Systems, devices, and methods related to drug dosage guidance

Abstract

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

Description

Related Applications

[0001] This application claims the priority of U.S. Patent Application No. 63 / 237,769, filed on August 27, 2021, U.S. Patent Application No. 63 / 225,140, filed on July 23, 2021, and U.S. Patent Application No. 63 / 145,131, filed on February 3, 2021, and incorporates the entire contents thereof by reference herein for all purposes. This application is also related to U.S. Patent Application No. 29 / 817,852, filed on December 3, 2021, U.S. Patent Application No. 29 / 817,851, filed on December 3, 2021, U.S. Patent Application No. 29 / 814,001, filed on November 2, 2021, and U.S. Patent Application No. 29 / 813,998, filed on November 2, 2021, and incorporates the entire contents thereof by reference herein for all purposes. This application is also related to U.S. Patent Application No. 16 / 944,736, filed on July 31, 2020, which claims the priority and benefit of U.S. Provisional Patent Application No. 62 / 882,249, filed on August 2, 2019, U.S. Provisional Patent Application No. 62 / 979,578, filed on February 21, 2020, U.S. Provisional Patent Application No. 62 / 979,594, filed on February 21, 2020, U.S. Provisional Patent Application No. 62 / 979,618, filed on February 21, 2020, and U.S. Provisional Patent Application No. 63 / 058,799, filed on July 30, 2020, and incorporates the entire contents thereof by reference herein for all purposes.

Technical Field

[0002] The subject matter described herein generally relates to systems, devices, and methods related to dosage guidance, such as determining insulin dosages for treating elevated glucose levels resulting from, for example, diabetes.

Background Art

[0003] The detection and / or monitoring of analytes such as glucose, ketones, lactate, oxygen, and hemoglobin A1C can be critical to the health of individuals with diabetes. Patients with diabetes may suffer complications including loss of consciousness, cardiovascular disease, retinopathy, neuropathy, and nephropathy. Generally, patients with diabetes need to monitor their glucose levels to ensure they are maintained within a clinically safe range, and this information can also be used to determine whether and / or when insulin is needed to lower glucose levels in the body, or when additional glucose is needed to raise glucose levels in the body.

[0004] Growing clinical data shows a strong correlation between the frequency of glucose monitoring and blood glucose control. However, despite this correlation, many individuals diagnosed with diabetes do not monitor their glucose levels as frequently as necessary due to a combination of factors, including inconvenience, difficulty in deciding whether to test, pain associated with glucose testing, and cost.

[0005] For patients who rely on medication (e.g., insulin) to treat or manage diabetes, it is desirable to have a system, device, or method that can automatically utilize glucose information collected by an analyte monitoring system to provide drug dosage guidance in an easily accessible manner as needed. Such a system, device, or method would further preferably take into account the physiology, diet, activity, and / or behavior of the user or patient receiving treatment when providing such drug dosage guidance, which could improve accuracy and reliability. Furthermore, in some situations, it would also be desirable that such a system, device, or method be able to automatically deliver the selected drug dose. [Overview of the project] [Problems that the invention aims to solve]

[0006] For these and other reasons, there is a need for improved systems, methods, and devices related to drug dosage guidance. [Means for solving the problem]

[0007] This specification provides exemplary embodiments of systems, devices, and methods relating to drug dose guidance, and in some embodiments, to drug delivery. According to one embodiment, many of the embodiments described herein include a dose guidance system (DGS) comprising a display device, a sensor control device, and a drug delivery device. The dose guidance system may include a dose guidance application (e.g., software) that can determine and output dose guidance (e.g., recommendations regarding dosage, correction, and titration) to the patient. Furthermore, according to some embodiments, the dose guidance system may learn the patient's medication strategy during a learning period in which the dose guidance system can estimate key dosing parameters. According to some embodiments, the dose guidance system may also provide guidance for titration and correction once the system is configured with the patient's current medication strategy. The dose guidance system may also provide guidance for different meal intake scenarios. For example, in some embodiments, the dose guidance system may provide dose guidance at or before the start of a meal, or after the start of a meal. The dose guidance system may also provide dose guidance for an added meal (e.g., dessert), or for a "touch-up" dose to address high glucose levels after a meal. An example of a dose guidance system and its safety features will also be described.

[0008] Many of the embodiments provided herein feature improved software capabilities or graphical user interfaces for use in analyte monitoring systems, are highly intuitive and user-friendly, and provide rapid access to the user's physiological information. More specifically, these embodiments enable the user (or HCP) to quickly determine appropriate pharmacotherapy based on information related to the user's physiological conditions, past dosing patterns, and other factors, without having to perform the tedious task of examining large amounts of analyte data. Furthermore, some of the GUI and GUI functions enable the user (and their caregivers) to better understand and improve the user's dosing patterns and subsequent hypoglycemic and hyperglycemic episodes. Similarly, many other embodiments provided herein feature improved software capabilities for dose guidance systems, improving dose guidance provided to the user by enabling safe titration strategies that minimize hypoglycemic episodes, methods for modifying dose guidance depending on the timing of dose administration relative to the start of a meal (e.g., before, at, or after the start of a meal), consideration of real-world events that affect the medication strategy, and postprandial alarms based on predicted probability of occurrence rather than thresholds, but these are just a few examples.

[0009] Furthermore, many embodiments described herein improve upon conventional bolus calculators, which require the HCP to configure numerous settings, a very time-consuming process. Alternatively, the system may require the patient to input numerous settings, which often confuses the patient. The embodiments described herein require little to no input from the patient (e.g., typical meal dosage), significantly reducing manual input required by both the patient and the HCP, and allowing the system to learn other necessary parameters related to the patient's insulin administration habits. Moreover, many patients have poor adherence to proposed medication regimens. During the learning period, the system can collect data on both glucose levels and insulin administration to assess the patient's level of adherence. In this way, the system's functionality can solve problems related to the HCP's time investment and the patient's level of progress. Another advantage of the system is that, unlike conventional bolus calculators which use fixed parameters that need to be set, this system can automatically titrate (i.e., optimize) medication regimen parameters over time, meaning it can optimize the medication regimen to reduce low-glucose or high-glucose patterns. This frees HCPs from the time burden of having to regularly monitor patients' glucose data and update dose parameters to address blood glucose issues. Another benefit is that, if many patients frequently forget their insulin doses, the system can detect and alert when a patient has missed a dose and provide a means for the patient to safely administer the dose later, which helps lower the patient's overall glucose level. The current standard practice is for patients to take a corrective dose to correct hyperglycemia either more than two hours after starting a meal or wait until the next meal dose. Since it can be safely administered within two hours of starting a meal, it can lower overall glucose levels while minimizing the additional risk of hypoglycemia. Other improvements and benefits are also offered.Various configurations of these devices will be described in detail by illustrative embodiments.

[0010] Other systems, devices, methods, features, and advantages of the subject matter described herein will become apparent to those skilled in the art by examining the following figures and detailed description. All such additional systems, devices, methods, features, and advantages are included herein, within the scope of the subject matter described herein, and are intended to be protected by the appended claims. Features of exemplary embodiments should not be construed as limiting the claims unless those features are expressly described in the appended claims. [Brief explanation of the drawing]

[0011] Details of the subject matter described herein, both in terms of its structure and operation, can be made apparent by examining the accompanying drawings (similar reference figures in the drawings refer to similar parts). Components in the drawings are not necessarily to scale, and instead the focus is on illustrating the principles of the subject matter. Furthermore, all drawings are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be shown schematically rather than literally or precisely. [Figure 1A] This is a block diagram of an exemplary embodiment of a dose guidance system. [Figure 1B] This is a block diagram of an exemplary embodiment of a dose guidance system. [Figure 2A] This is a schematic diagram illustrating an exemplary embodiment of a sensor control device. [Figure 2B] This is a block diagram illustrating an exemplary embodiment of a sensor control device. [Figure 3A] This is a schematic diagram illustrating an exemplary embodiment of a drug delivery device. [Figure 3B] This is a block diagram illustrating an exemplary embodiment of a drug delivery device. [Figure 4A] This is a schematic diagram illustrating an exemplary embodiment of a display device. [Figure 4B] This is a block diagram illustrating an exemplary embodiment of a display device. [Figure 5] This is a diagram illustrating an exemplary embodiment of a user interface device. [Figure 6A-1] This figure shows an exemplary layout of a glucose pattern report. [Figure 6A-2-1] This figure shows an exemplary layout of a glucose pattern report. [Figure 6A-2-2] This is a continuation of Figure 6A-2-1. [Figure 6A-3-1] This figure shows an exemplary layout of a glucose pattern report. [Figure 6A-3-2] This is a continuation of Figure 6A-3-1. [Figure 6A-4-1] This figure shows an exemplary layout of a glucose pattern report. [Figure 6A-4-2] This is a continuation of Figure 6A-4-1. [Figure 6B-1] This figure shows an exemplary glucose concentration profile. [Figure 6B-2] This figure shows an exemplary glucose concentration profile. [Figure 6C-1] This figure shows an exemplary glucose concentration profile. [Figure 6C-2] This figure shows an exemplary glucose concentration profile. [Figure 6D-1] This figure shows an exemplary glucose concentration profile. [Figure 6D-2] This figure shows an exemplary glucose concentration profile. [Figure 6E-1] This figure shows an exemplary glucose concentration profile. [Figure 6E-2] This figure shows an exemplary glucose concentration profile. [Figure 6F-1] This figure shows an exemplary glucose concentration profile. [Figure 6F-2] This figure shows an exemplary glucose concentration profile. [Figure 6G-1]This figure shows an exemplary glucose concentration profile. [Figure 6G-2] This figure shows an exemplary glucose concentration profile. [Figure 7A] This flowchart illustrates an exemplary embodiment of a part of the process flow of a dose guidance application aimed at a learning method for estimating patient insulin medication habits. [Figure 7B] This flowchart illustrates an exemplary embodiment of a method for parameterizing a patient's medication habits to configure dose guidance settings. [Figure 7C] Figure 7B is a flowchart showing any additional optional elements for the method shown. [Figure 7D] Figure 7B is a flowchart showing any additional optional elements for the method shown. [Figure 7E] Figure 7B is a flowchart showing any additional optional elements for the method shown. [Figure 8A] This flowchart illustrates an exemplary embodiment of the process flow for operation by a dose guidance application for evaluating meal bolus titration for frequent injectable (MDI) drug therapy. [Figure 8B] This flowchart illustrates an exemplary embodiment of the process flow for operation by a dose guidance application for glucose pattern analysis (GPA). [Figure 8C] This figure shows an exemplary embodiment of a graph that displays information for determining the risk of hypoglycemia and other indicators for GPA. [Figure 8D] This flowchart illustrates various exemplary embodiments of an algorithm for evaluating dietary bolus titration for MDI insulin therapy. [Figure 8E] This flowchart illustrates various exemplary embodiments of an algorithm for evaluating dietary bolus titration for MDI insulin therapy. [Figure 8F]This flowchart illustrates various exemplary embodiments of an algorithm for evaluating dietary bolus titration for MDI insulin therapy. [Figure 8G] This flowchart illustrates various exemplary embodiments of an algorithm for evaluating dietary bolus titration for MDI insulin therapy. [Figure 8H] This flowchart illustrates various exemplary embodiments of an algorithm for evaluating dietary bolus titration for MDI insulin therapy. [Figure 9A-1] This figure shows an example report for review by HCP. [Figure 9A-2] This figure shows an example report for review by HCP. [Figure 9B] This figure shows an exemplary adherence report. [Figure 9C] This figure shows an example plot related to clustering analysis of meal periods. [Figure 9D] This figure shows exemplary plots of meal dose clustering and dosage. [Figure 9E] This figure shows an exemplary plot related to the determination of the pre-meal correction factor. [Figure 9F] This flowchart illustrates an exemplary embodiment of the process for facilitating access to electronic medical records by HCP. [Figure 9G] This figure shows an example summary report for HCP. [Figure 9H] This figure shows an exemplary plot illustrating patient adherence to recommended medication regimens. [Figure 9I] This figure shows an example summary report of a patient's treatment. [Figure 10] This is a state transition diagram that defines when the insulin medication algorithm may be invoked during the guidance period. [Figure 11] This flowchart illustrates an exemplary embodiment of a method for displaying dose guidance related to meal doses and corrected doses. [Figure 12] This flowchart illustrates an exemplary embodiment of a method for displaying a dosage guidance screen. [Figure 13] This flowchart illustrates an exemplary embodiment of a method for displaying multiple meal icons. [Figure 14] This flowchart illustrates an exemplary embodiment of a method for displaying dose calculations. [Figure 15] This flowchart illustrates an exemplary embodiment of a method for issuing a meal administration forgetfulness alert. [Figure 16A] This flowchart illustrates an exemplary embodiment of a method for clearing a meal administration forgetfulness alert. [Figure 16B] This flowchart illustrates an exemplary embodiment of a method for clearing a meal administration forgetfulness alert. [Figure 16C] This flowchart illustrates an exemplary embodiment of a method for clearing a meal administration forgetfulness alert. [Figure 16D] This flowchart illustrates an exemplary embodiment of a method for clearing a meal administration forgetfulness alert. [Figure 17] This flowchart illustrates an exemplary embodiment of a method for issuing a corrected dose alert. [Figure 18A] This flowchart illustrates an exemplary embodiment of a method for clearing a corrected dose alert. [Figure 18B] This flowchart illustrates an exemplary embodiment of a method for clearing a corrected dose alert. [Figure 18C] This flowchart illustrates an exemplary embodiment of a method for clearing a corrected dose alert. [Figure 19] This figure shows the residual IOB from the previous injection as a function of the DIA of rapid-acting insulin. [Figure 20A] This flowchart illustrates an exemplary embodiment of a method for classifying dosages. [Figure 20B] This flowchart illustrates an exemplary embodiment of a method for classifying dosages. [Figure 20C]This flowchart illustrates an exemplary embodiment of a method for classifying dosages. [Figure 21A] This flowchart illustrates an exemplary embodiment of a method for providing dose guidance in response to analyte data. [Figure 21B] This flowchart illustrates an exemplary embodiment of a method for determining glucose pattern indicators. [Figure 21C] This flowchart illustrates certain additional operations that can be performed in combination with one or more of the methods illustrated in Figures 21A and 21B. [Figure 21D] This flowchart illustrates certain additional operations that can be performed in combination with one or more of the methods illustrated by Figures 21A and 21B. [Figure 21E] This flowchart illustrates supplementary or alternative operations for glucose pattern display. [Figure 22A] This is an exemplary data flow diagram. [Figure 22B] This is an exemplary data flow diagram. [Figure 22C] This flowchart illustrates an exemplary embodiment of a delivery device method for determining whether stored dose data is complete. [Figure 22D] This flowchart illustrates an exemplary embodiment of an application method for determining whether received dose data is complete. [Figure 22E] This flowchart illustrates an exemplary embodiment of an application method for determining whether received dose data is complete. [Figure 23A] This flowchart illustrates an exemplary embodiment of a method for tagging meals with recommended dosages. [Figure 23B] This flowchart illustrates an exemplary embodiment of a method for tagging meals with recommended dosages. [Modes for carrying out the invention]

[0012] Before describing the subject matter of the present invention in detail, it should be understood that this disclosure is not limited to the specific embodiments described herein and may, of course, be modified in itself. Furthermore, it should be understood that the terms used herein are solely for the purpose of describing specific embodiments and are not intended to limit the scope of this disclosure, as it is limited only by the appended claims.

[0013] Generally, embodiments of the present disclosure include systems, devices, and methods relating to drug dose guidance. Dose guidance can be based on a broad range of user-specific information and information categories, such as the user's current and past analyte levels, current and past diet, current and past physical activity, current and past medical history, and other physiological information about the user. According to one embodiment, the dose guidance provided by the systems, devices, and methods of the present disclosure can be based not only on individual information categories but also on the expected impact that such information categories will have on the user's future analyte levels.

[0014] The dose guidance function may be implemented as a dose guidance application (DGA) comprising software and / or firmware instructions stored in the memory of a computing device and executed by at least one processor or processing circuit. The computing device may be owned by a user or a healthcare professional (HCP), and the user or HCP may form an interface to the computing device via a user interface. According to some embodiments, the computing device may be a server or trusted computer system accessible over a network, and the dose guidance software may be presented to the user in the form of an interactive web page via a browser running on a local display device (having a user interface) that communicates with the server or trusted computer system over the network. In these embodiments and other embodiments, the dose guidance software may run across multiple devices, or may be partially executed on the processing circuit of a local display device and partially executed on the processing circuit of a server or trusted computer system. When the DGA is described as performing an operation, it will be understood by those skilled in the art that such operation is performed according to instructions stored in computer memory (including instructions hardcoded into read-only memory) that cause the DGA to perform the operation described when executed by at least one processor of at least one computing device. In all cases, the operation can be performed alternatively by physically embedded hardware (e.g., dedicated circuitry) to perform the operation, as opposed to execution by instructions stored in memory.

[0015] Furthermore, as used herein, a system in which DGA is implemented may be referred to as a dose guidance system. A dose guidance system may be configured solely for the purpose of providing dose guidance, or it may be a multifunctional system in which dose guidance is only one aspect. For example, in some embodiments, the dose guidance system may also monitor the user's analyte level. In some embodiments, the dose guidance system may also deliver the drug to the user using an injection or infusion device, for example. In some embodiments, the dose guidance system may be capable of both analyte monitoring and drug delivery.

[0016] These and other embodiments described herein represent improvements in the fields of computer-based dose determination, analyte monitoring, and drug delivery systems. Specific features and potential benefits of the disclosed embodiments are further described below.

[0017] Before describing in detail embodiments of dose guidance, it is desirable to first describe an example of a dose guidance system on which a dose guidance application can be implemented.

[0018] Exemplary Embodiment of a Dosage Guidance System Figure 1A is a block diagram showing an exemplary embodiment of the dose guidance system 100. In this embodiment, the dose guidance system 100 is capable of providing dose guidance, monitoring one or more analytes, and delivering one or more drugs. This multifunctional example is used to illustrate the high level of interoperability and performance achieved by the system 100. However, in the embodiments described herein, the analyte monitoring component, the drug delivery component, or both may be omitted as desired.

[0019] Here, system 100 includes a sensor control device (SCD) 102 configured to collect analyte level information from the user, a drug delivery device (MDD) 152 configured to deliver drugs to the user, and a display device 120 configured to present information to the user and to receive input or information from the user. The structure and function of each device are described in detail herein.

[0020] System 100 is configured for highly interconnected and highly flexible communication between devices. Each of the three devices 102, 120, and 152 can communicate with each other directly (without intermediate electronic devices) or indirectly (via the cloud network 190, or via another device and then through the network 190, etc.). The bidirectional communication capability between devices and between devices and the network 190 is indicated by double arrows in Figure 1A. However, a person skilled in the art will understand that one or more devices (e.g., SCDs) may be capable of unidirectional communication, such as broadcast, multicast, or advertising communication. In any case, whether bidirectional or unidirectional, communication can be wired or wireless. The protocols controlling communication on each path may be the same or different, and may be proprietary or standardized. For example, wireless communication between devices 102, 120, and 152 can be performed according to Bluetooth (including Bluetooth Low Energy) standards, NFC (Near Field Communication) standards, Wi-Fi (802.11x) standards, mobile telephony standards, etc. All communications across various paths can be encrypted, and each device in Figure 1A can be configured to encrypt and decrypt those communications being sent and received. In any case, the communication paths in Figure 1A can be direct (e.g., Bluetooth or NFC) or indirect (e.g., Wi-Fi, mobile telephony, or other internet protocols). Embodiments of System 100 do not need to have the ability to communicate across all of the paths shown in Figure 1A.

[0021] In addition, although Figure 1A shows a single display device 120, a single SCD 102, and a single MDD 152, those skilled in the art will understand that system 100 may comprise multiple of any of the aforementioned devices. For illustrative purposes only, system 100 may comprise a single SCD 102 communicating with multiple (e.g., two, three, four, etc.) display devices 120 and / or multiple MDD 152s. Alternatively, system 100 may comprise multiple SCD 102s communicating with a single display device 120 and / or a single MDD 152. Furthermore, each of the multiple devices may be the same or different device type. For example, system 100 may comprise multiple display devices 120, including smartphones, handheld receivers, and / or smartwatches, each of which may communicate with and with the SCD 102 and / or MDD 152.

[0022] Analyte data can be transferred autonomously between devices within system 100 (e.g., automatically according to a schedule) or in response to requests for analyte data (e.g., a request for analyte data is sent from the first device to the second device, and then the analyte data is sent from the second device to the first device). To accommodate more complex systems, other technologies for data communication, such as a cloud network 190, can also be employed.

[0023] Figure 1B is a block diagram showing another exemplary embodiment of the dose guidance system 100. Here, the system 100 includes an SCD 102, an MDD 152, a first display device 120-1, a second display device 120-2, a local computer system 170, and a trusted computer system 180 accessible by a cloud network 190. The SCD 102 and MDD 152 are able to communicate with each other and with the display device 120-1, which can aggregate information from the SCD 102 and MDD 152, process and display that information at a desired location, and function as a communication hub for transferring some or all of the information to the cloud network 190 and / or the computer system 170. Conversely, the display device 120-1 can receive information from the cloud network 190 and / or the computer system 170 and communicate some or all of the received information to the SCD 102, MDD 152, or both. The computer system 170 may be a personal computer, server terminal, laptop computer, tablet, or other suitable data processing device. The computer system 170 may include or present software for data management and analysis, as well as for communication with components within system 100. The computer system 170 can be used by a user or medical professional to display and / or analyze analyte data measured by the SCD 102. Furthermore, although Figure 1B shows a single SCD 102, a single MDD 152, and two display devices 120-1 and 120-2, those skilled in the art will understand that system 100 may include multiple of any of the aforementioned devices, and that each of the multiple devices may be of the same or different types.

[0024] Referring further to Figure 1B, according to some embodiments, a trusted computer system 180 may be owned physically or virtually via a secure connection by a manufacturer or distributor of the components of system 100 and may be used as a server to perform authentication of the devices of system 100 (e.g., devices 102, 120-n, 152), to securely store user data, and / or to provide data analysis programs (e.g., accessible via a web browser) for performing analysis of the user's measured analyte data and medical history. The trusted computer system 180 may also function as a data hub for routing and exchanging data among all devices communicating with system 180 via the cloud network 190. In other words, all devices of system 100 that can communicate with the cloud network 190 (e.g., directly using an internet connection or indirectly via other devices) may also communicate directly or indirectly with all other devices of system 100 that can communicate with the cloud network 190.

[0025] The display device 120-2 is shown communicating with the cloud network 190. In this example, device 120-2 may be owned by another user authorized to access the analytes and drug data of the person wearing the SCD 102. For example, the person owning display device 120-2 may be, as an example, the parent of a child wearing the SCD 102, or, as an example, the caregiver of an elderly patient wearing the SCD 102. System 100 can be configured to communicate the analytes and drug data about the wearer to another user authorized to access the data via the cloud network 190 (for example, via a trusted computer system 180).

[0026] Exemplary Embodiment of an Analytical Substance Monitoring Device The analyte monitoring function of the dose guidance system 100 can be implemented by including one or more devices capable of collecting, processing, and displaying the user's analyte data. Exemplary embodiments of such devices and methods of use are described in International Publication No. 2018 / 152241 and U.S. Patent Application Publication No. 2011 / 0213225, the entire contents of which are incorporated herein by reference for all purposes.

[0027] Analyte monitoring can be performed in numerous different ways. A "continuous analyte monitoring" device (e.g., a "continuous glucose monitoring" device) can, for example, automatically transmit data from a sensor control device to a display device automatically according to a schedule, with or without prompting. A "flash analyte monitoring" device (e.g., a "flash glucose monitoring" device or simply a "flash" device) can, as another example, transfer data from a sensor control device in response to a user-initiated data request (e.g., a scan) by a display device, using protocols such as NFC (Near Field Communication) or RFID (Radio Frequency Identification).

[0028] Analyte monitoring devices that utilize sensors configured to be partially or entirely placed within a user's body can be referred to as in vivo analyte monitoring devices. For example, an in vivo sensor can be placed within a user's body so that at least a portion of the sensor is in contact with bodily fluids (e.g., interstitial fluid (ISF), such as dermal fluid in the dermis or subcutaneous fluid below the dermis, or blood), and can measure the concentration of an analyte in those bodily fluids. In vivo sensors can utilize various types of sensing technologies (e.g., chemical, electrochemical, or optical). Some systems utilizing in vivo analyte sensors can also operate without requiring fingerstick calibration.

[0029] An "in vitro" device is a device that brings a sensor into contact with a biological sample outside the body (or "ex vivo"). These devices typically include a port to receive an analyte test strip carrying the user's bodily fluids, which can then be analyzed to determine the user's blood glucose level. Other ex vivo devices attempt to measure the user's internal analyte levels non-invasively, for example, by using optical techniques that can measure internal analyte levels without mechanically penetrating the user's body or skin. Both in vivo and ex vivo devices often include in vitro functionality (e.g., in vivo display devices that also include a test strip port).

[0030] This subject describes a sensor capable of measuring glucose concentration, but the detection and measurement of concentrations of other analytes are also within the scope of this disclosure. These other analytes include, for example, ketones, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropins, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, and troponin. Furthermore, the sensor can also monitor concentrations of drugs such as antibiotics (e.g., gentamicin, vancomycin), digitoxins, dytoxins, drugs of abuse, theophylline, and warfarin. The sensor can be configured to measure two or more different analytes at the same or different times. In some embodiments, the sensor control device can be coupled with two or more sensors, one sensor configured to measure a first analyte (e.g., glucose), and one or more other sensors configured to measure one or more different analytes (e.g., any of those described herein). In other embodiments, the user can wear two or more sensor control devices, each of which is capable of measuring a different analyte.

[0031] The embodiments described herein can be used with any type of in vivo, in vitro, and ex vivo device capable of monitoring the aforementioned analytes.

[0032] In many embodiments, the operation of the sensor can be controlled by the SCD102. The sensor can be mechanically and communicatively coupled to the SCD102, or simply communicatively coupled to the SCD102 using wireless communication technology. The SCD102 may include electronics and a power supply that enable and control the sensing of the analyte performed by the sensor. In some embodiments, the sensor or the SCD102 may be self-generating so as not to require a battery. The SCD102 may also include communication circuits for communicating with another device (e.g., a display device) which may or may not be localized to the user's body. The SCD102 can reside in the user's body (e.g., it can be attached to the user's skin, positioned in another way, or carried in the user's clothing). The SCD102 may also be implanted in the user's body along with the sensor. The functionality of the SCD102 can be divided between a first component implanted in the body (e.g., a component that controls the sensor) and a second component located on or outside the body (e.g., a relay component that communicates with the first component and further communicates with an external device such as a computer or smartphone). In other embodiments, the SCD102 may be located outside the body and configured to non-invasively measure the user's analyte levels. The sensor control device may also be referred to, to name a few, depending on the actual implementation or embodiment, as a "sensor control unit," an "on-body electronic device" device or unit, an "on-body" device or unit, an "in-body electronic device" device or unit, an "in-body" device or unit, or a "sensor data communication" device or unit.

[0033] In some embodiments, the SCD102 may include a user interface (e.g., a touchscreen) capable of processing analyte data and displaying the resulting calculated analyte levels to the user. In this case, the embodiments of dose guidance described herein can be implemented directly by the SCD102, either in whole or in part. In many embodiments, it may be desirable to have a display device that the user can use to read analyte levels and interface with the sensor control device, either because the physical form factor of the SCD102 is minimized (e.g., to minimize its appearance on the user's body), the sensor control device may be inaccessible to the user (e.g., if it is entirely embedded), or due to other factors.

