JP2021515613A - Autonomous drug delivery system - Google Patents

Abstract

Autonomous drug delivery systems are advantageous because they utilize physiological monitor output to automatically provide a bolus of rescue drugs or other required drugs when certain criteria and confidence levels are met. An emergency button is provided to manually trigger the administration of rescue medication. Rescue drugs should be opioid antagonists that respond to overdose of analgesia, hypotensive drugs to prevent excessive blood pressure drop, or anti-arrhythmic drugs that suppress abnormal heartbeat, to name a few. Can be done.

Description

Pulse oximetry is a widely accepted non-invasive procedure for measuring arterial oxygen saturation levels, which are indicators of human oxygen supply. A typical pulse oximetry system utilizes an optical sensor attached to the fingertip to measure the relative amount of oxidized hemoglobin in the pulsating arterial blood flowing into the fingertip. Accordingly, a plethysmograph waveform that visualizes oxygen saturation (SpO2), pulse rate, and pulsatile blood flow over time is displayed on the monitor.

Conventional pulse oximetry assumes that arterial blood is the only pulsating blood flow at the site of measurement. During the patient's body movement, venous blood also moves, which causes an error in conventional pulse oximetry. The latest pulse oximetry processes venous blood signals and reports true arterial oxygen saturation and pulse rate under the patient's kinetic conditions. Modern pulse oximetry also tends to fail with traditional pulse oximetry under conditions of low perfusion (small signal amplitude), strong ambient light (artificial light or sunlight), and interference of electrosurgical instruments. But it works.

The latest pulse oximetry is described at least in US Pat. No. 6,770,028, US Pat. No. 6,658,276, US Pat. No. 6,157,850, US Pat. No. 6,002,952, US Pat. No. 5,769,785, and US Pat. No. 5,758,644. These have been transferred to Masimo Corporation (“Masimo”), Irvine, California and are incorporated herein by reference in their entirety. For the corresponding low noise optical sensors, at least US Pat. No. 6,985,764, US Pat. No. 6,813,511, US Pat. No. 6,792,300, US Pat. No. 6,256,523, US Pat. No. 6,088,607, US Pat. No. 5,782,757, and US Pat. Disclosed in No. 5,638,818, these have also been transferred to Masimo and are also incorporated herein by reference in their entirety. Masimo SET® low-noise optical sensor for measuring SpO2, pulse rate (PR), and perfusion index (PI) and read through motion pulse oximetry The latest pulse oximetry systems, including monitor), are commercially available from Masimo. Optical sensors include either Masimo's LNOP®, LNCS®, SofTouch®, and BIue® adhesive or reusable sensors. The pulse oximetry monitor can be one of Masimo's Rad-8® monitor, Rad-5® monitor, Rad®-5v monitor, or SatShare® monitor. Be done.

For the latest blood parameter measurement system, at least US Pat. No. 7,647,083, filed March 1, 2006, entitled "Multiple Wavelength Sensor Equalization," and filed March 1, 2006, "Configurable Physiological Measurement System." US Pat. No. 7,729,733, filed March 1, 2006, US Patent Application Publication No. 2006/0211925, entitled "Physiological Parameter Confidence Measure," and filed March 1, 2006. It is described in US Patent Application Publication No. 2006/0238358 entitled "Noninvasive Multi-Parameter Patient Monitor", all of which have been transferred to Cercacor Laboratories, Inc. (Cercacor), Irvine, CA for reference in their entirety. Incorporated herein by. In addition to SpO2, the latest blood parameter measurement systems include total hemoglobin (SpHb ™), oxygen content (SpOC ™), methemoglobin (SpMet®), and carboxyhemoglobin (SpCO®). )), And Masimo's Rainbow® SET, which provides measurement results such as PVI®. The latest blood parameter sensors include Masimo's Rainbow® adhesive sensor, ReSposable ™ sensor, and reusable sensor. The latest blood parameter monitors include Masimo's Radical-7 ™ monitor, Rad-87 ™ monitor, and Rad-57 ™ monitor, all commercially available from Masimo. Such state-of-the-art pulse oximeters, low-noise sensors, and state-of-the-art blood parameter systems include surgical wards, intensive care units and neonatal rooms, general wards, home care, physical training, and virtually all types of surveillance scenarios. It has been rapidly accepted in a wide variety of medical applications, including.

U.S. Pat. No. 6,770,028 U.S. Pat. No. 6,658,276 U.S. Pat. No. 6,157,850 U.S. Pat. No. 6,002,952 U.S. Pat. No. 5,769,785 U.S. Pat. No. 5,758,644 U.S. Pat. No. 6,985,764 U.S. Pat. No. 6,813,511 U.S. Pat. No. 6,792,300 U.S. Pat. No. 6,256,523 U.S. Pat. No. 6,088,607 U.S. Pat. No. 5,782,757 U.S. Pat. No. 5,638,818 U.S. Pat. No. 7,647,083 U.S. Pat. No. 7,729,733 U.S. Patent Application Publication No. 2006/0211925 U.S. Patent Application Publication No. 2006/0238358

Inpatients and outpatients may be negatively affected by various dispensing medications prescribed for treatment. These negative effects can result from overdose, underdose, or side effects on these drugs. As an example, opioids are often given to postoperative patients for pain management, and opioid use is safe for most patients, while opioid analgesics are the result of overdose. It is often associated with side effects such as respiratory depression, improper monitoring, drug interactions, and side effects with other drugs. Complications from opioid use are unavoidable and not always avoidable. However, if a patient dies because such complications are not recognized in a timely manner or are not treated properly, it is called a "failure to rescue".

Rescue failure in the case of opioid overdose can be significantly prevented by the favorable autonomous administration of opioid antagonists. Similarly, other serious symptoms of the patient, such as cardiac arrhythmia, high or low heart rate, hypertension or hypotension, hypoglycemia, and anaphylactic shock, are autonomous with appropriate rescue medications, to name a few. It can be alleviated by administration. An autonomous drug delivery system (ADDS) utilizes physiological monitor output to meet at least one drug or at least one bolus of a drug when certain criteria and confidence levels are met. It is advantageous because it automatically gives bolus). In addition, an emergency button is provided to manually trigger the administration of such rescue drug or drug. In one embodiment, the emergency button may be triggered remotely. ADDS is advantageous because it responds with rescue medications when the delay in the clinician's response would normally result in injury or death of the patient.

One aspect of an autonomous drug delivery system is a housing with a front panel and drug compartment. The display and buttons are located on the front panel. The IV syringe is located in the drug compartment and a nozzle extending from the housing mechanically communicates with the IV syringe. In one embodiment, the IV syringe has a drug reservoir, a piston that is partially located within the drug reservoir and extends from the drug reservoir, and a drive motor that mechanically communicates with the piston. The drive motor operates to drive the piston against a drug-containing bolus located in the drug reservoir. The piston will push the drug out of the nozzle.

Another aspect of the autonomous drug delivery system is to receive physiological parameters that indicate a person's medical condition, as well as to calculate trigger conditions and corresponding confidence indicators. If the trigger condition exceeds a predetermined threshold, an alarm is generated. If the alarm is not manually confirmed within the prescribed time interval, the drug is administered to the person.

It is a front view of the drug reservoir of the embodiment of an autonomous drug delivery system (ADDS). It is a partial cut-out front view of the drug reservoir of embodiment of an autonomous drug delivery system (ADDS). FIG. 5 is a cut-out view of a drug reservoir of an embodiment of an autonomous drug delivery system (ADDS). It is a front view of the autonomous drug delivery system composed of the conventional IV drop injection. FIG. 5 is a front view of an autonomous drug delivery system consisting of a patient controlled analgesia (PCA) pump. FIG. 6 is a block schematic of an autonomous drug delivery system with fluid connectivity to a patient and electronic communications with sensors and monitors. It is a single-row drug administration decision flowchart. It is a cut-out view of two drug reservoirs of an autonomous drug delivery system embodiment. It is a two-row drug administration decision flowchart. It is a block schematic of signal processing and equipment management of an autonomous drug delivery system. It is a block schematic of an exemplary automated first responder system. FIG. 6 is a block schematic of an exemplary automated first responder method. It is a block diagram of an exemplary diagnostic model. It is a block schematic of another exemplary automated first responder method.

Figures 1A-1C show the Autonomous Drug Delivery System (ADDS) 100, which is generated by one or more patient monitors such as SpO2, PR, RR, EtCO2, and BP. Enter physiological parameters and evaluate specific criteria and confidence levels based on those parameters to automatically give a bolus of rescue drug. As shown in FIG. 1A, the autonomous drug delivery system 100 has a control portion 102 and a drug portion 108. The control portion 102 has a front panel 104 and an electrical interface (invisible). The front panel 104 features a user display 110, such as reading the LED or LCD of the device and patient status, among other data, a control button 120, such as power on / off, reset, mode, and rescue drug, among other functions. It provides a user interface including an emergency button 130 for manually triggering administration. The emergency button 130 may have other safety mechanisms, such as a flip-up cover, or simultaneous pressing with another button, to prevent inadvertent drug administration. The electrical interface communicates with the patient monitor, alarm, and drug portion 108. The drug portion 108 has a cover 109 that hides a drug administration device installed in a concave compartment, which will be described later.

