Patent Grant
12023127
This disclosure relates to a system for managing insulin administration or insulin dosing.
Managing diabetes requires calculating insulin doses for maintaining blood glucose measurements within desired ranges. Managing diabetes requires calculating insulin doses for maintaining blood glucose measurements within desired ranges. Manual calculation may not be accurate due to human error, which can lead to patient safety issues. Different institutions use multiple and sometimes conflicting protocols to manually calculate an insulin dosage. Moreover, the diabetic population includes many young children or elderly persons whom have difficulty understanding calculations for insulin doses.
One aspect of the disclosure provides a method. The method includes receiving subcutaneous information for a patient at data processing hardware and executing, at the data processing hardware, a subcutaneous outpatient process for determining recommended insulin dosages. The subcutaneous outpatient process includes obtaining, at the data processing hardware, blood glucose data of the patient from a glucometer in communication with the computing device. The blood glucose data includes blood glucose measurements of the patient, blood glucose times associated with a time of each blood glucose measurement, and dosages of insulin administered by the patient associated with each blood glucose measurement. The subcutaneous outpatient process further includes determining associated ones of scheduled blood glucose time intervals for each of the blood glucose measurements using the data processing hardware based on the blood glucose times and aggregating, using the data processing hardware, the blood glucose measurements associated with at least one of the scheduled blood glucose time intervals to determine a representative aggregate blood glucose measurement associated with the at least one scheduled blood glucose time interval. The method further includes determining a next recommended insulin dosage for the patient using the data processing hardware based on the representative aggregate blood glucose measurement and the subcutaneous information and transmitting the next recommended insulin dosage to a portable device associated with the patient, the portable device displaying the next recommended insulin dosage.
Implementations of the disclosure may include one or more of the following optional features. In some implementations, the method includes transmitting the subcutaneous outpatient process to an administration device in communication with the data processing hardware. The administration device includes a doser and an administration computing device in communication with the doser. The administration computing device, when executing the subcutaneous outpatient program, causes the doser to administer insulin specified by the subcutaneous outpatient program. The data processing hardware may obtain the blood glucose data by one or more of the following ways: receiving the blood glucose data from a remote computing device in communication with the data processing hardware during a batch download process, the remote computing device executing a download program for downloading the blood glucose data from the glucometer; receiving the blood glucose data from the glucometer upon measuring the blood glucose measurement; receiving the blood glucose data from a meter manufacturer computing device in communication with the data processing hardware during a batch download process, the meter manufacturer receiving the blood glucose data from the glucometer; or receiving the blood glucose data from a patient device in communication with the data processing hardware and the glucometer, the patient device receiving the blood glucose data from the glucometer.
In some examples, the method includes aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a breakfast blood glucose time interval to determine a representative aggregate breakfast blood glucose measurement and aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a midsleep blood glucose time interval to determine a representative aggregate midsleep blood glucose measurement. The method may further include selecting, using the data processing hardware, a governing blood glucose as a lesser one of the representative aggregate midsleep blood glucose measurement or the representative aggregate breakfast blood glucose measurement and determining, using the data processing hardware, an adjustment factor for adjusting a next recommended basal dosage based on the selected governing blood glucose measurement. The method may further include obtaining, using the data processing hardware, a previous day's recommended basal dosage and determining, using the data processing hardware, the next recommended basal dosage by multiplying the adjustment factor times the previous day's recommended basal dosage.
In some implementations, the method includes aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a lunch blood glucose time interval to determine a representative aggregate lunch blood glucose measurement and selecting, using the data processing hardware, a governing blood glucose as the representative aggregate lunch blood glucose measurement. The method may also include determining, using the data processing hardware, an adjustment factor for adjusting a next recommended breakfast bolus based on the selected governing blood glucose measurement, obtaining, using the data processing hardware, a previous day's recommended breakfast bolus, and determining, using the data processing hardware, the next recommended breakfast bolus by multiplying the adjustment factor times the previous day's recommended breakfast bolus.
In some examples, the method includes aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a dinner blood glucose time interval to determine a representative aggregate dinner blood glucose measurement and selecting, using the data processing hardware, a governing blood glucose as the representative aggregate dinner blood glucose measurement. The method may also include determining, using the data processing hardware, an adjustment factor for adjusting a next recommended lunch bolus based on the selected governing blood glucose measurement, obtaining, using the data processing hardware, a previous day's recommended lunch bolus, and determining, using the data processing hardware, the next recommended lunch bolus by multiplying the adjustment factor times the previous day's recommended lunch bolus.
