Systems and methods for drug administration and tracking

Abstract

A drug administration and tracking system includes a delivery device configured to deliver multiple doses of a drug over multiple days and a computing device configured to receive dose information for each dose. The computing device includes a software application operable to cause the computing device to: retrieve the dose information for all doses within a predetermined number of days cycle; classify each dose within the predetermined number of days cycle into one of a plurality of time blocks throughout a 24-hour time range; determine a time of interest based on the time block within the plurality of time blocks having the smallest dose classified therein; and select the physiological parameter reading as the physiological parameter reading of interest from a plurality of physiological parameter readings based on the proximity of the time of the physiological parameter reading to the determined time of interest.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit and priority of U.S. Patent Application No. 17 / 209,083, filed on March 22, 2021, which claims the benefit of U.S. Provisional Patent Application No. 62 / 993,592, filed on March 23, 2020, the entire contents of each of which are hereby incorporated by reference herein. Technical Field

[0003] The present disclosure relates to drug administration and tracking, and more particularly, to systems and methods for drug administration and tracking using contextual data. Background Art

[0004] Diabetes mellitus / diabetes is a metabolic disease associated with high blood sugar, which is caused by insufficient production or use of insulin by the body. Diabetes affects hundreds of millions of people worldwide and is one of the leading causes of death globally. Diabetes is divided into three types: type 1 diabetes, type 2 diabetes, and gestational diabetes. Type 1 diabetes is related to the body's inability to produce sufficient levels of insulin for cells to take up glucose. Type 2 diabetes is related to insulin resistance, where cells cannot properly use insulin. Gestational diabetes may occur during pregnancy when a pregnant woman has high blood sugar levels. Gestational diabetes usually resolves after pregnancy; however, in some cases, gestational diabetes develops into type 2 diabetes.

[0005] Various diseases and medical conditions, such as diabetes, require users to self - administer a certain dose of medication. When administering liquid medications, for example, by injection, an appropriate dose is set and then dispensed by the user, for example, using a syringe, a drug delivery pen, or a pump. Regardless of the specific device used for injecting liquid medications, it is important to track the administered medications, especially for managing lifelong or chronic conditions such as diabetes. To this end, some drug administration systems include software applications that assist users in drug administration and tracking, thereby helping users manage their diseases and / or medical conditions. Summary of the Invention

[0006] According to aspects of the present disclosure, there is provided a drug administration and tracking system including a drug delivery device and a computing device. The drug delivery device is configured to deliver multiple doses of a drug to a user over multiple days and send dose information indicating the delivery time of each dose of the multiple doses. The computing device is arranged to communicate with the drug delivery device and is configured to receive the dose information of each dose of the multiple doses. The computing device includes a data processing unit, which includes a processor and a software application. The software application is stored in the memory of the data processing unit and is operable to cause the computing device to: retrieve the dose information of all doses of the multiple doses within a predetermined number of days cycle; classify each dose of the multiple doses within the predetermined number of days cycle into one of multiple time blocks throughout a 24-hour time range; determine a time of interest based on the time block having the smallest dose classified therein among the multiple time blocks; and select a physiological parameter reading as an interesting physiological parameter reading from multiple physiological parameter readings based on the proximity of the time of the physiological parameter reading to the determined time of interest.

[0007] In aspects of the present disclosure, the system further includes a sensor device configured to obtain multiple physiological parameter readings and send the multiple physiological parameter readings to the computing device.

[0008] In another aspect of the present disclosure, the time of interest is the end time of a fasting period, the physiological parameter reading is a blood glucose reading, and the interesting physiological parameter reading is a fasting blood glucose reading. In such aspects, the system may further include a blood glucose sensor device, such as a continuous glucose monitor, configured to obtain blood glucose readings and send multiple blood glucose readings to the computing device.

[0009] In another aspect of the present disclosure, the instruction is further operable to cause the computing device to determine the start time of the fasting period based on the end time of the fasting period, and select a blood glucose reading as a pre-fasting blood glucose reading from multiple blood glucose readings based on the proximity of the time of the blood glucose reading to the start time of the fasting period.

[0010] In yet another aspect of the present disclosure, the start time of the fasting period is the start time of sleep, the end time of the fasting period is the end time of sleep, and the sleep period is defined between the start time of the sleep period and the end time of the sleep period.

[0011] In still another aspect of the present disclosure, the instruction is further operable to cause the computing device to determine whether the risk of hypoglycemia is too high, and in the case where it is determined that the risk of hypoglycemia is too high, calculate a dose reduction amount to adjust the dose delivered by the drug delivery device.

[0012] In yet another aspect of the present disclosure, determining whether the risk of hypoglycemia is too high is based on the pre - fasting blood glucose reading and the fasting blood glucose reading.

[0013] A method of drug administration and tracking provided according to an aspect of the present disclosure includes: retrieving dose information of a plurality of doses of a drug delivered from a drug delivery device to a user over multiple days; classifying each of the plurality of doses into one of a plurality of time blocks throughout a 24 - hour time range; determining a time of interest based on the time block among the plurality of time blocks that has the smallest dose among the plurality of doses; and instructions to select a physiological parameter reading as an interesting physiological parameter reading from a plurality of physiological parameter readings based on the proximity of the time of the physiological parameter reading to the determined time of interest.

[0014] In an aspect of the present disclosure, the time of interest is the end time of a fasting period, the physiological parameter reading is a blood glucose reading, and the interesting physiological parameter reading is a fasting blood glucose reading.

[0015] In another aspect of the present disclosure, the method further includes: determining the start time of the fasting period based on the end time of the fasting period; and selecting a blood glucose reading as a pre - fasting blood glucose reading from a plurality of blood glucose readings based on the proximity of the time of the blood glucose reading to the start time of the fasting period.

[0016] In yet another aspect of the present disclosure, the method further includes determining whether the risk of hypoglycemia is too high, and in the case where the risk of hypoglycemia is determined to be too high, calculating a dose reduction amount to adjust the dose delivered by the drug delivery device.