As shown in FIG. 1B, the cover 109, once flipped up, slid open, removed, or otherwise opened, with the drug reservoir 170 located within the drug compartment 152, The IV syringe 150 with the piston 180 and the drive motor 190 is exposed. The drug reservoir 170 is configured to receive a disposable bolus of rescue drug. When the motor 190 operates, it pushes the piston 180 into the drug reservoir 170, thereby causing the rescue drug contained in the drug reservoir 170 to move out of the nozzle 178, as described below with respect to FIGS. 1C) will be discharged into the attached IV tube, which will carry the rescue drug to the patient. The bolus remains in the drug reservoir 170 until it is used or expires. The expiration date can optionally be read from the bolus or otherwise entered into ADDS memory (part of DSP810 in Figure 8).

As shown in FIG. 1C, in the prepared position, the drug reservoir 170 has a substantially cylindrical enclosure 172 containing an unused rescue drug 174, and a piston 180. Specifically, the piston 180 has a piston head 182 arranged in the enclosure 172 and a piston shaft 184 extending from the enclosure 172. The motor acts on the piston shaft 184 to move the piston head 182 from the first position distal to the nozzle 178 to the second position proximal to the nozzle, thus the rescue drug is in the reservoir 170. Is discharged into the IV tube (not shown) attached to the nozzle 178.

FIG. 2 shows a conventional IV instillation configuration 200 incorporating an autonomous drug delivery system 100. Specifically, the IV pole 10 is fitted with an IV bag 20 that communicates fluidly with patient 1 via the IV line 30. The IV line is joined to a one-way valve 50 that communicates with the ADDS100 via a line shunt 40. If the ADDS100 is triggered either manually via the emergency button 130 (Figure 1A) or automatically via the patient monitor input communicating with control part 102 (Figure 1A), the ADDS100 will The rescue drug 174 (Fig. 1C) is injected into the shunt 40 and IV line 30 via a one-way valve 50, which stops the IV bag drop 20 and further fluid in the IV bag 20. To shut off.

FIG. 3 shows another conventional IV instillation configuration 300 incorporating an autonomous drug delivery system 100 in addition to the patient-controlled analgesia (PCA) pump 70. Similar to the configuration of FIG. 2 above, the IV pole 10 is fitted with an IV bag 20 that communicates fluidly with patient 1 via the IV line 30. The IV line is joined to a one-way valve 50 that communicates with the rescue device 100 via a line shunt 40. The IV line is also connected to the one-way valve 60 that communicates with the PCA pump. As mentioned above, the ADDS100 is triggered either manually via the emergency button 130 (FIG. 1A) or automatically via the patient monitor input communicating with control portion 102 (FIG. 1A). If ADDS100 injects its rescue drug 174 (Fig. 1C) into shunt 40 and IV line 30 via one-way valve 50, this one-way valve 50 stops IV bag instillation 20 and PCA analgesia 72. To do.

ADDS100 can be used in a variety of medical emergencies and conditions, along with a variety of rescue medications. In one embodiment, ADDS is advantageous because it is used with a patient-controlled analgesia (PCA) device that delivers painkillers at the same time as the patient's request. For example, a patient can push a button to request a morphine injection. In this case, the patient is particularly prone to inadvertent overdose so that respiratory failure occurs. ADDS is one of respiratory rate, oxygen saturation, pulse rate, blood pressure, and terminal exhalation CO2, according to the sensor and corresponding monitor attached to the patient, as described in more detail below with respect to FIG. Enter multiple parameters to deliver opioid antagonists such as naloxone (Nalcan).

The ADDS100 can optionally respond to cardiac arrhythmias and deliver rescue drugs specific to the type of rhythm detected, and optionally respond to high or low heart rate with a heart rate regulating drug. Can be delivered, optionally in response to hypertension or hypotension, can deliver blood pressure-regulating drugs, optionally, in response to hypotension, can deliver glucagon, or other specific In response to the symptoms of, the appropriate drug can be delivered. In various embodiments, ADDS100 delivers a formulated (mixed) formulation consisting of multiple drugs. This is achieved by a mixture of drugs stored in a single reservoir or by the use of multiple reservoirs. In all of these embodiments, the response is always bolus (reservoir) administration. The drug is infused at one time in a single dosing step (as opposed to pumping over a defined time dose). ADDS is ideal because it can deliver multiple bolus doses, in which case subsequent bolus doses can be activated after the first automatically delivered dose has been manually reviewed. ..

ADDS may be configured to administer the bolus at a specific date and time rather than in response to the monitored condition. Bolus administration can be performed at multiple individual times or over specified time intervals. If ADDS responds to multiple physiological parameters received from one or more monitoring instruments, a defined minimum and / or maximum interval from a previous dose or start time may be used.

In various situations, a physiological "guard rail" must be maintained. (See, for example, Figures 6-7.) As an example, hypotensive drugs can be used to raise a patient's blood pressure during an emergency. As another example, drugs for regulating cardiac rhythm or suppressing arrhythmias can be used. In one embodiment, display 110 (FIG. 1A) indicates that the device is working and is receiving the appropriate input. The ADDS100 has a memory that records data that can be called to display the trigger cause for administration of the rescue drug, such as the time and amount of the administered drug, and other symptoms logged. In one embodiment, the rescue drug is administered incrementally, not all at once. For example, given a dose of 10%, the patient can be monitored for improvement and additional doses will be given at intervals as needed. In one embodiment, a spring-driven piston with a releaseable clasp is used to administer the rescue drug in place of the motor-driven piston. In one embodiment, it is advantageous for the ADDS to be integrated with the PCA device as a single device.

FIG. 4 shows an autonomous drug delivery system 400 with a fluid coupling function 412 to patient 1 and an electronic communication unit 424 with one or more monitors 420. One or more sensors 430 interface with patient 1 either directly attached to patient 1 or by remote sensing 432. For example, the optical sensor 430 can clip to the fingertip site 432 and provide pulsating blood flow data via a cable to a pulse oximeter or blood parameter monitor 420 422. The pulse oximeter or blood parameter monitor 420 calculates physiological parameters such as oxygen saturation and pulse rate 424, which are sent to the ADDS 410. ADDS410 responds to parameter 424 by adjusting the drug dosage accordingly (starting, increasing, decreasing, or discontinuing) 412. The ADDS410 can raise one or more alarms to alert the caregiver. The caregiver can respond to the warning and at the same time reset the alarm 416. In one embodiment, communication between the ADDS410 and the caregiver may be via a wired network or via a wireless network and may be received at a nurse station or equivalent.

FIG. 5 shows an exemplary single-row drug administration decision. In this case, there is a single threshold (maximum or minimum), above which the ADDS device is triggered to administer the rescue drug to the patient. One or more parameters 501 are input to the ADDS device. ADDS algorithm 510 calculates trigger conditions and associated confidence indicators 512. If the ADDS trigger threshold is exceeded 520, alarm 530 is triggered 522. Otherwise 524 does nothing for ADDS. For example, if the alarm 532 is confirmed in a timely manner by the caregiver pressing the ADDS confirmation button 544, the ADDS will be terminated 560 and the alarm will be muted. If alarm 532 is not confirmed 542, ADDS administers the rescue drug 550. Note that the ADDS has a manual trigger 570 (button press) for 572 to manually trigger the drug administration 550 (see 130 in Figure 1A). In one embodiment, the alarm 530 and the corresponding confirmation time 540 can proceed from the local alarm, the network alarm, and finally the pager alarm before the autonomous drug delivery can be triggered.

FIG. 6 shows embodiment 600 of two drug reservoirs in an autonomous drug delivery system. The first drug reservoir 670 and the second drug reservoir 680 each contain the same drug for one or two separate doses, or different drugs for different single doses, depending on the monitored condition. May include. See, for example, Figure 7. The drug reservoirs 670 and 680 share a common nozzle 662. The nozzle 662 has a connecting portion 660 that connects the fluid tubes 678 and 688 to the first reservoir nozzle 676 and the second reservoir nozzle 686, respectively. In one embodiment, the clinician must re-enable ADDS before the second dose is administered.

FIG. 7 shows two rows of drug administration decisions. In this case, there is a maximum threshold and a minimum threshold. If the maximum threshold is exceeded, for example if one or more parameter values or combinations of parameter values exceed a predetermined limit, the ADDS device is triggered to administer the rescue drug to the patient. Also, if the minimum threshold is exceeded, for example if one or more parameter values or combinations of parameter values fall below a predetermined limit, the ADDS device is triggered to administer the rescue drug to the patient. To. In one embodiment, one rescue drug may be administered simultaneously above the maximum threshold and different rescue drugs may be administered simultaneously above the minimum threshold. One or more parameters 701 are input to the ADDS device. The ADDS algorithms 710 and 760 calculate the maximum (HI) and minimum (LO) trigger conditions, as well as the associated confidence indicators. If either the HI ADDS trigger threshold or the LO ADDS trigger threshold is exceeded, 720, 770 and the corresponding alarms 730, 780 are triggered. Otherwise, 724, 744, ADDS does nothing. If the HI or LO alarms 730, 780 are confirmed in a timely manner, for example by the caregiver pressing the ADDS confirmation button 744, 794, the ADDS is terminated 741, 791 and the alarm is muted. If alarms 730, 780 are not confirmed 742, 792, ADDS administers the appropriate HI or LO row rescue drug 745, 795. Note that the ADDS has a manual trigger 799 (LO or HI button press) for 745,795 to manually trigger either the LO column drug or the HI column drug.