[0034] Figure 2A is a side view of an exemplary embodiment of the SCD102. The SCD102 may include a case or mount 103 (Figure 2B) for sensor electronics, which can be electrically coupled to an analyte sensor 101, configured here as an electrochemical sensor. According to some embodiments, the sensor 101 may be configured to be partially present in the user's body (e.g., through the outermost surface of the skin), therein it may be in fluid contact with the user's bodily fluids and used together with the sensor electronics to measure the user's analyte-related data. An attachment structure 105, such as an adhesive patch, may be used to secure the case 103 to the user's skin. The sensor 101 may extend through the attachment structure 105 and protrude away from the case 103. Those skilled in the art will understand that other forms of attachment to the body and / or case 103 may be used in addition to, or instead of, adhesives, and are fully included within the scope of this disclosure.

[0035] The SCD102 can be applied to the body in any desired manner. For example, an insertion device (not shown), sometimes referred to as an applicator, can be used to position all or part of the analyte sensor 101 through the outer surface of the user's skin to come into contact with the user's bodily fluids. In this case, the insertion device can also position the SCD102 on the skin. In other embodiments, the insertion device can first position the sensor 101, and then subsequently couple (e.g., insert into a mount) accompanying electronic equipment (e.g., wireless transmission circuits and / or data processing circuits) with the sensor 101, either manually or with the help of a mechanical device. Examples of insertable devices are described in U.S. Patent Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661 and 2018 / 0235520, the entire contents of which are incorporated herein by reference for all purposes.

[0036] Figure 2B is a block diagram showing an exemplary embodiment of an SCD102 having an analyte sensor 101 and sensor electronics 104. The sensor electronics 104 can be implemented on one or more semiconductor chips (e.g., application-specific integrated circuits (ASICs), processors or controllers, memory, programmable gate arrays, etc.). In the embodiment of Figure 1B, the sensor electronics 104 includes an analog front-end (AFE) 110 configured to interface with the sensor 101 in an analog manner and convert analog signals to and / or from digital format (e.g., using an A / D converter), a power supply 111 configured to power the components of the SCD102, processing circuits 112, memory 114, timing circuits 115 (e.g., oscillators and phase-locked loops for providing clocks or other timings to the components of the SCD102), and communication circuits 116 configured to communicate wired and / or wirelessly with one or more devices outside the SCD102, such as a display device 120 and / or MDD 152.

[0037] The SCD102 can be implemented in a highly interconnected manner, where the power supply 111 is coupled to each component shown in Figure 2B, and these components that communicate or receive data, information, or commands (e.g., AFE110, processing circuit 112, memory 114, timing circuit 115, and communication circuit 116) can be coupled to communicate with all other such components, for example, via one or more communication connections or bus 118.

[0038] The processing circuit 112 may include one or more processors, microprocessors, controllers and / or microcontrollers, each of which may be a discrete chip or distributed among (and some of) a number of different chips. The processing circuit 112 may include onboard memory. The processing circuit 112 may interface with the communication circuit 116 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into formats suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuit 112 may also interface with the communication circuit 116 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0039] The processing circuit 112 can execute instructions stored in the memory 114. These instructions can cause the processing circuit 112 to process raw analyte data (or pre-processed analyte data) and reach a final calculated analyte level. In some embodiments, when executed, instructions stored in the memory 114 can cause the processing circuit 112 to process the raw analyte data and determine one or more of the following: a calculated analyte level, an average calculated analyte level within a predetermined time window, a calculated rate of change for the analyte level within a predetermined time window, and / or whether a calculated analyte index exceeds a predetermined threshold condition. These instructions can also cause the processing circuit 112 to read and act on received transmissions, adjust the timing of the timing circuit 115, process data or information received from other devices (e.g., calibration information, encryption or authentication information received from the display device 120), perform tasks to establish and maintain communication with the display device 120, interpret voice commands from the user, and transmit them to the communication circuit 116. In embodiments where the SCD102 includes a user interface, instructions can cause the processing circuit 112 to perform tasks such as controlling the user interface, reading user input from the user interface, displaying information on the user interface, and formatting data for display. The functions described herein, which are coded as instructions, can instead be implemented by the SCD102 using hardware or firmware designs that do not rely on the execution of software instructions stored to achieve the functions.

[0040] Memory 114 can be shared by one or more of the various functional units present in the SCD 102, or distributed among two or more functional units (for example, as separate memories present on different chips). Memory 114 can also be a separate chip itself. Memory 114 is non-temporary and can be volatile memory (e.g., RAM) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM).

[0041] The communication circuit 116 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication over their respective communication paths or links. The communication circuit 116 may include or be coupled to one or more antennas for wireless communication.

[0042] The power supply 111 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuits may also be included to regulate battery charging, monitor the usage of the power supply 111, increase power, perform DC conversion, and so on.

[0043] Furthermore, an optional temperature sensor (not shown) can collect readings or measurements of skin temperature or sensor temperature. These readings or measurements can be communicated from the SCD102 to another device (e.g., a display device 120) (individually or as aggregated measurements over time). However, the temperature readings or measurements can be used in combination with software routines executed by the SCD102 or the display device 120 to correct or compensate for analyte measurements output to the user, instead of actually outputting the temperature measurements to the user, or in addition to this.

[0044] Exemplary Embodiments of Drug Delivery Devices The drug delivery function of the dose guidance system 100 can be achieved by including one or more drug delivery devices (MDDs) 152. An MDD 152 can be any device configured to deliver a specific dose of drug. An MDD 152 may also include a device that transmits dose-related data to the DGA, such as a pen cap, even if the device itself does not deliver the drug. An MDD 152 can be configured as a portable injection device (PID) capable of delivering a single dose per injection, such as a bolus. A PID is essentially a manually operated syringe, where the drug must be pre-loaded into the syringe or drawn from a container into the syringe before injection. However, in most embodiments, the PID includes electronic components to form an interface with the user and perform drug delivery. While a pen-like appearance is not mandatory, PIDs are often referred to as medication pens. PIDs with user interface electronic components are often referred to as smart pens. PIDs may be discarded after being used to deliver a single dose, or they may be durable and reused to deliver multiple doses over a period of time, such as a day, a week, or a month. PIDs are commonly used by users undergoing frequent daily injection (MDI) therapy, where injections are administered multiple times a day.

[0045] The MDD may also include a pump and an infusion set. The infusion set includes a cannula that is at least partially present in the recipient's body. This cannula is in fluid communication with the pump, which can repeatedly deliver the drug in small amounts into the recipient's body over time via the cannula. The infusion set can be applied to the recipient's body using an infusion set applicator, and the infusion set is often left implanted for 2-3 days or longer. The pump device includes electronics that form an interface with the user to control the slow infusion of the drug. Both the PID and the pump can store the drug in a drug reservoir.

[0046] The MDD152 can function as part of a closed-loop system (e.g., an artificial pancreas system that does not require user intervention for operation), a semi-closed-loop system (e.g., an insulin loop system that requires little to no user intervention for operation, such as confirming dose changes), or an open-loop system. For example, the analyte levels of a diabetic patient can be repeatedly and automatically monitored by the SCD102, and this information can be used by the dose guidance embodiments described herein to automatically calculate or otherwise determine an appropriate drug intake to control the analyte levels of a diabetic patient, and then deliver that dose to the diabetic patient's body. This calculation can be performed in the MDD152 or any other device of system 100, and the resulting determined intake can then be transmitted to the MCD152.

[0047] In many embodiments, the dose guidance provided by the embodiments described herein pertains to the type of insulin (e.g., rapid-acting (RA), short-acting insulin, intermediate-acting insulin (e.g., NPH insulin), long-acting (LA), ultra-long-acting insulin, and mixed insulin) and the same drug delivered by MDD152. Examples of insulin types include human insulin and synthetic insulin analogs. Insulin may also include premix formulations. However, the embodiments of dose guidance and the drug delivery capabilities of MDD152 described herein can also be applied to other non-insulin drugs. Such drugs may include, but are not limited to, exenatide, sustained-release exenatide, liraglutide, lixisenatide, semaglutide, pramulintide, metformin, SLGT1-i inhibitors, SLGT2-i inhibitors, and DPP4 inhibitors. Embodiments of dose guidance may also include combination therapies. Combination therapies include, but are not limited to, insulin and glucagon-like peptide-1 receptor agonists (GLP-1RAs), and insulin and pramulintide.

[0048] To facilitate the description of the dosage guidance embodiments herein, the MDD152 is often described in the form of a PID, specifically a smart pen. However, those skilled in the art will readily understand that the MDD152 may alternatively be configured as a pen cap, pump, or any other type of drug delivery device.

[0049] Figure 3A is a schematic diagram showing an exemplary embodiment of the MDD152 configured as a PID, specifically as a smart pen. The MDD152 may include a case 154 for electronic devices, an injection motor, and a drug reservoir (see Figure 3B) from which a drug can be delivered via a needle 156. The case 154 may include a removable or detachable cap or cover 157, which, when attached, can cover the needle 156 when not in use and then be removed for injection. The MDD152 may also include a user interface (UI) 158, which can be implemented as a single component (e.g., a touchscreen for outputting information to the user and receiving input from the user) or as multiple components (e.g., a touchscreen or display combined with one or more buttons, switches, etc.). The MDD152 may also include an actuator 159 that can be moved, pressed, touched, or otherwise actuated to begin delivering a drug from the internal reservoir through the needle 156 into the recipient's body. According to some embodiments, the cap 157 and actuator 159 may also include one or more safety mechanisms to prevent separation and / or activation in order to mitigate the risk of injecting harmful drugs. Details of these safety mechanisms and others are described in U.S. Patent Application Publication No. 2019 / 0343385 ('385), which is incorporated herein by reference in its entirety for all purposes.

[0050] Figure 3B is a block diagram showing an exemplary embodiment of an MDD152 having electronic equipment 160 coupled to a power supply 161, and an electrically operated injection motor 162 similarly coupled to the power supply 161 and a drug reservoir 163. A needle 156 is shown in fluid communication with the reservoir 163, and a valve (not shown) may be present between the reservoir 163 and the needle 156. The reservoir 163 may be permanent or removable and replaceable with another reservoir containing the same or different drug. The electronic equipment 160 can be implemented on one or more semiconductor chips (e.g., application-specific integrated circuits (ASICs), processors or controllers, memory, programmable gate arrays, etc.). In the embodiment of Figure 3B, the electronic equipment 160 may include a high-level functional unit including processing circuitry 164, memory 165, communication circuitry 166 configured to communicate wired and / or wirelessly with one or more devices outside the MDD152 (e.g., display device 120), and user interface electronic equipment 168.

[0051] The MDD152 can be implemented in a highly interconnected manner, where the power supply 161 is coupled to each component shown in Figure 3B, and these components that communicate or receive data, information, or commands (e.g., processing circuit 164, memory 165, and communication circuit 166) can be coupled to all other such components in a communicative manner, for example, via one or more communication connections or bus 169.

[0052] The processing circuit 164 may include one or more processors, microprocessors, controllers and / or microcontrollers, each of which may be a discrete chip or distributed among (and some of) a number of different chips. The processing circuit 164 may include onboard memory. The processing circuit 164 may interface with the communication circuit 166 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into formats suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuit 164 may also interface with the communication circuit 166 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0053] The processing circuit 164 can execute software instructions stored in memory 165. These instructions can cause the processing circuit 164 to receive a selection or provision of a specified dose from the user (e.g., input via user interface 158 or received from another device), process a command to deliver the specified dose (e.g., a signal from actuator 159), and control the motor 162 to deliver the specified dose. These instructions can also cause the processing circuit 164 to read and operate on received transmissions, process data or information received from other devices (e.g., calibration information, encryption or authentication information received from display device 120), perform tasks to establish and maintain communication with display device 120, interpret voice commands from the user, and transmit them to communication circuit 166. In embodiments where the MDD 152 includes a user interface 158, instructions can cause the processing circuit 164 to control the user interface, read user input from the user interface (e.g., input of a drug dose for administration or input to confirm a recommended drug dose), display information on the user interface, format data for display, and so on. The functions described here, which are coded as instructions, can instead be implemented by the MDD152 using hardware or firmware designs that do not rely on the execution of software instructions stored to achieve the functions.

[0054] Memory 165 can be shared by one or more of the various functional units present in the MDD 152, or distributed among two or more functional units (for example, as separate memories present on different chips). Memory 165 can also be a separate chip itself. Memory 165 is non-temporary and can be volatile memory (e.g., RAM) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM).

[0055] The communication circuit 166 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication over their respective communication paths or links. The communication circuit 166 may include or be coupled to one or more antennas for wireless communication. Details of exemplary antennas can be found in Publication 385, the entire contents of which are incorporated herein by reference for all purposes.

[0056] The power supply 161 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuits may also be included to regulate battery charging, monitor the usage of the power supply 161, increase power, perform DC conversion, and so on.

[0057] The MDD152 may also include an integrated or attachable in vitro glucose meter, which includes an in vitro test strip port (not shown) for receiving in vitro glucose test strips for performing in vitro blood glucose measurement.

[0058] Communication function The connected insulin pen and pen cap device is a type MDD152 that measures the amount of insulin injected by a patient and then transmits that data to a display device 120, such as a smartphone. In the connected pen, the electronic and mechanical devices necessary for transmitting the data are built into the insulin pen. In the connected pen cap, the electronic and mechanical devices are built into a "cap" attached to the insulin pen.

[0059] The connected MDD152 is a crucial component of the DGS100. Traditionally, bolus calculator applications required patients to manually enter medication information, limiting the usability of the application. By having the connected MDD152 automatically transmit insulin delivery data to the DGA, the usability of the DGS100 is substantially improved.

[0060] The functionality related to how and what kind of information is communicated between the DGA and the connected MDD152 can significantly impact the usability of the DGS100.

[0061] The current connected MDD152 may include a circuit that can broadcast an insulin dose record once a dose is administered. Furthermore, many designs can rebroadcast the record until the receiving application confirms that it has received the dose record. As seen in Figure 22A, in the dataflow design 2300, the MDD152 can broadcast dose information 2302 to an application, and the application can send an acknowledgment of receipt to the MDD152. This dataflow design may work well for applications that use dose information for specific functions, such as dose logging.

[0062] However, this dataflow design may not adequately address the needs of a software application intended to provide insulin dosing guidance. In particular, dose calculation typically requires knowledge of previous insulin doses within the time frame of insulin's action (e.g., typically around 4.5 hours for most rapid-acting insulins). Dose calculators can track a metric commonly called IOB (Insulin Onboard). IOB is usually subtracted from the calculated dose before it is displayed to the user. When a user requests dose guidance, the application needs to calculate the insulin dose and then the patient's IOB. The problem with dataflow design 2300 is that if MDD152 malfunctions or the communication path is interrupted, the application may not receive information about recent doses, and subsequently miscalculate the user's IOB, potentially leading to user overdosing.

[0063] In one embodiment, this risk can be mitigated by prompting the DGA to the user to confirm that no doses other than those received by the DGA have occurred. If the DGA does not have a recent dose record to proceed with the dose guidance calculation, the DGA can provide the patient with means to manually enter the dose and time. In another embodiment, the DGA can provide the user with instructions to correct a communication interruption and means to retry the dose guidance calculation. However, this method is cumbersome and may add user steps to the process of requesting dose guidance, potentially severely reducing the usability of the DGA.

[0064] In another embodiment, as shown in Figure 22B, the MDD 152 may be designed to provide a way for the DGA to query the pen for the most recent dose record. In addition to or instead of a data flow 2300 in which the DGA monitors the MDD 152 for alert conditions, the DGA may, after the user initiates (e.g., requests) dose guidance in a data flow 2320, send a query to the MDD 152 to request dose information 2322. The query may be for recent dose information, or for dose information from a specific period, for example, a certain period since the last receipt of insulin dose data, as specified in the communication protocol. In response to the query, the MDD 152 may send the requested dose information back to the DGA 2324, and the DGA may send an acknowledgment 2326 back to the MDD 152. Once the DGA has received all recent dose records, the DGA may calculate and display the IOB and dose guidance amounts to the user, as described elsewhere in this application.

[0065] Dataflow design 2320 can handle situations where the communication channel is interrupted. However, to ensure that the IOB is accurate, the DGA may request confirmation from the MDD152 that the DGA has received all recent dose information, including confirmation that no other doses have been delivered by the MDD152 in the recent or specific period. Some scenarios in which this may occur include: (a) the MDD152's battery has run out or some other transient failure has prevented the MDD152 from properly recording the delivered dose; or (b) in the case of a pen cap delivery device, the pen cap may not have been properly fitted to the insulin pen.

[0066] In an exemplary embodiment, in method 2340 as shown in Figure 22C, in a first step 2342, the MDD 152 may store data on doses administered over a period of time. The data may include the amount and time of administration. The data may also include the remaining amount of drug in the drug delivery device, for example, insulin. In step 2344, the MDD 152 can determine whether the stored data includes all doses delivered over the period of time. To make this determination, the MDD 152 may include a self-test circuit that can periodically ensure proper function and battery power. This self-test circuit can maintain a counter that increments with each self-test cycle, for example, every minute. When the MDD 152 is queried, the MDD 152 can check the self-test counter to verify that the counter value is equal to an estimated counter value based on the current elapsed time, which may be provided by a separate circuit within the MDD 152 electronic device. In another embodiment, the MDD152 may send a counter value to the DGA as part of a query, and the DGA may perform a counter value check by comparing the MDD's 152 counter value with an estimated counter value based on the elapsed clock time in the DGA. The DGS100 may include some timing tolerance between the DGA clock and the MDD clock.

[0067] In step 2346, if it is determined that the stored data contains all doses delivered during a given period, the MDD can send the stored data to the application that sent the query.

[0068] In step 2348, if it is determined that the stored data does not include all doses delivered during a given period, MDD152 may create an incomplete dose data instruction. In step 2350, MDD152 may send an incomplete dose data instruction to the application that submitted the query.

[0069] In an alternative embodiment, the DGA may include a circuit to determine whether the data transmitted from the MDD152 includes all doses administered during a given period. In exemplary method 2360, as shown in Figure 22D, in step 2362, the DGA may query the MDD152, e.g., an insulin pen, and receive a first self-test counter value. This first counter value may be the current counter value at the time of the query. Later, in step 2364, the DGA may send an additional query to the MDD152 and receive a second self-test counter value. The additional query may be in response to a request for dose guidance from the user. The second self-test counter value may be the current counter value at the time of the additional query. In step 2366, the DGA may calculate an estimate of the second self-test counter value. The estimate may be calculated based on the first counter value + (time elapsed between the query and the additional query / self-test period). The self-test period may be the period during which the MDD152's self-test circuit is configured to increment the counter value by "1".

[0070] In step 2368, the DGA may compare the second counter value with the estimated counter value to determine if the values ​​are within an acceptable range. If the comparison of values ​​(e.g., the difference) is within an acceptable range, in step 2370, the DGA may calculate insulin dose guidance that may be displayed to the user. If the comparison is not within an acceptable range, in step 2372, the DGA may ask the user to confirm that no other doses were delivered in addition to the dose recorded by the DGA (e.g., received in data transfer). If the user confirms that no other doses were delivered, in step 2370, the DGA may calculate insulin dose guidance that may be displayed to the user. If the user does not confirm that no additional doses were delivered, the DGA will not calculate or display dose guidance, and the system may re-verify the counter values.

[0071] In another alternative embodiment, in method 2380 as shown in Figure 22E, the DGA may, in step 2382, query the MDD 152 for dose data over a period of time. In step 2384, the DGA may receive data from the MDD 152. The received data may include dose data or indications of incomplete dose data. In step 2386, the DGA may determine whether an indication of incomplete doses has been received from the MDD 152. If no indication of incomplete doses has been received, in step 2388, the DGA may calculate dose guidance based on the data transmitted from the MDD 152. If the DGA has received an indication of incomplete doses, in step 2390, the DGA may output a prompt requesting the user to confirm that the received dose data includes all doses administered over the period of time. In step 2392, if the DGA determines that it has received confirmation from the user that no other doses were delivered during the period of time, the DGA may calculate dose guidance in step 2388. If the DGA determines that it has not received confirmation from a user that no other doses were delivered during a given period, the DGA may query the MDD152 again for dose data, similar to step 2382.

[0072] In the case of the DGS100, which includes a pen cap system as MDD152, if the cap is removed from the insulin pen for a period of time and then reattached, the system can detect that a dose has been delivered, and (if more than one dose has been delivered) it may also be able to detect a cumulative dose. However, the actual timing of these one or more doses may be unknown. In one embodiment, the pen cap may include a mechanism to detect when it was attached to the insulin pen or removed from the insulin pen, in addition to a mechanism to detect the current amount of insulin remaining in the pen. The pen cap controller system may store the date and time of the last instance when the pen cap was reattached and the insulin level was different from when the pen cap was last removed. The date and time of this error indication may be sent to the DGA in response to an inquiry. The pen cap controller system may omit storing this error indicator in the special case where the pen cap is removed when the pen is empty (or nearly empty) and reattached when the pen is full. The DGA can process this indicator in the same way as the self-test indicator has been described.

[0073] Exemplary Embodiments of Display Devices The display device 120 can be configured to display information related to the system 100 to the user and to accept or receive user input also related to the system 100. The display device 120 can display recently measured analyte levels to the user in any number of forms. The display device can also display other indicators that describe the user's analyte information, in addition to the user's past analyte levels (e.g., time in range, external glucose profile (AGP), hypoglycemia risk level, etc.). The display device 120 can display past dose information as well as drug delivery information such as the time and date of administration. The display device 120 can display alarms, alerts, or other notifications related to analyte levels and / or drug delivery.

[0074] The display device 120 may be a dedicated device for use with system 100 (e.g., an electronic device designed and manufactured primarily to form an interface with an analyte sensor and / or a drug delivery device), or it may be a multifunctional, general-purpose computing device such as a handheld or portable mobile communication device (e.g., a smartphone or tablet), or a laptop, personal computer, or other computing device. The display device 120 may be configured as a mobile smart wearable electronic device assembly such as smart glasses, or a smartwatch or wristband. The display device and its variations may be referred to as, to name a few, “reading devices,” “readers,” “handheld electronic devices” (or handheld), “portable data processing” devices or units, “information receivers,” “receivers” devices or units (or simply receivers), “relay” devices or units, or “remote” devices or units.

[0075] Figure 4A is a schematic diagram showing an exemplary embodiment of the display device 120, where the display device 120 includes a user interface 121 and a case 124 that holds the display device electronics 130 (Figure 4B). The user interface 121 can be implemented as a single component (e.g., a touchscreen capable of input and output) or as multiple components (e.g., a display and one or more devices configured to receive user input). In this embodiment, the user interface 121 includes a touchscreen display 122 (configured to display information and graphics and to accept user input by touch) and input buttons 123, both of which are coupled to the case 124.

[0076] The display device 120 may have stored software (e.g., software downloaded by the manufacturer or by the user in the form of one or more "apps" or other software packages) that forms an interface to the SCD 102, MDD 152, and / or the user. In addition, or instead, the user interface may be controlled by a web page displayed in a browser or other internet interface software that can run on the display device 120.

[0077] Figure 4B is a block diagram of an exemplary embodiment of a display device 120 with a display device electronic device 130. Here, the display device 120 includes a user interface 121 including a display 122 and input components 123 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog dials, microphones, speakers, etc.), processing circuitry 131, memory 125, communication circuitry 126 configured to communicate with and / or from one or more other devices outside the display device 120, power supply 127, and timing circuitry 128 (e.g., oscillators and phase-locked loops for providing clocks or other timing to components of the SCD 102). Each of the aforementioned components can be implemented as one or more different devices or integrated into a multifunction device (e.g., integration of processing circuitry 131, memory 125, and communication circuitry 126 on a single semiconductor chip). The display device 120 can be implemented in a highly interconnected manner, with the power supply 127 coupled to each component shown in Figure 4B, and those components that communicate or receive data, information, or commands (e.g., user interface 121, processing circuit 131, memory 125, communication circuit 126, and timing circuit 128) can be coupled communicatively to all other such components, for example, via one or more communication connections or bus 129. Figure 4B is a simplified representation of typical hardware and functionality present in the display device, and those skilled in the art will readily recognize that other hardware and functionality (e.g., codecs, drivers, glue logic) may also be included.

[0078] The processing circuit 131 may include one or more processors, microprocessors, controllers and / or microcontrollers, each of which may be a discrete chip or distributed among (and some of) a number of different chips. The processing circuit 131 may include onboard memory. The processing circuit 131 may interface with the communication circuit 126 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate the conversion of data signals into formats suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuit 131 may also interface with the communication circuit 126 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0079] The processing circuit 131 can execute software instructions stored in memory 125. These instructions can cause the processing circuit 131 to process raw analyte data (or pre-processed analyte data) and reach the corresponding analyte level suitable for display to the user. These instructions can cause the processing circuit 131 to read, process, and / or store administration instructions from the user and transmit the administration instructions to the MDD 152. These instructions can cause the processing circuit 131 to execute user interface software adapted to present an interactive group of graphical user interface screens to the user for the purpose of configuring system parameters (e.g., alarm thresholds, notification settings, display preferences, etc.), presenting current and past analyte level information to the user, presenting current and past drug delivery information to the user, collecting other non-analyte information from the user (e.g., information on meals consumed, activities performed, drugs administered, etc.), and presenting notifications and alarms to the user. These instructions can also cause the processing circuit 131 to transmit to the communication circuit 126, and the processing circuit 131 to read and act on the received transmission, read input from the user interface 121 (e.g., input of the drug dose to be administered or input to confirm the recommended drug dose), display data or information on the user interface 121, adjust the timing of the timing circuit 128, process data or information received from other devices (e.g., analyte data, calibration information, encryption or authentication information received from the SCD 102), perform tasks to establish and maintain communication with the SCD 102, interpret voice commands from the user, and so on. The functions described herein, which are coded as instructions, can instead be implemented by the display device 120 using a hardware or firmware design that does not rely on the execution of software instructions stored to achieve the functions.

[0080] The memory 125 can be shared by one or more of the various functional units present within the display device 120, or distributed among two or more functional units (for example, as separate memories present on different chips). The memory 125 can also be a separate chip itself. The memory 125 is non-temporary and can be volatile memory (e.g., RAM) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM).

[0081] The communication circuit 126 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication over their respective communication paths or links. The communication circuit 126 may include or be coupled to one or more antennas for wireless communication.

[0082] The power supply 127 may include one or more batteries, which may be rechargeable or single-use disposable batteries. Power management circuits may also be included to regulate battery charging, monitor the usage of the power supply 127, increase power, perform DC conversion, and so on.

[0083] The display device 120 may also include one or more data communication ports (not shown) for wired data communication with external devices such as a computer system 170, SCD102, or MDD152. The display device 120 may also include an integrated or attachable in vitro glucose meter, which includes an in vitro test strip port (not shown) for receiving in vitro glucose test strips for performing in vitro blood glucose measurements.

[0084] The display device 120 can display analyte data received from the SCD 102 and can also be configured to output alarms, alert notifications, glucose values, etc., which may be visual, auditory, tactile, or any combination thereof. In some embodiments, the SCD 102 and / or MDD 152 can also be configured to output alarms or alert notifications in visual, auditory, tactile form, or a combination thereof. Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011 / 0193704, the entire contents of which are incorporated herein by reference for all purposes.

[0085] Exemplary embodiments related to dosage guidance The following exemplary embodiments relate to the dose guidance function provided by the dose guidance system 100. In many embodiments, the dose guidance function is implemented as a set of software instructions stored and / or executed on one or more electronic devices. This dose guidance function will be referred to herein as a dose guidance application (DGA). In some embodiments, the DGA is stored, executed, and presented to the user on the same single electronic device. In other embodiments, the DGA may be stored and executed on one device and presented to the user on different electronic devices. For example, the DGA may be stored and executed on a trusted computer system 180 and presented to the user via a web page displayed through an internet browser running on a display device 120. The DGA may be a standalone application or may be incorporated in whole or in part into another software application.

[0086] Therefore, many different embodiments exist in relation to the number and type of electronic devices used to store, execute, and present the DGA to the user. With regard to presentation to the user, a device configured to implement this capability will be referred to herein as a user interface device (UID) 200. Figure 5 is a block diagram showing an example of one embodiment of the UID 200. In this embodiment, the UID 200 includes a case 201 coupled with a user interface 202. The user interface 202 can output information to the user and receive input or information from the user. In some embodiments, the user interface 202 is a touchscreen. As shown herein, the user interface 202 includes a display 204 which may be a touchscreen and input components 206 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog dials, microphones, touchpads, soft keys, keyboards, etc.).