[0017] Another drug administration and tracking system provided according to the present disclosure includes a drug delivery device and a computing device. The drug delivery device is configured to deliver a plurality of doses of a drug to a user and send dose information indicating the delivery time of each of the plurality of doses. The computing device is arranged to communicate with the drug delivery device and is configured to receive the dose information of each of the plurality of doses. The computing device includes a data processing unit that includes a processor and a software application. The software application is stored in the memory of the data processing unit and has instructions operable to cause the computing device to automatically label or receive input instructions to label a dose among the plurality of doses as an interesting dose associated with an event, identify a first physiological parameter reading from a plurality of physiological parameter readings as a pre - event physiological parameter reading, identify a second physiological parameter reading from a plurality of physiological parameter readings as a post - event physiological parameter reading, and determine whether the risk of an adverse physiological condition is too high based on the pre - event physiological parameter reading and the post - event physiological parameter reading.

[0018] In aspects of the present disclosure, the system further includes a sensor device configured to obtain a plurality of physiological parameter readings and send the plurality of physiological parameter readings to the computing device.

[0019] In another aspect of the present disclosure, the event is the consumption of a meal, the physiological parameter reading is a blood glucose reading, the pre-event physiological parameter reading is a pre-meal blood glucose reading, the post-event physiological parameter reading is a post-meal blood glucose reading, and the adverse physiological condition is hypoglycemia. In such aspects, the system may include a blood glucose sensor device configured to obtain a blood glucose reading and send the blood glucose reading to the computing device.

[0020] In yet another aspect of the present disclosure, determining whether the risk of hypoglycemia is too high is based on the pre-meal blood glucose reading and the post-meal blood glucose reading.

[0021] Another method for drug administration and tracking according to the present disclosure includes: retrieving dose information of a plurality of doses of a drug delivered from a drug delivery device to a user over multiple days; automatically marking or receiving an input instruction to mark the plurality of doses as doses of interest associated with an event; identifying a first physiological parameter reading from the plurality of physiological parameter readings as a pre-event physiological parameter reading; identifying a second physiological parameter reading from the plurality of physiological parameter readings as a post-event physiological parameter reading; and determining whether the risk of an adverse physiological condition is too high based on the pre-event physiological parameter reading and the post-event physiological parameter reading.

[0022] In an aspect of the present disclosure, the event is the consumption of a meal, the physiological parameter reading is a blood glucose reading, the pre-event physiological parameter reading is a pre-meal blood glucose reading, the post-event physiological parameter reading is a post-meal blood glucose reading, and the adverse physiological condition is hypoglycemia.

[0023] Details of one or more aspects of the present disclosure are set forth in the accompanying drawings and description below. Other features, objects, and advantages of the present disclosure are apparent from the specification and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1A is a schematic diagram of a drug administration and tracking system provided according to the present disclosure, which includes a drug delivery device, a computing device, and in aspects, a sensor device and / or a data processing system;

[0025] Figure 1B is configured to be used as Figure 1A a block diagram of a drug delivery pen of a drug delivery device of the system;

[0026] Figure 1C is according to the present disclosure Figure 1A a block diagram of one configuration of a computing device of the system;

[0027] Figure 2 is a block diagram showing a software architecture of various data processing modules configured to be used with a Figure 1A system in accordance with the present disclosure;

[0028] Figure 3A is a flowchart showing a method of identifying fasting glucose readings and determining a dosage recommendation based thereon in accordance with the present disclosure;

[0029] Figure 3B is a flowchart showing a method of determining a sleep cycle in accordance with the present disclosure; and

[0030] Figure 4 is a flowchart showing a method of identifying a meal time window based on which a dosage recommendation is determined in accordance with the present disclosure. DETAILED DESCRIPTION

[0031] Figure 1A Illustrated is a drug administration and tracking system 10 provided in accordance with the present disclosure. Although the present disclosure is described herein for diabetes management involving an insulin pen, a health management application associated with the insulin pen (e.g., a software application or app), and / or a glucose monitoring device, it should be understood that aspects and features of the present disclosure are also applicable to managing other diseases and medical conditions and / or for use with other drug delivery devices and / or monitoring devices.

[0032] System 10 includes a drug delivery device 20 that wirelessly communicates with a computing device 30. The drug delivery device 20 is detailed herein and illustrated as a drug delivery pen, although any other suitable drug delivery device may be provided, such as, for example, a syringe, a pump, etc. The pen 20 is operable to select, set, and / or dispense a drug dosage. The computing device 30 is detailed herein and illustrated as a smart phone, although any other suitable computing device may be provided, such as, for example, a tablet computer, a wearable computing device (e.g., a smart watch, smart glasses, etc.), a laptop computer and / or a desktop computer, a smart TV, a web-based server computer, etc. The pen 20 and / or the smart phone 30 includes a health management application 40 associated with the pen 20 and / or a portion of the system 10 or other devices connected to the system 10.

[0033] In various aspects, system 10 further includes a data processing system 50 that communicates with the pen 20 and / or the smart phone 30. The data processing system 50 may include one or more computing devices in a computer system and / or a communication network (also referred to as the “cloud”) accessible via the Internet, such as including servers and / or databases in the cloud.

[0034] The health management application 40 is paired with the pen 20, which can be a prescription-only medical device, although other suitable devices are also contemplated. In various aspects, the pairing of the smartphone 30 with the pen 20 at least partially unlocks the health management application 40 to enable the user to utilize some or all of the features of the application 40 while providing secure access and ensuring that the pen 20 belongs to the user, such as a user who may have a prescription for the use of some or all of the features of the health management application 40. Thus, the pairing action unlocks and enables the functions of the health management application 40 and / or the system 10; at least a portion of the health management application 40 may be disabled without a specific pairing and / or the health management application 40 may provide only limited features without a specific pairing.

[0035] In various aspects, the health management application 40 can monitor and / or control the functions of the pen 20 and provide a dose calculator and / or decision support module, which can calculate and recommend a drug dose for the user to administer using the pen 20.