FIG. 8 shows an autonomous drug delivery system (ADDS) controller 800 with a digital signal processor (DSP) 810, an instrument manager 820, and an interface 830. The DSP810 has the algorithm 812 described above for determining trigger conditions, alarms, and trigger timings, eg, FIGS. 5-7. Instrument Manager 820 interfaces with DSP810 for driving ADDS displays and alarms, receives input from buttons / keypads 835, and communicates with an external monitor to extract triggering parameters from patient sensors. 837, controls the syringe motor and reads the syringe status such as bolus full / empty and syringe running (delivering rescue drug) 839. In one embodiment, the DSP firmware records in memory the time and duration of the drug delivery-triggered event, including before and after the history of the drug administration-triggered monitor parameters.

Additional Embodiments The following additional embodiments may be implemented in any combination with any of the devices, algorithms, or features described above.

Figure 9 shows an exemplary automated First Responder System 900. In this automated first responder system 900, patient 902 is shown communicating with patient monitor 910 and autonomous drug delivery device 920. As an example, the patient monitor 910 may wirelessly or telecommunications with one or more sensors connected to the patient. Examples of such sensors are described above and may include, for example, optical sensors, acoustic respiration sensors, ECG sensors, electroencephalogram measurement sensors, blood pressure sensors and the like.

The patient monitor 910 and the autonomous drug delivery device 920 can have some or all of the functions of similar devices described above. As an example, the patient monitor 910 can include any of the controller functions described above with respect to FIG. In general, the patient monitor 910 can include one or more hardware processors, memory, and display, as an alternative, the display can be optional and the patient monitor 910 displays patient data separately. Can be output to. Similarly, the autonomous drug delivery device 920 can have some or all of the functions of the ADDS embodiment described above.

The patient monitor 910 is shown communicating with the gateway device 940. The patient monitor 910 can communicate with the gateway device 940 wired or wirelessly via a hospital network (not shown). The hospital network may include a local area network, a wide area network, an intranet, the Internet, or a combination thereof. The gateway device 940 can facilitate communication over the network between the plurality of devices shown in the system 900. These devices can include a clinician device 960, which can include tablets, pagers, smartphones, laptops, computers, nurse station computers, and the like. In addition, the gateway device 940 is shown communicating with an electronic medical record (EMR) database 950, which is the sample result 930 obtained from a sample test applied to patient 902. Can be memorized. The patient monitor 910 accesses the EMR950 via the gateway device 940 to obtain sample results 930 and other data including data on medical records such as patient symptoms, medications, tests, and those applied during hospitalization. be able to. Of course, System 900 is also applicable not only in hospitals but also in any clinical setting. However, for convenience, the present specification, without loss of generality, specifically illustrates hospital embodiments.

The automated First Responder System 900 can implement any of the features described above with respect to FIGS. 1-8. In addition, in certain embodiments, the automated First Responder System 900 can also implement additional features described below. As an example, the patient monitor 910 can automatically initiate a treatment workflow based on the analysis of patient data. Patient data can include data measured from physiological sensors, data obtained from sample results, and the like. Using this patient data, the patient monitor 910 determines whether the data corresponds to or matches any diagnostic model or multiple diagnostic models stored in physical computer storage. Can be done. Diagnostic models can be represented as data structures and the like, and patient data entry sets can be associated with possible diagnoses and treatment workflows to address possible diagnoses. An exemplary treatment workflow will be described later with reference to FIG.

If the patient data matches the diagnostic model (examples described below), the patient monitor 910 can automatically initiate a treatment workflow to begin treatment of the patient. The treatment workflow can include administration of substances such as drugs using any of the techniques described above. The treatment workflow can also include other actions, such as directing a specimen test and warning the clinician.

As part of the treatment workflow, the patient monitor 910 can transmit commands or signals to the autonomous drug delivery device 920 so that the autonomous drug delivery device 920 supplies the patient 902 with a dose or bolus of the drug. In one embodiment, the autonomous drug delivery device 920 is an intravenous therapy device, such as an intravenous (IV) infusion device. Therefore, the autonomous drug delivery device 920 can administer a non-drug substance such as a physiological saline solution to patient 902. In some embodiments, the saline solution may be administered to patient 902 in combination with the drug.

As another exemplary embodiment of the treatment workflow, the treatment workflow can include first administering a first dosage of the substance to the patient without requiring initiation by a clinician. However, optionally, the patient monitor 910 can generate output (eg, on the display) that gives the clinician the option to intervene and pause the administration of the substance, or to pause any other aspect of the treatment workflow. ..

Later, if the patient data reconcile with the diagnostic model, thereby guaranteeing additional administration of the substance, the treatment workflow should include warning the clinician instead of administering a second dosage. Can be done. In this scheme, without human intervention, the patient is first given a relatively harmless or low dosage of the substance to provide an automated first response, but then the patient accidentally overdose the substance. It is possible to involve the clinician so as not to let them.

As another example, the patient monitor 910 can detect that the diagnostic model is consistent and at the same time warn the clinician who may be away from the patient about the patient's potential symptoms. The clinician can then respond to the warning in a variety of ways, described below.

The warning to the clinician may be sent as a message to the clinician's device (such as a smartphone, tablet, laptop, desktop, or other computing device) via a network such as a hospital network. The alert may be sent as an email message, as a text message, or as another type of message, including a custom message for a custom mobile application or web application that is installed or accessed on the clinician's device. Warnings include the following information: patient identification and / or demographic information, patient data or an overview thereof (such as waveforms and / or physiological parameter values), and indications that a particular diagnostic model has been matched ("this". Display of patient symptoms, such as "Patient may be septic" or "Patient overdose of opioid was detected", as well as recommended course of treatment (recommendations for administering bolus of drug, specimen testing) Can include any subset of (such as any other aspect of the workflow discussed herein).

Upon receiving the alert, the software on the clinician's device can optionally output a message to the clinician, with the ability for the clinician to respond. As an example, the clinician's device may have a mobile application or browser installed that can receive the alert and output the user interface with the alert to present to the clinician. The user interface can include buttons, input fields, or other user interface controls that allow the clinician to interact with the alert. As an example, the user interface can provide the clinician with the ability to accept or coordinate recommended treatments. If the clinician accepts the recommended treatment, the software on the clinician's device can send the consent to the patient monitor 910, whereby the autonomous drug delivery device 920 will receive the recommended dosage. It will be possible to deliver to the patient (or direct a sample test or perform another workflow action). If the clinician adjusts the recommended treatment, this adjustment can be communicated to the patient monitor 910, whereby the autonomous drug delivery device 920 delivers the adjusted dosage to the patient (or specimen). Can be instructed or another workflow action can be performed).

Similarly, clinicians can remotely create or adjust treatment workflows, whether sample-directed, drug-administered, or other workflows, and send this adjustment to patient monitor 910. Then, it becomes possible to apply it to the patient. For example, a clinician can receive patient data with or without treatment recommendations. The clinician can then access the user interface on the clinician's device to enter the desired treatment or workflow, even if not recommended by the patient monitor 910. The clinician device can send this selected treatment or workflow to the patient monitor 910. If the treatment or workflow involves administration of a drug, the patient monitor 910 allows the autonomous drug delivery device 920 to deliver the selected dosage to the patient. Any of the autonomous drug delivery devices described herein can include a number of different interventions that are ready to be injected or delivered intravenously to the patient, if desired.

FIG. 10 shows an exemplary automated first responder method 1000. This automated first responder method 1000 can be implemented by any of the systems described above. As an example, the patient monitor 910 can implement method 1000. However, in another embodiment, at least a portion of Method 1000 can be implemented on any server, eg, in a cloud computing environment. For example, method 1000 can be implemented in a server that receives information from patient monitor 910, which provides output to monitor 910, where monitor 910 initiates delivery of material (by autonomous drug delivery device 920). Etc.) To be able to perform part of the treatment workflow. However, for the sake of brevity, Method 1000 will be described as being implemented primarily by the patient monitor 910.

In block 1002, patient monitor 910 receives patient data containing one or more physiological parameter values. One or more physiological parameter values can include values for one or more physiological parameters such as respiratory rate, oxygen saturation, blood pressure, and heart rate (and any other parameters mentioned above). .. In addition to receiving one or more physiological parameter values, the patient monitor can receive additional patient data, such as sample test values. One example of sample test values that may be useful is white blood cell count (WBC). More generally, any blood test value, urine test value, stool test value, or any other sample test value may be used.

