of the subject over a period of time; one or more processors coupled to;
The instructions, when executed by the one or more processors, cause the device to:
displaying a summary of the subject's treatment including doses administered over a period of time and an analyte index determined from the measured analyte data received;
displaying a graphic summarizing doses missed during the period;
displaying a graphic summarizing override doses taken by the subject at a certain time, including doses that are different from the recommended dose at that time;
Device. A device for displaying indicators related to an object, the device comprising:
an input configured to receive measured analyte data from a plurality of subjects, drug administration data from a plurality of subjects, and data related to dosing recommendations for the plurality of subjects;
a display configured to visually present information;
the input, the display, instructions and time-correlated data characterizing the analyte of each of the plurality of subjects, the dosage of drug taken by each of the plurality of subjects over a period of time and medication recommendations for the plurality of subjects; one or more processors coupled to a memory for storing data related to the matter;
When executed by the one or more processors, the apparatus includes:
at least two of the following: time in target range, time below low threshold, time above high threshold, percentage of basal dose taken, and average bolus dose taken per day for each of the plurality of subjects. Display a summary of analyte indicators including
displaying a summary of information related to medication recommendations, including displaying medication recommendations for a subject of the plurality of subjects that require approval from a health care provider;
Device. A device for displaying treatment information related to a subject, the device comprising:
an input configured to receive measured analyte data and drug administration data;
a display configured to visually present information;
one coupled to the input, the display, and a memory storing instructions and time-correlated data characterizing the subject's analyte, the dose of medication taken by the subject over a period of time, and the subject's meal times; Equipped with a processor of
The instructions, when executed by the one or more processors, cause the device to:
receiving estimated dose parameters and estimated meal administration time ranges from the subject;
determining a representative amount of each of a plurality of basal doses and a plurality of dietary doses ingested by the subject during a period of time based on the drug administration data;
determining a typical meal administration time range for the subject during the period based on the drug administration data;
determining a recommended dose of 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 administration time ranges received from the subject;
displaying the representative amount and the representative meal administration time range of each of the plurality of basal doses and the plurality of meal doses;
displaying the recommended dose of at least one of the basal dose, the breakfast dose, the lunch dose and the dinner dose;
Device.

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