[0036] The smartphone 30 can be used to obtain, process, and / or display context data, which can be related to the user's health condition, including the condition being treated by the pen 20. For example, the smartphone 30 can be used to track the user's location; the user's physical activity, including steps, distance moved, and / or intensity, estimated calories burned, and / or duration of activity; and / or the user's interaction pattern with the smartphone 30. In various aspects, the health management application 40 can aggregate and process the context data to generate decision support outputs to guide and assist the user in using the pen 20 and / or managing their behavior to promote treatment and better health outcomes.

[0037] In various aspects, the system 10 can include a sensor device 60 to monitor one or more health metrics and / or physiological parameters of the user. Examples of health metric and physiological parameter data monitored by the sensor device 60 include analytes (e.g., glucose), heart rate, blood pressure, user movement, temperature, etc. The sensor device 60 can be a wearable sensor device, such as a continuous glucose monitor (CGM) to obtain transcutaneous or blood glucose measurements, which are processed to produce continuous glucose values. For example, the CGM can include a glucose processing module implemented on an independent display device and / or on the smartphone 30, which processes, stores, and displays the user's continuous glucose values.

[0038] Figure 1BThe pen 20 of the system 10 is shown, although any suitable pen or any other suitable drug delivery device as described above may be used. The pen 20 includes a cap 21 configured to protect a drug dispensing element (e.g., the needle 29) and a body 22 configured to receive a drug cartridge 23 (e.g., which may be replaceable). The pen 20 further includes: a dose dispensing mechanism 24 to dispense (e.g., deliver) the drug contained in the drug cartridge 23 out of the pen 20 (e.g., through the needle 29); a dose setting mechanism 25 to enable selection and / or setting of the drug dose to be dispensed; an operation monitoring mechanism 28 (e.g., including one or more switches, sensors (electrical, optical, acoustic, magnetic, etc.), encoders, etc.) to qualitatively determine that the pen 20 is being operated and / or to monitor the operation of the pen 20 (e.g., to quantitatively determine the amount of drug set and / or administered); and an electronic unit 27, which may include a processor, a memory, a transceiver, and a battery or other suitable power source. The pen 20 is configured to pair with and communicate with a health management application 40 operating on a smart phone 30 (or other suitable computing device as described above).

[0039] In various aspects, to operate the pen 20, the user first sets, e.g., dials in, the dose using the dose knob 26a of the dose setting mechanism 25. For example, the dose can be adjusted up or down before administration to achieve the desired dose by rotating the dose knob 26a in the appropriate direction.

[0040] Once the appropriate dose has been set, the user applies a force to the dose dispensing button 26b of the dose setting mechanism 25 to begin dispensing. More specifically, to begin dispensing, the user presses the portion of the dose dispensing button 26b that protrudes from the body 22 of the pen 20, thereby driving a plunger 26c of the dose dispensing mechanism 24 against a seat (not explicitly shown) of the drug cartridge 23 to dispense a quantity of the drug from the cartridge 23 through the needle 29 into the user according to the dose set by the dose setting mechanism 25, e.g., the dose knob 26a.

[0041] In various aspects, the operation monitoring mechanism 28 of the pen 20 senses the movement of the rotational and / or translational components of the dose dispensing mechanism 24 (e.g., the plunger 26c, the drive shaft or screw associated with the plunger 26c (not shown), or other suitable components). For this purpose, the operation monitoring mechanism 28 can include one or more switches, sensors, and / or encoders. More specifically, any suitable (one or more) switches, (one or more) sensors, and / or (one or more) encoders can be utilized to sense rotational and / or linear movement. Such non-limiting examples include rotary and linear encoders, Hall effect and other magnet-based sensors, linear variable displacement transducers, etc. For example, with respect to an encoder, the encoder can be configured to sense the rotation of a drive screw that rotates to linearly drive the plunger 26c; thus, by sensing the rotation of the drive screw, the movement of the plunger 26c can be easily determined. The movement of the encoder can be detected as data processed by the processor of the electronic unit 27 of the pen 20, whereby the amount of the administered drug can be determined.

[0042] In various aspects, the processor of the electronic unit 27 of the pen 20 can store the dose along with the timestamp of the dose and / or any other information associated with the dose. In various aspects, the transceiver of the electronic unit 27 enables the pen 20 to transmit the dose and the associated information to the smartphone 30. In such aspects, once the dose is transmitted, the dose data and any associated information associated with that particular transmitted dose are marked as transmitted in the memory of the electronic unit 27 of the pen 20. If the dose has not been transmitted to the smartphone 30, for example because there is no available connection between the pen 20 and the smartphone 30, the dose and the associated data can be saved and transmitted the next time a successful communication link is established between the pen 20 and the smartphone 30.

[0043] The timestamp can be the current time or the time from an up-counting timer. When the dose and the associated information are transmitted to the health management application 40 running on the smartphone 30, the timestamp and / or the "dose start time" parameter (as determined by the up-counting timer) are transmitted by the pen 20 and received by the smartphone 30 for storage in the memory 33 of the data processing unit 31 of the smartphone 30 (see Figure 1C ). In the case of utilizing an up-counting timer, the time of the dose can be determined without the pen 20 having to know the current time, which can simplify the operation and setup of the pen 20. That is, the health management application 40 can determine the time of the dose based on the current time and the value returned from the up-counting timer.

[0044] The dose dispensing mechanism 24 of the pen 20 can include a manually powered mechanism, a motorized mechanism, or an assisted mechanism (e.g., a mechanism that operates partially relying on manual power and partially relying on motorized power). Regardless of the specific configuration of the dose dispensing mechanism 24, as described above, when a force (e.g., hand power, electric motor force, or a combination thereof) is applied to the plunger 26c of the dose dispensing mechanism 24, the plunger 26c in turn provides a force to push the drug from the drug cartridge 23 to deliver a set or dialed dose. In various aspects, the dose dispensing mechanism 24 can be adjusted to deliver a dose over different time periods. In various aspects, the dose dispensing mechanism 24 can be operated such that the plunger 26c is pushed by an adjustable tension spring or by a variable speed motor to inject a dose within a specific time range (e.g., 1 second, 5 seconds, etc.) to help reduce the pain of administration and / or for other purposes. In other aspects, the dose dispensing mechanism 24 can be operated over much longer time periods, e.g., to better match the kinetics of carbohydrates, similar to an extended bolus delivered by a pump.