In block 1004, patient monitor 910 analyzes patient data based on one or more diagnostic models. For example, the patient monitor 910 can compare patient data to any diagnostic model stored in memory or other physical computer storage. The diagnostic model can include one or more criteria for determining whether a treatment workflow should be initiated. Evaluation of this criterion may include performing a comparison of patient data with specific values, thresholds, trends, changes, combinations, ratios, and so on. As an example, a particular combination of parameter tendencies can indicate that a patient may experience either opioid overdose, sepsis, hypovolemia, or some other symptom. Specific examples of diagnostic models will be presented later with respect to FIG.

In block 1006, the patient monitor 910 determines if the patient data matches the diagnostic model. If they do not match, method 1000 loops back to block 1002 and patient monitor 910 continues to receive patient data. However, if patient monitor 910 identifies a match in the diagnostic model, method 1000 proceeds to block 1008, where patient monitor 910 determines if this is the first time patient data matches this model. judge. For the first time, at block 1010, patient monitor 910 initiates a treatment workflow specific to the diagnostic model. Method 1000 then loops back to block 1002.

Diagnostic models can be represented as data structures or data definitions stored in physical computer storage or memory. Diagnostic models can also be empirically generated by collecting data from multiple patients, measuring the same or similar physiological parameters, and analyzing patient treatment and subsequent outcomes. The diagnostic model may also be a rule-based model or a combination of an empirical model and a rule-based model. A model based on some exemplary rules will be described later with reference to FIG.

In some embodiments, in block 1006, the patient monitor 910 calculates a confidence value that represents the likelihood that the patient data will match the diagnostic model. The confidence value can be a percentage value, a score on any scale, and so on. The patient monitor 910 can calculate higher confidence values based on patient data that are more closely matched to the diagnostic model, or lower confidence values based on patient data that are less closely matched to the diagnostic model.

The confidence value may also be determined based at least in part on the parameter value confidence. As an example, the patient monitor 910 can calculate the confidence value of a parameter value that indicates that the parameter value may be accurate. Therefore, the confidence value to match the diagnostic model can take into account the parameter confidence value. For example, if two parameters are analyzed for the diagnostic model and the two parameters have a calculated confidence of 95% and 75%, respectively, then the patient monitor 910 is the average of these two values (85%). You can calculate a confidence value, or some other combination of these two values, such as choosing a lower confidence value for the confidence value as a whole. The patient monitor 910 can then compare the two parameter values with the diagnostic model to determine how closely the parameters match this model. The patient monitor 910 can give a high confidence score if the parameters match the model well, but the patient monitor 910 then modifies this high confidence score based on the parameter value confidence score. can do. As an example, if the confidence value to match the model is 95%, but the parameter value confidence is 75%, then the patient monitor 910 (for example, multiplyes the two numbers to be about 71%). You can reduce the confidence to match the model (by doing so). Many other ways of calculating confidence are possible.

The patient monitor 910 can determine that the diagnostic model is not met if the confidence value is too low. Alternatively, the patient monitor 910 can determine that the diagnostic model is met, even if the confidence value is low, but allow the clinician to determine if the diagnostic model match should be trusted. In addition, a display of reliability values can be output (for example, on a display). The patient monitor 910 can output a display of the reliability value regardless of whether the value is high or low. The display may be the value itself or some representation of the value, such as a graphic representation (eg, a bar of a size corresponding to the confidence value).

However, in block 1008, if patient data is not the first to match this model, in block 1012, patient monitor 910 warns the clinician, thereby associating the clinician with the treatment workflow. It will be possible to determine whether to give additional substances to be treated or to perform additional actions. The patient monitor 910 can alert the clinician by outputting warnings or alarms audibly and / or visually. Patient Monitor 910 also informs clinicians by sending warning or alarm messages to clinicians over networks such as hospital networks, local area networks (LANs), wide area networks (WANs), the Internet, or intranets. Can warn you.

Therefore, Method 1000 can provide automated treatment of the patient as a first response and postpone the clinician's intervention until a subsequent response. As an example, when patient data is first determined to indicate a potential opioid overdose, patient monitoring allows the patient to be provided with an initial dosage of an overdose therapeutic agent such as Nalcan. However, if patient data later re-indicates that the patient is suffering from the possibility of overdose, the patient monitor 910 warns the clinician to determine if the patient needs an additional dose. can do.

In one embodiment, if the clinician's response is not fast enough (eg, within a predetermined time period), the patient monitor 910 can automatically deliver a second dose to the patient. The patient monitor 910 supplies the patient a second dose unless the patient data contains one or more critical parameter values (such as very low pulse rate, oxygen saturation, or blood pressure). Can be restricted. More generally, the patient monitor 910 can take into account one of the following variables, among other things, when deciding whether to administer subsequent doses of the drug to the patient: the clinician Without clearing the alarm, or otherwise not responding to the alarm, the time elapsed since the alarm; severity of patient data; preceding and / or subsequent dosages (lower doses are detrimental) It may be less likely, and therefore subsequent lower doses may be allowed without the intervention of a clinician); the type of drug administered (some drugs may be followed by subsequent doses). The risk of administration may be lower than other drugs); specific facts about the patient, including demographics, or comorbidities (eg, newborns may be at higher risk than adults for subsequent doses, or Patients with a particular disease may be at higher risk for a particular dose than patients without that disease); other drugs taken by the patient (patients may have side effects for subsequent doses) Subsequent doses can potentially be more detrimental if taking the drug; previous permission by the clinician (Patient Monitor 910 is one or more subsequent doses as needed by the clinician) It is possible to output a user interface that allows explicit permission to be given for automatic drug administration); or a combination of these.

FIG. 11 shows exemplary diagnostic models 1110, 1120, and 1130. These diagnostic models show exemplary criteria corresponding to possible diagnostics, as well as associated exemplary treatment workflows. The first model 1110 is an opioid overdose model. In this model, changes in these parameters can indicate opioid overdose when respiratory rate, heart rate, and blood pressure decrease (eg, tend to decline or fall below a threshold). , Can trigger the treatment workflow. Decreased body temperature may also indicate an opioid overdose in the model. The exemplary treatment workflow shown includes administration of Nalcan and notification to the clinician. Notifications to the clinician can include sending the warnings or alarms described above to the clinician, recording the display on the patient's medical record (eg, EMR, etc.), or a combination thereof. The treatment workflow shown can be modified to include more or fewer steps. In addition, the treatment workflow and other treatment workflow steps described herein can be implemented in any order.

The second model 1120 is a hypovolemia model. In this model 1120, blood pressure is reduced (eg, downward trend or below threshold), heart rate is increased (eg, upward trend or above threshold), and hemoglobin levels are (non-invasive). Stable (eg, no positive or negative trend, or mean trend gradient within acceptable range), whether measured in (SpHb) or sample (THb). Or if the parameter value has not changed more than the threshold amount over a period of time), this may be an indication that the patient is suffering from hypovolemia. Treatment workflows that can be triggered are increasing saline infusion rate (to increase blood volume and / or sodium levels in the blood), supplying oxygen (not shown, of the patient's remaining blood supply). Efficiency can be increased), and notification to the clinician (similar to above) can be included. Although not shown, other steps, such as directing other drugs and recommending possible surgery (for example, to stop internal bleeding in some patients with hypovolemia), are these other treatment workflows. May be included in.

The third model 1130 shown is the sepsis model. In this model, if the blood pressure drops, the white blood cell count rises, and the body temperature rises (eg, fever, or conversely, the body temperature is too low), the patient may be suffering from sepsis. Increased heart rate (eg above the threshold), increased respiratory rate (eg above the threshold), and decreased systolic blood pressure (eg below the threshold) are other exemplary examples that can be included in this model. It is a factor. The exemplary treatment workflow shown is to run a sepsis team (eg, notify a team of clinicians with different duties who can assist in the treatment of sepsis), administer the drug, and Includes directing testing of specimens, drugs, and radiation. The treatment workflow can also include automatic administration of intravenous infusions and / or antibiotics. The treatment workflow can also include recommending the transfer of the patient to the intensive care unit (ICU).

As mentioned above, there are many other diagnostic models available by patient monitors to initiate treatment workflows. All models may be combined or modified by other criteria. As an example, administration of some drugs can cause side effects, which can be detected or inferred from patient data. As an example, administration of some drugs can have a negative effect on blood pressure. When a change in blood pressure is detected after a drug has been administered, the treatment workflow asks the clinician to discontinue administration of the drug (if it is being administered automatically) or to consider discontinuing administration of the drug. You can start making recommendations.

As another example, a diagnostic model for detecting bedsore formation (or risk factors for bedsore formation) can be provided. This diagnostic model can capture input from a pressure mat on the patient's bed or on the floor next to the bed, which provides electricity when the patient moves on or steps on the mat. A signal output can be provided. No movement on the mat, or no stepping on the mat, may indicate that the patient is not moving sufficiently and is therefore prone to bedsores. Possible treatment workflows may be implemented, including warning the caregiver that the patient should be moved in some way to prevent bedsore formation.