[0087] Many of the devices described herein can be implemented as UID200. For example, the display device 120 is used as UID200 in many embodiments. In some embodiments, the MDD 152 can be implemented as UID200. In embodiments where the SCD 102 includes a user interface, the SCD 102 can be implemented as UID200. The computer system 170 can also be implemented as UID200.

[0088] the purpose The Dose Guidance System (DGS100) leverages glucose and insulin data to learn, provide, and titrate insulin doses. DGS includes applications integrated with connected insulin pens and sustained-release glucose sensors, such as smartphone-based mobile applications, to improve treatment management for insulin-intensive diabetes patients (PWD) using frequent injectors (MDIs).

[0089] The DGS100 can perform three main tasks. First, the DGS100 can learn the patient's insulin dose regimen (i.e., the frequency and amount of insulin dose administered) during the DGS's "learning period." Second, the DGS100 can provide the patient with dose recommendations for mealtime administration and postprandial adjustments. Third, the DGS100 can titrate the current dose settings to maximize glycemic control. These second and third tasks may be performed in parallel during the DGS's "guidance period."

[0090] Similar to insulin data, various forms of continuous glucose data (scan, history, and streaming) can serve as input for performing all three functions described above. The DGS100 can receive glucose data by various means and in various forms, including scan, history, and streaming. Scan data, including the most recent glucose values ​​and trend values, may be acquired by the user on demand. Glucose history data can be generated by a component of the DGS, which can generate and record glucose values ​​and trend values ​​at regular intervals, e.g., every 15 minutes. Historical data can be acquired by the user through scanning. Streaming data may include glucose values ​​and trend values ​​that are generated and recorded at regular intervals, e.g., every minute, and automatically sent to the DGS. Similarly, insulin data can be obtained from multiple sources. Insulin data may be manually logged or transferred from an MDD152, e.g., an insulin pen. Glucose and insulin data may be transferred by any known means, e.g., wireless communication technologies such as Bluetooth or NFC.

[0091] During the "learning period," DGA can determine the user's insulin dosage regimen through clustering of insulin data by time of day and subsequent curve fitting when combined with glucose data. This learning phase of DGA may require glucose and insulin data from the user for a certain period, for example, about 14 days.

[0092] The guidance period, including the provision of dose recommendations and dose setting titration, may begin after the learning period is complete and the initial insulin dose regimen has been determined. During the guidance period, DGS can provide mealtime dose recommendations upon user request. Users can request mealtime dose recommendations for meals that DGS has determined the user is currently being treated with insulin. Dose calculations can provide dose recommendations that modify the learned fixed dose by utilizing the form of a bolus calculator and taking into account pre-meal high glucose and residual insulin remaining in the bloodstream from previous injections.

[0093] DGS can also notify the user if mealtime administration is missed and recommend a modified dose. Rapid-acting insulin analogs for mealtime administration are recommended to be taken either during or immediately before a meal. Forgetting mealtime insulin administration is common and is known to be a factor affecting treatment consistency. To address these behavioral tendencies, DGS can leverage streaming data, such as data transmitted at regular intervals (e.g., every minute), to detect significant glycemic excursion periods without an associated insulin dose. Such periods may indicate instances where the user ate a meal but did not take the prescribed insulin dose. In this case, a prompt will be displayed to notify the user. Once a missed meal event is confirmed by the user, a modified mealtime insulin administration may be recommended, taking into account the timing discrepancy between meal initiation and insulin injection.

[0094] DGS can also notify the user when it is appropriate to take a corrective dose and recommend a dosage to correct high glucose between meals. Every minute, DGS can process streaming data to identify periods when the user is experiencing high glucose between meal administrations. In this case, DGS can display a prompt to notify the user of its occurrence. The user can then request a dose recommendation from DGS to correct the high glucose. Alternatively, DGS can provide the dose recommendation within the notification, eliminating the need for the user to request it.

[0095] DGS can also titrate the user's insulin dose regimen to lower the user's glucose levels while avoiding excessive periods of low thresholds, e.g., below 70 mg / dL, and ultimately maximizing blood glucose levels within the target range, e.g., 70-180 mg / dL. When the user transitions to a "guidance period," DGS can periodically analyze insulin and glucose data to titrate previously learned or titrated insulin dose regimen parameters.

[0096] Detection of MDI dosing strategies Turning to the DGA aspect, more specifically, the DGA can use the patient's dosing strategy and analyte-level knowledge to provide accurate dose guidance. This specification describes exemplary embodiments relating to the automated detection of a patient's dosing strategy, which can facilitate and expedite the setup of the DGA. The detection of the dosing strategy can be based on numerous characteristics of the monitored drug (e.g., insulin) dose. For example, in this embodiment, doses can be identified as basal or bolus based on the MDD152 used to administer the dose. Some patients may have multiple MDD152s. For example, a patient may have one MDD for administering long-acting insulin (e.g., basal dose) and another MDD for administering rapid-acting insulin (e.g., meal dose). Additionally, the basal strategy can be classified as a “single” or “split” basal dosing strategy using the count of basal doses per administration (e.g., number of doses) and timing. For example, in a "split" basal dosing strategy, the daily basal dose of 20U can be divided into two 10U doses, one of which can be administered before bedtime and the other upon waking.

[0097] When consecutive bolus doses are administered in quick succession, the system can attempt to distinguish between the original meal dose, an increase to the original meal dose, or a corrective dose for inter-meal high glucose. When DGA detects a large dose following a small dose (both occurring near the start of a meal), it can group these doses as a single meal dose, even if the initial dose was a priming dose not injected into the patient. Subsequently, if a dose occurs much later than a known meal and / or dose(group) tagged as a meal dose, DGA can tag the later dose as a corrective dose for post-meal high glucose or as a dose increasing the previous meal dose to account for any extra food consumed. Once a meal event is recognized based on the meal detector algorithm or a user-entered meal event, DGA uses the amount of the previous dosing event and its timing relative to the currently detected meal to help determine whether the previous dosing was the first of multiple meal doses or a corrective for inter-meal high glucose. Corrective doses are expected to be smaller in size than meal doses. Furthermore, if the time elapsed between the previous medication and the current meal event is sufficiently long, it is reasonable to assume that these two events are not related to the treatment of the same glucose excursion event, thus ruling out the possibility that the previous medication was the first of multiple medications related to a given meal. Therefore, if the previous dose is sufficiently small compared to the meal dose recorded within this window over the past few days and sufficiently far from the current meal, the previous dose can be classified as a corrected dose event.

[0098] DGA can be configured to use a real-time meal detection algorithm and medication timing to identify doses added to the basal dose as bolus doses for breakfast, lunch, and / or dinner, as well as / or corrective doses. DGA can also be configured to use the number of daily bolus doses to identify medication strategies as basal only, basal + 1, basal + 2, etc.

[0099] These different scenarios and aspects of DGA will be discussed in more detail elsewhere in this specification.

[0100] Onboarding To enhance the safety profile of DGA, HCP can approve the learned insulin dosing parameters and the subsequent titrations calculated by DGA. Embodiments of DGA include numerous interaction methods between HCP and DGA, thereby providing HCP with relevant evidence for approving proposed dose learning and titrations in a concise and informative manner that improves the workflow.

[0101] For diabetic patients already on an insulin regimen, HCPs can leverage existing reports that provide insights into the patient's glucose patterns to identify users who may benefit from dose guidance. An embodiment of DGA provides a learning period during which the patient's medication strategy and tendencies can be categorized (e.g., while using DGS100). If insulin and glucose binding data further confirms that a user is a suitable candidate for DGA, for example, that DGA can learn a specific medication strategy, the insulin medication parameters learned during the learning period can serve as initial conditions for dose guidance that DGA may then titrate as needed. A method for HCP notification for DGA dose parameter initialization and titration can also be provided. This process streamlines DGA onboarding and titration, assisting both HCPs and users while ensuring DGA is used only by those who are directed to it. If DGA is unable to learn the patient's medication parameters, it can indicate a patient medication mismatch, which HCPs can use to address the mismatch.

[0102] The first step in identifying potential users of DGA could include an initial analysis of a patient's blood glucose control using glucose concentration profiles. To ensure that as many users as possible have access to DGA, this process can be made independent of the glucose monitoring methods currently being used by the user.

[0103] For diabetic patients currently using SCD102, a glucose pattern report is available, which includes key indicators, glucose concentration profiles (e.g., outpatient glucose profiles (AGP)), patterns identified at different time points, and titration and lifestyle suggestions to improve glucose levels if they are consistently outside the target range, as will be discussed in more detail later in Figures 6A-1 to 6G-2. This pattern can be identified using the GPA algorithm, as will be discussed in more detail elsewhere.

[0104] As described above, for individuals who are not currently using a device or system (e.g., SCD102) associated with an application capable of generating a glucose pattern report 250, the HCP may suggest monitoring the patient with a different device or system so that a report 250 or similar can be generated. For example, a patient may wear an SCD102, configured in a masking or blind mode, in which the user is unable to access measured glucose levels and therefore unable to modify their behavior during this period, in order to collect glucose data over a period of several days or weeks. From this data, a glucose pattern report can be generated. If a proposed insulin titration is included in the glucose pattern report, the glucose pattern report 250 may also include a suggestion that the patient is a suitable candidate for DGS100 and may suggest a period for learning the drug dosing strategy.

[0105] During the learning period, MDD152 can be integrated into the glucose sensing system used for the initial screening to provide a more complete portrait of insulin-intensive diabetes management. The learning period can utilize algorithms, such as those described elsewhere in this specification, to detect the user's insulin dosing strategy. During the learning period, DGA can be configured to determine how the user determines mealtime doses. For example, DGA can determine whether the user determines mealtime doses based on carbohydrate counting, whether the user determines mealtime doses based on empirical methods such as learning appropriate dosages based on past or similar meal experiences, whether the user administers a fixed amount of insulin with meals, whether the user modifies the mealtime insulin dose (determined from fixed dose, carbohydrate counting, or empirical dose) based on pre-meal glucose levels, whether the user considers residual insulin (IOB) from previous injections or other techniques when determining dosages (determined from fixed dose, carbohydrate counting, or empirical dose). DGA can also determine whether the user's mealtime dose is constant or variable depending on the meal type (e.g., breakfast, lunch, and dinner). A determination that mealtime doses are changing may indicate that the user is basing mealtime doses on carbohydrate counting. DGA can also determine whether the user is adjusting mealtime doses to account for high pre-meal glucose levels. In some embodiments, DGA can also determine a target glucose level, and the user adjusts or corrects mealtime doses if their level is above or expected to be above the target glucose level. DGA can also determine which meals are associated with insulin administration. DGA can also determine patterns of forgotten mealtime doses. For example, DGA can detect whether the user has missed at least two, or instead at least three, doses associated with meals or time periods during a given period (e.g., one or two weeks).

[0106] In some embodiments, the DGS may also prompt the user to enter a typical meal dose and a typical time period during which the meal dose is usually administered. In some embodiments, the DGS may prompt the user to enter the amount of rapid-acting insulin usually taken with each meal when the glucose level is at a particular level. For example, the DGS may prompt the user to enter the amount of rapid-acting insulin usually taken with each meal when the glucose level is approximately 120 mg / dL. For rapid-acting insulin, the DGS may prompt the user for the start and end times of breakfast, lunch, and dinner, respectively. The rapid-acting dose for each of the various meals during the specified time period may then be logged as the dose for that meal. In some embodiments, the rapid-acting dose may be logged as the dose for that meal without any additional input from the user, and the user may not be prompted after the dose is administered to confirm, for example, that the dose is for a particular meal. For example, the bolus time period for breakfast may be from around 2 a.m. to around 11 a.m. The bolus period for lunch can be from around 11:30 a.m. to around 2:00 p.m. The bolus period for dinner can be from around 5:00 p.m. to around 8:00 p.m. In this exemplary embodiment, a rapid-acting insulin dose administered at 12:30 p.m. can be automatically logged as the lunch dose without further confirmation from the user.

[0107] In some embodiments, the DGS may also prompt the user about the amount of a typical basal dose to be administered and the time or duration over which that typical basal dose is administered. The DGS may also prompt the user to review and verify all dosages and administration times and durations before making a final decision.

[0108] The learning period can be any duration sufficient to obtain the necessary information. In many embodiments, this period is at least two days, more preferably one week or longer (e.g., 14 days), and may vary depending on how well the DGA can learn the trends. The results can be compiled into a summary report for both the user and the physician.

[0109] Glucose Pattern Report As shown in Figures 6A-1 to 6G-2, the glucose pattern report 250 can include various elements that can be arranged in different layouts. Those skilled in the art will understand that the glucose pattern report 250 may be a graphical user interface output to the display of a computing device. Elements may include identification of the most important glucose patterns 278, medication guidance 260, variability statement 286, lifestyle guidance 284, and excursion statement 288. The glucose pattern report 250 may also include identification of the time period 264 covered by the report, identification of the amount of time 266 in which the CGM sensor is active, average number of scans or average views per day 268, glucose indicator or statistics section 270, time in target range (TIR) ​​section 272, guidance for clinicians 276 section, and glucose pattern 282 section. The guidance for clinicians 276 section may include medication guidance 260, variability statement 286, lifestyle guidance 284, and excursion statement 288.

[0110] The time period 264 covered by the report may be included in the glucose pattern report 250. The time period 264 may be approximately 7 days, approximately 14 days, approximately 1 month, approximately 2 months, or approximately 3 months. The time period 264 may be reported as a start date and an end date, a total number of days, and / or both the start date and an end date and the total number of days (for example, "May 31, 2018 - June 13, 2018 (14 days)"). The time period 264 may be listed at the top of the report, for example, below the report name, or at the bottom of the report, in the header or footer, or elsewhere in the report layout.

[0111] The amount of time 266 during which the CGM sensor is active may be reported in the glucose pattern report 250, for example, as a percentage. The amount of time 266 during which the CGM sensor is active may be listed at the top of the report, for example, near the time period 264. Alternatively, the amount of time 266 during which the CGM sensor is active may be listed at the top of the report, for example, below the report name, or at the bottom of the report, in the header or footer, or elsewhere in the report layout.

[0112] The average number of scans or views per day 268 during the time period 264 can also be included in the glucose pattern report 250. The average number of scans or views per day 268 may be listed at the top of the report, for example, near the amount of time 266 in which the CGM sensor is active. Alternatively, the average number of scans or views per day 268 may be listed at the top of the report, for example, below the report name, or at the bottom of the report, in the header or footer, or elsewhere in the report layout.

[0113] The glucose indicator section 270 may also be included in the glucose pattern report 250. The glucose indicator section 270 may include average glucose over a time period 264. The glucose indicator section 270 may also include glucose management indicators (GMI) for the time period 264. Targets for each of the average glucose and GMI may optionally be listed next to the average glucose and GMI values ​​so that the user can quickly see how close or far they are to achieving their targets for the time period 264. The targets may be displayed in a different color (e.g., gray text against black for the actually calculated average glucose and GMI values) and in a smaller font size.

[0114] The Time Within Target Range (TIR) ​​section 272 may include a TIR graphical representation 252 and text components 274 that describe the amount of time in each of the different ranges. The TIR graphical representation 252 may be a bar graph, a pie chart, a histogram graph, or any other graphical representation that shows the relative amount of time in a number of different concentration ranges. The TIR graphical representation 252 may include at least three, at least four, at least five, or at least six different concentration ranges. The graphical representation of each concentration range may reflect the time within target range for that concentration range. For example, the relative area or relative height of the graphical representation of each concentration range may be proportional to, or related to, the determined proportion of time for that concentration range in the time period 264.

[0115] The range may include very low thresholds less than 290 (e.g., less than approximately 54 mg / dL), very low thresholds 290 to low thresholds 291 (e.g., approximately 54 mg / dL to approximately 69 mg / dL), low thresholds 291 to high thresholds 292 (e.g., approximately 70 mg / dL to approximately 180 mg / dL), high thresholds 292 to very high thresholds 293 (e.g., approximately 181 mg / dL to approximately 250 mg / dL), and very high thresholds greater than 293 (e.g., greater than approximately 250 mg / dL).

[0116] A very low threshold of 290 can be approximately 50 mg / dL to 65 mg / dL, or approximately 50 mg / dL to 60 mg / dL, or approximately 53 mg / dL, or approximately 54 mg / dL, or approximately 55 mg / dL, or approximately 56 mg / dL, or approximately 57 mg / dL, or approximately 58 mg / dL, or approximately 59 mg / dL, or approximately 60 mg / dL, or approximately 61 mg / dL, or approximately 62 mg / dL, or approximately 63 mg / dL, or approximately 64 mg / dL, or approximately 65 mg / dL. The low threshold can be approximately 60 mg / dL to 75 mg / dL, or approximately 65 mg / dL to 75 mg / dL, or approximately 67 mg / dL to 72 mg / dL, or approximately 67 mg / dL, or approximately 68 mg / dL, or approximately 69 mg / dL, or approximately 70 mg / dL, or approximately 71 mg / dL, or approximately 72 mg / dL, or approximately 73 mg / dL, or approximately 74 mg / dL, or approximately 75 mg / dL. The high threshold can be approximately 170 mg / dL to 190 mg / dL, or approximately 175 mg / dL to 185 mg / dL, or approximately 175 mg / dL, or approximately 176 mg / dL, or approximately 177 mg / dL, or approximately 178 mg / dL, or approximately 179 mg / dL, or approximately 180 mg / dL, or approximately 181 mg / dL, or approximately 182 mg / dL, or approximately 183 mg / dL, or approximately 184 mg / dL, or approximately 185 mg / dL. Very high thresholds can be approximately 230 mg / dL to 270 mg / dL, or approximately 240 mg / dL to 260 mg / dL, or approximately 245 mg / dL, or approximately 246 mg / dL, or approximately 247 mg / dL, or approximately 248 mg / dL, or approximately 249 mg / dL, or approximately 250 mg / dL, or approximately 251 mg / dL, or approximately 252 mg / dL, or approximately 253 mg / dL, or approximately 254 mg / dL, or approximately 255 mg / dL. Various ranges of thresholds and limits may be customizable by the user. Alternatively, various ranges of thresholds and limits may not be customizable by the user.

[0117] Different intensity ranges in the TIR graphical display can be assigned different colors. For example, graphical displays below a very low threshold (graphical display) can be colored dark red or chestnut, graphical displays in the range between the very low threshold and the low threshold can be colored light red, graphical displays in the range between the low threshold and the high threshold can be colored green, graphical displays in the range between the high threshold and the very high threshold can be colored yellow, and graphical displays in the range above the very high threshold can be colored orange.

[0118] The text component 274 of the TIR display 272 may include labels for each range. Ranges below the very low threshold may be labeled "very low," ranges between the very low threshold and the low threshold may be labeled "low," ranges between the low threshold and the high threshold may be labeled "target," ranges between the high threshold and the very high threshold may be labeled "high," and ranges above the very high threshold may be labeled "very high." The text component 274 of the TIR display 272 may include numerical limits for multiple intensity ranges, or alternatively, include descriptions thereof. Different intensity ranges may be listed next to or immediately adjacent to the corresponding graphical element and / or the label for that range. For example, in the case of a graphical element for time below the very low threshold of 290, the text could read as "Very Low <54" or "Less than 54 mg / dL", "Low 54-69" or "54-69 mg / dL", "Target 70-180" or "Target 70-180 mg / dL", "High 181-250" or "181-250 mg / dL", and / or "Very High >250" or "Greater than 250 mg / dL". The text component 274 can also include numerical values, such as percentages, of the time spent in each range of the concentration range. Instead of, or in addition to, individual numerical values ​​for each concentration range, the text component 274 can include combined numerical values, such as combined percentages, of two or more ranges. For example, the numerical value for time below the very low threshold of 290 and the numerical value for time between the low threshold of 291 and the very low threshold of 290 may be reported as a single combined numerical value. Furthermore, the time values ​​for periods exceeding the very high threshold of 293 and the time values ​​for periods between the high threshold of 292 and the very high threshold of 293 may be reported as a single combined value. The values ​​or combined values ​​may be placed next to or immediately adjacent to the graphical elements and / or explanatory text for each concentration range. If both individual values ​​and combined values ​​are reported, the combined values ​​may be visually distinct from the individual values.For example, combined values ​​may be displayed in bold, italics, or different colors. The sum of the reported values ​​or combined values ​​may be equal to 100 or equal to a value other than 100. The text component 274 may also include the target 275 for each concentration range (see, for example, Figures 6A-2 and 6A-3), for example, a percentage. Alternatively, the text component 274 may include combined targets for two or more concentration ranges. For example, the time target below a very low threshold and the time target between a low threshold and a very low threshold may be reported as a single target. Furthermore, the time target above a very high threshold and the time target between a high threshold and a very high threshold may also be reported as a single target. The targets and / or combined targets for each concentration range may be listed next to or immediately adjacent to the determined values ​​for the time periods 264 for each concentration range.

[0119] A section detailing guidance for clinicians, HCPs, or patients 276 may also be included in the glucose pattern report 250. The clinician guidance section 276 may include a critical pattern section 278, a medication guidance section 260, and a lifestyle guidance section 284.

[0120] The most important pattern section 278 can identify the most important patterns of a time period 264 determined by an algorithm including, but not limited to, the GPA algorithm described elsewhere in this application. The most important pattern section 278 can identify patterns including, but not limited to, “Lows,” “Highs with Some Lows,” and “Highs.” The most important pattern section 278 can also identify periods of the day in which the most important patterns occurred, e.g., nighttime, morning, afternoon, and / or evening. Each pattern and period of the day in which the identified patterns occurred may be identified by text, e.g., a sentence or phrase, or each may be identified by a tag 280. If multiple patterns including “Lows” patterns are detected, the glucose pattern report 250 can identify the “Lows” patterns in the most important pattern section 278, allowing clinicians to address these “Lows” patterns first before addressing “Highs with Some Lows” or “Highs.” If multiple patterns are identified, the patterns identified in the most important patterns section 278 may be prioritized to identify the “low value” pattern first, then the “high value with some low value” pattern, and then the “high value” pattern. However, additional patterns may be identified in the outline or boxes of the glucose pattern profile 256 even if they are not identified in the most important patterns statement 278. The pattern tags 280 in the most important patterns section 278 may be color-coded to match the color of the outline or boxes or partial boxes or brackets that highlight these sections of the glucose concentration profile 256 in the glucose pattern section 282. For example, the tag 280 identifying the evening “low value” may be colored red (e.g., white text on a red background), the box or partial box enclosing the evening period in the glucose concentration profile 256 may have a red line color, and the color-coded red tag identifying the low value may be placed at the top of the box.The time periods in which the most important pattern occurs may be listed next to tag 280. The time periods can be displayed as text according to the order of the glucose concentration profile 256, for example, from left to right, “nighttime,” “morning,” “afternoon,” and “evening.” Alternatively, the time periods may be identified by tags (not shown), but in a different color from the pattern tags. For example, the time period tags may be gray. If the pattern occurs in all four time periods of a day, the most important pattern section 278 may contain two tags: “all day” and “nighttime.” If the pattern occurs in all time periods of a day except “nighttime,” the most important pattern section 278 may contain a single tag labeled “all day,” or it may contain three tags: “morning,” “afternoon,” and “evening.” If the pattern occurs in multiple time periods, a single box or a partial box may outline adjacent time periods with a single label (see, for example, Figures 6C–6E).

[0121] Medication guidance 260 can also be provided in the glucose pattern report 250 if the patient's current therapy (e.g., basal + RA insulin, basal only, basal + SU, etc.) is known. Medication guidance can be provided in the form of text recommendations. General advice regarding insulin dose titration can be provided based on identified high-glucose and low-glucose patterns highlighted in box 258 in the glucose concentration profile 256. This general advice may be determined without access to data on the actual insulin dose administered. Recommendations can generally follow the rule of reducing any low patterns before reducing high patterns. If the glucose pattern report includes suggestions regarding insulin dose titration, the glucose pattern report 250 may also include suggestions that the patient is a suitable candidate for DGS100, facilitating conversations between the HCP and the patient before transitioning to the learning period.

[0122] Medication guidance section 260 may include different descriptions and / or findings regarding medications administered during time period 264. Medication guidance section 260 may include questions asking whether a medication is contributing to the low levels. Alternatively, medication guidance section 260 may include a statement that medications added to address high levels may worsen low levels. Alternatively, medication guidance section 260 may include a statement that if a patient has initiated or adjusted medication to address high levels, they should consider how that medication may induce low levels. Medication guidance section 260 may also include a statement advising clinicians and / or patients to consider different therapies to address glucose fluctuations. Medication guidance section 260 may also indicate that, for T1 patients, insulin adjustment should be considered. For T2 patients, medication guidance section 260 may include a statement advising that adjustment of medication should be considered for T2 patients currently taking insulin or sulfonylurea drugs, or, for other T2 patients, that adjustment of medication or initiation of medication other than insulin or sulfonylurea drugs should be considered. Medication guidance section 260 may also indicate that initiating insulin should be considered for other T2 patients.

[0123] Medication Guidance Section 260 may include one or more statements relating to the following topics, but is not limited to these: - Could medication be a contributing factor to the low levels? - Medications added to address high levels may worsen low levels.

[0124] - When initiating or adjusting medication to address high levels, consider how that medication might induce low levels.

[0125] - Consider different treatment methods to address glucose fluctuations.

[0126] - For T1 patients, consider adjusting insulin levels.

[0127] - For T2 patients currently taking insulin or sulfonylurea drugs, consider adjusting their medication.

[0128] - For other T2 patients, consider initiating insulin.

[0129] - For other T2 patients, consider adjusting medication or initiating medication other than insulin or sulfonylurea.

[0130] The lifestyle guidance section 284 may include variability statements 286, excursion statements 288, and self-care guidance 262. If variability is determined to be low for a given period, no variability statements may be included. In some embodiments, if no pattern is detected for any period, the variability statement 286 may not be displayed. In some embodiments, the variability statement 286 may be displayed if high variability is determined and a pattern is determined to exist. If variability is determined to be high for a given period, the glucose pattern report 250 may include variability statements 286. The variability statement 286 may indicate that low values ​​are often associated with high glucose variability. The variability statement 286 may instead indicate, or in addition to, that high values ​​are often associated with high glucose variability. The variability statement 286 may also indicate that certain behaviors may contribute to high glucose variability, in which case it may include a list of specific behaviors. The variability statement 286 may also indicate that certain behaviors may contribute to glucose variability, in which case it may include a list of specific behaviors that contribute to glucose variability.

[0131] Variable statement 286 may include, but is not limited to, one or more statements relating to the following topics: - Low values ​​are often associated with high glucose fluctuations.

[0132] - The following actions may contribute to glucose fluctuations.

[0133] - The following actions may contribute to high glucose fluctuations.

[0134] - High levels are often associated with high glucose fluctuations.

[0135] The determination of variation is described elsewhere in this application. Alternative determinations for determining variation are described in International Publication No. 2014 / 145335 and International Publication No. 2014 / 106263, both of which are expressly incorporated herein by reference in their entirety for all purposes.

[0136] As shown in Figure 6A-3, an excursion statement 288 may be included in the glucose pattern report 250 when an excursion is detected. An excursion may be an example in which a glucose level below a very low threshold is detected. The very low threshold may be approximately 50 mg / dL to approximately 65 mg / dL, or approximately 50 mg / dL to approximately 60 mg / dL, or approximately 53 mg / dL, or approximately 54 mg / dL, or approximately 55 mg / dL, or as described elsewhere in this application. If an excursion is detected, the excursion statement 288 may suggest that the clinician discuss with the patient the occasional occurrence of hypoglycemia below the very low threshold, and may refer to a Weekly Summary Report or Daily View Report that can list excursions below the very low threshold (e.g., below approximately 54 mg / dL). Alternatively, the excursion statement 288 may indicate that hypoglycemia occurs occasionally at a very low threshold of less than 290, and may refer to the weekly summary report or daily view report (e.g., "Hypoglycemia below 54 mg / dL occurs occasionally. Refer to the daily view report"). If one or more excursions below the very low threshold are detected, the excursion statement 288 may be included in the glucose pattern report 250. In some embodiments, even if one or more excursions below the very low threshold are detected, the excursion statement may not be included in the glucose pattern report 250 if a low pattern is detected. In some embodiments, the excursion statement 288 may be displayed if a "high" pattern, a "high with some low" pattern, or no pattern is detected.