[0045] The health management application 40 of the smartphone 30 provides a user interface to allow the user to manage health-related data. For example, the health management application 40 can be configured to control some functions of the pen 20 and / or provide an interactive user interface to allow the user to manage the settings of the pen 20 and / or the settings of the smartphone 30, which may affect the function of the system 10( Figure 1A ).

[0046] Figure 1C A smartphone 30 of the system 10 is shown( Figure 1A ), which includes a data processing unit 31, a wireless communication unit 35, and a display unit 36. The data processing unit 31 includes: a processor 32 that processes data, a memory unit 33 that communicates with the processor 32 to store data, and an input / output unit (I / O) 34 that connects the processor 32 and / or the memory 33 to other modules, units, and / or devices of the smartphone 30 and / or external devices. The processor 32 can include a central processing unit (CPU) or a microcontroller unit (MCU). The memory 33 can include and store processor-executable code that, when executed by the processor 32, configures the data processing unit 31 to perform various operations, e.g., such as receiving information, commands, and / or data, processing information and data, and transmitting or providing information / data to another device. In various aspects, the data processing unit 31 can transmit raw or processed data to the data processing system 50( Figure 1A) To support the various functions of the data processing unit 31, the memory 33 can store information and data such as instructions, software, values, images, and other data processed or referenced by the processor 32. For example, various types of random access memory (RAM) devices, read-only memory (ROM) devices, flash memory devices, and other suitable storage media can be used to implement the storage function of the memory 33. The I / O 34 of the data processing unit 31 can connect the data processing unit 31 to the wireless communication unit 35 to utilize various types of wired or wireless interfaces compatible with typical data communication standards, such as, for example, data communication between the data processing unit 31 and other devices such as the pen 20 can be achieved via a wireless transmitter / receiver (Tx / Rx), including but not limited to Bluetooth, Bluetooth Low Energy, Zigbee, IEEE 802.11, Wireless Local Area Network (WLAN), Wireless Personal Area Network (WPAN), Wireless Wide Area Network (WWAN), WiMAX, IEEE 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)), 3G / 4G / LTE cellular communication methods, NFC (Near Field Communication), and parallel interfaces. The I / O 34 of the data processing unit 31 can also be connected to other external interfaces, data storage sources, and / or visual or audio display devices, etc., to retrieve and transfer data and information that can be processed by the processor 32, which are stored in the memory 33 and / or presented on the output unit of the smartphone 30 and / or external devices. For example, the display unit 36 of the smartphone 30 can be configured to communicate data with the data processing unit 31 via the I / O 34, for example, to provide visual display, audio display, and / or generate other sensory displays of the user interface of the health management application 40( Figure 1A ) In some instances, the display unit 36 can include various types of screen displays, speakers, or printing interfaces, for example, including but not limited to light-emitting diode (LED) or liquid crystal display (LCD) monitors or screens, cathode ray tubes (CRT) as visual displays; audio signal converter devices as audio displays; and / or toner, liquid inkjet, solid ink, dye sublimation, inkless (e.g., thermal or UV) printing devices, etc.

[0047] Also refer to Figure 2Once the smartphone 30 receives the dose and associated information (e.g., it may include time information, dose setting and / or dose dispensing information, and other information about the pen 20 and / or the environment associated with the administration event), the smartphone 30 stores the dose-related information in the memory 33, e.g., it may be included in a list of doses or administration events. In various aspects, via a user interface associated with the health management application 40, the smartphone 30 allows the user to browse the list of previous doses to view an estimate of the current drug activity (“active drug”) in the patient's body based on calculations performed by the health management application 40, and / or utilize the dose determination module 240 to assist the patient with dose setting information regarding the size of the next dose to be delivered. For example, the patient may input the carbohydrates to be consumed and the current blood glucose, and the health management application 40 already knows the active insulin. Using these parameters, the recommended drug dose (e.g., such as an insulin dose) calculated by the dose determination module 240 can be determined. In various aspects, the smartphone 30 may also allow the user to manually input dose data, e.g., tablets, which may be useful if the battery in the pen 20 has run out or if another drug delivery device such as a syringe is used for administration.

[0048] Other features of the system 10, including the health management application 40 that may operate on the smartphone 30, the pen 20, and / or other suitable devices and is configured to communicate with a medical device (e.g., the pen 20 or other drug delivery device), can be found in U.S. Patent No. 9,672,328, entitled “Medicine Administering System Including Injection Pen and Companion Device,” the entire content of which is hereby incorporated by reference herein.

[0049] Continuing to refer Figure 2 , in conjunction with Figures 1A to 1C, which shows the software architecture 200 of various data processing modules of the health management application 40 of the system 10, generally including a data aggregation module 210, a device pairing module 220, a user information database 230, a dose determination module 240, a fasting glucose recognition module 250, and / or a meal time window detection module 260. Some or all of the data processing modules 210 to 260 of the software architecture 200 of the health management application 40 can be provided on the smartphone 30; additionally or alternatively, some or all of the data processing modules 210 to 260 can be provided on the pen 20 (e.g., on its electronic unit 27), on the data processing system 50 (in the cloud, e.g., residing on one or more cloud computers), and / or on other devices. The data processing modules 210 to 260 control the functions of the smartphone 30, the pen 20, and / or other devices, including aggregating and processing the output signals sent by the pen 20 to perform device pairing for verifying the pen 20 and unlocking some or all of the functions of the health management application 40, for example, using the device pairing module 220 as described above.