Diagnostic models can also take into account more than the physiological parameters and sample tests described herein as examples of patient data. Additional patient data can be involved in assessing the applicability of a particular model. As an example, data about a patient's gender, age, or other comorbidities may affect whether a particular diagnostic model determines that it is applicable. Similarly, any of these factors can be used to modify the treatment workflow if a diagnostic model is shown to be applicable. For example, if a patient is shown to be a smoker (which can be a data point stored in the patient's EMR record), the smoker's lungs will have higher carbon dioxide levels when breathing. A treatment workflow that involves providing ventilation from a ventilator can include supplying more carbon dioxide than is supplied to a non-smoking patient.

In another embodiment, previous or current treatment can be entered into the diagnostic model as part of patient data. Therefore, previous or current treatment may affect whether a diagnostic model is applied and / or how the treatment workflow is performed. As an example, if a patient has just undergone hip replacement surgery (which can be a datapoint stored in the patient's EMR record), the patient monitor will enhance the warning and show signs of sepsis. You can look for it. Therefore, patient monitors can be more sensitive to changes in blood pressure, body temperature, and white blood cell count to determine if sepsis is occurring. More specifically, lower changes in blood pressure, white blood cell count, or body temperature may be applied to trigger a sepsis treatment workflow than for other patients who have not recently undergone hip arthroplasty.

Moreover, the level of warning provided to the clinician can vary depending on the severity of the diagnostic model detected, as seen in the exemplary workflow shown in Figure 11. As an example, in the case of sepsis, the entire team can be warned, but in the case of other symptoms such as hypovolemia, one clinician can be warned. Of course, the number of clinicians warned for any given symptom can be determined by the severity of the symptom, the severity of the physiological parameter values, etc., from those shown in FIG. You may deviate.

Patient data in any of the models described herein can be analyzed according to a variety of different types of threshold values. Two broad types of thresholds, namely static thresholds and dynamic thresholds, can be used. The static threshold can include a predetermined threshold that is the same for the patient's class, such as the same threshold set for all adults or a different threshold set for all newborns. Dynamic thresholds can include thresholds that are based on current patient mean or baseline physiological parameter values. Table 1 below shows exemplary static and dynamic thresholds for adult patients for several physiological parameters.

In Table 1, "SD" indicates "standard deviation", "MAP" indicates mean arterial pressure, and "RPM" indicates per minute. It indicates respirations or breaths per minute, and "BPM" indicates beats per minute.

The static threshold category can be further subdivided into two subcategories, the critical threshold and the normal threshold. The critical threshold can represent a threshold at which physiological parameters are below (or above) a critical or fatal value. The normal threshold can represent a value that is less severe or less fatal, but can nevertheless indicate a diagnosis for each diagnostic model. Some diagnostic models can use critical thresholds and others can use normal thresholds. Some diagnostic models can use the critical threshold for one parameter and the normal threshold for the other.

The patient monitor can evaluate the static or dynamic threshold to determine if the diagnostic model is met. In addition, the patient monitor can evaluate both static and dynamic thresholds to determine if the diagnostic model is met. As an example, a patient monitor can perform a logical "OR" operation based on a comparison of a physiological model with both static and dynamic thresholds. To illustrate one exemplary embodiment, if the patient's heart rate satisfies either (or both) a static threshold or a dynamic threshold, the patient monitor will show that the patient's heart rate is It can be determined that the heart rate portion of the diagnostic model is satisfied. In another embodiment, the patient monitor performs a logical "AND" operation (or some other logical operation) based on a comparison of the physiological model with both static and dynamic thresholds.

In one embodiment, the patient monitor may rely on static thresholds early in the patient's hospital stay, in which case the mean or baseline value of the patient's parameters is unknown or the patient data is available. Not very reliable to calculate based on no. After the patient has been hospitalized (even minutes or hours after reception), the patient monitor has sufficient data about the patient to determine the mean or baseline value. After that, it may depend on the dynamic threshold.

FIG. 12 shows another exemplary automated first responder method 1200. The automated first responder method 1200 can be implemented by any of the systems described above. As an example, the patient monitor 910 can implement method 1200. However, in another embodiment, at least a portion of Method 1200 can be implemented in a server, eg, in a cloud computing environment. Method 1200 can optionally be implemented on a server that receives information from the patient monitor 910, which provides output to the monitor 910 so that the monitor 910 can perform part of the treatment workflow. However, for the sake of brevity, Method 1200 will be described as being implemented primarily by the patient monitor 910.

Blocks 1202-1206 may proceed the same as or similar to Blocks 1002-1006 of Method 1000 (see Figure 10). In block 1208, the patient monitor 910 outputs an alarm or warning to the clinician if the patient data matches the diagnostic model in block 1206. This alarm or warning may be provided in a manner similar to the alarm or warning output in block 1012 of Method 1000 (see Figure 10). However, in this method 1200, the patient monitor 910 blocks whether the alarm (or warning) is confirmed within a given time period (such as 30 seconds, 1 minute, 2 minutes, or some other time period) 1210 Judge in. As an example, the patient monitor 910 can determine whether the clinician has cleared the alarm as an indication that the alarm has been confirmed. Releasing the alarm can indicate that the clinician has visited the patient's bedside to see the situation that led to the alarm.

If the alarm is confirmed within a predetermined time period, method 1200 can be terminated. If the alarm is not confirmed within a predetermined time period, in block 1212, the patient monitor 910 initiates a diagnostic model-specific treatment workflow, as described above for block 1010 in FIG. Can be done.

As such, Method 1200 allows a patient to receive some form of medical attention, for example, as specified by a treatment workflow, if the clinician is unable to treat the patient within a certain amount of time. To do. This method 1200 can save lives and improve patient comfort in busy hospitals or other triage situations.

Of course, if the patient data matches the diagnostic model again, the patient monitor 910 can implement blocks 1008 and 1012, such as Method 1000.

CONCLUSIONS: Autonomous drug delivery systems and automated first responder systems have been disclosed in detail in relation to various embodiments. These embodiments are disclosed only as examples and do not limit the scope of the present disclosure or the claims that follow. Those skilled in the art will recognize many variants and modifications. As an example, many variants other than those described herein will become apparent from this disclosure. For example, depending on the embodiment, any particular action, event, or function of any of the algorithms described herein may be performed in different sequences, added, fused, or It may be completely excluded (for example, not all described actions or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, actions or events can be performed at the same time, not sequentially, for example, by multithreading, interrupt handling, or by multiple processors or processor cores, or in other parallel architectures. In addition, different tasks or processes can be performed by different machines and / or computing systems that can work together.

The various exemplary logical blocks, modules, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination thereof. To articulate this compatibility between hardware and software, various exemplary components, blocks, modules, and steps have been outlined above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the entire system. The features described may be implemented in different ways for each particular application, but such implementation decisions should not be construed as causing deviations from the scope of this disclosure.

The various exemplary logic blocks and modules described in connection with the embodiments disclosed herein are hardware processors, general purpose processors, digital signal processors (DSPs), application specific integrated circuits, including digital logic circuit mechanisms. Integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, individual gate or transistor logic units, individual hardware components, or those designed to perform the functions described herein. Can be implemented or executed by a machine such as any combination of. The general purpose processor can be a microprocessor, but in alternative forms, the processor can be a controller, a microcontroller, or a state machine, or a combination thereof. The processor can include an electrical circuit mechanism that is configured to process computer executable instructions. In another embodiment, the processor includes an FPGA or other programmable device that performs logical operations without processing computer executable instructions. Processors are also implemented as a combination of computing devices, such as a DSP and microprocessor combination, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration. Can be done. Computing environments include, but are not limited to, microprocessors, mainframe computers, digital signal processors, portable computing devices, device controllers, or computer systems based on computer computing engines in appliances. Any type of computer system can be included.

The methods, processes, or algorithmic steps described in connection with the embodiments disclosed herein are stored directly in hardware in one or more memory devices and performed by one or more processors. It can be implemented in a software module or a combination of the two. Software modules are RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of non-temporary computer-readable storage medium, multiple media, or It can reside in physical computer storage devices known in the art. An exemplary storage medium can be coupled to the processor, which allows the processor to read information from the storage medium and write the information to the storage medium. In the alternative form, the storage medium may be integrated with the processor. The storage medium may be volatile or non-volatile. The processor and storage medium can reside in the ASIC.

Conditional as used herein, among others, such as "can," "might," "may," and "for example." A language generally includes a particular function, element, and / or state, but other embodiments, unless otherwise specified or understood within the context otherwise used. Intended to tell you that there isn't. Therefore, such conditional languages generally require a function, element, and / or state in some way in one or more embodiments, or one or more embodiments are input or by the author. Make sure that these features, elements, and / or states, with or without prompting, include a logical part to determine if they are included in or should be performed in any particular embodiment. Not intended to be implied. Terms such as "comprising," "including," and "having" are synonymous and are used comprehensively in an open-ended format, with additional elements, functions, actions, operations, etc. Do not exclude. Also, the term "or" is used in its inclusive sense (not in its exclusive sense), and thus, for example, "or" when used to connect a list of elements. The term means one, some, or all of the elements in the list. Furthermore, the term "each" as used herein means any subset of the set of elements to which the term "each" applies, in addition to having its usual meaning. be able to.