[0137] Self-care guidance 262 can also be included in the glucose pattern report 250. Self-care guidance 262 can be displayed in the glucose pattern report 250 if the GPA algorithm identifies a highly variable pattern. Alternatively, the glucose concentration profile 256 may have such high variability values ​​that the logic behind the report cannot make specific suggestions; in this case, instead of the aforementioned case, the default is for the user to consult with an HCP about lifestyle or treatment changes.

[0138] The self-care guidance 262 displayed in the glucose pattern report 250 may depend on the type of pattern detected, the amount of variation, the median glucose level, the presence or absence of hypoglycemia risk, and the presence or absence of an excursion. The self-care guidance 262 may include one or more statements on the following topics: - Do you sometimes forget to eat, or is your carbohydrate intake inconsistent? - Is your activity level different every day? - Does your alcohol intake vary from day to day? - Do you sometimes forget to take your medication? - Do your meals or snacks frequently contain large amounts of carbohydrates? - Do your meals or snacks sometimes contain a lot of carbohydrates? The glucose pattern section 282 may include a glucose concentration profile 256 displaying glucose data over a 24-hour period. The glucose concentration profile 256 may be an exogenous glucose profile (AGP). Alternatively, the glucose pattern profile may display various data points as points or dots on a graph. These points or dots may or may not be connected by lines showing the analyte curve for each day. Exemplary glucose concentration profiles 256 are shown in Figures 6B-1 to 6G-2. The glucose concentration profile 256 may also be a graph of glucose data for a time period 264 of the report 250, where the various data points on the graph may be color-coded (not shown) to correspond to the concentration ranges in which the glucose analyte levels fall. This color coding may correspond to the color coding of the TIR display 252. Boxes or partial boxes 295 enclosing different parts of the glucose concentration profile 256 highlight the detected patterns (e.g., "high," "low," and "high with some lows"). Various thresholds and boundaries for different concentration ranges may be highlighted in the glucose concentration profile 256, including labeling or other highlighting (e.g., text labels and / or horizontal lines) for very low thresholds 290, low thresholds 291, high thresholds 292, and very high thresholds 293. The target or desired range between the low thresholds 291 and high thresholds 292 may also be labeled in the glucose concentration profile 256, for example, by shading or highlighting with a different color (e.g., green). The median glucose level for each time period may be highlighted with a solid line 294 and labeled as median or 50%. The glucose median line 294 may change color depending on the median of that portion of the line.For example, the median glucose line 294 may be colored dark chestnut or dark red for medians below a very low threshold, red for medians between a very low threshold and a low threshold, green for medians between a low threshold and a high threshold, yellow for medians between a high threshold and a very high threshold, and orange for medians above a very high threshold. The four time periods can be nighttime 296 (e.g., approximately 12am to 8am), morning 297 (e.g., approximately 8am to 12pm), afternoon 298 (e.g., approximately 12pm to 6pm), and evening 299 (e.g., approximately 6pm to 12am).

[0139] For each time zone, the algorithm can determine whether one of three possible patterns exists or whether no pattern exists in that time zone. Thus, each of the four time zones may be determined to be assigned one of the following patterns: (1) low 281, (2) high 283 with a partial low, (3) high 285, or (4) no harmful pattern detected. Each identified pattern is labeled with a colored tag above the appropriate section of the graph, and the time zones are outlined with boxes or partial boxes, which may also be color-coded. The "low" 281 pattern may be displayed in red (see, for example, Figures 6B-1 to 6B-2). The "high" 283 with a partial low may be displayed in amber or chestnut (see, for example, Figures 6E-1 to 6E-2). The "high" 285 may be displayed in orange (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). As seen in Figures 6B-1 to 6B-2, if multiple patterns are detected, multiple boxes or multiple partial boxes 295 can display all of the patterns in the relevant sections on the glucose concentration profile 256. In some embodiments, the “low value” pattern 281 may be prioritized and highlighted in a different color, such as red, compared to the boxes and labels of other patterns. As seen in Figures 6C-1 to 6C-2, if the same pattern occurs in two or more adjacent time periods, a single box or partial box 295 can outline all of the relevant time periods and have a single label above the box. As seen in Figures 6D-1 to 6D-2 and Figures 6E-1 to 6E-2, if the same pattern occurs in all time periods of the day (night, morning, afternoon, and evening), a single box or partial box 295 can outline all of the time periods and have a single label above the box 295.

[0140] In some embodiments, the pattern may be determined according to the GPA algorithm discussed elsewhere in this specification. Multiple variables can be used to determine the output of the GPA algorithm and the contents of the glucose pattern report 250. These variables include, but are not limited to, the preferred pattern, additional patterns combined with the preferred pattern, variability (high or low), median glucose (e.g., greater than 180 mg / dL or less than 180 mg / dL), moderate hypoglycemia risk (yes or no), and excursion below 54 mg / dL (yes or no). The layout and text associated with each element may vary depending on the output of the GPA algorithm.

[0141] Specific guidance text displayed in any given report may be based on a combination of the three patterns described herein, a “hidden” pattern defined as a “moderate hypoglycemia risk” pattern determined according to the GPA algorithm described elsewhere herein, glucose variability, and hyperglycemia defined by the overall median glucose compared to a 180 mg / dL threshold. In some embodiments, the patterns and indicators described herein may be replaced with similar patterns and indicators. In some embodiments, the low pattern may be replaced by a calculation of the number of low events (e.g., glucose below 70 mg / dL) exceeding a threshold. For example, the threshold may require that the number of low events be at least four low events in 14 days. In some embodiments, the variability indicator may be replaced with any other common glucose variability indicator. In some embodiments, the overall hyperglycemia indicator may be defined as the mean glucose value instead of the median. In some embodiments, the “highs with some lows” pattern may be replaced by a calculation of approximately two to three low events and approximately four or more high events (where a high event may be defined as glucose >180 mg / dL) occurring within a 14-day time period. Thus, guidance can be driven by comparable patterns and indicators.

[0142] Table 1 below outlines an example mapping of guidance or lookup tables that may be provided in a glucose pattern report. Inputs include: (1) whether there is significant variability, (2) whether it is low, high with some low, high, or no pattern, (3) whether there is a risk of hypoglycemia, and (4) median glucose (G med Whether the value is greater than 180 mg / dL. Table 1 allows us to define the output of guidance text based on the input. The output may include (1) medication guidance, (2) lifestyle statements and guidance, (3) low excursion, and (4) identification of the most important patterns. In other embodiments, the input to the lookup table may be less. In some embodiments, the table may exclude, for example, overall hyperglycemia and corresponding guidance, or additional input. The various scenarios outlined in Table 1 are described in more detail below.

[0143] [Table 1-1]

[0144] [Table 1-2]

[0145] [Table 1-3]

[0146] "Either" = at least one of the four TOD periods "None" = 0 out of 4 TOD periods G med = Median glucose concentration Medication guidance Statement A1: For T1 patients, consider adjusting insulin levels.

[0147] Statement A2: For T2 patients currently taking insulin or sulfonylurea drugs, consider adjusting their medication regimen.

[0148] Statement A3: For other T2 patients, adjust medication or consider initiating medication other than insulin or sulfonylurea.

[0149] Statement A4: For other T2 patients, consider initiating new medications such as insulin.

[0150] Statement A5: When initiating or adjusting medication to address high levels, consider how that medication may induce low levels.

[0151] Statement A6: Consider different treatment methods to address glucose fluctuations.

[0152] Statement A7: Could medication be a contributing factor to the low levels? Statement A8: Medications added to address high levels may worsen low levels.

[0153] Lifestyle statements and guidance Statement B1: The following actions may contribute to high glucose fluctuations.

[0154] Statement B2: The following actions may contribute to glucose fluctuations.

[0155] Statement B3: Low values ​​are often associated with high glucose fluctuations.

[0156] Statement B4: Highs are often associated with high glucose fluctuations.

[0157] Statement B5: Do your meals or snacks frequently contain large amounts of carbohydrates? Statement B6: Do your meals or snacks sometimes contain a lot of carbohydrates? Statement B7: Do you sometimes forget to take your medication? Statement B8: Do you sometimes forget meals, or is your carbohydrate intake inconsistent? Statement B9: Is your activity level different from day to day? Statement B10: Does your alcohol intake vary from day to day? As seen in Figures 6A-1 to 6A-4, the layout of the glucose pattern report 250 may have, for example, a time period 264 and a time period 266 in which the CGM is active, which may be displayed at the top of the report, for example, below the report title. In some embodiments, the average number of scans / views per day may also be displayed at the top of the report. The time period 264, the time period 266 in which the CGM is active, and the average number of scans / views per day 268 may be listed adjacent to each other, separated by lines, or outlined by boxes. The glucose index section 270 and the TIR section 272 may be displayed side by side below the list of period 264, the time period 266 in which the CGM is active, and the average number of scans / views per day 268. The glucose index section 270 may display the average glucose value 271a together with the target value 271b, and the GMI 273a together with the target value 273b. In some embodiments, the average glucose value with a target value may be displayed above the GMI display with the target value. The TIR display section 272 may be displayed on the left and the glucose index section 270 may be displayed on the right. Alternatively, the TIR display section 272 may be displayed on the right and the glucose index section 270 may be displayed on the left. The glucose pattern report 250 may include a clinician guidance section 276 below the glucose index section 270 and the TIR display section 272. The clinician guidance section 276 may include identification of the most important pattern 278 at the top of section 276, and below the most important pattern 278, medication guidance 260 and lifestyle guidance 284 may be listed side by side. In the lifestyle guidance section 284, if all three statements are reported, the variation statement 286 may be listed first, the self-care guidance 262 may be listed second, and the excursion statement 288 may be listed last.Alternatively, each of the three statements 286, 262, and 288 may be listed in a different order, for example, self-care guidance 262 may be listed first, in the middle, or last, variation statement 286 may be listed first, in the middle, or last, and excursion statement 288 may be listed first, in the middle, or last. A glucose pattern section 282, which may include a glucose concentration profile 256, may be displayed below the guidance for clinicians 276. Thus, in one embodiment, the glucose indicator section 270 and the TIR display section 272 may be displayed in the upper third of the report 250, the guidance for clinicians 276 may be displayed in the middle third of the report 250, and the glucose pattern section 282 may be displayed in the lower third of the report. Alternatively, in other embodiments, the glucose indicator section 270 and the TIR display section 272 may be displayed in the top, middle, or bottom third of the report 250; the guidance for clinicians 276 may be displayed in the top, middle, or bottom third of the report 250; and the glucose pattern section 282 may be displayed in the top, middle, or bottom third of the report.

[0158] In some embodiments, if no pattern is found and no excursion is detected during time period 264, the guidance for clinicians 276 may include a statement that no harmful glucose pattern was detected. Furthermore, the glucose concentration profile may not include boxes highlighting any time period within the day.

[0159] In some embodiments, if no pattern was found but at least one excursion was detected during time period 264, the guidance for clinicians 276 may include a critical pattern statement 278 stating that no harmful glucose pattern was detected. The guidance for clinicians 276 may also include an excursion statement 288 in which the clinician may suggest that the clinician discuss with the patient the occasional occurrence of very low threshold hypoglycemia and may encourage the clinician to refer to additional reports, such as weekly summary reports. The glucose concentration profile may not include boxes highlighting any time period of the day but may include several data points highlighted in dark red or chestnut below the very low threshold 290 corresponding to one or more detected excursions.

[0160] In some embodiments, if a “low value” pattern 281 is detected during a certain time period and low variability is detected, the guidance for clinicians 276 may include a critical pattern statement 278 in which the “low value” pattern 281 can be highlighted with a red-colored tag, and may also include tags for the time periods of day in which the “low value” pattern occurred. If the “low value” pattern 281 occurs during all time periods of the day, the critical pattern statement 278 may include two tags: “all day” and “nighttime.” As seen in Figures 6F-1 to 6F-2, if the “low value” pattern 281 occurs during all time periods of the day, the glucose concentration profile may include a single box 295, for example, a red box, highlighting the entire graph with the heading “low value” at the top. Alternatively, as seen in Figures 6G-1 to 6G-2, if the “low value” pattern 281 occurs during at least two non-adjacent time periods, those time periods may be highlighted by separate boxes or partial boxes 295.

[0161] In some embodiments, if a “low value” pattern 281 is detected in at least one time period and high variability is detected, or if the “low value” pattern 281 and other patterns are detected, the guidance for clinicians 276 may include a critical pattern statement 278 that can highlight the “low value” pattern 281 with a red-colored tag 280, and may also include identification of the time of day in which the “low value” pattern occurred, which may be colored in a different color, such as gray. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “low value” pattern. These statements may include, but are not limited to, “Does the medication contribute to the low value?” and “Medications added to address the high value may worsen the low value.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. These statements may include, but are not limited to, “Low values ​​are often associated with high glucose variability” and “The following actions may contribute to glucose variability (this may be followed by a list of actions).” Clinician guidance 276 may also include self-care guidance 262. Self-care statements 262 may include, but are not limited to, “Do you sometimes forget meals or have variability in carbohydrate intake?”, “Is your activity level different from day to day?”, and “Is your alcohol intake different from day to day?”. In the glucose concentration profile, time periods with a “low value” pattern, as seen, for example in Figures 6G-1 to 6G-2, may be highlighted with a red box and labeled “Low Value” at the top. In some embodiments, where adjacent time periods have the same pattern, a single box may outline the adjacent time periods having the same pattern (see, for example, Figures 6F-1 to 6F-2). If different types of patterns are detected in time periods, as seen in Figures 6G-1 to 6G-2, all patterns may be identified with a box schematically showing the relevant time period of the day and an appropriate heading label identifying the type of pattern.In some embodiments, if a “low value” pattern 281 is detected together with a “high value” 285 and / or a “high value with part of a low value” 283 pattern, the “low value” pattern 281 may be highlighted in a different color, such as red, compared to the other patterns, such as gray (see, for example, Figures 6B-1 to 6B-2 and Figures 6G-1 to 6G-2).

[0162] In some embodiments, if a “high with some low” pattern 283 and high variability are detected, or if a “high with some low” pattern 283 and a “high” pattern 285 are detected, the guidance for clinicians 276 may include a critical pattern statement 278 that can highlight the “high with some low” pattern 283 with a red-colored tag 280, and may also include identification of the time of day in which the “high with some low” pattern 283 occurred. In some embodiments, the “high with some low” pattern may take precedence over the “high” pattern as being more important for the clinician to address first. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the low pattern. These statements may include, but are not limited to, “consider how medication may induce lows when initiating or adjusting medication to address highs” and “consider different treatments to address glucose variability.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. Statements may include, but are not limited to, “The following behaviors may contribute to high glucose fluctuations (this may be followed by a list of behaviors).” Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Do you sometimes forget to take your medication?”, “Do you sometimes forget to eat or have inconsistent carbohydrate intake?”, “Is your activity level different from day to day?”, and “Is your alcohol intake different from day to day?”. In the glucose concentration profile 256, time periods with a “high with some low” pattern 283 may be highlighted with a dark red box or partial box with the heading “High with some low” at the top (see, for example, Figures 6E-1 to 6E-2), and time periods with a “high” pattern 285 may be highlighted with a box 295, for example, a gray box with the heading “High” at the top of the box.

[0163] In some embodiments, if only “high” pattern 285 is detected, with small variability, median glucose <180 mg / dL, no risk of hypoglycemia, and no excursion detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. To distinguish the “high” pattern 285 from the “low” pattern 281 and the “high with partial low” pattern 283, each “high” pattern 285 may be highlighted in orange, the “low” pattern 281 may be highlighted in red, and the “high with partial low” pattern 283 may be highlighted in dark red / purple when its particular pattern is detected, reported, and identified as the critical pattern. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “high” pattern 285. This statement may include, but is not limited to, “For T1 patients, consider adjusting insulin,” “For T2 patients currently taking insulin or sulfonylurea drugs, consider adjusting medication,” and “For other T2 patients, consider adjusting medication or initiating medication other than insulin or sulfonylurea drugs.” The glucose pattern report 250 may not include the variability statement 286 because low variability was detected. Guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Are meals or snacks frequently high in carbohydrates?” Time periods with a “high” pattern 285 in the glucose concentration profile may be highlighted with a box, for example, an orange box. If all adjacent time periods are determined to have a “high” pattern 285, as seen in Figures 6D-1 to 6D-2, a single box 295 may outline the adjacent time periods.

[0164] In some embodiments, if only “high” pattern 285 is detected, with small variability, median glucose > 180 mg / dL, no risk of hypoglycemia, and no excursion detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for “high” patterns. These statements may include, but are not limited to, “Consider adjusting insulin for T1 patients,” “Consider adjusting medication for T2 patients currently taking insulin or sulfonylurea drugs,” and “Consider initiating insulin for other T2 patients.” The guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Are meals or snacks frequently high in carbohydrates?” In the glucose concentration profile, time periods having the “high” pattern 285 may be highlighted with a box, for example, an orange box (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have the “high” pattern 285, a single box or a partial box 295 may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0165] In some embodiments, if only a “high” pattern is detected, the variability is small, the median glucose is arbitrary, there is a moderate hypoglycemia risk, and no excursion is detected, the clinician guidance 276 may include a critical pattern statement 278 that identifies the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include a statement for clinicians to consider when deciding on treatment for the “high” pattern 285. This statement may include, but is not limited to, “When initiating or adjusting medication to address highs, consider how that medication may induce lows.” The clinician guidance 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Are meals or snacks frequently high in carbohydrates?” In the glucose concentration profile, time periods exhibiting a "high" pattern may be highlighted with an orange box (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to exhibit a "high" pattern, a single box may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0166] In some embodiments, if only the “high” pattern 285 is detected, with high variability, median glucose <180 mg / dL, no risk of hypoglycemia, and no excursion detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “high” pattern. These statements may include, but are not limited to, “For T1 patients, consider adjusting insulin,” “For T2 patients currently taking insulin or sulfonylurea, consider adjusting medication,” and “For other T2 patients, consider adjusting medication or initiating non-insulin or non-sulfonylurea medication.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. Statements may include, but are not limited to, “Highs are often associated with high glucose fluctuations” and “The following behaviors may contribute to high glucose fluctuations (this may be followed by a list of behaviors).” Clinician guidance 276 may also include self-care guidance 262. Self-care statements may include “Do you sometimes forget to take your medication?” and “Do you sometimes forget to eat or have variability in carbohydrate intake?” Time periods with a “high” pattern 285 in the glucose concentration profile may be highlighted with a box 295, for example, an orange box (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0167] In some embodiments, if only the “high” pattern 285 is detected, with high variability, median glucose > 180 mg / dL, no risk of hypoglycemia, and no excursion detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “high” pattern 285. These statements may include, but are not limited to, “Consider adjusting insulin for T1 patients,” “Consider adjusting medication for T2 patients currently taking insulin or sulfonylurea drugs,” and “Consider initiating insulin for other T2 patients.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. These statements may include, but are not limited to, “Highs are often associated with high glucose variability” and “The following actions may contribute to high glucose variability (this may be followed by a list of actions).” Clinician guidance 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Do I sometimes forget to take my medication?” and “Do my meals or snacks sometimes contain too many carbohydrates?” In the glucose concentration profile 256, time periods with a “high” pattern may be highlighted with an orange box 295 (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0168] In some embodiments, if only “high” pattern 285 is detected, with high variability, an arbitrary median glucose value, no risk of hypoglycemia, and no excursion detected, the clinician guidance 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include a statement for clinicians to consider when deciding on treatment for the “high” pattern. This statement may include, but is not limited to, “when initiating or adjusting medication to address highs, consider the possibility that the medication may induce lows.” The clinician guidance 276 may also include a variability statement 286 regarding the detected high variability. This statement may include, but is not limited to, “highs are often associated with high glucose variability” and “the following actions may contribute to high glucose variability (this may be followed by a list of actions).” The clinician guidance 276 may also include self-care guidance 262. Self-care statements may include "Do I sometimes forget to take my medication?" and "Do my meals or snacks sometimes contain too many carbohydrates?". In the glucose concentration profile 256, time periods with a "high" pattern 285 may be highlighted with a box, for example, an orange box (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0169] In some embodiments, if only “high” pattern 285 is detected, with low variability, median glucose <180, no risk of hypoglycemia, and low excursion, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for “high” pattern 285. These statements may include, but are not limited to, “For T1 patients, consider adjusting insulin,” “For T2 patients currently taking insulin or sulfonylurea, consider adjusting medication,” and “For other T2 patients, consider adjusting medication or initiating non-insulin or non-sulfonylurea medication.” The glucose pattern report 250 may not include a variability statement 286 because low variability was detected. The guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Are meals or snacks occasionally high in carbohydrates?". Clinician guidance 276 may also include an excursion statement 288, which may include, but is not limited to, "Hypoglycemia < 54 mg / dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with a “high” pattern may be highlighted with an orange box, for example (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0170] In some embodiments, if only a “high” pattern 285 is detected, with low variability, median glucose > 180, no risk of hypoglycemia, and low excursion, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “high” pattern 285. These statements may include, but are not limited to, “Consider adjusting insulin for T1 patients,” “Consider adjusting medication for T2 patients currently taking insulin or sulfonylurea drugs,” and “Consider initiating insulin for other T2 patients.” The glucose pattern report 250 may not include a variability statement 286 because low variability was detected. The guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, "Are meals or snacks frequently high in carbohydrates?" Clinician guidance 276 may also include an excursion statement 288. This statement may include, but is not limited to, "Hypoglycemia < 54 mg / dL occurs occasionally. See weekly summary report." In the glucose concentration profile 256, time periods with a “high” pattern 285 may be highlighted with a box, for example, an orange box (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0171] In some embodiments, if only a “high” pattern 285 is detected, with low variability, an arbitrary median glucose value, a moderate hypoglycemia risk, and low excursion detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include a statement for clinicians to consider when deciding on treatment for the “high” pattern 285. This statement may include, but is not limited to, “When initiating or adjusting medication to address highs, consider how that medication may induce lows.” The glucose pattern report 250 may not include a variability statement 286 because low variability was detected. The guidance for clinicians 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Are meals or snacks frequently high in carbohydrates?” Guidance for clinicians 276 may also include an excursion statement 288, which may include, but is not limited to, “Hypoglycemia < 54 mg / dL occurs occasionally. See weekly summary report.” Time periods with a “high” pattern 285 in the glucose concentration profile 256 may be highlighted with a box, for example, an orange box (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0172] In some embodiments, if only “high” pattern 285 is detected, and high variability, median glucose <180, no risk of hypoglycemia, and low excursion are detected, the guidance for clinicians 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for “high” pattern 285. These statements may include, but are not limited to, “For T1 patients, consider adjusting insulin,” “For T2 patients currently taking insulin or sulfonylurea, consider adjusting medication,” and “For other T2 patients, consider adjusting medication or initiating non-insulin or non-sulfonylurea medication.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. Statements may include, but are not limited to, “High levels are often associated with high glucose fluctuations” and “The following behaviors may contribute to high glucose fluctuations (this may be followed by a list of behaviors).” Clinician guidance 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Do you sometimes forget to take your medication?” and “Do your meals or snacks sometimes contain too many carbohydrates?” Clinician guidance 276 may also include an excursion statement 288. This statement may include, but is not limited to, “Hypoglycemia below 54 mg / dL occurs occasionally. See weekly summary report.” In the glucose concentration profile 256, time periods with a “high” pattern 285 may be highlighted with a box, for example, an orange box (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).If all adjacent time periods are determined to have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0173] In some embodiments, if only a “high” pattern 285 is detected, and it is characterized by high variability, median glucose > 180, no risk of hypoglycemia, and low excursion, the guidance for clinicians 276 may include a critical pattern statement 278 that identifies the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include statements for clinicians to consider when deciding on treatment for the “high” pattern 285. These statements may include, but are not limited to, “Consider adjusting insulin for T1 patients,” “Consider adjusting medication for T2 patients currently taking insulin or sulfonylurea drugs,” and “Consider initiating insulin for other T2 patients.” The guidance for clinicians 276 may also include a variability statement 286 regarding the detected high variability. These statements may include, but are not limited to, “Highs are often associated with high glucose variability” and “The following actions may contribute to high glucose variability (this may be followed by a list of actions).” Clinician guidance 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Do you occasionally forget to take your medication?” and “Do you occasionally have too many carbohydrates in your meals or snacks?” Clinician guidance 276 may also include excursion statements 288. These statements may include, but are not limited to, “Hypoglycemia of less than 54 mg / dL occurs occasionally. Refer to the weekly summary report.” Time periods with a “high” pattern 285 in the glucose concentration profile 256 may be highlighted with an orange box (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0174] In some embodiments, if only a “high” pattern 285 is detected, with high variability, an arbitrary median glucose value, a moderate hypoglycemia risk, and low excursion, the clinician guidance 276 may include a critical pattern statement 278 identifying the “high” pattern 285 and the time period in which the pattern was detected. The “high” pattern tag 280 may be highlighted in orange, and the time period may be identified in text format. Medication guidance 260 may include a statement for clinicians to consider when deciding on treatment for the “high” pattern 285. This statement may include, but is not limited to, “when initiating or adjusting medication to address highs, consider how that medication may induce lows.” The clinician guidance 276 may also include a variability statement 286 regarding the detected high variability. This statement may include, but is not limited to, “highs are often associated with high glucose variability” and “the following actions may contribute to high glucose variability (this may be followed by a list of actions).” The clinician guidance 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, “Do you occasionally forget to take your medication?” and “Do you occasionally have too many carbohydrates in your meals or snacks?” Guidance for clinicians 276 may also include an excursion statement 288, which may include, but is not limited to, “Hypoglycemia of less than 54 mg / dL occurs occasionally. See weekly summary report.” Time periods with a “high” pattern 285 in the glucose concentration profile 256 may be highlighted with an orange box (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2). If all adjacent time periods are determined to have a “high” pattern 285, a single box 295 may outline the adjacent time periods (see, for example, Figures 6C-1 to 6C-2 and Figures 6D-1 to 6D-2).

[0175] As shown in Figures 6B-1 to 6B-2, if multiple types of patterns are detected, all patterns may be identified in the glucose concentration profile 256. In some embodiments, the glucose concentration profile 256 may contain up to three patterns per patient. The boxes or partial boxes or brackets 295 that schematically represent the various patterns may be prioritized. The priority order from beginning to end may be "low," "high with some low," and "high." The various patterns may be color-coded based on their priority. In some embodiments, the highest priority pattern among the detected patterns may be the only pattern highlighted in color. The remaining lower priority patterns may be colored in different colors, such as gray or black. For example, if at least one of each of the "low" pattern 281, the "high with partial low" pattern 283, and the "high" pattern 285 is detected, the "low" pattern 281 may be displayed in red (in partial box 295 and label format), and the "high with partial low" pattern 283 and the "high" pattern 285 may be displayed in gray on the glucose concentration profile 256. In other embodiments, if at least one of each of the "high with partial low" pattern 283 and the "high" pattern 285 is detected, the "high with partial low" pattern 283 may be displayed in dark red or chestnut (in partial box 295 and label format), and the "high" pattern 285 may be displayed in gray on the glucose concentration profile 256.

[0176] If a patient is found to have met all TIR targets for a given period, but a “low” pattern is detected for at least one period, the report may still identify the “low” pattern in all the appropriate sections, which include a box or partial box or bracket 295 illustrating the “low” pattern on the glucose concentration profile 256, and a critical pattern statement 278. In an alternative embodiment, if a patient is found to have met all TIR targets for a given period, the report may not identify or highlight the pattern.