[0050] In various aspects, some of the data processing modules 210 to 260 can be configured to process health metric data (e.g., CGM data, dose data, etc.) and context data obtained by the health management application 40 from sensor devices 60, other devices of the system 10, other devices of the user, and / or other applications on the smartphone 30. More specifically, the data aggregation module 210 can be configured to obtain health metric data from one or more devices, such as the pen 20, the sensor device 60, and / or other devices or applications communicating with the health management application 40. The database 230 stores health data and context data associated with the user and / or user group. In various aspects, the database 230 can reside on the data processing system 50, e.g., in the cloud.

[0051] The dose determination module 240, e.g., a dose calculator module, is configured to calculate the dose of the drug to be delivered from the pen 20 based on user-specific time-related and context or environment-related data of the pen 20.

[0052] In various aspects, the software architecture 200 includes a fasting glucose recognition module 250, which is configured to determine whether a glucose reading is a fasting glucose reading based, in whole or in part, on user-specific time-related and context or environment-related dose data of the pen 20. The fasting glucose recognition module 250 can further be configured to adjust or recommend an adjustment of the basal dose based on the identified fasting glucose reading. The fasting glucose recognition module 250 is described in more detail below.

[0053] In various aspects, the software architecture 200 includes a meal time window detection module 260 that is configured to determine whether a dose delivered by the pen 20 is a bolus dose associated with a meal. The meal time window detection module 260 can further be configured to determine which meal of the day the delivered dose is associated with if it is determined that the bolus dose is associated with a meal, evaluate the response to the bolus dose, and adjust or recommend an adjustment to the bolus dose. The meal time window detection module 260 is described in more detail below.

[0054] The various data processing modules 210-260 of the software architecture 200 can be organized in various ways, including providing data to each other directly (e.g., module to module) and / or indirectly (e.g., via an intermediate module) on a periodic, intermittent, and / or on-demand basis. Additional or alternative data processing modules can also be envisioned.

[0055] Generally referring Figures 1A to 2 , fasting glucose is a glucose reading taken after a period of fasting (e.g., intentionally abstaining from food intake for a period of time). Fasting glucose readings can be used as a primary data point in clinical decision-making processes related to decisions to increase or decrease the amount of basal or long-acting insulin (long-acting insulin titration). In many cases, the fasting period occurs during the overnight period when the user is asleep, and the fasting glucose reading is obtained before the user's first meal (or other food intake) of the day; however, this is not necessarily the case, e.g., in cases where the user may work at night and sleep during the day, or in cases where there is an extended fasting period during the day or night.

[0056] There are several different methods for identifying fasting periods, sleep schedules, and / or fasting glucose readings. These methods can be fully manual, partially manual and partially automatic, or fully automatic. For example, one manual method requires the user to manually specify a blood glucose (BG) reading as fasting. This is the most straightforward method, but since it is fully manual, it relies on user consistency and may impose an additional burden on the user. Another method requires the user to manually configure a reader device (e.g., the health management application 40) with a user-specific typical overnight cycle, allowing the health management application 40 to search for BG readings at or near the end of the pre-configured overnight cycle and before the first rapid-acting or bolus dose of the day. Although this method alleviates some of the burden, it can be problematic, e.g., if the user does not maintain a consistent sleep schedule. Systems that automatically label glucose readings as fasting remove the burdens and / or problems associated with other methods and can increase the number of data points available for clinical decision support. The fasting glucose identification module 250 implements such a fully automatic method, as detailed below.

[0057] Go to Figure 3A Figure 3A , the full - automatic method according to the present disclosure implemented by the fasting glucose recognition module 250 (or any other suitable module or group of modules) is shown as method 300. Method 300 enables the identification of fasting glucose readings and, based thereon, if necessary, the adjustment or recommendation of an adjustment to the user's basal dose. As described in more detail below, this is achieved by identifying a fasting (e.g., sleep) cycle, determining the fasting glucose value and time, determining the pre - fasting glucose value and time, generating a report including a basal assessment graph, and, if necessary, using the generated report to adjust or recommend an adjustment to the user's basal dose.

[0058] For example, when the user selects to initiate a report on the interactive display of the smart phone 30, method 300 is initiated. When method 300 is initialized, the user's sleep cycle (or other fasting cycle) is determined, as indicated in step 302. In various aspects, the sleep cycle can be defined as a single static sleep cycle based on the most recent history of the user's activity relative to the sleep time. In other aspects, time - specific data related to the actual user's bedtime and wake - up time can be used to dynamically define the sleep cycle on a daily basis. In still other aspects, only the user's dose data is used to determine the sleep cycle. In yet other aspects, only the user's blood glucose data (e.g., using CGM data to find glucose peaks indicating eating, dawn phenomenon, etc.) is used to determine the sleep cycle. In still yet other aspects, a combination of the user's dose and blood glucose data is used to determine the sleep cycle. In other aspects, in addition to applying one or more of interactive data, activity data from sensors on a wearable (e.g., smart watch, smart health monitor, etc.), activity data from sensors on a smart phone or other connected device, a motion sensor (e.g., accelerometer) for determining whether the user is moving, and / or an orientation sensor (e.g., gyroscope) for determining whether the user is lying down, standing, etc., the user's dose and / or blood glucose data is also used to determine the sleep cycle.

[0059] Figure 3B A exemplary sub - method for determining the sleep cycle in step 302 of method 300 is provided. Refer to Figure 3B Figure 3B , initially, the processing unit collects all bolus dose data from a predetermined past time period, e.g., the previous thirty (30) calendar days stored in the memory unit, and uses this information to define a "sleep cycle" value. It can be obtained from the pen 20 ( Figure 1A ) to the health management application 40 ( Figure 1A) and / or transmit bolus data, such as dose data with timestamp information, between other devices. In various aspects, a specific minimum number of days of bolus dose data may be required to enable analysis. For example, if the bolus dose data stored in the memory unit is less than a specific minimum number of days (e.g., three days), the processing unit may define the sleep cycle as from 12:00 midnight to 6:00 am, and the sub-method for determining the sleep cycle ends. On the other hand, if the number of days of bolus dose data stored in the memory unit is greater than the specific minimum number of days, the processing unit defines the sleep cycle value based on the analysis. As detailed below, the analysis may be a rolling sum of a calculated bolus frequency vector associated with a specific time period, such as a rolling period, e.g., 9 hours, a sum of the calculated bolus frequency vectors. Other suitable methods for determining the sleep cycle value are also contemplated.