In some implementations, the method includes aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a bedtime blood glucose time interval to determine a representative aggregate bedtime blood glucose measurement and selecting, using the data processing hardware, a governing blood glucose as the representative aggregate bedtime blood glucose measurement. The method may also include determining, using the data processing hardware, an adjustment factor for adjusting a next recommended dinner bolus based on the selected governing blood glucose measurement, obtaining, using the data processing hardware, a previous day's recommended dinner bolus, and determining, using the data processing hardware, the next recommended dinner bolus by multiplying the adjustment factor times the previous day's recommended dinner bolus.
In some examples, the method includes aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with a selected time interval to determine a representative aggregate blood glucose measurement associated with the selected time interval and selecting, using the data processing hardware, a governing blood glucose as the representative aggregate blood glucose measurement associated with the selected time interval. The method may further include determining, using the data processing hardware, an adjustment factor for adjusting a next recommended carbohydrate-to-insulin ratio governed by the selected time interval based on the selected governing blood glucose measurement, obtaining, using the data processing hardware, a previous day's recommended carbohydrate-to-insulin ratio governed by the selected time interval, and determining, using the data processing hardware, the next recommended carbohydrate-to-insulin ratio by multiplying the adjustment factor times the previous day's recommended carbohydrate-to-insulin ratio. The selected time interval may include one of lunch blood glucose interval, a dinner blood glucose time interval, or a bedtime blood glucose time interval. Each scheduled blood glucose time interval may correlate to an associated blood glucose type including one of a pre-breakfast blood glucose measurement, a pre-lunch blood glucose measurement, a pre-dinner blood glucose measurement, a bedtime blood glucose measurement and a midsleep blood glucose measurement.
In some examples, the method includes determining, using the data processing hardware, the blood glucose type for each of blood glucose measurement, the blood glucose type is tagged by the patient when measuring the blood glucose measurement. A portion of the scheduled blood glucose time intervals are associated with time intervals when the patient is consuming meals and a remaining portion of the scheduled blood glucose time intervals are associated with time intervals when the patient is not consuming meals.
In some examples, the method includes receiving, at the data processing hardware, a specified date range from a remote healthcare provider computing device in communication with the data processing hardware and aggregating, using the data processing hardware, one or more of the blood glucose measurements associated with at least one scheduled blood glucose time intervals and within the specified date range. The representative aggregate blood glucose measurement may include a mean blood glucose value for the associated scheduled blood glucose time interval. The representative aggregate blood glucose measurement may further include a median blood glucose value for the associated scheduled blood glucose time interval.
Another aspect of the disclosure provides a system. The system includes a dosing controller receiving subcutaneous information for a patient and executing a subcutaneous outpatient process for determining recommended insulin dosages, during subcutaneous outpatient program. The dosing controller includes obtaining, at the data processing hardware, blood glucose data of the patient from a glucometer in communication with the computing device, the blood glucose data including blood glucose measurements of the patient, blood glucose times associated with a time of each blood glucose measurement, and dosages of insulin administered by the patient associated with each blood glucose measurement. The system also includes determining associated ones of scheduled blood glucose time intervals for each of the blood glucose measurements based on the blood glucose times and aggregating the blood glucose measurements associated with at least one of the scheduled blood glucose time intervals to determine a representative aggregate blood glucose measurement associated with the at least one scheduled blood glucose time interval. The system also includes determining a next recommended insulin dosage for the patient based on the representative aggregate blood glucose measurement and the subcutaneous information and transmitting the next recommended insulin dosage to a portable device associated with the patient, the portable device displaying the next recommended insulin dosage.
This aspect may include one or more of the following optional features. In some implementations, the dosing controller transmits the subcutaneous outpatient process to an administration device in communication with the dosing controller. The administration device includes a doser and an administration computing device in communication with the doser. The administration computing device, when executing the subcutaneous outpatient process, causes the doser to administer insulin specified by the subcutaneous outpatient process. The dosing controller may obtain the blood glucose data by one or more of the following: receiving the blood glucose data from a remote computing device in communication with the dosing controller during a batch download process, the remote computing device executing a download program for downloading the blood glucose data from the glucometer; receiving the blood glucose data from the glucometer upon measuring the blood glucose measurement; receiving the blood glucose data from a meter manufacturer computing device in communication with the dosing controller during a batch download process, the meter manufacturer receiving the blood glucose data from the glucometer; or receiving the blood glucose data from a patient device in communication with the dosing controller and the glucometer, the patient device receiving the blood glucose data from the glucometer.