[0177] In embodiments where only a single pattern is detected, the single pattern can be color-coded on the glucose concentration profile 256. For example, a "low" pattern 281 may be displayed in red (in a partial box 295 and label format), a "high with partial low" pattern 283 may be displayed in dark red or chestnut (in a partial box 295 and label format), and a "high" pattern 285 may be displayed in orange (in a partial box 295 and label format).

[0178] Learning methods Manual configuration of the DGS100 requires time from the Health Care Planner (HCP), but sufficient time may not always be available. Furthermore, even if time is available, the configuration can be complex and prone to errors. To mitigate these issues, a Patient Parameter Initialization (PI) module can be included in the DGA, requiring either no configuration or minimal configuration. The PI module learns the patient's medication strategy, which may include, for example, basal only, basal +1, basal +2, etc., and parameterizes the patient's medication habits to configure the DGA's dose guidance settings.

[0179] According to one embodiment, the learning process of the PI module may include a step of automatically configuring the patient's dose guidance settings from observed data. Once the settings are successfully learned, the DGS100 can enter guidance mode, allowing the patient to request dose guidance and receive notifications regarding administration. During the learning process preceding guidance mode, the DGA can process glucose and insulin data collected by the patient's SCD102, UID202, and / or other devices, and determine administration information based on the processed data.

[0180] Dosage information may include, for example, the medication regimen, meal-dose type, dose parameters, and dosage range. Medication regimens may include, for example, basal dose + BF, basal dose + LU, basal dose + DI, basal dose + BF / LU, basal dose + BF / DI, basal dose + LU / DI, and basal dose + 3, where BF is "breakfast," LU is "lunch," and DI is "dinner." Additional regimens, such as the dose for an afternoon snack, may also be included. Meal-dose types may include, for example, fixed meal doses or variable meal doses. Dose parameters may include, for example, the nominal fixed dose or carbohydrate ratio for each meal, the pre-meal correction factor (CF), and the post-meal CF. The dose range may include an estimate of the minimum meal dose. CF is also known as the insulin sensitivity factor. It is a ratio that reports how much 1 U of insulin lowers blood glucose, either in a fasting or pre-meal state. DGA can have two CF values ​​that take into account the difference in insulin sensitivity between the fasting state and the pre-meal state, i.e., the difference in insulin sensitivity between before and after meals. The unit of CF is (mg / dL) / insulin units.

[0181] For each of the above-mentioned types of dosage information, the DGA can determine whether the accumulated data is sufficient or insufficient to determine the dosage information. In some embodiments, the patient's SCD102 can be configured to operate for a predetermined time period, for example, 14 days. In these embodiments, after the predetermined time period (or earlier if the sensor stops operating before the end of the period), the DGA can determine whether the available analytes and dosage data are sufficient to determine each of the above-mentioned dosage information. If sufficient, the DGA can perform the parameterization method 300 and initiate the dose guidance mode. In alternative embodiments, periodically during the learning period (e.g., once a day), the DGA can determine whether the data is sufficient to determine each of the above-mentioned types of dosage information. In any case, if the collected data is sufficient, the DGA can terminate the learning period, perform parameterization, and initiate the guidance period. Otherwise, the DGA can continue the learning process.

[0182] Referring to Figure 7A, the DGA can be configured to run Method 300 alone or in any combination on a suitable computing device, for example, UID200, SCD102, and MDD152. The program instructions for running Method 300 can be grouped in a PI module or any other suitable code configuration. Outline, Method 300 may include the step in step 302 in which the DGA classifies each of the drug doses received by the patient during the analysis period based on data characterizing the patient's analytes and drug doses received during the analysis period. Method 300 may further include the step in step 304 in which each dose is grouped into one of a set of mealtime groups. The Method may further include the step in step 306 in which it generates patient dose parameters at least partially by applying the data for each mealtime group to a model. The Method may include the step in step 308 in which it stores the dose parameters in computer memory and configures dose guidance settings. In the embodiments described herein, the analyte may be glucose or include an indicator of the patient's glucose level, and the drug may be insulin or include insulin. The dose guidance settings can be used by the DGA to formulate dose guidance or provided for output to an interface device, such as UID200 or a healthcare worker's terminal. More detailed aspects of each operation in Method 300 are described below. As used herein, “PI module” refers to a part or portion of the DGA that performs the operations of Method 300 and any auxiliary operations. The PI module is not limited to a specific configuration and can encompass various arrangements of computer code.

[0183] In one embodiment, the classification operation 302 may include the step of classifying each dose of a drug (e.g., insulin) into a meal dose, a corrective dose, and / or an ambiguous dose. If the DGA cannot classify a drug dose as a meal dose or a corrective dose with a defined degree of confidence, the DGA may classify this dose as an ambiguous dose and omit it from use when generating dose parameters for dose guidance.

[0184] The DGA can classify drug doses by a sequence of two operations, referred herein as feature extraction and classification. Relating this to Figure 7A, the classification operation 302 may include the step of creating a feature matrix that correlates a set of classification features to each of the doses. In some embodiments, the DGA may consist of a vector of insulin injection timestamps, a data file containing analyte measurements from the patient's SCD102, and results from a meal detection algorithm module, discussed elsewhere herein, as inputs to a function that outputs a feature matrix for insulin dose classification. The number of rows in the feature matrix may represent the amount of injections, or equivalent drug administration events, during the relevant analysis period. Each row in the feature matrix may be, or contain, a feature vector for a single drug administration event. In embodiments for classifying insulin injections, each vector may contain elements, referred herein as classification features, as described below. The DGA may determine each element of the feature vector based on a time range for insulin injection time, e.g., a corresponding segment of glucose monitoring data from -2.5 hours to 1.5 hours.

[0185] In the embodiment, the classification feature may include the time of administration for each dose, for example, the time period recorded by MDD152 or the time period recorded by the patient using UID200.

[0186] The classification features may further include time-filtered analyte values, such as glucose values ​​filtered using non-parametric smoothing filters like Savitsky-Golay filters, low-pass filters, band-pass filters, or locally estimated scatterplot smoothing filters, or other filters. In one embodiment, the Savitsky-Golay filter may be of order 2 with a frame length of 7 and a sampling interval of 15 minutes.

[0187] The classification features may further include the rate of change in analyte values ​​closest to the time of administration, for example, the rate of change in analyte (e.g., glucose) values ​​calculated by linear regression of five analyte data points (e.g., using a 15-minute sampling interval) centered on the data point closest to the time of administration (e.g., injection).

[0188] The classification features may further include an Area Under the Curve (AUC) index, which shows the integral of the difference between the analyte value and the analyte value closest to the time of administration, over the interval prior to the time of administration. For example, to obtain the left-side AUC index, DGA can collect all data points from filtered analyte data within a time window (e.g., 2.5 hours), count back from the injection time, then calculate the difference between the average analyte value of the collected data points and the data point closest to the injection time (i.e., the reference data point), and calculate the left-side AUC index by multiplying this difference by the duration of the time window to calculate the increment of the left-side AUC.

[0189] The classification features may further include a right-hand AUC index, which represents the integral of the difference between the analyte value and the analyte value closest to the time of administration, over an interval after the time of administration. For example, DGA can calculate the right-hand AUC index by collecting all data points from filtered analyte data within a time window (e.g., 1.5 hours), counting back from the injection time, then calculating the difference between the average analyte value of the collected data points and the data point closest to the injection time (reference data point), and multiplying this difference by the duration of the time window to calculate the increment of the right-hand AUC.

[0190] Classification features can further include the elapsed time between medication times. For example, DGA can calculate the elapsed time between the previous injection time and the current injection time for each injection time by subtracting the previous injection time from the current injection time. For the first injection time in the insulin log, since there is no available previous injection time, DGA can calculate the elapsed time from the first SCG time data point to the current injection time. In addition, as a further example, DGA can calculate the elapsed time between the current injection time and the next injection time by subtracting the current injection time from the next injection time. For the last injection time in the insulin log, since there is no available next injection time, DGA can calculate the elapsed time from the current injection time to the last SCG time data point. In both backward and forward calculations, if the elapsed time is greater than a predetermined maximum value (e.g., 12 hours), DGA can set the elapsed time value to equal the maximum time.

[0191] The classification features may further include the probability that a meal will be initiated within a defined interval prior to the time of administration, for example, the maximum probability of a meal being initiated within a time window before injection (e.g., 1.5 hours). This probability can be calculated by a meal detection module described elsewhere in this specification.

[0192] The classification features can further include the most accurate interval of the elapsed time from the most recent meal, for example, the elapsed time from the maximum point of the meal start probability relative to the injection time (e.g., determined by the meal detection module).

[0193] The classification features can further include the probability that a meal is started within a defined interval after the dosing time, for example, the maximum point of the meal start probability within 2 hours after injection (determined by the meal detection module).

[0194] The classification features can further include the most accurate interval until the next meal, for example, the predicted elapsed time from the injection time to the maximum point of the meal start probability after the meal injection (e.g., determined by the meal detection module).

[0195] As described above, the step of calculating a part of the classification features includes the step of estimating the time of each meal taken by the patient during the analysis period, and the method of estimating the meal time will be described in more detail below. Briefly, the step of estimating the time of each meal can further include the step of creating a feature matrix based on the time-correlated analyte data by DGA, and the feature matrix correlates a set of analyte (e.g., glucose) data features to each of the separate regions classified as rising, before falling, and falling. The set of analyte data features can be or can include the maximum analyte change rate, the maximum analyte acceleration, the analyte value at the maximum analyte acceleration point, the duration of the region, the height of the region, the maximum deceleration, the average change rate within the region, and the time of the maximum analyte acceleration. The estimating step can further include the step of creating an estimated meal time based on the feature matrix using the algorithm described below.

[0196] More detailed embodiments of the retrospective mealtime detection algorithm for use in Method 300 or elsewhere are described in the following paragraphs. The description of other embodiments of Method 300 follows. The DGA can perform retrospective mealtime detection based on time-correlated analyte data by executing one or more code modules, for example, a feature extraction module and a meal detection module. When executed by the DGA, the feature extraction module can cause the DGA to output a feature matrix that takes a glucose time series as input and passes through the retrospective meal detection module to detect glucose excursions in response to meal events.

[0197] DGA can perform feature extraction using the following operations, which can be divided into a sequence of three sub-operations: smoothing, segmentation, and extraction.

[0198] In the smoothing sub-operation, the DGA can smooth the analyte (e.g., glucose) time series using a Savitzky-Golay filter (order 2) and calculate the rate of change and acceleration rate at each analyte data point. The filter's frame length parameter may be the number of data points collected in a first time interval (e.g., 60 minutes), and therefore sampling is interval-dependent. The DGA can calculate the rate of change by taking the average of the before-and-after differences in the smoothed analyte values ​​between the point of interest and points in a second interval (e.g., 15 minutes) before and after that point, where the second interval is smaller than the first interval, for example, equal to one-quarter of the first interval. Similarly, the DGA can calculate the acceleration rate by taking the average of the before-and-after differences in the rate of change of the analyte between the point of interest and points in a second interval (e.g., 15 minutes) before and after that point.

[0199] In the segmentation suboperation, DGA can segment the smoothed analyte trace into monotonically increasing regions (i.e., rising regions) and decreasing regions (i.e., falling regions). Each rising region can be considered a candidate for glucose excursion in response to a meal event.

[0200] In the extraction suboperation, DGA can extract features from the data. For example, it can extract 16 features that may or may not be features from each ascending region (e.g., 8 features), the preceding descending region (e.g., 4 features), and the next descending region (e.g., 4 features). Features that DGA can extract from ascending features may include, for example, the following: 1) maximum analyte rate of change, 2) maximum analyte acceleration, 3) analyte value at the point of maximum analyte acceleration (reference point), 4) duration of the ascending region (elapsed time from the reference point to the last point of the region), 5) height of the region (difference in smoothed analyte values ​​between the last point and the reference point), 6) maximum deceleration (negative acceleration at the maximum absolute value), 7) average rate of change within the region (height / duration), and 8) time of the data at the reference point. To give a further example, the four features extracted from the preceding and succeeding descending regions may include the following: Specifically, these are 1) the height of the downward region, 2) the duration of the downward region, 3) the average rate of change of the region (height / duration), and 4) the maximum absolute value of the rate of change of glucose. The number of rows in the feature matrix output by the feature extraction module may be the same as the number of upward regions in the smoothed glucose time series.

[0201] According to another embodiment, a retrospective meal detection module can take a feature matrix as input and output a binary detection result for each rise region. Such outputs may include a binary classification result and a probability value that each rise region is an analytic (e.g., glucose) excursion in response to a meal event. The DGA can assign the probability value of each rise region to its reference point. In some embodiments, for example, a pre-trained machine learning model for meal detection can be implemented using a Random Forest Classifier by scikit-learn (https: / / scikitlearn.org / stable / modules / generated / sklearn.ensemble.RandomForestClassifier.html). The meal detection module can detect meal-induced postprandial glucose excursions based on a number of decision trees built and optimized during the training process. In alternative embodiments, the DGA can build a pre-trained model based on alternative classification algorithms, such as gradient boosting, Ada boosting, artificial neural networks, linear discriminant analysis, and extra trees.

[0202] Referring again to method 300 in Figure 7A, the classification operation 302 can take a patient feature matrix as input and output a binary classification result for each relevant medication event (e.g., for each insulin injection). For example, the DGA can output binary data "1" representing meal doses and "0" representing non-meal doses. According to some embodiments, the classification operation 302 can use meal detection results, in which case meal detection can be performed before insulin dose classification. As described for retrospective meal time detection, the classification operation 302 can include a pre-trained machine learning model, for example, a model implemented using a random forest classifier with scikit-learn (see above). The machine learning model implemented by the DGA can perform classification based on tree construction rules and thresholds for various features in each decision tree, which are optimized during the training process. Alternatively, this model can also be trained with other machine learning algorithms, including gradient boosting, Ada boost, artificial neural networks, linear discriminant analysis, and extra trees. Once the DGA successfully classifies each dose, the determination of the medication regimen and administration parameters can be initiated.

[0203] In step 304, method 300 may include the step of DGA grouping each dose into one of a set of mealtime groups or clusters. For example, DGA can determine a medication strategy by clustering an analysis of the timing of medication administration (e.g., injection) of meal doses. DGA can run a clustering module implemented using the K-means algorithm along with the elbow method, which takes injection times as input and outputs the optimal number of clusters K (up to 3) and cluster index for each injection time. The optimal number of clusters K can be the number of meal doses a patient takes in a day. Using the cluster index for each injection, DGA can classify meal doses into K groups according to the cluster index.

[0204] DGA can identify these groups as breakfast, lunch, or dinner (B,L,D) as follows: For each group, DGA can determine the typical time of day (TOD) by calculating the median TOD of the group. Alternatively, DGA can use other centroid indicators. If K=3, DGA can associate breakfast with the group after the longest period between the group's typical TODs. The next group is lunch, and the last group is dinner. If K=2, DGA can estimate which groups are associated with breakfast, lunch, or dinner using a rule of assumptions about the time between each meal. For example, if two groups are more than 6 hours apart from each other, DGA can identify the group as breakfast and dinner. In addition, if the first group occurs before 10 a.m., DGA can identify the group as breakfast and lunch; otherwise, it can identify the group as lunch and dinner. In an alternative embodiment, after DGA has identified the typical times of meal events, the user can be prompted to identify the meals associated with each typical time. As a further example, in an alternative embodiment, the DGA can combine the two methods described herein by prompting the user for confirmation after estimating the relevance of meals. Further alternative methods include analyzing glucose data to identify meals and clustering meal times to detect typical meal times. This may be useful for distinguishing meals in the case of K=2, i.e., identifying meals for which no dose was consumed.

[0205] Once the doses are grouped into mealtime clusters, in step 306, the DGA can perform the step of generating patient dose parameters, at least partially, by applying the data for each mealtime group to a model. For example, for each meal group (B, L, D), the DGA can pair each set of corresponding pre-meal glucose levels with the corresponding meal dose. The DGA can then fit each group with an appropriate model, e.g., a linear function with zero slope, a linear function with a non-zero slope, a piecewise linear function joined at one point, or a nonlinear function that approximates a piecewise model but has smooth curvature around the joint point. Other models are also suitable.

[0206] DGA can perform model fitting and parameter estimation by minimizing the sum of squared residuals (SSR) of the model parameters. Then, using a search algorithm, DGA can find the optimal parameters that minimize the SSR. For linear models, DGA can perform fitting using the Nelder-Mead simplex method. For nonlinear models, DGA can use the Levenberg-Marquardt algorithm. In other words, DGA can use the Nelder-Mead simplex numerical optimization method for linear models and the Levenberg-Marquardt optimization method for nonlinear models. Other methods for fitting data to these models are also possible.

[0207] If the number of iterations during optimization exceeds the convergence criterion, the model will not fit, and DGA can exclude the unfit model from the candidate models. Furthermore, DGA can apply certain rules to minimize the uncertainty of parameter estimation, for example, by validating the estimated correction coefficient by requiring at least three pre-meal glucose data points greater than the estimated threshold glucose, or by validating the estimated fixed volume by requiring at least three pre-meal glucose data points less than the estimated threshold glucose, or by requiring the 95% confidence interval for the parameter intercept to exclude zero, or by requiring the 95% confidence interval for the model slope to exclude zero.

[0208] If the data is insufficient, model fitting may fail, resulting in certain models being excluded as candidate models. DGA can evaluate each model using the Akaike Information Criterion (AIC) and select the model with the lowest AIC value as the preferred model for each dietary group.

[0209] Once the DGA selects a model for each mealtime cluster, it can then determine dose parameters, including, for example, a fixed dose of insulin, a target glucose level, and a correction factor, based on the selected model for each mealtime cluster. The DGA can determine the target glucose level and correction factor as a single value for each group, as will be described in more detail in the following paragraphs. In an alternative embodiment, the DGA may determine the target glucose level and correction factor separately for each group and use the separately determined parameters for downstream dose guidance operations.

[0210] According to another embodiment, DGA can form combined data groups to obtain a more accurate correction factor for the patient. For example, after fitting dose data to various models for each meal group to select the best model and estimating a fixed insulin dose, DGA can subtract the fixed dose insulin amount from the relevant meal dose for each meal group. The remaining non-zero values ​​correspond to doses with a correction dose. These non-zero values ​​can then be joined from all three meal groups (B, L, D) to form a combined group. If a fixed dose insulin amount cannot be determined for a group, DGA can exclude the data for that group from the combined group. The system can then repeat the operation to find the best model for the combined group, or use the same model identified when analyzing the groups individually. By using this combined group approach, assuming that the patient has the same (or constant) correction factor and target glucose for all meals, the combined group can provide a more accurate fitting with a larger sample size. After determining the target glucose level and correction factor based on the best-fitting model, DGA completes the estimation of dose parameters. Next, in step 308, the DGA can store the dose parameters in computer memory and configure the dose guidance settings.

[0211] In an additional embodiment, the DGA may determine whether a patient is potentially engaging in carbohydrate counting (e.g., adjusting meal portions to account for carbohydrate consumption) by comparing the AIC value of a preferred model to a threshold such as 50, 75, or 100. If the AIC value is greater than the threshold, the DGA determines that the patient is engaging in carbohydrate counting and may seek confirmation from the patient via UID200.

[0212] In alternative embodiments, one or more of the operations described above may be omitted and replaced by requiring the patient or HCP to manually provide information, or by extracting information from another source, such as EMR or other software programs. Nevertheless, Method 300 should be useful for a variety of applications that do not have more information than what SCD and MDD can provide.

[0213] Alternative learning methods In alternative embodiments, the DGS100 (e.g., SCD102, display device 120, or MDD152) may be configured using an automated or semi-automated learning method that classifies and characterizes drug doses based on patient input and the patient's GPA analyzed over the learning period. Alternative learning embodiments include processing of regimen input from the user, recorded glucose measurements (glucose readings and associated date / timestamps), and insulin administration information (dosage and associated date / timestamps). However, the inputs and algorithms may be optimized to reduce the burden on the patient and HCP. For example, in the embodiments described herein, HCP involvement may not be required to initiate dose guidance. This is advantageous because, in many cases, HCPs do not have time to set up the system for the patient. Furthermore, this embodiment provides an additional safety mechanism compared to the common practice of having the patient or HCP directly set up the dose guidance system (or, more commonly, a bolus calculator). Specifically, as connected insulin pens become more common and are now being used in research, it is clear that many MDI patients are not adhering to their medication regimens, such as forgetting or delaying meal administration or taking less insulin than necessary. The first two are likely due to inconvenience or forgetfulness, while the third may stem from a fear of hypoglycemia, coupled with a lack of confidence in managing it.

[0214] A fundamental problem with common practice is that the dosing guidance system may be set based on what the patient or HCP believes is needed, based on the blood glucose profile. However, that profile may be based on previous regimens with poor adherence. For example, the HCP may advise the patient to increase the fixed insulin dose for breakfast from 10 to 12 to mitigate post - breakfast hyperglycemia, but this hyperglycemia may actually be triggered by the patient missing 50% of the breakfast dose or under - dosing, often taking only 8 units. If the patient suddenly decides to increase adherence, there may be a problem with hypoglycemia. Therefore, it is important that the dosing guidance setting process includes an analysis of past adherence before presenting dosing guidance to the patient.

[0215] As described above, the patient or HCP may enter all of the major dosing guidance parameters or only a subset of them. Here, as an example, it is assumed that the patient enters the following portions of the dosing guidance parameters, although other examples are possible. Specifically, the system provides means for entering the following parameters before providing dosing guidance: typical fixed doses for breakfast, lunch, and dinner; and the typical time of day when breakfast (and / or each meal) is taken.

[0216] The above is an example of a compact parameter set that should be easy enough for most patients to understand and input correctly. Complex parameters such as correction factors that many patients would not understand are avoided. Alternatively, the system may allow patients to input more parameters at will for patients with more sophisticated medication regimens and / or who understand the details of these regimens well enough to input the parameters correctly. In another embodiment, the patient may input information about their level of adherence, such as how often they forget to take a dose at a certain time of day, or how often they skip meals and therefore do not need to take insulin. Another embodiment is a system that prompts the patient to manually categorize the recorded doses. However, this type of system may be undesirable for most patients because it is likely to require a high degree of interaction with the patient due to the lack of readily available values.

[0217] During a learning mode to analyze patient adherence to a user-entered regimen, the system acquires glucose data and insulin dose information over a period of, for example, two weeks. If the system does not initiate dose guidance after this period, it may allow the patient to begin a subsequent observation period.

[0218] Once the learning period is complete, the system processes the input regimen parameters and observed glucose and insulin dose information using an analytical algorithm executed by the processor. This algorithm may include classifying doses as described above. Alternatively, or additionally, the classification may be based on user-entered medication information and may take into account the absolute and relative time of day between measured analyte (e.g., glucose) data and user-supplied administration data. Alternatively, or additionally, the classification may be based on dynamic relationships between different data inputs, e.g., the rate of change in glucose. The system can perform classification using classification models that can be developed and trained using common machine learning techniques applied to clinical and simulated data.

[0219] Once the system processor classifies the doses, it associates each dose with one of several meal events during the day. The aforementioned clustering analysis is one way to perform this association. For example, the system can associate breakfast with the cluster whose central measurement (e.g., median, mean) for that time period is temporally closest to the value entered by the user for breakfast, the next cluster can be associated with lunch, and the next cluster with dinner.

[0220] Alternative clustering methods may include glucose data as well as dose information in cluster determination using algorithms to identify clusters and determine which doses are associated with which clusters. Furthermore, the processor may include data indicating the absolute and relative time of day between glucose data and dose data in its cluster analysis. For example, a dose of 10 units in the morning may be associated with the lunch cluster if 10 units is a typical dose for this cluster, not a dose for the breakfast cluster, and if that dose was taken sufficiently late in the morning. Associations may also be based on dynamic relationships between different data inputs, such as the rate of change in glucose. Determination is performed by a classification model, which can be developed and trained using common machine learning techniques applied to clinical and simulated data.

[0221] Once the clustering process is complete, the system estimates the regimen parameters, as described above. The parameter estimation process may involve determining the degree of confidence of the parameter estimates using standard numerical analysis techniques. In the following description, for brevity, the degree of confidence may be described as a confidence interval, but other common measures of confidence can be used. These parameters are referred to herein as “learned” regimen parameters, and include parameter values ​​and their associated confidence intervals.

[0222] Once a regimen is learned, the system can perform the following checks between the input parameters and the learned parameters: The system processor can check the confidence intervals (CIs) of the learned meal dose parameters and typical administration times by comparing each parameter to an appropriate maximum threshold. If the CI exceeds that threshold, the processor can flag the system configuration as questionable. For meal doses, appropriate maximum CIs are ±30%, 20%, or 50%, and for typical meal administration times, appropriate maximum CIs are ±1.5 hours, 1 hour, or 2 hours.

[0223] In another embodiment, the system processor can compare learned parameters with user-input parameters, specifically the dosages of breakfast, lunch, and dinner. If the input parameters and learned parameters differ by more than a confidence interval (or a multiple of the confidence interval), the processor can flag the system configuration as suspicious.

[0224] The system processor can further compare the input typical breakfast administration time with the learned meal administration time of the temporally closest dose cluster. If these parameters differ significantly, the processor can flag the confidence interval (or a multiple of the confidence interval) as questionable.

[0225] Furthermore, the system can check whether additionally learned parameters are valid. For example, the maximum gap between learned typical meal times must occur before the learned typical meal time, ensuring that nighttime periods are properly accounted for. If the validity check fails, the system configuration can be flagged as questionable. Some parameters do not need to be estimated with high confidence so that the system configuration is not flagged as questionable. For target glucose (GT), pre-meal CF, and post-meal CF, the learned value is used in the dose guidance regimen if the CI is within an appropriate maximum threshold for that parameter; otherwise, a conservative default value is used. Appropriate maximum CI thresholds for GT and CF are ±2, 5, 10 mg / dL or ±5, 10, 20 mg / dL / unit, respectively. Appropriate default values ​​for GT, pre-meal CF, and post-meal CF are 120, 125, 130 mg / dL or 40, 50 mg / dL / unit, or 60, 70 mg / dL / unit.

[0226] Furthermore, the system can evaluate the acquired data regarding patient compliance with the entered regimen. For example, the system's processor can estimate the frequency of meal forgetting (or meals with little impact on postprandial glucose levels). This is useful for calculating subsequent indicators. This estimate can be calculated using a model developed with machine learning or other techniques from glucose data and insulin dose data as well as meal records—this model can be trained with clinical data, real-world data, or simulated data. Generally, if insulin is not administered during a time period when meal administration is expected, and the blood glucose pattern during this period does not show an increase in glucose, this indicates meal forgetting.

[0227] The system processor can estimate the frequency of forgotten meals for breakfast, lunch, and dinner. For example, the frequency can be calculated by dividing (number of periods of forgotten meals - number of periods tagged as forgotten meals) by (total number of periods - number of periods tagged as forgotten meals).

[0228] In another embodiment, the system can estimate the difference between the median dose and the input dose for each meal (breakfast, lunch, and dinner), i.e., how much the dose is under- or over-administered compared to the dose entered by the patient. Before initiating dose guidance, the system processor may output the administration adherence findings to a display device or equivalent device. Similarly, the system can report to the patient the estimated impact of lack of adherence on blood glucose control and A1c, or conversely, indicate to the patient the possibility of improvement in glucose indicators such as mean glucose or time in target range, or clinical laboratory values ​​such as A1c, if the patient corrects the adherence problem. This information can be generated by the processor from a model developed by correlating each diabetes management measure with adherence measurements for a specific regimen. In a simple model, correlation parameters that link adherence measurements to blood glucose measurements can be found based on actual or simulated population data, and then linked to A1c measurements. It is also possible to develop more advanced models to correlate specific patient characteristics, such as tracked regimens and blood glucose profiles, with adherence measurements, and to implement them using system processors.