Interdisciplinary languages, such as the phrase "at least one of X, Y and Z," generally include items, terms, etc., unless otherwise stated. It should be understood within the context used to convey that it can be either X, Y, or Z, or a combination thereof. Thus, such adjacent languages generally imply that a particular embodiment requires at least one of X, at least one of Y, and at least one of Z for each to be present. Not intended to be.

Unless otherwise stated, articles such as "a" or "an" should generally be construed to include one or more described items. Therefore, a phrase such as "a device configured to" is intended to include one or more enumerated devices. Such one or more enumerated devices may also be configured together to perform the enumeration described. For example, "a processor configured to carry out recitations A, B and C" includes a first processor configured to perform enumeration. be able to. A, which works with the second processor, is configured to perform enumerations B and C.

The detailed description above has shown, described, and pointed out new features that apply to various embodiments, but various abbreviations, substitutions, and modifications in the embodiments and details of the illustrated device or algorithm. It will be appreciated that the form can be created without departing from the gist of this disclosure. As will be appreciated, certain embodiments of the invention described herein combine all of the functions and benefits described herein when some functions may be used or performed separately from others. It can be embodied within a range of forms that are not always provided.

1 patient
10 IV pole
20 IV bag
30 V line
40 line shunt
50, 60 one-way valve
70 Patient Control Analgesia (PCA) Pump
72 PCA analgesia
100 Autonomous Drug Delivery System (ADDS)
102 Control part
104 Front panel
108 drug part
109 cover
110 user display
120 control buttons
130 emergency button
150 IV syringe
152 Drug compartment
170 drug reservoir
172 enclosure
174 Rescue drug
178 nozzle
180 piston
182 Piston head
184 Piston shaft
190 drive motor
200, 300 IV drop injection configuration
400 Autonomous drug delivery system
410 ADDS
412 Fluid connection function; adjust
414 generate
416 reset
420 monitor
422 Provide data
424 Electronic Communication Department; Calculate
430 sensor
432 Take an interface
501 parameters
510 ADDS algorithm
512 to calculate
If it exceeds 520
522 Triggered
524 otherwise
530 alarm
540 If confirmed
542 If not confirmed
544 press
550 administer
560 Finish
570 Manual trigger
572 trigger
600 Embodiment
662 nozzle
670 Drug reservoir
676 Reservoir nozzle
678, 688 Fluid tube
680 drug reservoir
686 Reservoir Nozzle
701 parameters
710, 760 ADDS algorithm
If it exceeds 720 or 770
724, 744 otherwise
730, 780 alarm
741, 791 Finish
742, 792 If not confirmed
745, 795 administer
744, 794 press
799 Manual trigger
800 Autonomous Drug Delivery System (ADDS) Controller
810 Digital Signal Processor (DSP)
812 algorithm
820 Equipment Manager
830 interface
832, 834 to drive
835 receive
837 Communicate
839 read
900 Automated First Responder System
902 patient
910 patient monitor
920 Autonomous drug delivery device
930 sample results
940 gateway device
950 database
960 Clinician equipment
1000 Automated First Responder Method
1110, 1120, 1130 Diagnostic model
1200 automated first responder method

Claims (193)