[0060] Generally, this determination includes obtaining the bolus data for each day, binning or classifying the bolus data for all days into time blocks over a 24-hour time range, and determining the start and end sleep times and thus the sleep cycle therebetween based on the least filled bin. This determination is described in more detail below.

[0061] Continuing to refer to Figure 3B , in the case where it is determined that there are more than a specific minimum number of days of bolus dose data stored in the memory unit, the processing unit analyzes the stored dose data to determine whether the bolus dose for each day occurs within a predetermined time period, such as, for example, each 15-minute time period (or 30-minute or other suitable time period) of a 24-hour day, and assigns a data value to each predetermined time period in which at least one bolus dose occurs. Next, the processing unit sums the stored data values associated with the presence or absence of a bolus dose in each predetermined (e.g., 15-minute) time period over all days (e.g., between 3 days and 30 days) of the analyzed period. This results in generating a bolus frequency vector by predetermined (e.g., 15-minute) periods.

[0062] Using the generated bolus frequency vector, the processing unit calculates a rolling 9-hour (e.g.) sum value of the bolus frequency vector (e.g., using thirty-six 15-minute periods). For the first 8 hours and 45 minutes of the time vector (in this example), the rolling sum calculation includes the previous night values, if any. For example, at 6:00 am, the rolling sum calculation will include the sum of the frequency values from 9:00 pm to 12:00 midnight (e.g., 21:00 to 24:00) of the previous day and the frequency values from 12:01 am to 6:00 am of the next day.

[0063] Then, the processing unit identifies the daily time value with the minimum rolling frequency sum. If the processing unit determines only one daily time value with the minimum rolling sum of frequency values, the sleep cycle is determined by the processing unit by first calculating the "sleep end time" as the time with the minimum rolling sum minus 1 hour, and then calculating the "sleep start time" as the sleep end time minus 6 hours. If the processing unit identifies more than one daily time value with the minimum rolling sum of frequency values, then the processing unit determines whether any of the identified time values occur during a defined early morning time range (e.g., 5:00 a.m. to 9:00 a.m.) or a defined noon time range (e.g., 9:00 a.m. to 1:00 p.m.). If any of the identified times occur during the early morning time range, the sleep cycle is determined by the processing unit by first calculating the sleep end time as the maximum time value of the early morning time values with the minimum rolling sum minus 1 hour, and then calculating the sleep start time as the sleep end time minus 6 hours. If the processing unit determines that no identified time occurs during the early morning time range and if any of the identified times occur during the noon time range, the sleep cycle is determined by the processing unit by first calculating the sleep end time as the minimum time value of the noon time values with the minimum rolling sum minus 1 hour, and then calculating the sleep start time as the sleep end time minus 6 hours.

[0064] If the processing unit determines that no identified time occurs during the early morning time range or the noon time range, then the processing unit extends the rolling sum window by a predetermined (e.g., 15 - minute) period on the leading edge / front side, and then recalculates as detailed above. The processing unit continues this process until there is one remaining minimum sum value or the window size exceeds 12 hours. If there is one remaining minimum sum value, the sleep cycle is determined by the processing unit by first calculating the sleep end time as the time value with the minimum rolling sum minus 1 hour, and then calculating the sleep start time as the sleep end time minus 6 hours. If the window size exceeds 12 hours and there is no minimum sum value, the sleep cycle is determined by the processing unit by first calculating the sleep end time as the minimum time of the minimum rolling sum of the most recent rolling sum result minus 1 hour, and then calculating the sleep start time as the sleep end time minus 6 hours.

[0065] Although the above has been detailed with respect to the sleep cycle, the above can equally be used to identify fasting cycles. In various aspects, different time ranges suitable for a particular fasting cycle (e.g., instead of or in addition to the early morning and noon time ranges) can be utilized. Additionally, any exemplary time period, such as a 6 - hour sleep cycle, can vary according to settings and / or for other purposes.

[0066] Return reference Figure 3A, once the sleep cycle (or fasting cycle) is determined at step 302, the method proceeds to step 304, where the fasting glucose reading at the end of the sleep cycle (or fasting cycle) is identified. For CGM users, for example, where the sensor device 60 ( Figure 1A ) is a CGM, the CGM 60 can be configured to send all data related to the glucose readings during the determined fasting cycle to the processing unit of the smartphone 30, which will include the fasting glucose reading at the end of the fasting period. For BG users, for example, where the sensor device 60 ( Figure 1A ) is a blood glucose meter, the processing unit accesses the data stored in the memory unit and excludes any glucose reading data within two hours after a bolus dose data point.

[0067] The processing unit identifies the fasting glucose value and time for each day of a predetermined past time period (e.g., up to 30 days). Generally, the fasting glucose value is determined based on the proximity of the glucose value to the sleep end time value. More specifically, in the glucose reading dataset for each day, the processor analyzes the glucose data within a specific time amount before and after the determined sleep end time (e.g., a 7-hour period that includes two hours before the determined sleep end time to five hours after the sleep end time) to identify the glucose value that occurs closest to the sleep end time, excluding any glucose values that occur within a specific time period (e.g., up to 2 hours) after a bolus dose. This glucose value is identified as the fasting glucose value, and the fasting glucose time is recorded as the time when the fasting glucose value is recorded. If the processing unit cannot identify the fasting glucose value within these parameters, then the processing unit will use the sleep end time value to define the fasting glucose time as a placeholder for that particular day in the analysis.

[0068] Proceeding to step 306, the pre-fasting or pre-bedtime glucose value and time are identified for each day of a predetermined time period (e.g., up to 30 days). To achieve this, the processor analyzes the glucose data for each day within a specific time period before the determined sleep start time (e.g., a 7-hour period that includes 12 hours before the determined sleep start time to 5 hours before the sleep start time) to identify the glucose value that occurs closest to the sleep start time (not before a bolus dose). This glucose value is identified as the pre-fasting glucose value, and the pre-fasting glucose time is recorded as the time when the pre-fasting glucose value is recorded.