[0229] As a summary of the foregoing, and as an additional example, the DGS100 or its components (e.g., one or more of the SCD102, display device 120, or MDD152) for parameterizing a patient's medication habits to configure dose guidance settings may include an input component configured to receive measured analyte data, dietary data, and medication data; a display component configured to visually present the information; and one or more processors coupled to the input, display, and memory. The memory may hold time-correlated data characterizing the patient's analytes over an instruction and analysis period, and the instruction, when executed by one or more processors, causes the device to perform method 1000, as shown in Figure 7B. In one embodiment, the drug may be insulin, or may include insulin.

[0230] Method 1000 may, in 1002, include receiving patient dose regimen information for the analysis period by one or more processors. One or more processors may hold the patient dose regimen information in memory for processing. Method 1000 may further, in 1004, include evaluating a measure of consistency between time-correlation data and patient dose regimen information. In one embodiment, patient dose regimen information may be, or include, typical fixed drug doses taken with meals and typical times when breakfast is eaten. As a further example, patient dose regimen information may be, or include, information defining the frequency of patient compliance to scheduled doses or meals. In some embodiments, the patient dose regimen may include information about the type, amount, and / or timing of medication administration. In particular, information about the administration schedule of one or more medications.

[0231] Measures of consistency may include any numerical measure for comparing the consistency between datasets, such as mean and standard deviation, or interquartile range (IQR). Evaluating measures of consistency may include comparison with fixed or variable thresholds. Measures of consistency may also be measures of variability between, for example, time-correlated data and predicted data calculated based on patient dose regimens. Variable thresholds can be determined using machine learning or other algorithms. More specific examples are provided above and below in this specification.

[0232] Method 1000 may further include determining dose guidance information based on a measure of consistency in 1006. Once determined, the processor may output the dose guidance information to a display or other output device for use by the patient or HCP, or it may store it in memory for later use. In one embodiment, the dose guidance information may be or may include dose guidance information as illustrated above herein. The dose guidance may also include information that modifies the type, amount, or timing of administration of a particular drug.

[0233] Method 1000 may include additional operations 1100, 1200, and / or 1300 shown in Figures 7C to 7E. The additional operations may be executed by one or more processors of the DGS 100 in any operable order, and the presence or absence of one or more operations shown in any figure does not necessarily imply the presence or absence of other corresponding operations shown in that figure. Instructions for executing one or more of Method 1000 and / or additional operations 1100, 1200, and 1300 may be held in memory for execution by one or more processors of the DGS.

[0234] As shown in Figure 7C, operation 1100 of method 1000 may include, in 1102, outputting dose guidance information to a display or other output device. The method may further include, in 1104, receiving patient dose regimen information from an input component, for example, via input from a touchscreen of a display device. Alternatively or additionally, the method may include, in 1106, receiving patient dose regimen information by transmission from a remote data server.

[0235] Method 1000 may further include an action 1200 for evaluating a measure of consistency, as shown in Figure 7D. Method 1000 may include classifying each dose of a patient dose regimen into a medication class based on time correlation data in 1202. Method 1000 may include grouping each dose into a set of mealtime groups, e.g., breakfast, lunch, and dinner, based on time of day and / or other factors in 1204. Alternatively, Method 1000 may include grouping each dose by time of day, e.g., hourly analysis. Method 1206 may include generating patient dose parameters, at least partially, by applying data from each mealtime group to a model. In some embodiments, the model may be based on historical data from users or historical data from a population survey. Method 1208 may further include storing dose parameters to constitute dose guidance settings.

[0236] Method 1000 may include a further operation 1300, as shown in Figure 7E. Method 1000 may include, in 1302, accumulating time-correlated data characterizing the patient's analytes over a period of time, e.g., 10, 14, or 21 days, before evaluating a measure of agreement. Method 1304 may include determining dose guidance information at least partially by reducing medication recommendations based on detecting analyte excursions that exceed the lower threshold in the time-correlated data. Recommendations should correlate with the meal dose associated with the excursion.

[0237] The method may include, in 1306, determining patient adherence to patient medication regimen information based on time-correlated data. The method may also include, in 1308, determining whether to output dose guidance parameters based on a measure of consistency. For example, if the measure of consistency indicates sufficient patient adherence, dose guidance information is provided for determination by the DGS. If adherence is slightly consistent, the DGS provides dose guidance but also provides a warning about adherence. If adherence is inconsistent, the DGS informs the patient or HCP about what needs to be corrected before providing guidance.

[0238] In one embodiment, the method in 1310 may include outputting dose guidance parameters, including a predetermined dose suggestion, if the consistency metric indicates an unreliable system configuration, such as unreliable data. The predetermined dose suggestion may be retrieved from memory as a fallback if dose guidance is not available by the method in use.

[0239] HCP involvement during the learning period If the system configuration is tagged as suspicious, dose guidance may not start automatically. The DGS100 can offer the patient two options: (a) repeat the learning period by addressing the deficiencies described in the learning / adherence report, or (b) review the results / outputs of the learning period at the next HCP visit. In the second option, the HCP can configure the system and start dose guidance or advise the patient on how to address the deficiencies described in the report and repeat the learning period.

[0240] Many HCPs do not have the time to obtain a web portal account and set up a patient's dose guidance system. Therefore, DGS100 can provide a highly efficient means to facilitate HCP assistance with this setup. Once the learning period is complete and the system configuration is tagged as questionable, DGA can display control functions that allow the patient to initiate the following processes to facilitate HCP configuration of the system.

[0241] When a patient is about to have their next contact with an HCP (e.g., in person or via telemedicine), the patient can press a button or select the option to initiate this process. The DGA can then display a URL and / or code for the HCP to enter into a web browser. The code can be randomly generated by the DGA and associated with that patient. Once the HCP enters the URL into their internet browser, the DGA can open a webpage that provides UI controls for entering these codes. The code may be, for example, a four-character alphanumeric string, or any other acceptable format. The DGA may treat this code as valid for a limited time, for example, five or fifteen minutes. Once the HCP enters the code, the DGA checks if the code is valid, and if so, provides the HCP with access to patient dose guidance learning and configuration information. In an alternative embodiment, the HCP may further be asked to enter their medical license, and the system may grant the HCP access only if the entered license matches the medical license format requirements. The HCP's browser may store the medical license number for subsequent interactions with this webpage.

[0242] If the HCP has access rights, the DGA may display a GUI report 1330, an example of which is shown in Figures 9A-1 and 9A-2, which provides information on what the patient entered into DGS100 1332 and the observed and learned values ​​1334 of DGS100 determined during the learning period. Parameters listed in report 1330 may include dose 1342, administration time 1341 (times when various doses were typically taken or will be taken), administration time range 1344 (start and end administration times for each meal dose), and correction parameters 1345. Dose 1342 may include basal dose and doses for each meal (breakfast, lunch, and dinner). Correction parameters 1345 may include target glucose (mg / dL), pre-meal correction factor (mg / dL / U), and post-meal correction factor (mg / dL / U). Patient input data 1332 may include dose 1342, administration time 1341 (e.g., the time when the basal dose and each meal (breakfast, lunch, and dinner) dose are typically taken), and administration time range 1344 (e.g., start and end times that define the time range in which meal doses are typically taken for each meal). Observed or learned values ​​1334 may include dose 1342 for the basal dose and each meal dose, administration time 1341 for the basal dose and each meal dose, administration time range 1344 (observed start and end times for each meal administration time range), and correction parameters 1345 (target glucose, pre-meal correction factor, and post-meal correction factor). Report 1330 may also provide information and / or warnings regarding adherence issues, for example, in an observation note 1336 for any or all of the listed parameters. The report may also provide conservative values ​​1340 for the correction parameter 1345 or other parameters listed in Report 1330. For example, if adherence observations indicate insufficient dosage, a warning message can be provided to the HCP indicating that it may be safer to set the initial medication regimen to a lower learned dose (e.g., a conservative value) of 1340.Report 1330 may also provide means for the HCP to manually enter medication regimen parameters, or means for copying values ​​to the report from other appropriate sections, for example, by entering values ​​in the fields under the initial therapy 1338 for dose guidance. The report may include means for the HCP to approve this initial regimen. If the HCP selects the approval UI function 1337, the system can download this initial medication regimen to the patient's device and initiate dose guidance. The HCP may also reject the treatment proposed in Report 1330 by selecting the treatment rejection option 1339. If the medication regimen parameters are not fully entered or are out of range, an error message may be provided to the HCP. In another alternative embodiment, the DGA control function may initiate the same process (i.e., a user interface enabling the entry of dose guidance parameters) on the patient's mobile phone, except for the display of a URL and code for the HCP to enter. In yet another embodiment, instead of displaying a URL and code (or in addition thereto), the system may provide a UI means for entering an email address, for example, the patient may enter the email address of the HCP, and the system may then send an email with an embedded hyperlink to this address, which, when selected by the user who receives the email, can open a report webpage containing the patient's data and information on the user's web browser.

[0243] In some embodiments, the DGA can provide a status summary of patients under the care of an HCP or treatment facility. The DGA can display a GUI report 1450, an example of which is shown in Figure 9G, which provides information on all patients under the care of an HCP or treatment facility, along with a summary of various statistics related to the patient's diabetes management. The report 1450 may include records for multiple subjects, including columns or fields for subject identifier 1452, study type 1454, subject status 1456, approval request pending 1458, time within target range (TIR) ​​1460, time below low threshold 1462 (e.g., 70 mg / dL (TB70)), time above high threshold 1464 (e.g., 180 mg / dL (TA180)), basal dose ingested % 1466, mean number of bolus doses ingested per day 1468, glucose capture amount (%) 1470, and number of days of subject in study 1472. Filter options can be included for any field, allowing HCP to reorder the records as needed. Report 1450 can display at least one, at least two, at least three, at least four, at least five, or at least six of the following: time within target range, time below low threshold, time above high threshold, percentage of basal dose taken, and average bolus dose taken per day.

[0244] Report 1450 can display a field containing a target identifier 1452, such as the target number or the target name.

[0245] Report 1450 may display a column containing the type of research 1454 to which the subject is registered. The research may be exploratory research or other types of research.

[0246] Report 1450 may display a column containing the status 1456 of the subject. The status may be one of the following: “Pre-observation,” “Observation 1,” “Observation 2,” “Regimen Approved,” “Regimen Updated,” or “Study Completed.” If the status is “Regimen Approved” or “Regimen Updated,” the date 1474 on which the regime was approved or updated may be listed below the status. In some embodiments, the date 1474 may be grayed out or in a lighter font than the status.

[0247] Report 1450 may display a column 1458 regarding the status of approval requests. The approval request column 1458 may include an icon 1476 (for example, an orange triangle with an exclamation mark in the center) indicating that approval by the HCP is required if there are outstanding requests awaiting review and approval by the HCP. The report may also include a statement 1478 indicating the number of subjects who have medication regimens requiring approval by the HCP.

[0248] Report 1450 may display a column for reporting the time ("Time in Target Range" or "TIR") 1460 of the subject's glucose level being within the target range, as described elsewhere in this application. The TIR can be calculated based on the number of days the subject was participating in the study and / or the number of days the subject was following the current medication regimen.

[0249] Report 1450 may display a column reporting the time when the subject's glucose level was below the low threshold, for example, 70 mg / dL ("Time <70 mg / dL" or "TB70") 1462. The time below the low threshold can be calculated based on the number of days the subject was participating in the study and / or the number of days the subject was following the current medication regimen. In some embodiments, the low threshold may be approximately 60 mg / dL, or approximately 65 mg / dL, or approximately 70 mg / dL, or approximately 75 mg / dL, or approximately 80 mg / dL, or approximately 85 mg / dL, or approximately 90 mg / dL.

[0250] Report 1450 may display a column reporting the time the subject's glucose level exceeds a high threshold, for example, 180 mg / dL ("Time above 180 mg / dL" or "TA180") 1464. The time above the high threshold can be calculated based on the number of days the subject was participating in the study and / or the number of days the subject was following the current medication regimen. In some embodiments, the high threshold may be approximately 170 mg / dL, or approximately 175 mg / dL, or approximately 180 mg / dL, or approximately 185 mg / dL, or approximately 190 mg / dL, or approximately 195 mg / dL, or approximately 200 mg / dL.

[0251] Report 1450 may display a field for reporting the basal dose ingested 1466, for example, as a percentage. The basal dose ingested % 1466 can be calculated for the number of days the subject was participating in the study and / or the number of days the subject was following the current medication regimen.

[0252] Report 1450 may display a column reporting the average number of bolus doses ingested per day. The average number of bolus doses ingested per day can be calculated based on the number of days the subject participated in the study and / or the number of days the subject was following their current medication regimen.

[0253] Report 1450 may display a field for reporting the glucose capture amount 1470, for example, as a percentage. The glucose capture percentage 1470 can be calculated as the percentage of glucose readings received by the system from the sensor during a set time period, for example, the number of days the subject participated in the study and / or the number of days the subject was on the current medication regimen (e.g., before titration).

[0254] Report 1450 can display a column that reports the number of days (1472) the subject was enrolled in the study.

[0255] In some embodiments, the DGA can provide a report summarizing approved treatments along with statistical analyses of glucose levels and doses different from those of the approved treatments. The DGA can display a GUI report 1486, an example of which is shown in Figure 9I, which provides information on approved treatments 1488 for a specific patient 1496, along with an AGP plot 1490, a graph of dose forgettings 1492, and a graph of user overrides 1494. The clinician 1498 can switch back to the complete list of patients under their care to view a GUI report 1486 for a different patient.

[0256] Approved treatments 1488 may be presented in a table including columns for the date the treatment was approved, the type of medication strategy (e.g., initial, titration, manual override), various insulin doses, the length of treatment (e.g., in days), the average number of bolus doses per day, the number of low glucose events, TIR (%), time below low threshold (e.g., TB70 (%)), time above high threshold (e.g., TA180 (%)), and glucose capture amount (%). The type of medication strategy may include, but is not limited to, initial, titration, and manual override. The types of insulin doses for which amounts are reported may include, but are not limited to, basal doses; fixed doses for breakfast, lunch, and dinner; and doses with adjustment factors for breakfast, lunch, and dinner. Percentages in the table may be calculated for data over a specified time period, e.g., data from the last two weeks, the period the subject was enrolled in the study, or the period the subject followed a particular medication strategy.

[0257] AGP graph 1490 displays an exogenous glucose profile (AGP) graph showing hourly 5th, 25th, 50th (median), 75th, and 95th percentiles of glucose readings, presented over a "typical" 24-hour day based on all days within a selected time frame. Alternatively, the AGP can display other hourly percentiles of glucose readings, e.g., the 10th, 25th, 50th (median), 75th, and 90th percentiles, presented over a "typical" 24-hour day based on all days within a selected time frame. The AGP graph may also include two horizontal lines indicating the boundaries of the target range. For example, the first line may correspond to the lower limit of the target range (e.g., 70 mg / dL), and the second line may correspond to the upper limit of the target range (e.g., 180 mg / dL). The first and second lines may also be color-coded. Data points below the lower limit can be highlighted by coloring them a different color from other data points, such as red. In this way, AGP graphs easily illustrate the amount of time spent within the target range (or the amount of readings that fall within the target range) and the amount of time spent outside the target range. Other exemplary AGP graphs can be found, for example, in U.S. Patent Application Publication No. 2018 / 0235524, U.S. Patent Application Publication No. 2014 / 0188400, U.S. Patent Application Publication No. 2014 / 0350369, and U.S. Patent Application Publication No. 2018 / 0226150, all of which are incorporated herein by reference in their entirety for all purposes.

[0258] Graph 1492 of dose forgetting graphs the percentage of doses forgotten relative to the total doses received or administered over a given period, for each of the basal dose, breakfast dose, lunch dose, and dinner dose. This graph can be easily shown to the HCP if a subject forgets a large amount of a particular type of dose, and corrective action can be recommended. The percentages in the graph can be calculated using data for a specified time period, for example, the period the subject was enrolled in the study, or the most recent two weeks during the period the subject followed a particular medication strategy. The graph may be configurable so that the HCP can select the desired time window from a dropdown menu. Graph 1492 of dose forgetting may be a bar graph where one axis represents the type of dose (basal, breakfast, lunch, and dinner) and the other axis represents the percentage of forgotten doses relative to the total dose.

[0259] The User Override Graph 1494 graphs the percentage of doses that were not taken at the recommended dose level for each of the basal dose, breakfast dose, lunch dose, and dinner dose, out of the total number of doses received or administered by the subject. This graph can easily show the Health Care Planner (HCP) when a subject is ignoring the recommended dose for a particular type of administration or meal, and can recommend corrective action. The percentages in the graph can be calculated using data for a specified time period, for example, the period the subject was enrolled in the study, or the most recent two weeks during which the subject followed a particular medication strategy. The graph may be configurable to allow the HCP to select the desired time window from a dropdown menu. The User Override Graph 1494 may be a bar graph where one axis represents the type of dose (basal, breakfast, lunch, and dinner) and the other axis represents the percentage of user overrides relative to the total dose.

[0260] In some embodiments, the DGA can provide a graph showing the correlation between the recommended dose and the dose actually taken by the subject. The DGA can display a recommended dose versus taken dose GUI1480, an example of which is shown in Figure 9H, which graphs the dose taken versus the recommended dose at various times throughout the day. As seen in GUI1480, the X-axis 1481 can be time in minutes, hours, days, or weeks. In some embodiments, the graph can be from 12am to 12pm. The graph may display data accumulated over a period of several days, weeks, or months, and the doses may be graphed on the same graph. For example, the graph may include all doses taken by the subject during a period of time or during a period of time in a particular study period or during a period following a particular medication regimen.

[0261] The y-axis 1482 can represent the difference between the dose taken and the recommended dose. If the dose taken is the same as the recommended dose, the y-coordinate of that dose is zero, and the x-coordinate is the time the dose was administered or taken by the user. If the dose taken is X1 units greater than the recommended dose, the y-coordinate of that dose is X1. (For example, if the dose taken is 2 units more than the recommended dose, the y-coordinate of that dose is 2). If the dose taken is X2 units less than the recommended dose, the y-coordinate of that dose is -X2. (For example, if the dose taken is 1 unit less than the recommended dose, the y-coordinate of that dose is -1). For example, if a fixed breakfast dose is taken at 7:50 AM, and the dose taken is the same as the recommended dose, the corresponding point 1483 on the graph will be (7:50 AM, 0). If the basal dose taken at 9 PM was 2 units more than the recommended basal dose, the corresponding point 1484 on the graph would be (9 PM, 2).

[0262] The doses that may be presented in the graph include, but are not limited to, basal doses; fixed doses for breakfast, lunch, and dinner; and doses with adjustment factors for breakfast, lunch, and dinner.

[0263] In some embodiments, a report 1350 detailing regimen adherence may also be displayed, as shown in Figure 9B. The report 1350 may be accessible to the patient, HCP, or other stakeholders. The adherence report 1350 may cover a period (e.g., one week, two weeks, or a learning period) and may include a table 1352 listing different analyses for each of the different types of doses 1351. The different types of doses 1351 may include basal doses, breakfast doses, lunch doses, dinner doses, and corrected (e.g., postprandial corrected) doses. For each of the different types of doses, table 1352 may list the dose count 1354, dose forgettings 1356, requested guidance 1358, the delta mean 1360 of the doses taken and guidance doses, and the delta IQR 1362 of the doses taken and guidance doses.

[0264] Definitions and / or descriptions of each of these categories 1364 may be listed below Table 1352. Dosage count 1354 may be a simple count of dose types in the relevant period (e.g., the most recent week). Dosage forgetfulness 1356 may be reported as a percentage and can be calculated by (7 - basal count dose) / 7 for basal doses in the most recent week. Dosage forgetfulness 1356 can be calculated as (number of dosage forgetfulness detections without a relevant dose) / (total number of dosage forgetfulness detections). Postprandial correction dosage forgetfulness 1356 can be calculated as (postprandial correction alerts without a relevant dose) / (total postprandial correction alerts). Requested guidance 1358 may be reported in units of insulin and can be calculated as (dose associated with guidance indication) / (total dose). Delta mean (intake guidance) 1360 may be reported in units of insulin and can be calculated by calculating the mean difference for doses associated with guidance. The Delta IQR (Intake Guidance) 1362 can be calculated by calculating the IQR for the dosage associated with the guidance. The Delta IQR has a standard meaning to those skilled in the art and refers to the difference between the third quartile and the first quartile.

[0265] Additional indicators 1366 and related statistics can also be listed in Report 1350, which include late dose frequency, postprandial frequency, automated dose classification versus manual dose classification, non-meal and non-postprandial adjustment frequency, snack dose frequency, dose percentage taken per guidance (which may be not only aggregated but also doses time-linked to guidance), and dose percentage taken per alert.

[0266] Figure 9C shows an exemplary plot 1370 relating to clustering analysis for determining meal intervals, which can be optionally displayed in HCP. Graph 1372 plots the cumulative counts of all doses within the learning period against the time period in which administration was performed (e.g., ascending time period). User-entered meal time range values ​​1374, along with user-entered meal time midpoints 1376, may be shown in plot 1370, for example, near the x-axis, if available. The rapidly increasing portion of the curve represents each meal time cluster. In alternative embodiments, the graph may be plotted as a histogram or pie chart of counts. A typical learned meal time 1378 may be indicated by a vertical line in plot 1370. The line segment 1378 has a length equal to the amount of insulin injected. If there are multiple injections within 15 minutes, the length will be equal to the sum of all injected amounts. This is necessary because patients may inject multiple times with a single meal, for example, if the insulin pen a patient is using dispenses less than the full amount needed to cover the required dose, and the patient refills the pen (or obtains a new full pen) to complete the required dose; or if some patients require a dose of insulin that exceeds the pen's injection capacity, thus requiring multiple injections; or if some patients experience pain from injecting a large amount of insulin in one injection, thus requiring multiple injections. The learned meal time range 1380 can be indicated by a horizontal line located at the intersection of the typical time and dose line.

[0267] Figure 9D shows an exemplary plot 1390 associated with meal dose clustering and dosage. Graph 1392 plots each dose within the learning period against the TOD at which the dose was administered. Each dose is indicated by a dot 1394 to show different doses at the same time. The bar thickness associated with the time can be changed in accordance with the number of doses to show when identical doses occurred at the same time. As seen in the inset window, the dots 1394 and bar thickness allow the viewer to distinguish between different doses taken at the same time of day. Doses taken in a short period of time on the same day, e.g., within about 15 minutes, may be added together (e.g., 7.5 + 1.5 = 9 units). Additional doses may be indicated by lines 1396 of a different color or type. Other means may be employed to illustrate this.

[0268] User interface controls can be provided to switch between displaying all data results and displaying only the results associated with a certain threshold, for example, pre-meal glucose below 150 mg / dL. Alternatively, user interface controls can be provided to allow the user to set the threshold.

[0269] Figure 9E shows an exemplary plot 1400 associated with the determination of the pre-meal correction coefficient. Graph 1402 plots the dots 1404 of each insulin dose for all initial meal doses during the learning period against the pre-meal glucose associated with each dose of a particular meal. The curves of each model fit performed in the learning analysis are overlaid with the associated learned parameters. Models may include a P1 zero-gradient model (uncorrected) 1406, a P2 piecewise linear function 1408, and a P3 nonlinear function 1410. The best-fit curve can be highlighted in some way, for example, with a thicker curve (comparing 1406 with 1408 and 1410). The DGA can provide four plots (one for each meal and one overall), as described above.

[0270] If the DGS fails to adequately learn the patient's medication regimen, instead of further enhancing dose guidance, the DGS may provide a means for sharing a report on the learning progress with the HCP. It should also be noted that this method can be used to share any type of report. A method for facilitating efficient access by the HCP to reports generated by the DGS100 while protecting the privacy of the patient's health data1420 is described in Figure 9F.

[0271] In step 1422, the session with the patient is authenticated by at least one processor of a portable display device. This authentication may be achieved when the patient logs into the DGA, or by other known authentication methods. Most commonly, this function is satisfied by the authentication function provided by the patient's smartphone.

[0272] In step 1424, at least one processor may determine whether, during the session, it has received input from the patient indicating a request to share the EMR with the HCP. The DGA may provide an optional feature of the user interface in which the user can indicate their desire to share the report. If it is determined that a request to share the EMR has been made, in step 1426, at least one processor may generate a report sharing identification code (ID). The ID may be an alphanumeric code, a numeric code, or other appropriate format. This code may be displayed by the DGA along with the URL of the remote report access server.

[0273] In step 1428, at least one processor may provide the ID, along with the data necessary for generating the report, to a remote report access server that controls access to the report. In step 1430, the HCP may launch a standard web browser on the PC and enter the URL displayed by the DGA as appropriate. The report access server can then display a screen in the browser that accepts the ID. The HCP can enter the ID, and the browser can send this ID to the server. If the ID matches the ID sent from the DGA and is received within a certain period of time, for example 20 minutes, since the ID was sent from the DGA, the server can send the report to the browser.

[0274] In another embodiment, method 1420 may also include a step of determining whether the DGS does not meet the criteria for agreement between the patient-entered regimen and the learned regimen. If the EMR determines that the agreement criteria are not met, the method may include a step of providing the patient with the option of providing a learning results report to the HCP. In one embodiment, the step of generating a report access ID may be conditional on the determination that the EMR does not meet the agreement criteria.

[0275] In another embodiment, the method may include a remote server that creates a user interface, such as a web page, at least partially addressed by ID, for displaying the report. The report may include, for example, as shown in Figures 9A-1 to 9A-2, a determination of the drug administration parameters of medications administered to the patient over a defined period of time, and a measure of the agreement between this determination and the drug administration information provided by the patient.

[0276] In alternative embodiments, a DGA may be included that provides a user interface means for directly generating a report for the patient to view on the DGA, which may be shown to the HCP. It should be understood that each of these steps is optional and not necessarily required in this method.

[0277] Opportunities for titration after learning During the learning period, insulin doses may not be fully characterized as they are during the guidance period. Therefore, initial titrations using data from the learning period may differ from those during the guidance period. Specifically, DGS100 may titrate only fixed doses during the learning period, while CF may not. Furthermore, titration may only be performed if a low pattern is detected by the fixed-dose titration algorithm. A detected low pattern may trigger a dose reduction of insulin administration prior to that period, as described in the fixed-dose titration section above. The GPA algorithm can be used to evaluate fixed meal periods. Bedtime can be defined as 6 hours after the fixed dinner administration time or approximately 6 hours before the fixed breakfast time, whichever comes first.

[0278] User feedback during the learning period Next, exemplary embodiments of how to obtain user feedback during or after the DGA's learning period are described. During the initial learning phase with the DGA, the user may be prompted for feedback. User feedback can indicate to the user that the system is progressing. The DGA may prompt the user for feedback (e.g., input or confirmation) on any aspect of dose guidance, such as the dose administered, the history of the analytes, the patient's behavior or activities, the overall medication strategy, the type of specific dose, and confirmation that the type of dose or strategy determined by the DGA (e.g., learned by the system) is correct.

[0279] During the learning period (or thereafter), the DGA may output prompts or other displays to UID200 requesting user feedback. This feedback may relate to a medication strategy, for example, a strategy regarding insulin action type (e.g., long-acting and / or short-acting or rapid-acting). If the feedback (or other decision) indicates the use of a long-acting strategy, the DGA may monitor the patient's basal dosing pattern during a first time period, e.g., the first three days, classifying each dose or dosing pattern into single-dose or divided-dose types and / or characterizing doses by time period (e.g., a single dose in the morning, a single dose in the evening, or divided doses (e.g., both morning and evening)). The DGA may also determine trends in dosage (e.g., median, mean) and associated dose variability values. From this information, the DGA can formulate expected basal doses. After the first time period, if the actual dose administered (e.g., automatically registered by MDD152 or entered by the user) differs from expectations, the user may be prompted for feedback.