It ’s an automated first responder method,
Under the control of the hardware processor
A step of receiving patient data containing multiple physiological parameter values, wherein at least some of the physiological parameter values are physiological signals obtained from one or more physiological sensors connected to the patient. Derived from the steps and
A step of analyzing the patient data to determine if the patient data meets at least one criterion associated with a diagnostic model represented in computer memory.
A step of determining that the patient data meets at least one criterion associated with the diagnostic model.
In response to the determination, a step of initiating a treatment workflow involving the automatic administration of a substance to the patient, and
Following the administration, a step of determining that the patient data re-satisfies at least one criterion associated with the diagnostic model, and
The warning is a step of sending a warning to the clinician via the hospital network in response to the determination that the patient data re-satisfies at least one criterion associated with the diagnostic model. An automated first responder method, including steps, indicating that an additional amount of said substance may be needed for said patient. The method of claim 1, wherein the step of receiving the patient data further comprises a step of receiving the sample data. The method according to claim 2, wherein the sample data includes data representing a white blood cell count. From claim 1, the step of analyzing the patient data includes a step of identifying a tendency in the physiological parameter value and a step of determining whether the tendency corresponds to a predetermined criterion in the diagnostic model. The method described in any one of 3. The step of determining that the patient data meets at least one criterion associated with the diagnostic model is a reduction in one of the physiological parameter values and another physiological of the physiological parameter values. The method of any one of claims 1 to 4, comprising the step of identifying an increase in the parameter value. The method according to any one of claims 1 to 5, wherein the diagnostic model represents one of the following overdose diagnostic model, hypovolemia diagnostic model, or sepsis diagnostic model. The method of claim 6, wherein the overdose diagnostic model corresponds to a decrease in respiratory rate, a decrease in heart rate, and a decrease in blood pressure. The method according to any one of claims 1 to 7, wherein the substance contains naloxone. The method of claim 6, wherein the hypovolemia diagnostic model corresponds to a decrease in blood pressure, an increase in heart rate, and a stable hemoglobin level. The method according to any one of claims 1 to 9, wherein the substance comprises physiological saline. The method of claim 6, wherein the sepsis diagnostic model corresponds to a decrease in blood pressure, an increase in white blood cell count, and an increase in body temperature. The step of initiating the workflow is one of the following steps of warning the clinician, instructing a sample test, instructing a drug, and instructing a radiological examination. The method according to any one of claims 1 to 11, further comprising a plurality. The method of any one of claims 1-12, further comprising the step of automatically giving at least one drug or at least one bolus of the drug when a particular criterion and confidence level are met. The method of any one of claims 1-13, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in respiratory rate. The method of any one of claims 1-14, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in respiratory rate. The method of any one of claims 1 to 15, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in heart rate. The method of any one of claims 1-16, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in heart rate. The method of any one of claims 1-17, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in blood pressure. The method of any one of claims 1-18, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in blood pressure. The method of any one of claims 1-19, wherein the at least one criterion associated with the diagnostic model corresponds to stable hemoglobin levels. The method of any one of claims 1-20, wherein the at least one criterion associated with the diagnostic model corresponds to a change in hemoglobin level. The method of any one of claims 1 to 21, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in white blood cell count. The method of any one of claims 1 to 22, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in white blood cell count. The method of any one of claims 1 to 23, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in body temperature. The method of any one of claims 1 to 24, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in body temperature. The method of any one of claims 1-25, wherein the at least one criterion associated with the diagnostic model corresponds to a high heart rate. The method of any one of claims 1-26, wherein the at least one criterion associated with the diagnostic model corresponds to a low heart rate. The method of any one of claims 1-27, wherein the at least one criterion associated with the diagnostic model corresponds to an arrhythmia. The method of any one of claims 1-28, wherein the at least one criterion associated with the diagnostic model corresponds to hypertension. The method of any one of claims 1-29, wherein the at least one criterion associated with the diagnostic model corresponds to hypotension. The method of any one of claims 1-30, wherein the at least one criterion associated with the diagnostic model corresponds to a high respiratory rate. The method of any one of claims 1-31, wherein the at least one criterion associated with the diagnostic model corresponds to a low respiratory rate. The method of any one of claims 1-32, wherein the at least one criterion associated with the diagnostic model corresponds to high oxygen saturation. The method of any one of claims 1-33, wherein the at least one criterion associated with the diagnostic model corresponds to hypoxic saturation. The method of any one of claims 1-34, wherein the at least one criterion associated with the diagnostic model corresponds to low expiratory terminal CO2. The method of any one of claims 1-35, wherein the at least one criterion associated with the diagnostic model corresponds to high expiratory terminal CO2. The method of any one of claims 1-36, wherein the at least one criterion associated with the diagnostic model corresponds to hypoglycemia. The method of any one of claims 1-37, wherein the at least one criterion associated with the diagnostic model corresponds to an acoustic respiration measurement result. The method according to any one of claims 1 to 38, wherein the at least one criterion associated with the diagnostic model corresponds to an ECG measurement result. The method according to any one of claims 1 to 39, wherein the at least one criterion associated with the diagnostic model corresponds to an optical sensor measurement result. The method according to any one of claims 1 to 40, wherein the at least one criterion associated with the diagnostic model corresponds to an electroencephalogram measurement result. The method according to any one of claims 1 to 41, wherein the automatic administration of the substance to the patient comprises a single administration. The method of any one of claims 1-42, wherein said automatic administration of the substance to the patient comprises delivering a plurality of bolus doses. The method according to any one of claims 1 to 43, wherein the automatic administration of the substance to the patient comprises a gradual administration of the substance. The method of any one of claims 1-44, wherein the substance comprises a formulated formulation. The method according to any one of claims 1 to 45, further comprising the step of automatically administering a second dose of the substance to the patient. The method according to any one of claims 1 to 46, further comprising the step of automatically administering the second substance to the patient. The method of any one of claims 1-47, wherein the at least one criterion associated with the diagnostic model is associated with a confidence level. The method of any one of claims 1-48, further comprising the step of administering the substance to the patient in response to input from the clinician. The method of any one of claims 1-49, further comprising the step of administering the substance to the patient in response to a remote input from the clinician. The method of any one of claims 1-50, further comprising administering the substance to the patient after a predetermined time interval following the alarm without receiving confirmation by the clinician of the alarm. The method of any one of claims 1-51, wherein the patient data comprises information received from an electronic medical record. The method of any one of claims 1-52, wherein the patient data comprises a drug. The step of analyzing the patient data to determine if the patient data meets at least one criterion associated with a diagnostic model represented in computer memory is to analyze the patient data and say that Each diagnostic model represents a possible diagnosis of a different symptom, including determining if the patient data matches at least one of a plurality of diagnostic models represented in computer memory, and as a result, The method of any one of claims 1-53, which results in the administration of different drugs. The step of analyzing the patient data to determine if the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is consistent with the patient data. The method of any one of claims 1-54, comprising the step of calculating a confidence value that represents the possibility of doing so. The step of analyzing the patient data to determine whether the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is the parameter value reliability of the patient data. The method of any one of claims 1-55, comprising the step of calculating a confidence value that represents the likelihood of matching the diagnostic model, which is determined on the basis of at least a portion. Following the determination that the patient data re-satisfies at least one criterion associated with matching with the diagnostic model, a second treatment workflow involving the automatic administration of the substance to the patient. The method according to any one of claims 1 to 56, which is automatically supplied. 22. The step of automatically supplying the second treatment workflow, if the clinician does not clear the alarm or does not, does not respond to the alarm and takes into account the time elapsed since the alarm, claim 57. the method of. 58. The method of claim 57, wherein the step of automatically supplying a second treatment workflow takes into account the severity of the patient's symptoms. 58. The method of claim 57, wherein the step of automatically supplying the second treatment workflow takes into account the preceding dosage. 58. The method of claim 57, wherein the step of automatically supplying a second treatment workflow takes into account the patient's demographics. 58. The method of claim 57, wherein the step of automatically supplying a second treatment workflow takes into account comorbidities. 58. The method of claim 57, wherein the step of automatically supplying a second treatment workflow takes into account other agents ingested by said patient. The method of any one of claims 1-63, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold. The method of any one of claims 1-64, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold that is the same for the social layer of the patient. The method of any one of claims 1-65, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold. The method of any one of claims 1-66, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's baseline physiological parameter values. The method of any one of claims 1-67, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's average physiological parameter value. An automated first responder system,
A patient monitor with a hardware processor configured to derive physiological parameter values from physiological signals obtained from one or more physiological sensors connected to the patient.
An autonomous delivery device that communicates with the patient monitor and includes an autonomous delivery device that includes at least one substance.
The hardware processor
Analyzing the physiological parameter values to determine whether at least one physiological parameter value corresponds to at least one criterion of the diagnostic model.
Determining that the at least one physiological parameter value corresponds to the at least one criterion of the diagnostic model.
In response to the determination, the autonomous delivery device initiates a treatment workflow that automatically administers the substance to the patient.
Following the administration, determining that the at least one physiological parameter value again corresponds to at least one criterion of the diagnostic model.
Sending a warning to the clinician via the hospital network in response to the determination that the physiological parameter value is in agreement with the diagnostic model again, the warning is the addition of the substance. It is configured to send and to indicate that the amount of the patient may be needed.
Automated first responder system. Analyzing the physiological parameter values to determine whether the physiological parameter values correspond to a diagnostic model identifies a tendency in the physiological parameter values, and the tendency is predetermined in the diagnostic model. 69. The system of claim 69, comprising determining whether to respond to a trend of. The system according to claim 69 or 70, wherein the diagnostic model represents one of the following opioid overdose diagnostic model, hypovolemia diagnostic model, or sepsis diagnostic model. The system according to any one of claims 69 to 71, wherein the opioid overdose diagnostic model corresponds to a decrease in respiratory rate, a decrease in heart rate, and a decrease in blood pressure. The system according to any one of claims 69 to 72, wherein the substance comprises naloxone. The system according to any one of claims 69 to 73, wherein the hypovolemia diagnostic model corresponds to a decrease in blood pressure, an increase in heart rate, and a stable hemoglobin level. The system according to any one of claims 69 to 74, wherein the substance comprises saline. The system according to any one of claims 69 to 75, wherein the sepsis diagnostic model corresponds to a decrease in blood pressure, an increase in white blood cell count, and an increase in body temperature. Initiating the workflow is one of the following, warning the clinician, instructing a sample test, instructing a drug, and instructing a radiological examination. The system according to any one of claims 69 to 76, further comprising a plurality. The system of any one of claims 69-77, further comprising automatically giving at least one drug or at least one bolus of a drug when certain criteria and confidence levels are met. The system of any one of claims 69-78, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in respiratory rate. The system of any one of claims 69-79, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in respiratory rate. The system of any one of claims 69-80, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in heart rate. The system of any one of claims 69-81, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in heart rate. The system according to any one of claims 69 to 82, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in blood pressure. The system according to any one of claims 69 to 83, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in blood pressure. The system of any one of claims 69-84, wherein the at least one criterion associated with the diagnostic model corresponds to stable hemoglobin levels. The system of any one of claims 69-85, wherein the at least one criterion associated with the diagnostic model corresponds to changes in hemoglobin levels. The system according to any one of claims 69 to 86, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in white blood cell count. The system according to any one of claims 69 to 87, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in white blood cell count. The system according to any one of claims 69 to 88, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in body temperature. The system according to any one of claims 69 to 89, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in body temperature. The system of any one of claims 69-90, wherein the at least one criterion associated with the diagnostic model corresponds to a high heart rate. The system of any one of claims 69-91, wherein the at least one criterion associated with the diagnostic model corresponds to a low heart rate. The system of any one of claims 69-92, wherein the at least one criterion associated with the diagnostic model corresponds to an arrhythmia. The system according to any one of claims 69 to 93, wherein the at least one criterion associated with the diagnostic model corresponds to hypertension. The system according to any one of claims 69 to 94, wherein the at least one criterion associated with the diagnostic model corresponds to hypotension. The system of any one of claims 69-95, wherein the at least one criterion associated with the diagnostic model corresponds to a