[0069] Next, at step 308, the statistics of the overnight basal response are calculated and a report is generated based on this for the user, doctor, and / or health management application 40 ( Figure 1A)is capable of acting to adjust or recommend an adjustment to therapy, such as a basal bolus dose. If the user is a continuous glucose monitor (CGM) user, then the basal assessment fasting window is the same as the sleep cycle, which is recorded daily by the user's CGM device and can be sent to a smart phone 30 for data storage and / or data processing. If the processing unit determines that there are zero days with both an identified prefasting glucose value and a fasting glucose value, and the requested report is a 30-day or 90-day report (for example), then the processing unit can include additional days (such as up to 30 days) until the processing unit identifies a day in which both a prefasting glucose value and a fasting glucose value are determined.

[0070] After step 308, in various aspects, method 300 can proceed to step 310, where the processing unit excludes from the generated report days in which the glucose data includes a bolus dose during the determined fasting / sleep cycle. This exclusion addresses the problem of disqualifying the night or subtracting the modeling effect from the fasting designation when the glucose response is modified, such as by eating food or by administering a drug, such as a mealtime insulin injection, during the sleep cycle or a predetermined time period prior to the cycle, to obtain a pure basal glucose response. If not utilized or required, method 300 proceeds to step 312.

[0071] In step 312 of method 300, the current hypoglycemia frequency and risk based on the generated fasting glucose values and the basal assessment report are evaluated by a decision maker. The decision maker, such as the user, a doctor, and / or a health management application 40( Figure 1A )(via the processing unit) can then analyze the data and determine at step 314 whether the risk of hypoglycemia is too high. If the decision maker determines that the risk of hypoglycemia is too high ("yes" at step 314), then method 300 proceeds to step 316, where a recommended dose reduction is calculated. If the decision maker determines that the risk of hypoglycemia is not too high ("no" at step 314), then method 300 proceeds to step 318 to determine whether to recommend an increase in the basal dose or whether to recommend no change.

[0072] Go to Figure 4 , according to the present disclosure, there is also provided a fully automated method 400 implemented by a meal time window assessment module 260( Figure 2 ) for detecting a meal time window by identifying a meal time window, labeling a meal time bolus dose to the meal time window, labeling pre-meal glucose readings, labeling post-meal glucose readings, generating a report including a bolus assessment, and using the generated report to detect the meal time window and, if necessary, adjusting or recommending an adjustment to the user bolus dose associated with the detected meal time window to adjust or recommend an adjustment to the patient user's bolus dose associated with each meal window.

[0073] Method 400 begins at step 402 by marking a meal dose. This can be achieved by the user manually marking the meal dose, or by the processing unit executing a set of instructions in the health management application 40( Figure 1A ) to automatically mark the meal dose. The automatic marking of the meal bolus dose can be achieved by the processing unit collecting and marking meal dose data from a predetermined past time period (e.g., the previous fourteen calendar days) stored in the memory unit. The processing unit then classifies the meal dose for a given meal according to specific criteria. For example, in the carbohydrate counting mode, the processing unit determines the dose with the largest amount obtained within the corrected meal window network as the meal dose. More specifically, the processing unit compares the dose values (e.g., the total dose size minus the corrected amount of active insulin (IOB) and glucose based on the meal time) that can be attributed to the size value of the associated meal, and determines the dose with the largest dose value as the meal dose. For the meal estimation and fixed dose modes, if there is a paired recommended dose for the meal in the meal window, the processor determines that dose as the meal dose. If multiple doses match these criteria in a given meal window, the processing unit determines the first such dose as the meal dose.

[0074] If the processing unit identifies a paired recommended dose that is marked for a different meal than the evaluated meal window, the processing unit will exclude the glucose data for the evaluated meal window for a particular day. For example, the processing unit will exclude the dose recommendation marked for breakfast in the dinner time window from the dinner window on that day. In the case where there are multiple dose recommendations in a meal window for a particular day, if one of the recommendations is for the correct meal window, the day is still included.

[0075] When there is no paired recommended dose in the meal window, the processing unit determines the meal dose as the dose with the largest dose value obtained within the corrected meal window network. When there is no paired recommended dose and the user is using the meal estimation function, the processing unit determines the size of the meal by matching the corrected meal dose value network with the most recent meal size setting. If the value is equidistant from two meal sizes, the processing unit classifies the dose as the smaller of the two meal sizes.

[0076] In various aspects, the correction amount can be calculated and subtracted only when the insulin sensitivity factor (ISF), target blood glucose, and blood glucose within a subsequent predetermined time period (e.g., the last 10 minutes) of the dose are available.

[0077] Once the meal dose is marked in step 402, method 400 proceeds to step 404, where the pre-meal glucose values within each day are identified. To achieve this, the processing unit accesses the data stored in the memory unit and identifies the glucose values and times for each meal within a predetermined past time period (e.g., the previous 14 days). In the glucose dataset for each day, the processor analyzes the glucose data within a specific (e.g., 1-hour) period before each meal dose to identify the glucose value that occurs closest to the meal time. This glucose value is identified as the pre-meal glucose value, and the pre-meal glucose time is recorded as the time when the pre-meal glucose value is recorded.

[0078] Next, at step 406, the post-meal glucose values within each day are identified. To achieve this, the processing unit accesses the data stored in the memory unit and identifies the glucose values and times for each meal within a predetermined past time period (e.g., 14 days). In the glucose dataset for each day, the processor analyzes the glucose data (e.g., values and times) within a specific time period (e.g., 2 hours) after each meal dose up to an earlier 8 hours (e.g.) after the meal dose, as well as the time of the next marked meal dose. The processing unit determines the post-meal glucose value as the glucose value at the time closest to a predetermined time (e.g., 4 hours) after the meal dose.