[0280] According to one embodiment, the DGA can prompt the user in many different situations. For example, the DGA can be configured to detect missed doses, such as when the user fails to administer a basal or bolus dose during the time period in which a previous basal or bolus dose would have been administered. If a missed dose is detected, the DGA can be configured to prompt the user for input regarding whether a basal dose was administered during that time period. According to some embodiments, the DGA can be configured to detect differences in administration timing. For example, the DGA can be configured to detect when the user administers a basal dose at a different time than when a previous basal dose was administered (for example, when a basal dose usually administered in the morning is administered in the evening). If such a difference in administration timing is detected, the DGA can be configured to prompt the user for input regarding whether the basal dose was administered during a different time period. In another embodiment, the DGA can also be configured to detect when an extra dose has been administered. For example, the DGA can be configured to detect changes in the number of basal doses administered per day. In yet another embodiment, the DGA can be configured to detect whether the medication strategy on day 1 (e.g., one basal dose administered) differs from the medication strategy on day 2 (e.g., two basal doses administered). If a different medication strategy is detected, the DGA can be configured to prompt the user for input regarding whether the user has adopted the medication strategy used on day 2 as the new medication strategy. In yet another embodiment, the DGA can also be configured to detect whether different dosages have been administered. For example, the DGA can be configured to detect whether a first dose administered at a certain time is different (smaller or larger) from a preceding dose administered at a certain time on the previous day. If a different dosage is detected, the DGA can be configured to prompt the user for input regarding whether the user has changed the dosage.

[0281] The user's responses to these prompts allow the DGA to either confirm that it has identified the correct pattern (for example, the user confirms that they have forgotten to take their morning basal dose but usually do) or to provide the user with an opportunity to correct the pattern (for example, the user informs the DGA that they will adjust their basal dose based on glucose before taking it).

[0282] In rapid-acting insulin administration strategies, in addition to the prompts mentioned above, DGA may include prompts regarding dose classification. Dose classifications may include, but are not limited to, bolus, corrected, divided dose, bolus + corrected, and bolus + carbohydrate count + corrected.

[0283] Regarding the administration of rapid-acting insulin, the DGA can prompt the user in various situations. The DGA can be configured to detect whether a dose unrelated to a meal has been administered. For example, the DGA can be configured to determine whether a dose was taken during a time period in which no meal is identified or detected. If the DGA detects dose intake and no meal is detected within the time period of administration (e.g., within about 1 hour of administration), the DGA can prompt the user for input regarding the reason for the dose being administered (e.g., because a meal was eaten, to lower glucose, or because the administration of the previous meal has finished). The DGA can also be configured to detect whether a meal dose does not match a previous meal dose related to the same meal type. For example, the DGA can be configured to determine whether a bolus dose administered at a certain time in relation to a first meal type is not the same as a previous bolus dose administered at the same time the previous day in relation to the first meal type. If such a difference in bolus doses is detected, the DGA can be configured to determine the reason for the different doses. For example, DGA can be configured to determine the difference in pre-meal glucose levels associated with a bolus dose and a previous bolus dose, and to determine whether the detected difference is a correction. DGA can also prompt the user for input regarding the reason for the difference in bolus doses (e.g., a small / large amount of food eaten and / or correcting for hyperglycemia and / or other factors).

[0284] In addition to enabling DGA to determine what type of rapid-acting dose is being taken throughout the day, it can also facilitate the timing of dose prediction. After a learning period in which no prompts are provided, DGA can provide these prompts to the user when the dose differs from the expected dose in order to improve DGA's model of the user's medication strategy.

[0285] In both long-acting and fast-acting cases, DGA can aim to minimize the number of prompts as time passes and the user responds. The focus can be on frequent prompting in the initial stages, gradually reducing it once repetitive patterns are observed.

[0286] Glucose pattern analysis and dietary bolus titration for MDI insulin therapy Next, an exemplary embodiment of a method for determining meal bolus titration is described. The system can learn (or configure to match) a patient's current medication strategy and provide titration guidance for frequent daily injectable (MDI) therapy. For patients using fixed meal medication, fixed doses (e.g., breakfast, lunch, dinner, snacks, etc.) can be titrated. For patients performing carbohydrate counting, the carbohydrate ratio can be adjusted for the same meal or different time periods. For patients performing empirical administration, dose titration can be performed for each meal. Titration guidance by DGA can provide recommendations for changing doses or carbohydrate ratios in a particular direction. The amount of change can be changed by appropriate percentages, for example, 5%, 10%, 15%. Dose guidance may also include initiating meal administration. For example, if a patient is using basal + 1 (e.g., lunch medication regimen) and breakfast shows a high-value pattern, DGA can provide recommendations for administering RA insulin at breakfast.

[0287] DGA may require defining administration categories such as time-of-day (TOD) periods, meal types (e.g., breakfast), and meal composition (e.g., cereal with milk). For example, an administration category could be a time-of-day period defined by the duration associated with meal insulin administration during that period. In a further example, a post-breakfast time-of-day period could be defined as beginning when a meal insulin dose is taken during a defined time period, e.g., 5 a.m. to 10 a.m., and ending after a defined post-meal period (e.g., 6 hours later) or when the next meal insulin dose is taken, whichever comes first. One or more indicators may be needed to define whether a post-meal glucose response is nominal or requires correction, or to rank post-meal glucose patterns favorably or unfavorably compared to others. The likelihood of low glucose (LLG) index and median glucose can be used to quantify the degree of hypoglycemia risk and hyperglycemia risk, respectively.

[0288] U.S. Patent Application Publication No. 2018 / 0188400 ('400), whose entire contents are incorporated herein by reference for all purposes, describes an example of an embodiment for deriving and determining risk indicators available in glucose pattern analysis (GPA) of an embodiment of DGA. This embodiment, in particular, uses central trend (e.g., mean, median, etc.) and variation data from a multi-day period to determine a risk indicator corresponding to the degree of hypoglycemia risk ("hypo risk"). This embodiment is summarized herein, and a more comprehensive description of embodiments and variations thereof can be obtained by referring to '400.

[0289] An alternative to the embodiment described in the '400 publication is described in U.S. Patent Application Publication No. 2014 / 0350369, which is hereby incorporated by reference in its entirety for all purposes. For example, instead of using the median and variance values, this method can employ any two statistical measures that define the distribution of the data. As described in the '369 publication, the statistical measures can be based on a glucose target range (e.g., G LOW = 70 mg / dL and G HIGH = 140 mg / dL). Common measurements related to the target range are the time in range (TIR), the time above target (t AT ), and the time below target (t BT ). When the glucose data is modeled as a distribution (e.g., gamma distribution), for defined thresholds G LOW and G HIGH , t AT and t BT can be calculated. For the thresholds, the algorithm can also define t BT_HYPO , and when it exceeds t BT , it can be determined that the patient has a high risk of hypoglycemia. For example, a high risk of hypoglycemia can be defined as when t LOW exceeds 5% when G BT = 70 mg / dL. Similarly, an indicator of t AT_HYPER can be defined, and when it exceeds t AT , it can be determined that the patient has a high risk of hyperglycemia. The degree of the risk of hypoglycemia and hyperglycemia can be adjusted by adjusting either G LOW or t BT_HYPO , G HIGH or t AT_HYPER . A control grid can be defined using any two of the three measures of TIR, t BT and t AT . Using these options (and other options), the risk indicators of the embodiments of the DGA described herein can be determined.

[0290] Embodiments of DGA described herein can operate based on a quantitative assessment of the user's analyte data during a TOD period. This quantitative assessment can be performed in various ways. For example, embodiments described herein can assess analyte data over several days to determine one or more indicators that describe the relevant risks that the analyte data exhibits for a given TOD. Using these indicators, the analyte data during a TOD period can be classified into one of several patterns. For example, these patterns may indicate glucose behavior or trends that are common to or generalized for that TOD. Embodiments of DGA can utilize any number of two or more patterns. For ease of reference, these patterns are referred to as glucose pattern types herein, and embodiments described herein refer to embodiments that utilize three glucose pattern types (e.g., low-value pattern, high / low pattern, and high-value pattern), but other embodiments may utilize only two types or three or more types, and these types may differ from those described herein.

[0291] Taking fixed meal doses as an example, once the DGA has learned the medication strategy and the amount of dose or carbohydrate ratio, it can begin titration evaluation, which can be categorized into four titration categories: “nighttime,” “after breakfast,” “after lunch,” and “after dinner.” For each of these categories, the DGA can map the two indicators mentioned above (LLG and median glucose) to four logical “pattern” variables using the GPA method described later. Figure 8A shows the operation of an exemplary method 400 by the DGA for evaluating meal bolus titration for frequent injectable (MDI) medication therapy. Method 400 may further include a step in 402 in which the DGA determines at least one TOD analyte pattern type by running a glucose pattern analysis (GPA) algorithm that takes time-correlated analyte data transmitted from a sensor-controlled device worn by the patient during the analysis period as input. Method 400 may further include the step in 406 of storing recommended action guidelines in computer memory for output to at least one of UID200 or MDD152, which administers the drug to the patient, by the DGA. UID200 can use the recommended action guidelines to control its user interface, for example, by displaying a human-readable representation of the guidelines on a display or by creating an audio output that represents the guidelines in human language. MDD152 can use the guidelines to adjust or maintain the next relevant dose administration. Further details of Method 400 are described below.

[0292] Figure 8B is a flowchart illustrating an exemplary embodiment of GPA method 410, which can be implemented as the GPA algorithm referenced in 402. Method 410 can be performed for an entire day (e.g., a 24-hour period), or for specific TOD periods that may be time blocks (e.g., three 8-hour periods), or for parts of the day defined by user activities (e.g., meals, exercise, sleep, etc.). In many embodiments, multiple TOD periods may correspond to meals (e.g., after breakfast, after lunch, after dinner) and sleep (e.g., nighttime). These TOD periods may correspond to fixed times of the day when activities would normally take place (e.g., from 5 a.m. after breakfast to 10 a.m.), and such time blocks may be conditional on the meals or activities actually taking place, as can be set by the user or determined by automatic detection of meals or activities, or by user instructions indicating these (e.g., using UID 200).

[0293] The DGA can independently perform method 410 for each TOD period to obtain individual pattern assessments for that period. In 412, the DGA can determine the central trend and variation values ​​from the user's analyte data for a particular TOD period. The user's analyte data may be available from the user's own records or from the records of the user's healthcare professional, or the user's analyte data may be collected by, for example, DGS100. The analyte data preferably spans a period of multiple days (e.g., 2 days, 2 weeks, 1 month, etc.) so that there is enough data within the TOD period to make a reliable judgment. In other embodiments, this method can be performed in real time for limited data. The DGA can use any type of central trend index that correlates with the central trend of the data, including but not limited to the median or mean. Additionally, any variability index can be used, including but not limited to variability ranges spanning the entire dataset (e.g., from minimum to maximum), variability ranges spanning the majority of the data but not the entire dataset to minimize the significance of outliers (e.g., from the 90th percentile to the 10th percentile, or from the 75th percentile to the 25th percentile), or variability ranges targeting specific asymmetric ranges (e.g., low-range variability, which can span, for example, from the central trend value to lower values ​​of the data, such as the 25th percentile, the 10th percentile, or the minimum value). The choice of indexes to represent central trend and variability can vary depending on the embodiment.

[0294] In 414, DGA can assess hypoglycemia risk ("hypo risk") indicators based on central trend and variability values. One such methodology for determining hypoglycemia risk is described with respect to Figure 8C, which illustrates an exemplary embodiment of a framework for determining hypoglycemia risk and other indicators. While Figure 8C is intended to convey the framework to the reader, this framework can be implemented electronically in a number of different ways, including software algorithms (e.g., formulas, sets of if-else statements, etc.), lookup tables, firmware, and combinations thereof.

[0295] Figure 8C is a graph of central trend pair variance (e.g., low-range variance) that can be used to hold or evaluate or identify a region or zone corresponding to a determined central trend and variance data pair for a particular TOD. Any number of two or more zones can be used. In this embodiment, the data pair may correspond to one of the target zone 425 or three hypo-risk zones: low 426, medium 428, or high 430. A first hypo-risk function (e.g., a curve or linear boundary), referred to as the medium-risk function 422, distinguishes between low 426 and medium 428. A second hypo-risk function, referred to as the high-risk function 424, distinguishes between medium 428 and high 430. The data pair of central trend and variance can be evaluated against or compared to a zone to determine a hypo-risk index for the corresponding TOD period.

[0296] The hypoglycemia risk functions 422 and 424 can be explicitly implemented in the DGA as mathematical functions (e.g., polynomials), or implicitly implemented by defining each zone by the included pairs, using lookup tables, sets of if-else statements, threshold comparisons, etc. The hypoglycemia risk functions 422 and 424 can be preloaded into the DGA, downloaded from a trusted computer system 480, or configured by another party such as an HCP. Once implemented in the DGA, the hypoglycemia risk functions 422 and 424 can be treated as fixed or can be adjusted by the user or HCP. An exemplary methodology for determining the hypoglycemia risk functions is described in Publication No. 400.

[0297] In 416, DGA can assess a hyperglycemia risk index ("hyper risk") based on the central trend value. In this embodiment, hyperglycemia risk can be assessed by comparing the central trend value for a particular TOD period to a target or threshold 432 of the central trend. The magnitude and / or sign of the difference between the central trend value and the target 432 can identify the amount of hyperglycemia risk. For example, if the central trend value is less than the target 432 (e.g., a negative value), a low hyperglycemia risk may exist. A moderate hyperglycemia risk may exist if the central trend value is below the threshold (e.g., 5 percent, 10 percent, etc.) but above the target 432 (e.g., a positive value). A high hyperglycemia risk may exist if the central trend value is greater than the threshold and above the target 432. The use of three separate groupings for hyperglycemia risk (e.g., low, medium, high) is an example, and any number of two or more groupings can be used.

[0298] In other embodiments, DGA can assess the hyperglycemia risk index at 416 before assessing the hypoglycemia risk at 414. Alternatively, in another embodiment, the assessment of hypoglycemia risk at 414 and the assessment of hyperglycemia risk at 416 can be performed simultaneously and in parallel.

[0299] Other indicators, such as volatility risk, can also be evaluated. For example, volatility values ​​smaller than the first volatility threshold of 434 may indicate low volatility risk, volatility values ​​greater than the first volatility threshold of 434 but smaller than the second volatility threshold of 436 may indicate moderate volatility risk, and volatility values ​​greater than the second volatility threshold of 436 may indicate high volatility risk. Here again, the use of three separate groupings for volatility risk is just one example. DGA can use any number of two or more groupings.

[0300] In step 418, DGA can determine the pattern type of the TOD period based on one or more evaluated risk indicators. In one exemplary embodiment, pattern determination can be evaluated using hypoglycemia risk indicators and hyperglycemia risk indicators. If the hypoglycemia risk indicator is high, the pattern can be set as a low pattern (or "low" pattern). Alternatively, if the hypoglycemia risk is moderate and the hyperglycemia risk is high or moderate, the pattern can be set as a high / low (or moderate) pattern (or "low and some high" or "high with some low"). Alternatively, if the hyperglycemia risk is high or moderate and the hypoglycemia risk is low, the pattern can be set as a high pattern (or "high" pattern). If both the hyperglycemia risk and hypoglycemia risk are low, the identified pattern can be "No Problem" (e.g., an "OK" message is displayed and output) (or "No Pattern").

[0301] Thus, method 410 is an example of how the DGA outputs one of several pattern types for each TOD period. The number of pattern types themselves may differ from those described in this embodiment (e.g., low, high / low, high). Once the pattern types for the TOD periods are determined, the DGA can store the indicators of the pattern types in memory for use in determining the titration recommendations. Referring again to Figure 8A, in 404, after completing the GPA for each relevant TOD period, the DGA can proceed to determine the titration recommendations.

[0302] The recommended approach may branch depending on other factors such as pattern type (e.g., low, high / low, high), TOD period, medication strategy, adherence to the strategy (e.g., whether the dose is insufficient), and whether sufficient data is available to perform the evaluation. The DGA will not recommend titration until sufficient data is available for the corresponding TOD period. For example, if the amount of available data falls below a threshold, such as if the number of other days with more than the minimum portion of available data (e.g., 90%) is below the threshold (e.g., 5), the DGA may skip the evaluation and generate an error message.

[0303] Figures 8D to 8H show examples of branching recommendation algorithms or methods for determining dose titration recommendations based on the input information described above. Other branches may also be useful. Figure 8D shows recommendation method branch 440 for TOD where sufficient data is available and there is a possible cause of a low pattern type including one or more of the basal dose, meal dose, pre-meal corrected dose, or post-meal dose that is higher than the optimal amount. In 442, DGA assesses whether the pattern type for the nocturnal TOD period is low. If the pattern is low, in 444, DGA generates a recommendation to reduce all relevant doses by the same amount, e.g., 10%, including at least the basal dose and optionally one or more of the meal dose, pre-meal corrected dose, or post-meal dose. The titration recommendation rule for low patterns may include a step in 444 to generate a recommendation to reduce the dose or basal rate of long-acting insulin for the nocturnal TOD period. In 446, if other TOD periods are low patterns, DGA may generate a recommendation in 448 to reduce the fixed meal dose only for the relevant TOD period.

[0304] In this embodiment, if there is at least one low-value pattern, no titration guidance is provided for TOD periods with high-value patterns. The idea here is to prioritize the prevention of hypoglycemia and to increase the dose only when the risk of hypoglycemia is low throughout all TOD periods. Also, in some cases, if a TOD period has a high-value pattern, this may be due to a low-value pattern in the previous TOD period, and the patient overeating to compensate for this, so addressing the low-value pattern itself may help address the subsequent high-value pattern. In 449, if the pattern is not high, process 440 either waits or terminates without generating a recommendation, or proceeds to high-value pattern assessment 450.

[0305] Therefore, in the case of high / low patterns, DGA does not generate titration guidance. If there is no TOD period for which titration guidance can be given and sufficient data is available for all time periods, DGA can provide the patient with a message indicating that glucose fluctuations need to be addressed before further titration guidance can be given. DGA can also provide the patient's HCP with a report to consider alternative drug therapies or treatments that can address glucose fluctuations.

[0306] Figure 8E shows how DGA works to generate titration recommendations for a high-value pattern when there are no low-value TOD periods. In 452, if the nocturnal period has a high-value pattern and there are no other periods with a moderate risk of hypoglycemia, DGA can increase the long-acting insulin dose or basal rate recommendation in 454. In 456, if the nocturnal period has a high-value pattern and there is at least one other non-dinner period with a moderate risk of hypoglycemia, DGA can decrease the meal insulin dose associated with any period with a moderate risk of hypoglycemia in 458. In 460, if the nocturnal TOD period does not have a moderate risk of hypoglycemia and does not have a high-value pattern, DGA can generate a recommendation to increase the meal insulin dose associated with the first TOD period with a high-value pattern in 462. In 464, if the nocturnal period has a moderate risk of hypoglycemia and the only postprandial period with a high-value pattern is dinner, DGA can generate a recommendation to increase the long-acting insulin dose or basal rate in 466. If the nocturnal period carries a moderate risk of hypoglycemia, and the post-dinner period does not, then in 462, DGA can generate a recommendation to increase the meal insulin dose associated with the first TOD period with a high-value pattern.

[0307] In an alternative embodiment, pre-meal glucose may be higher or lower than the target glucose (e.g., 120 mg / dL). The glucose data for each meal contributing to the calculation of hypoglycemia and hyperglycemia risk indicators can be modified to compensate for the effects of previous meals or conditions that affect glucose not attributable to the current meal. DGA can modify these data by subtracting an offset so that the resulting starting glucose is at the target level. Alternatively, DGA can modify these data using a “trigonometric” function, which subtracts the difference between the meal start glucose and the target glucose for the meal start time, but this modification is reduced linearly over a defined period (e.g., 3 hours) or by another decay function.

[0308] Alternatively, this function can itself be a function of the glucose level or glucose trend at the start of a meal, and / or when the previous meal dose was consumed.

[0309] According to another embodiment, the algorithm for generating dietary bolus titration recommendations may become more complex when considering additional aspects such as forgotten meal administration, forgotten basal administration, postprandial and preprandial adjustments. The algorithm for providing appropriate recommendations when these factors are present may need to exclude some data while still satisfying a data sufficiency threshold after excluding data to provide guidance.

[0310] For example, referring to Figure 8F, if a high-value pattern is detected at 461 and there are several days when meal doses are forgotten, the days with forgotten meal doses are excluded, and the GPA analysis 410 is repeated at 463. Subsequently, if a high-value pattern is detected at 465, the dose can be increased at 467 based on patterns identified in other TODs, or further input or return can be awaited at 469. Alternatively, the system can evaluate only the high-value pattern using data excluding days when meal doses were forgotten. Algorithm 470 with this branching pattern is shown in Figure 8F. If the system detects a low-value pattern at 473, the low-value pattern algorithm 472, described in the next paragraph, can be executed. If the system does not detect a high-value or low-value pattern, it may return to block 469 for further input or return.

[0311] In case 472, if a meal is missed, DGA can detect a low-value pattern during the TOD period, and if a meal is missed on any of the days during this TOD, DGA can generate a recommendation to reduce the dose. Recommendations may include, for example, reducing the fixed dose or corrected dose portion.

[0312] Regarding basal dose forgetting, if DGA detects a low pattern of 473 in nocturnal TOD, basal dose forgetting should not affect the dose titration logic. Similarly, if a low pattern is detected in non-nocturnal TOD, basal dose forgetting should not affect the dose titration logic.

[0313] If DGA detects a high pattern 461 in TOD using data that includes at least one day (or TOD) in which basal administration was forgotten, the pattern analysis 410 can be repeated by excluding data 463 from any day (or TOD) in which basal administration was forgotten. Subsequent actions may depend on the specific TOD in which the high pattern was detected. For example, if DGA detects a high pattern in nighttime TODs using data that includes at least one day in which basal administration was forgotten, the pattern analysis can be repeated by excluding data from any day in which basal administration was forgotten. If a high pattern is detected in nighttime TODs when basal administration was forgotten days are excluded, the results of nighttime TODs can be used as a guideline for adjusting basal administration, and the basal dose can be increased. If a high pattern is detected in non-nighttime TODs, the pattern analysis can be repeated by excluding days in which basal administration was forgotten. If a high pattern is detected when basal administration was forgotten days are excluded, the meal doses associated with TODs having a high pattern can be analyzed and titrated as described herein. In either case, the logical flow 470 is as shown in Figure 8F.

[0314] Figure 8G shows an example of a logical flow 474 for formulating recommendations with postprandial adjustments. After GPA 410, if DGA detects a low-value pattern 479 of TOD over several days including postprandial adjustments, the following analysis can be used to perform adjustment or titration of the meal dose. In 475, if DGA first detects a low-value pattern 479, it can first test in 487 whether sufficient data is available and exclude data from days without postprandial adjustments. If sufficient data is not available, DGA may execute an error recovery routine 489, for example, by displaying an error message. If sufficient data is available, DGA may repeat the pattern analysis 410. Subsequently, if DGA detects a low-value pattern, it can reduce the postprandial adjustment dose (i.e., increase the adjustment coefficient) in 476, subject to the pattern analysis results for other TODs. If DGA does not subsequently detect a low-value pattern, it can reduce the meal dose in 477.

[0315] In all embodiments described herein, a unidirectional correction of the correction dose (e.g., titration) can be achieved by a unidirectional correction of the correction coefficient. These two parameters have an inverse relationship, with an increase in the correction dose being achieved by a decrease in the correction coefficient, and a decrease in the correction dose being achieved by an increase in the correction coefficient. Thus, in all embodiments described herein, the DGA can recommend or implement correction by either a correction of the correction coefficient or a correction of the correction dose. Thus, where the correction of the correction coefficient or titration is described herein, the embodiment can be configured to achieve the same effect by an inverse correction of the correction dose, and conversely, where the correction of the correction dose or titration is described herein, the embodiment can be configured to achieve the same effect by an inverse correction of the correction coefficient. Given this compatibility, both options are available in all embodiments described herein, although not all embodiments are described solely for the sake of clarity.

[0316] Additionally, or alternatively, starting from the original dataset 491, at 478, the DGA may exclude days on which meal administration was missed. After finding sufficient data at 487, if pattern analysis 410 of these data, excluding days on which postprandial correction was performed at 490, does not show a low-value pattern, the DGA may recommend reducing the postprandial correction dose at 476. Otherwise, the DGA may implement logic 510 of Figure 10B of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, which is incorporated herein by reference in its entirety for all purposes, and as a result may recommend reducing either the mealtime insulin or preprandial correction portion of the dose guidance. If the DGA does not detect low values ​​at 479 and does not detect a high-glucose pattern at 492, it may wait for further input or return at 469. If the DGA detects a high-value pattern at 492, process 480 may be performed at block 471 (Figure 8H).

[0317] Referring to Figure 8H, if DGA detects a high-value pattern 493 for TODs that include several days with postprandial adjustments, the following procedure 480 may be performed to formulate recommendations for adjustment and meal dose titration. In 481, DGA may repeat pattern analysis 410, including data for days with missed doses and days with postprandial adjustments. Subsequently, if DGA detects a high-value pattern in 494, in 482, the postprandial adjustment dose may be increased (i.e., the adjustment coefficient reduced), subject to the pattern analysis results for other TODs. If no high-value pattern is detected in 494, a low-value pattern may be checked in 495, and if a low-value pattern is detected, the process may return to 474 in Figure 8G, or otherwise wait for further input in 469. Although not shown in Figure 8H, after excluding any data from GPA 410, before running GPA, DGA may test for data sufficiency and run an error recovery routine if the available data is insufficient.

[0318] Alternatively, or additionally, if pattern analysis of the data excluding days when meal administration was forgotten, starting from the original dataset at 493, shows a high-value pattern along either branch 2.1 or 2.2, the DGA can continue step 480 as follows: In the case of branch 2.1, if pattern analysis 410 of the data for days when postprandial correction was excluded (i.e., bolus administration only) at 484 does not show a high-value pattern at 497, the DGA can generate a recommendation to increase the postprandial correction dose at 482, subject to pattern analysis of other TODs. Otherwise, the DGA can generate a recommendation to increase either the mealtime insulin or the preprandial correction portion, according to step 550 in Figure 10C of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, which was previously incorporated by reference in its entirety.

[0319] In branch 2.2, if the pattern analysis of data only for days with postprandial correction in 485 does not show a high-value pattern in 496, DGA may increase either the mealtime insulin or the preprandial correction portion according to step 550 in Figure 10C of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, which was previously referenced in its entirety. If no high-value pattern is detected in 496, DGA may return to block 484.

[0320] If the recommendations for correction factor titration from different TODs are contradictory, and the patient is currently using the same correction factor for all TODs, the DGA may increase the correction factor. Procedure 480 suggests that if all three components—meal dose, pre-meal adjustment, and post-meal adjustment—are not optimal, the meal dose may be increased first. The pre-meal adjustment can be increased by titration after the meal dose has been titrated. The post-meal adjustment can be increased by titration after the meal dose and pre-meal adjustment have been completed.

[0321] During subsequent analysis, TODs for which DGA previously generated a recommendation to "increase the correction factor" using the method described above will now be recommended to "keep the correction factor unchanged." Conversely, if a TOD for which DGA previously generated a recommendation to "decrease the correction factor" using the method described above is now recommended to "decrease the correction factor," it is highly likely that different TODs are being optimized using different correction factors.

[0322] Figures 8D to 8H show various aspects of the recommended algorithm 404 for use in method 400, but it should be understood that these are illustrative examples. Various other algorithms may also be suitable.

[0323] Guidance period Algorithm description During the guidance period, the DGS100 can (1) provide users with recommended insulin doses for meals and postprandial adjustments, and (2) titrate the dose settings initially learned from the learning period to improve blood glucose control.

[0324] Before starting the guidance period, it may be necessary to initialize the insulin dose settings. Initialization can be achieved by manually entering the initialized values ​​if learning was successful, or if learning was unsuccessful. Once the insulin dose settings are initialized, the user can receive insulin dose advice in several ways. The user can request a specific meal dose through DGA. Alternatively, DGS100 can detect and notify the user of meal dose forgetting events. If the user confirms a meal dose forgetting event, DGS100 can provide a "late meal dose" recommendation, taking into account the fact that insulin is being administered after the meal instead of at or before the prescribed dose at the start of the meal. Alternatively, DGS100 can detect and notify the user if glucose levels are too high between meals. If the user confirms that glucose levels are too high between meals, DGS100 can suggest a "postprandial correction dose" to bring the user's glucose to the target value or target range before the next meal.