high respiratory rate. The system of any one of claims 69-96, wherein the at least one criterion associated with the diagnostic model corresponds to a low respiratory rate. The system of any one of claims 69-97, wherein the at least one criterion associated with the diagnostic model corresponds to high oxygen saturation. The system of any one of claims 69-98, wherein the at least one criterion associated with the diagnostic model corresponds to hypoxic saturation. The system of any one of claims 69-99, wherein the at least one criterion associated with the diagnostic model corresponds to low expiratory terminal CO2. The system according to any one of claims 69 to 100, wherein the at least one criterion associated with the diagnostic model corresponds to high expiratory terminal CO2. The system according to any one of claims 69 to 101, wherein the at least one criterion associated with the diagnostic model corresponds to hypoglycemia. The system according to any one of claims 69 to 102, wherein the at least one criterion associated with the diagnostic model corresponds to an acoustic respiration measurement result. The system according to any one of claims 69 to 103, wherein the at least one criterion associated with the diagnostic model corresponds to an ECG measurement result. The system according to any one of claims 69 to 104, wherein the at least one criterion associated with the diagnostic model corresponds to an optical sensor measurement result. The system according to any one of claims 69 to 105, wherein the at least one criterion associated with the diagnostic model corresponds to an electroencephalogram measurement result. The system according to any one of claims 69 to 106, wherein the automatic administration of the substance to the patient comprises a single administration. The system according to any one of claims 69 to 107, wherein the automatic administration of the substance to the patient comprises delivering a plurality of bolus doses. The system according to any one of claims 69 to 108, wherein the automatic administration of the substance to the patient comprises a gradual administration of the substance. The system according to any one of claims 69 to 109, wherein the substance comprises a formulated formulation. The system according to any one of claims 69 to 110, further comprising automatically administering a second dose of the substance to the patient. The system according to any one of claims 69 to 111, further comprising automatically administering a second substance to the patient. The system of any one of claims 69-112, wherein the at least one criterion associated with the diagnostic model is associated with a confidence level. The system of any one of claims 69-113, further comprising administering a substance to said patient in response to input from a clinician. The system of any one of claims 69-114, further comprising administering a substance to said patient in response to remote input from a clinician. The system according to any one of claims 69 to 115, further comprising administering the substance to the patient after a predetermined time interval following the alarm without receiving confirmation by the clinician of the alarm. The system according to any one of claims 69 to 116, wherein the patient data includes information received from an electronic medical record. The system according to any one of claims 69 to 117, wherein the patient data comprises a drug. Analyzing the patient data to determine if the patient data meets at least one criterion associated with a diagnostic model represented in computer memory can be analyzed. Each diagnostic model represents a possible diagnosis of different symptoms, including determining if the patient data matches at least one of a plurality of diagnostic models represented in computer memory, and as a result, The system according to any one of claims 69 to 118, which results in administration of different drugs. Analyzing the patient data to determine if the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is consistent with the diagnostic model. The system according to any one of claims 69 to 119, comprising calculating a confidence value that represents the possibility of doing so. Analyzing the patient data to determine if the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is the parameter value reliability of the patient data. The system of any one of claims 69-120, comprising calculating a confidence value that represents the likelihood of matching the diagnostic model, which is determined on the basis of at least a portion. Following the determination that the patient data re-satisfies at least one criterion associated with matching with the diagnostic model, a second treatment workflow involving the automatic administration of the substance to the patient. The system according to any one of claims 69 to 121, which is automatically supplied. 22. The automatic supply of a second treatment workflow takes into account the time elapsed since the alarm, if the clinician does not clear the alarm or does not respond to the alarm. System. The system of claim 122, wherein the automatic supply of a second treatment workflow takes into account the severity of the patient's symptoms. 22. The system of claim 122, wherein the automatic supply of a second treatment workflow takes into account the preceding dosage. The system of claim 122, wherein the automatic supply of a second treatment workflow takes into account the patient's demographics. The system of claim 122, wherein the automatic supply of a second treatment workflow takes into account comorbidities. The system of claim 122, wherein the automatic supply of a second treatment workflow takes into account other agents ingested by said patient. The system of any one of claims 69-128, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold. The system of any one of claims 69-129, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold that is the same for the social layer of the patient. The system of any one of claims 69-130, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold. The system of any one of claims 69-131, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's baseline physiological parameter values. The system of any one of claims 69-132, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's average physiological parameter value. An automated first responder system,
A patient monitor with a hardware processor configured to derive physiological parameter values from one or more physiological signals obtained from one or more physiological sensors connected to the patient.
An autonomous delivery device that communicates with the patient monitor, including an autonomous drug delivery device containing a substance.
The hardware processor
Analyzing the physiological parameter values to determine that at least one of the physiological parameter values corresponds to at least one criterion of the diagnostic model.
To output an alarm based on the determination that at least one of the physiological parameter values corresponds to at least one criterion of the diagnostic model.
Determining that the response to the alarm was not received within a predetermined time period
The response to the alarm is programmed to administer the substance to the patient following the determination that the response was not received within the predetermined time period.
Automated first responder system. Following the administration of the substance to the patient by the hardware processor
Derivation of the second physiological parameter value and
Determining that at least one of the second physiological parameter values also corresponds to at least one criterion of the diagnostic model.
A second indication that an additional amount of the substance may be required for the patient in response to the determination that the second physiological parameter value corresponds to at least one criterion of the diagnostic model. The system according to claim 134, which is further programmed to output an alarm and to do so. The system according to claim 134 or 135, wherein the diagnostic model represents one of the following opioid overdose diagnostic model, hypovolemia diagnostic model, or sepsis diagnostic model. The system of any one of claims 134-136, further comprising automatically giving at least one drug or at least one bolus of a drug when certain criteria and confidence levels are met. The system of any one of claims 134-137, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in respiratory rate. The system of any one of claims 134-138, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in respiratory rate. The system of any one of claims 134-139, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in heart rate. The system of any one of claims 134-140, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in heart rate. The system according to any one of claims 134 to 141, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in blood pressure. The system of any one of claims 134-142, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in blood pressure. The system according to any one of claims 134 to 143, wherein the at least one criterion associated with the diagnostic model corresponds to stable hemoglobin levels. The system of any one of claims 134-144, wherein the at least one criterion associated with the diagnostic model corresponds to changes in hemoglobin levels. The system according to any one of claims 134 to 145, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in white blood cell count. The system according to any one of claims 134 to 146, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in white blood cell count. The system according to any one of claims 134 to 147, wherein the at least one criterion associated with the diagnostic model corresponds to a decrease in body temperature. The system according to any one of claims 134 to 148, wherein the at least one criterion associated with the diagnostic model corresponds to an increase in body temperature. The system according to any one of claims 134 to 149, wherein the at least one criterion associated with the diagnostic model corresponds to a high heart rate. The system of any one of claims 134-150, wherein the at least one criterion associated with the diagnostic model corresponds to a low heart rate. The system of any one of claims 134-151, wherein the at least one criterion associated with the diagnostic model corresponds to an arrhythmia. The system of any one of claims 134-152, wherein the at least one criterion associated with the diagnostic model corresponds to hypertension. The system according to any one of claims 134 to 153, wherein the at least one criterion associated with the diagnostic model corresponds to hypotension. The system according to any one of claims 134 to 154, wherein the at least one criterion associated with the diagnostic model corresponds to a high respiratory rate. The system according to any one of claims 134 to 155, wherein the at least one criterion associated with the diagnostic model corresponds to a low respiratory rate. The system according to any one of claims 134 to 156, wherein the at least one criterion associated with the diagnostic model corresponds to high oxygen saturation. The system according to any one of claims 134 to 157, wherein the at least one criterion associated with the diagnostic model corresponds to hypoxic saturation. The system of any one of claims 134-158, wherein the at least one criterion associated with the diagnostic model corresponds to low expiratory terminal CO2. The system of any one of claims 134-159, wherein the at least one criterion associated with the diagnostic model corresponds to high expiratory terminal CO2. The system according to any one of claims 134 to 160, wherein the at least one criterion associated with the diagnostic model corresponds to hypoglycemia. The system of any one of claims 134-161, wherein the at least one criterion associated with the diagnostic model corresponds to an acoustic respiration measurement result. The system according to any one of claims 134 to 162, wherein the at least one criterion associated with the diagnostic model corresponds to an ECG measurement result. The system according to any one of claims 134 to 163, wherein the at least one criterion associated with the diagnostic model corresponds to an optical sensor measurement result. The system according to any one of claims 134 to 164, wherein the at least one criterion associated with the diagnostic model corresponds to an electroencephalogram measurement result. The system according to any one of claims 134 to 165, wherein the automatic administration of the substance to the patient comprises a single administration. The system according to any one of claims 134 to 166, wherein the automatic administration of the substance to the patient comprises delivering a plurality of bolus doses. The system according to any one of claims 134 to 167, wherein the automatic administration of the substance to the patient comprises a gradual administration of the substance. The system according to any one of claims 134 to 168, wherein the substance comprises a formulated formulation. The system according to any one of claims 134 to 169, further comprising automatically administering a second dose of the substance to the patient. The system according to any one of claims 134 to 170, further comprising automatically administering a second substance to the patient. The system of any one of claims 134-171, wherein the at least one criterion associated with the diagnostic model is associated with a confidence level. The system of any one of claims 134-172, further comprising administering the substance to said patient in response to input from a clinician. The system of any one of claims 134-173, further comprising administering the substance to said patient in response to remote input from a clinician. The system according to any one of claims 134 to 174, further comprising administering the substance to the patient after a predetermined time interval following the alarm without receiving confirmation by the clinician of the alarm. The system according to any one of claims 134 to 175, wherein the patient data includes information received from an electronic medical record. The system according to any one of claims 134 to 176, wherein the patient data comprises a drug. Analyzing the patient data to determine if the patient data meets at least one criterion associated with a diagnostic model represented in computer memory can be analyzed. Each diagnostic model represents a possible diagnosis of different symptoms, including determining if the patient data matches at least one of multiple diagnostic models represented in computer memory, and as a result, The system according to any one of claims 134 to 177, which results in administration of different drugs. Analyzing the patient data to determine if the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is consistent with the diagnostic model. The system of any one of claims 134-178, comprising calculating a confidence value that represents the possibility of doing so. Analyzing the patient data to determine if the patient data meets at least one criterion associated with the diagnostic model represented in computer memory is the parameter value reliability of the patient data. The system of any one of claims 134-179, comprising calculating a confidence value that represents the likelihood of matching the diagnostic model, which is determined on the basis of at least a portion. Following the determination that the patient data re-satisfies at least one criterion associated with matching with the diagnostic model, a second treatment workflow involving the automatic administration of the substance to the patient. The system according to any one of claims 134 to 180, which is automatically supplied. 21. The automatic supply of a second treatment workflow takes into account the time elapsed since the alarm, if the clinician does not clear the alarm or does not respond to the alarm. System. The system of claim 181, wherein the automatic supply of a second treatment workflow takes into account the severity of the patient's symptoms. The system of claim 181, wherein the automatic supply of a second treatment workflow takes into account the preceding dosage. The system of claim 181, wherein the automatic supply of a second treatment workflow takes into account the patient's demographics. The system of claim 181, wherein the automatic supply of a second treatment workflow takes into account comorbidities. The system of claim 181, wherein the automatic supply of a second treatment workflow takes into account other agents ingested by said patient. The system according to any one of claims 134 to 187, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold. The system of any one of claims 134-188, wherein the at least one criterion associated with the diagnostic model corresponds to a static threshold that is the same for the social layer of the patient. The system of any one of claims 134-189, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold. The system of any one of claims 134-190, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's baseline physiological parameter values. The system of any one of claims 134-191, wherein the at least one criterion associated with the diagnostic model corresponds to a dynamic threshold based on the patient's average physiological parameter value. The system of any one of claims 69 to 192, further configured to implement the method of any one of claims 1 to 68.

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