[0079] Method 400 continues to step 408, where statistics for the meal-time bolus response are calculated and a report is generated for the user, doctor, and / or health management application 40( Figure 1A ) for use in adjusting or recommending adjustments to the treatment. As an example, the reported meal assessment window can be a graph that is zero-aligned with the available pre-meal glucose times and wide enough to show the most recently available post-meal glucose values. If the processing unit determines that there are zero days with both identified pre-meal glucose values and post-meal glucose values, and the requested report is a 30-day or 90-day report (e.g.), then the processing unit can include additional days (e.g., up to 30 days) until the processing unit identifies a day where both pre-meal glucose values and post-meal glucose values are determined.

[0080] The next step 410 in method 400 is to evaluate the current hypoglycemia frequency and risk based on the generated meal window assessment report. The decision maker, e.g., the user, doctor, and / or health management application 40( Figure 1A(via a processing unit) can analyze the data and determine at step 412 whether the risk of hypoglycemia is too high. If the decision maker determines that the risk of hypoglycemia is too high ("Yes" at step 412), then method 400 proceeds to step 414 to calculate a recommended dose reduction for a specific dietary window. If the decision maker determines that the risk of hypoglycemia is not too high ("No" at step 412), then method 400 advances to step 416, where it is determined at step 416 whether a bolus dose increase associated with a specific dietary window is recommended or whether no change is recommended.

[0081] The various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and the drawings. It should also be understood that, according to an example, certain acts or events of any of the processes or methods described herein can be performed in a different order, can be added, combined, or completely excluded (e.g., all described acts and events may not be necessary for the implementation of the technique). Additionally, although certain aspects of the present disclosure are described for clarity as being performed by a single module or unit, it should be understood that the techniques of the present disclosure can be performed by a combination of units or modules associated with, for example, a medical device.

[0082] In one or more instances, the described functionality and / or operational aspects can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality can be stored on a computer-readable medium in the form of one or more instructions or code and executed by a hardware-based processing unit. The computer-readable medium can include a non-transitory computer-readable medium, which corresponds to a tangible medium, such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store the desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0083] The instructions can be executed by one or more processors, such as one or more digital signal processors (DSPs), general microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, as used herein, the term "processor" or "processing unit" can refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Similarly, the techniques can be implemented entirely in one or more circuits or logic elements.

[0084] Although several aspects of the present disclosure have been detailed and illustrated in the drawings, the present disclosure is not intended to be limited thereby, as the scope of the present disclosure is intended to be as broad as the art will permit and should be read in this manner as well. Thus, the above description and drawings should not be construed as restrictive, but only as exemplary of particular aspects. Those skilled in the art will envision other modifications within the scope and spirit of the appended claims.

Claims

1. A drug administration and tracking system, comprising: A drug delivery device configured to deliver a plurality of doses of a drug to a user over multiple days, the drug delivery device including a processor configured to generate dose information associated with drug doses dispensed from the drug delivery device and wirelessly transmit, for each of the plurality of doses, dose information indicating the delivery time of the dose; and a computing device that communicates with the drug delivery device and is configured to receive the dose information for each of the plurality of doses, the computing device including a data processing unit that includes a processor and a software application stored in a memory of the data processing unit and having instructions that, when executed by the processor, are operative to cause the computing device to: retrieve dose information for all of the plurality of doses within a predetermined number of days period based on a signal wirelessly transmitted from the drug delivery device to the computing device; classify each of the plurality of doses within the predetermined number of days period into one of a plurality of time blocks throughout a 24-hour time range; determine an end time of a fasting period of the user based on the time block among the plurality of time blocks that has the smallest dose classified therein; and select a physiological parameter reading as a physiological parameter reading of interest from among a plurality of physiological parameter readings based on the proximity of the time of the physiological parameter reading to the determined end time of the user's fasting period.

2. The drug administration and tracking system according to claim 1, further comprising: a sensor device configured to obtain the plurality of physiological parameter readings and transmit the plurality of physiological parameter readings to the computing device.

3. The drug administration and tracking system according to claim 1, wherein the physiological parameter readings are a plurality of blood glucose readings and the physiological parameter reading of interest is a fasting blood glucose reading.

4. The drug administration and tracking system according to claim 3, further comprising: a blood glucose sensor device configured to obtain the plurality of blood glucose readings and transmit the plurality of blood glucose readings to the computing device.

5. The drug administration and tracking system according to claim 4, wherein the blood glucose sensor device is a continuous glucose monitor.

6. The drug administration and tracking system according to claim 3, wherein the instructions, when executed by the processor, are further operative to cause the computing device to: determine a start time of the fasting period based on the end time of the fasting period; and select the blood glucose reading as a pre-fasting blood glucose reading from among the plurality of blood glucose readings based on the proximity of the time of the blood glucose reading to the start time of the fasting period.

7. The drug administration and tracking system according to claim 6, wherein the start time of the fasting period is a sleep start time, the end time of the fasting period is a sleep end time, and wherein a sleep period is defined between the sleep period start time and the sleep period end time.

8. The drug administration and tracking system according to claim 6, wherein the instructions, when executed by the processor, are further operative to cause the computing device to: determine whether the risk of hypoglycemia is too high; and when it is determined that the risk of hypoglycemia is too high, calculate a dose reduction amount to adjust the dose delivered by the drug delivery device.

9. The drug administration and tracking system according to claim 8, wherein determining whether the risk of hypoglycemia is too high is based on the pre-fasting blood glucose reading and the fasting blood glucose reading.

10. The drug administration and tracking system according to claim 1, wherein the instructions, when executed by the processor, are further operable to cause the computing device to: Set a user's default fasting period based on the computing device receiving the dose information on less than a predetermined threshold number of days within the predetermined number of days cycle.

11. The drug administration and tracking system according to claim 1, wherein the instructions, when executed by the processor, are further operable to cause the computing device to: For each of the plurality of time blocks, generate a bolus frequency vector based on the dose information received for that time block over all days of the predetermined number of days cycle; Calculate a rolling sum value for each bolus frequency vector; and Identify at least one daily time value having the minimum rolling sum value over all days of the predetermined number of days cycle.

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