[0325] The frequency at which the subroutines for dosage suggestions are processed may vary. Those subroutines associated with meal administration forgetting and postprandial hyperglucose detection may be executed continuously, while those associated with dose calculation may be executed only when requested by the user. The implementation of when meal time or postprandial dose suggestions may be invoked can be managed by the state transition diagram shown in Figure 10. Periodically, DGS100 can titrate fixed doses and correction factor values ​​to improve the user's blood glucose levels. These titration algorithms may be processed daily and may operate independently of the administration algorithms described above.

[0326] Customization settings In some embodiments, users can customize certain settings. For example, in some embodiments, users can indicate that certain data should be ignored by the algorithm. Or, if a user is ill or taking new medication, they may want to ignore a certain period of time. DGS may include a “vacation mode,” where a user can notify DGS that DGS should ignore certain past or future days or time periods where their behavior may or may have been irregular. For example, if a user wants to go on a cruise or exclude the Labor Day weekend, the user can select the period to ignore when DGS determines the titration.

[0327] In some embodiments, the user may also adjust previously selected dosages and time periods if there are changes in the user's routine. In some embodiments, if the user's usual mealtime changes, the user may enter the new time slot for that meal so that DGS can correctly record the meal dosage.

[0328] In some embodiments, the user may also input a dose adjustment as a result of a new medication. In some embodiments, this change may only be made if the user's HCP advises that the change be input. For example, the HCP may have prescribed a new oral diabetes medication, and the DGS needs to take that into consideration. For example, it may be necessary to input a 10% increase in a glucose-raising agent (e.g., a steroid) or a 10% decrease in a glucose-lowering agent (e.g., metformin). If such a change has occurred, the DGS will immediately adjust the instructed dose, and the DFA can continue to learn and adjust based on the user's glucose results even after the dose change.

[0329] State transition diagram of the administration algorithm DGS100 can recommend two types of rapid-acting insulin doses to users: meal dose and corrective dose. The meal dose is the dose the user requests to address the glucose elevation in response to a meal. The corrective dose is the dose recommended by DGS100 to correct for the high glucose levels between meal doses.

[0330] The state transition diagram determines when the functions that calculate these meal and corrective doses are called. Figure 10 shows the state transition diagram for managing insulin administration, also known as the Dose Guidance State Machine (DGSM) 2000.

[0331] The current state of dose guidance is determined by two factors: the time since dose delivery and the classification of recent historical doses. Insulin administration time is defined by the timestamp received by the system from the connected insulin pen. The classification depends on the rules used to define insulin doses in other parts of the application that describe real-time dose classification.

[0332] The meal dose status machine 2002 - the standby status, indicated as "Meal Dose Guidance Available" 2004, can be defined as a state where more than two hours have elapsed since the initial meal insulin administration. In Meal Dose Guidance Available 2004, the user can receive meal dose guidance by querying the DGS100 for dose recommendations. Once the insulin dose (including timestamp and amount) is received by the DGS100 and correctly classified as the initial meal dose, it transitions to "Post-Meal Dose Status" 2006, where the DGS100 remains for a certain period following the timestamp reported by the connected pen, e.g., two hours. In this "Post-Meal Dose Status" 2006, meal administration may not be recommended to the user. Additional doses received by the DGS100 while in Post-Meal Dose Status 2006 may also be considered meal doses and may not affect the time spent in this state.

[0333] Corrected Dose Status Machine 2010 - The criteria for triggering a corrected dose notification are defined elsewhere in this application. When any non-primed dose is received and classified as a corrected dose, a transition occurs from standby status (corrected-only dose guidance available) status 2014 to corrected-dosing status 2016, in which corrected-only dose guidance may not be available. The DGS100 may remain in this status for a certain period, e.g., 2 hours, from the timestamp reported by the connected pen. While in corrected-dosing status 2016, a corrected dose may not be recommended. If any non-primed insulin dose is recorded in corrected-dosing status 2016, the timer for that period, e.g., 2 hours, is restarted with the timestamp of the new dose. Priming before administering an insulin dose removes air from the needle and cartridge, ensuring that the correct amount of insulin is administered in its entirety. The primed dose is part of a safety test in which a small amount of insulin (e.g., 2 units) is sprayed into the air, which the user can observe to confirm that the insulin is coming out of the tip of the needle.

[0334] As shown in Figure 11, in exemplary method 2020, beginning in step 2022, DGS100 can receive or otherwise access the relevant insulin data (e.g., from MDD152). For example, DGS100 can check for the latest insulin delivery information by requesting delivery information from various sources, including but not limited to MDD152, MDD-related applications, or interfaces that store the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information.

[0335] In step 2024, DGS100 may classify the most recent dose administered as one of a specific dose category. For example, the most recent dose administered may be a meal dose, a corrective dose, or a prime dose. The most recent dose administered may be classified automatically by DGS100 or manually by the user, as described elsewhere in this application. If the most recent dose is a prime dose, no further action is taken. If the most recent dose administered is a meal dose or a corrective dose, in step 2026, DGS100 may enter a period during which no recommendations for additional drug doses are displayed. This period may be approximately 1 to 3 hours, or approximately 1.5 to 2.5 hours, or approximately 1.5 hours, or approximately 2 hours, or approximately 2.5 hours, or approximately 3 hours.

[0336] In step 2028, DGS100 can determine whether at least one additional drug dose was administered, i.e., whether it was administered after the time the previous “most recent drug dose” was administered. If at least one additional drug dose was administered, in step 2030, DGS100 can classify at least one additional drug dose as either a meal dose or a corrective dose. If at least one additional drug dose was a meal dose, in step 2032, no change is made to the period during which no additional drug dose recommendation may be displayed. However, if at least one additional drug dose was a corrective dose, in step 2034, the start of the period during which no additional drug dose recommendation may be displayed may be restarted and set to begin from the time at least one additional drug dose was administered.

[0337] Algorithm for calculating meal dosage Interaction with the user interface The DGS100 can display the dose guidance screen only if the dose guidance state machine (DGSM) and dose classification state machine DCSM are up-to-date. In exemplary method 2040, as shown in Figure 12, starting from step 2042, the DGS100 can receive or otherwise access the relevant insulin data (e.g., from MDD152). For example, the DGA can check for up-to-date insulin delivery information by requesting delivery information from different sources, including but not limited to MDD152, MDD-related applications, or interfaces that store up-to-date insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for up-to-date insulin delivery information. The DGA can record the dose when the DGA receives insulin dose information (e.g., dosage and administration time) electronically.

[0338] In step 2044, DGS100 can determine whether the received data includes the most recent drug dose data administered, i.e., whether the data is "up-to-date". To make the data up-to-date, DGSM requires that (a) the most recently recorded insulin dose is confirmed by the user, and / or (b) the reset time (t resetAll doses recorded after ) may need to be categorized automatically by the system or manually by the user. Verification may be performed manually; for example, the DGS100 can prompt the user to confirm that the last recorded dose was indeed the last dose. Alternatively, verification may be performed by the DGA querying the connected pen to confirm that it has the most up-to-date dose information. In some embodiments, the display device 120 may be configured to send data requests (e.g., queries) to the MDD152 via a wired or wireless link. In response to a received request, the MDD152 can send data to the display device 120. In some embodiments, for example, the display device 120 may be configured to communicate with the insulin pen according to the NFC (Near Field Communication) protocol. In other embodiments, the MDD152 can autonomously send data to a reader device via a wired or wireless link. The MDD152 may be configured to send data according to a schedule, based on a triggering event or condition, and / or when it enters the wireless range of the reader device. In some embodiments, the MDD152 can be configured to communicate with the display device 120 according to the Bluetooth or Bluetooth Low Energy networking protocol. However, those skilled in the art will recognize that other wireless communication protocols may be implemented (e.g., infrared, UHF, 802.11x, etc.). DCSM is t reset It may be reset at the reset time (t reset The TOD may be set to midnight, or it may be different depending on the timing parameters of the medication regimen. Please understand that each of these conditions is optional and not necessarily required.

[0339] In step 2046, if it is determined that DGS100 has data related to the most recent drug dose administered, a screen may be displayed. This screen may be the DGA home screen, including dose guidance recommendations. This screen may be displayed only for a predetermined period of time after the user has confirmed the most recent dose. The predetermined period of time may be at least approximately 10 minutes, or at least approximately 15 minutes, or at least approximately 20 minutes, or approximately 15 minutes. If a bolus record is recorded while either the home screen or a subordinate flow screen is active, the DGA may exit these screens and request new user confirmation (if the dose is not automatically classified) and subsequent flow and logic, so that the state machine is updated before the home screen is displayed again.

[0340] Dose Classification State Machine DCSM2000 has the information necessary to determine how to display meal carousel icons on the DGA home screen and how to function when an icon is selected. As seen in Figure 13, in exemplary method 2050, starting from step 2052, DGS100 can receive or otherwise access the relevant insulin data (e.g., from MDD152). For example, DGA can check for the latest insulin delivery information by requesting delivery information from different sources, including but not limited to MDD152, MDD-related applications, or interfaces that store the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information. Insulin dose data may relate to meal doses administered since the reset time.

[0341] In step 2054, the DGS100 can determine whether meal doses received since the reset time have been classified. Meal doses may be classified as relating to a specific meal, namely breakfast, lunch, or dinner. As described in relation to other methods and embodiments described herein, meal doses may be classified automatically by the DGS100 or manually by the user.

[0342] In step 2056, if it is determined that all meal doses received since the reset time have been classified, the DGS100 may display a screen containing multiple meal icons. These multiple meal icons may include icons for breakfast, lunch, and dinner. Each meal icon may have a first appearance or display associated with a first state and a second appearance or display associated with a second state. The first appearance or display may be associated with a first state where meal doses administered since the reset time have been classified as meal types corresponding to the breakfast, lunch, or dinner icon. The second appearance or display may be associated with a second state where meal doses administered since the reset time have not been classified as meal types corresponding to the breakfast, lunch, or dinner icon. For example, a meal icon (one each for breakfast, lunch, or dinner) may represent the associated meal record for that meal. reset If present thereafter, it can be "shaded" (first state). When selected, the shaded meal icon will display recorded dose information and, if available, may provide an option to display meal dose guidance calculations. Within this time period (t reset If no meal record exists related to (second state), the icon will not be shaded, and if selected, it can display the meal dosage calculation if available. reset After that point, all icons are reset and the shading is removed (second state), and the meal portion calculation can be displayed.

[0343] variable t resetThis can be determined by regimen parameters, dinner dose time range and breakfast dose time range. If the dinner dose time range is the last meal dose time range before midnight, t reset It is equal to midnight (this is the most likely case). Otherwise, t reset It is necessary to set this point midway between the end of the dinner dose time range and the start of the breakfast dose time range.

[0344] Calculation of meal portions When a meal icon is selected and meal dose calculation is displayed, the DGA can determine whether the standard meal dose calculation or the late dose calculation should be used to determine an appropriate dose recommendation. To determine which meal dose calculation is appropriate and to obtain the relevant calculation results, the DGA can determine whether the dose forgetting alert described elsewhere in this application is enabled.

[0345] The displayed dose guidance may be based on a later meal dose calculation if a meal dose forgetting alert has been issued. Additionally, insulin doses may not have been recorded in the past two hours. Please understand that each of these conditions is optional and not necessarily required. Note that DGA may verify that all alerts have been cleared before providing dose guidance to avoid race conditions. For example, a dose may not have been received and an alert may not have been cleared until the user is prompted to connect to or scan the MDD152. If the above conditions are not met, the dose displayed by DGA may be based on a normal meal dose calculation.

[0346] As shown in Figure 14, in the exemplary method 2060, starting from step 2062, the DGS 100 can receive or otherwise access the target insulin data (e.g., from MDD 152). For example, the DGS can check for the latest insulin delivery information by requesting delivery information from different sources, including but not limited to MDD 152, MDD-related applications, or interfaces that store the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information.

[0347] In step 2064, the DGA can determine whether insulin doses have been recorded over a period of time, for example, two hours. As seen in step 2066, if insulin doses have been recorded over the past two hours, the DGA does not need to display a dose recommendation.

[0348] If no insulin dose has been recorded in the past two hours, in step 2068, DGA can determine whether a dose forgetting alert is enabled. If a dose forgetting alert is enabled or has been issued, in step 2070, DGA can display a dose recommendation based on the calculation of the later meal dose. If a dose forgetting alert is not enabled or has not been issued, in step 2072, DGA can display a dose recommendation based on the calculation of the normal meal dose.

[0349] The calculation of normal meal doses (i.e., not late-stage meal doses) may be based on recent glucose levels (e.g., scanned glucose or streaming glucose) and may be expressed by the following logic and equations: If G prm ≤G T I guide =I fixed -IOB(Equation 1) Else I guide =I fixed -IOB+CorrectionAdj+TrendAdj(Equation 2) During the ceremony, G prm =Current scanned glucose value G T =Target glucose (regimen parameter lookup) I guide = Insulin dosage calculation result I fixed = Fixed insulin dose associated with the applicable breakfast, lunch, or dinner (regime parameter lookup) IOB = Insulin Onboard (calculated by the Insulin Onboard module) CF prm = Pre-meal correction factor (regimen parameter lookup) CorrectionAdj=(G prm -G T ) / CF prm TrendAdj = Trend adjustment value (based on table lookup by Kudva, et al.), C F =CF prm Based on.

[0350] Definition of glucose trend and CF prm TrendAdj is described in the following table from Kudva, et al., "Approach to Using Trend Arrows in the FreeStyle Libre Flash Glucose Monitoring Systems in Adults," J Endocr Soc. 2018;2(12):1320-1337, which is incorporated herein by reference in its entirety for all purposes.

[0351] I guideThe calculation can be rounded to the nearest unit of insulin according to standard rounding rules. If the calculated dose is negative, the displayed dose may be set to zero. In some embodiments, CorrectionAdj may be negative if pre-meal glucose is less than the target glucose. Note that in alternative embodiments, the IOB value is simply subtracted from the corrected and trend-adjusted values. For example, if there are no corrected and trend-adjusted values, Iguide = Ifixed. If there are adjustment values, IOB is subtracted from that value before being added to Ifixed. If the value obtained by subtracting IOB from the adjustment value is less than zero, then Iguide = Ifixed.

[0352] As an additional safety measure against the timeout described above (see, for example, step 2046 in Figure 12), the DGS may decide whether to calculate and present a recommended meal dose to the user based on a schedule, a triggering event or condition, and / or using glucose data received when it begins to communicate wirelessly with the reading device. In some embodiments, if the user's current glucose level is below a threshold when a meal dose calculation request is made, the DGS may not present the calculated meal dose suggestion to the user. Instead, the DGS may provide the user with an alert regarding the current glucose level and suggest raising the current glucose level before administering an insulin dose. In some embodiments, if the current glucose level is determined to be below a threshold, no dose guidance is displayed. This additional safety measure is to avoid hypoglycemia resulting from administering insulin at too low a glucose level. This threshold may be configurable based on the user's tolerance to hypoglycemia.

[0353] IOB (Insulin Onboard) is a measure of the amount of active insulin remaining in the user's bloodstream after injection. IOB is subtracted from the currently calculated dose to account for the active insulin from previous injections, in order to avoid insulin-induced hypoglycemia. It is calculated by multiplying the previous dose by a fraction representing the percentage of insulin remaining at that point in time after the injection. IOB is measured in insulin units (U).

[0354] [Table 2]

[0355] [Table 3A]

[0356] [Table 3B]

[0357] The calculation of meal dose may be triggered by a user request for meal dose in DGA. The input data stream may include requested scan glucose, requested scan glucose trend data, requested IOB, and late dose checks. Input parameters may be a fixed dose of the requested meal, mealtime CF, and target glucose. The output may be a suggestion for mealtime insulin administration.

[0358] Calculation of late-stage meal dosage Both the normal meal dose and the late meal dose can be calculated by selecting the meal dose within the DGA. The presentation of the late meal dose value may depend on the DGS100 for detecting meal administration forgetting events, as described elsewhere in this application.

[0359] The calculation of the late-stage meal dosage is G prmAnd TrendAdj may be the same as the calculation of normal meal doses described above, except that it may be calculated using continuous streaming glucose data, e.g., streaming glucose once per minute, rather than scanned glucose. For the calculation of late meal doses, G prm DGS100 may be the glucose value at the estimated time of meal initiation, and TrendAdj may be determined using the glucose trend at the estimated time of meal initiation. The estimated time of meal initiation may be calculated based on streaming data once per minute and may be the output from a meal administration forgetfulness detection algorithm described elsewhere in this application. Furthermore, in the case of calculating the later meal dose, IOB may be calculated according to the time the later meal dose was requested by the user, rather than the estimated time of meal initiation. If the calculated result for the later meal dose is smaller than the calculated result for the normal meal dose, DGS100 may display a lower value.

[0360] As an additional safety measure against the aforementioned timeout (see, for example, step 2046 in Figure 12), the DGS may decide whether to calculate and present a late meal dose recommendation to the user based on a schedule, a triggering event or condition, and / or using glucose data received when it begins to communicate wirelessly with the reading device. In some embodiments, if the user's glucose level at the scheduled meal time is below a threshold, the DGS may not present the calculated dose recommendation to the user. Instead, the DGS may provide the user with a warning to correct low glucose before administering insulin, instead of a dose recommendation. This additional safety measure is to avoid hypoglycemia caused by administering insulin at excessively low glucose levels. This threshold may be configurable based on the user's tolerance to hypoglycemia.

[0361] The calculation of the later meal dose may be triggered by a user responding to a meal administration forgetfulness notification, in which case the user selects a meal and scans for glucose. The input data stream may include the estimated meal initiation time, historical glucose at the estimated meal initiation time, and IOB at the requested later dose time. Input parameters may be a fixed dose for the requested meal forgetfulness, meal time CF, and target glucose. The output may be the proposed mealtime insulin dose at the estimated meal initiation time.

[0362] Dosage calculation explanation display When the user selects a displayed dosage, a pop-up screen may appear explaining how the dosage was calculated. The dosage is calculated as follows: fixed and I guide -I fixed The following two components may be displayed. Additionally, explanatory text may be displayed according to Table 4.

[0363] [Table 4]

[0364] Meal tracking and tagging In some embodiments, users may be able to tag meals that result in fluctuating glucose levels, for example, glucose levels above or below a target range. In some embodiments, based on tagged meals and relevant postprandial glucose data from multiple tagged meals, DGS may provide a novel proposed dose specific to the tagged meal.

[0365] In some embodiments, a user might eat the same pancake and egg breakfast at a local restaurant once a week. However, each time they eat the pancake and egg breakfast, their post-meal glucose levels fall outside the target range. In some embodiments, DGS can notify the user that the meal did not bring them back to the target value after the meal and prompt the user to tag or track this meal. The user can then enter a description tag for this meal as a favorite.

[0366] Subsequently, after a user has eaten or consumed the same meal, the user can tag that meal, and DGS can associate the meal dose administered in relation to the tagged meal and the postprandial glucose dataset associated with that meal with this tag. Once DGS has acquired a sufficient amount of data, DGS can determine a specific dose recommendation for this tagged meal based on at least the previously administered dose and the postprandial glucose dataset associated with the tagged meal. DGS may also consider the user's current glucose level at the time the dose recommendation request was made when determining the dose recommendation for a tagged meal. DGS may require data from at least three, or at least four, or at least five meals before calculating a recommended dose for a tagged pancake and egg meal. Tags may be foods and / or beverages consumed at breakfast, lunch, dinner, or as a snack. The dose recommendation may include multiple components, such as a base dose and a corrected dose. The base dose is the user's standard or typical meal dose, and the corrected dose may take into account the user's previous response to the same tagged meal. In some embodiments, the corrected dose may take into account the user's current glucose level at the time the dose recommendation request is made.

[0367] In an exemplary embodiment, as shown in Figure 23A, in Method 2400, in the first step 2402, the DGS may prompt the user to enter a tag associated with a meal type. For example, the meal type may be a description of the specific food and / or beverage consumed by the user during the meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda."

[0368] In step 2404, DGS can receive the entered tags for an instance of a meal type. The user can enter this meal description tag as a favorite. Alternatively, if a meal type is already entered in DGS, the user can select a tag from, for example, a list of favorite meal types.

[0369] In step 2406, the DGS can determine whether a threshold number of instances of a food type tag have been received by the DGS. For example, the DGS may require a minimum of three instances of a particular food type tag. If the DGS determines that only two instances of a food type tag have been received, it may not calculate the recommended drug dose for that food type. However, if the DGS determines that three or more instances of that food type tag have been received, it can calculate the recommended drug dose for that food type, as shown in step 2408.

[0370] In some embodiments, after a user tags a meal, the DGS can detect if the administered drug dose differs from the drug dose previously administered while consuming the same meal. If the DGS detects a significant difference in drug dosage, for example, a difference of 1 unit, 2 units, 3 units, 4 units, 5 units, or a difference of approximately 1 to 5 units, or a difference of approximately 2 to 5 units, or a difference of approximately 3 to 5 units, the DGS can prompt the user. In some embodiments, the DGS can prompt the user to create a new tag. The new tag may include an explanation to account for the different drug dosage. For example, the new tag may include the amount of food, or another reason for the different drug dosage (e.g., exercise before the meal). In some embodiments, the DGS can prompt the user in real time, i.e., within 5 minutes, 4 minutes, 3 minutes, 2 minutes, or 1 minute after the user tags the meal type.

[0371] In one exemplary embodiment, as shown in Figure 23B, in method 2420, in the first step 2422, DGS may prompt the user to enter a tag associated with the meal type. For example, the meal type may be a description of the specific food and / or beverage consumed by the user during the meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda." 【037...

Claims

1. A system for providing dosage guidance, An on-body unit configured to be worn on the surface of a user's skin, comprising: a glucose sensor positioned to be in contact with the user's interstitial fluid and to detect the user's glucose level; and electronic equipment connected to the glucose sensor and configured to transmit the user's glucose data; A drug delivery device configured to manage drug dosages and transmit the user's drug dosage data, wherein the drug dosage data includes the time elapsed since the last drug administration, A display configured to visually present dosage guidance, The on-body unit, the drug delivery device, the display, and one or more processors that are communicative and coupled with memory for storing instructions. Equipped with, When the instruction is executed by the one or more processors, the one or more processors will be instructed to: An operation to determine whether a dose-forgotten alert, which is based on a dose-forgotten detection algorithm and includes a notification on the display, is enabled, In response to the determination that the dose forgetting alert is not effective, the operation causes the display to show dose guidance recommendations on the screen based on a calculation of a normal meal dose, which is at least in part based on the user's current blood glucose level, target blood glucose level, fixed insulin dose associated with the meal, and the amount of active insulin remaining in the user's body. In response to the determination that the dose forgetting alert is effective, the operation of displaying on the display a delayed dose guidance recommendation based on a calculation of a delayed meal dose, which is at least in part based on the user's estimated meal start time, target blood glucose level, fixed insulin dose associated with the meal, and the amount of residual active insulin in the user's body based on the time the user requested the delayed dose guidance recommendation; This will cause it to be executed. system.

2. A method for providing computer-assisted dose guidance, wherein the method is: A step of receiving the user's glucose level from an on-body unit configured to be attached to the user's skin surface and receiving the user's drug dose data from a drug delivery device, wherein the on-body unit includes a glucose sensor positioned to be in contact with the user's interstitial fluid and to detect the user's glucose level, and electronic equipment connected to the glucose sensor and configured to transmit the user's glucose data, and the drug delivery device is configured to manage drug doses and transmit the user's drug dose data, wherein the drug dose data includes the time elapsed since the last drug administration, A step of determining whether a dose forgetting alert is enabled, wherein the dose forgetting alert is based on a dose forgetting detection algorithm and includes a notification on the display, Steps include: displaying a dose guidance recommendation on the display based on a calculation of a normal meal dose in response to a determination that the dose forgetting alert is not effective, wherein the calculation of the normal meal dose is performed at least in part based on the user's current blood glucose level, target blood glucose level, fixed insulin dose associated with the meal, and the amount of active insulin remaining in the user's body; In response to a determination that the dose forgetting alert is valid, a step of displaying a delayed dose guidance recommendation on the display based on a calculation of a delayed meal dose, wherein the calculation of the delayed meal dose is performed at least in part based on the user's estimated meal start time, target blood glucose level, fixed insulin dose associated with the meal, and the amount of residual active insulin in the user's body based on the time the user requested a delayed dose guidance recommendation; Methods that include...

3. The operation to determine whether the dose forgetting alert is effective is: This procedure determines whether more than two hours have passed since the last drug administration, If it is determined that more than two hours have passed since the last drug administration, the operation to display the delayed dose guidance recommendation, The system according to claim 1, including the following:

4. The system according to claim 1, wherein, if the user's current glucose level is below the target glucose level, the calculation of the normal meal dose is performed based on a fixed insulin dose associated with the meal and the amount of residual active insulin in the user's body.

5. The system according to claim 1, wherein if the user's current glucose level exceeds the target glucose level, the calculation of the normal meal dose is performed based on a fixed insulin dose associated with the meal, the amount of residual active insulin in the user's body, corrective adjustments, and trend adjustments.

6. The system according to claim 1, wherein the calculation of the delayed meal dose is further based on trend adjustment at the estimated meal start time.

7. The step of determining whether the dose forgetting alert is valid is: A step to determine whether more than two hours have passed since the last drug administration, The method according to claim 2, further comprising the step of displaying the delayed dose guidance recommendation if it is determined that more than two hours have elapsed since the last drug administration.

8. The method according to claim 2, wherein, if the user's current glucose level is below the target glucose level, the calculation of the normal meal dose is performed based on a fixed insulin dose associated with the meal and the amount of residual active insulin in the user's body.

9. The method according to claim 2, wherein if the user's current glucose level is above the target glucose level, the calculation of the normal meal dose is performed based on a fixed insulin dose associated with the meal, the amount of residual active insulin in the user's body, corrective adjustments, and trend adjustments.

10. The method according to claim 2, wherein the calculation of the delayed meal dose is further based on trend adjustment at the estimated meal start time.

11. The system according to claim 1, wherein the drug delivery device includes a drug dispensing pen.

12. The system according to claim 11, wherein the drug delivery device further comprises a pen cap detachably coupled to the drug dispensing pen and configured to detect the time of one or more drug administrations administered by the drug dispensing pen.

13. The system according to claim 1, wherein the drug delivery device includes a portable infusion pump.

14. The operation that the instruction causes the processor to perform is: An operation to compare the value of the normal meal dosage calculation with the value of the delayed meal dosage calculation, If the value calculated for delayed meal intake is less than the value calculated for normal meal requirements, the recommended value for delayed meal intake guidance is displayed. The system according to claim 1, including the following:

15. The operation that the instruction causes the processor to perform is: If the glucose level at the estimated meal start time is below the low glucose threshold, the operation will prevent the display of the delayed dose guidance. The system according to claim 1, including the following:

16. The method according to claim 2, wherein the drug delivery device includes a drug dispensing pen.

17. The method according to claim 16, wherein the drug delivery device further comprises a pen cap detachably coupled to the drug dispensing pen and configured to detect the time of one or more drug administrations administered by the drug dispensing pen.

18. The method according to claim 2, wherein the drug delivery device includes a portable infusion pump.

19. A step of comparing the value of the normal meal dose calculation with the value of the delayed meal dose calculation, If the value of the delayed meal dose calculation is less than the value of the normal meal requirement calculation, the step of displaying the recommended value of the delayed dose guidance, The method according to claim 2, further comprising:

20. A step of preventing the display of delayed dose guidance if the glucose level at the estimated meal start time is below the low glucose threshold, The method according to claim 2, further comprising:

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