The dosing controller may further include aggregating one or more of the blood glucose measurements associated with a breakfast blood glucose time interval to determine a representative aggregate breakfast blood glucose measurement and aggregating one or more of the blood glucose measurements associated with a midsleep blood glucose time interval to determine a representative aggregate midsleep blood glucose measurement. The dosing controller may further include selecting a governing blood glucose as a lesser one of the representative aggregate midsleep blood glucose measurement or the representative aggregate breakfast blood glucose measurement, determining an adjustment factor for adjusting a next recommended basal dosage based on the selected governing blood glucose measurement, obtaining a previous day's recommended basal dosage, and determining the next recommended basal dosage by multiplying the adjustment factor times the previous day's recommended basal dosage.
The dosing controller may also include aggregating one or more of the blood glucose measurements associated with a lunch blood glucose time interval to determine a representative aggregate lunch blood glucose measurement and selecting a governing blood glucose as the representative aggregate lunch blood glucose measurement. The dosing controller may further include determining an adjustment factor for adjusting a next recommended breakfast bolus based on the selected governing blood glucose measurement, obtaining a previous day's recommended breakfast bolus, and determining the next recommended breakfast bolus by multiplying the adjustment factor times the previous day's recommended breakfast bolus.
In some examples, the dosing controller includes aggregating one or more of the blood glucose measurements associated with a dinner blood glucose time interval to determine a representative aggregate dinner blood glucose measurement and selecting a governing blood glucose as the representative aggregate dinner blood glucose measurement. The dosing controller may also include determining an adjustment factor for adjusting a next recommended lunch bolus based on the selected governing blood glucose measurement, obtaining a previous day's recommended lunch bolus, and determining the next recommended lunch bolus by multiplying the adjustment factor times the previous day's recommended lunch bolus.
In some implementations, the dosing controller includes aggregating one or more of the blood glucose measurements associated with a bedtime blood glucose time interval to determine a representative aggregate bedtime blood glucose measurement and selecting a governing blood glucose as the representative aggregate bedtime blood glucose measurement. The dosing controller may also include determining an adjustment factor for adjusting a next recommended dinner bolus based on the selected governing blood glucose measurement, obtaining a previous day's recommended dinner bolus, and determining the next recommended dinner bolus by multiplying the adjustment factor times the previous day's recommended dinner bolus.
The dosing controller may further include aggregating one or more of the blood glucose measurements associated with a selected time interval to determine a representative aggregate blood glucose measurement associated with the selected time interval and selecting a governing blood glucose as the representative aggregate blood glucose measurement associated with the selected time interval. The dosing controller may also include determining an adjustment factor for adjusting a next recommended carbohydrate-to-insulin ratio governed by the selected time interval based on the selected governing blood glucose measurement, obtaining a previous day's recommended carbohydrate-to-insulin ratio governed by the selected time interval, and determining the next recommended carbohydrate-to-insulin ratio by multiplying the adjustment factor times the previous day's recommended carbohydrate-to-insulin ratio. The selected time interval may include one of lunch blood glucose time interval, a dinner blood glucose time interval, or a bedtime blood glucose time interval. Each scheduled blood glucose time interval may correlate to an associated blood glucose type including one of a pre-breakfast blood glucose measurement, a pre-lunch blood glucose measurement, a pre-dinner blood glucose measurement, a bedtime blood glucose measurement and a midsleep blood glucose measurement. The dosing controller may determine the blood glucose type for each of blood glucose measurement. The blood glucose type is tagged by the patient when measuring the blood glucose measurement.
A portion of the scheduled blood glucose time intervals may be associated with time intervals when the patient is consuming meals and a remaining portion of the scheduled blood glucose time intervals may be associated with time intervals when the patient is not consuming meals. The dosing controller may receive a specified date range from a remote healthcare provider computing device in communication with the data processing hardware and aggregate one or more of the blood glucose measurements associated with at least one scheduled blood glucose time intervals and within the specified date range. The representative aggregate blood glucose measurement may include a mean blood glucose value for the associated scheduled blood glucose time interval. The representative blood glucose measurement may also include a median blood glucose value for the associated scheduled blood glucose time interval.
The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.
Like reference symbols in the various drawings indicate like elements.
Diabetic outpatients must manage their blood glucose level within desired ranges by using insulin therapy that includes injection dosages of insulin corresponding to meal boluses and basal dosages. Meal boluses without meals cause hypoglycemia; meals without meal boluses cause hyperglycemia. Different providers may use different methods of adjusting doses: some may use formulas of their own; some may use paper protocols that are complex and difficult for the outpatient to follow, leading to a high incidence of human error; and some may use heuristic methods. Therefore, it is desirable to have a clinical support system 100 (
Referring to
Hyperglycemia is a condition that exists when blood sugars are too high. While hyperglycemia is typically associated with diabetes, this condition can exist in many patients who do not have diabetes, yet have elevated blood sugar levels caused by trauma or stress from surgery and other complications from hospital procedures. Insulin therapy is used to bring blood sugar levels back into a normal range.
Hypoglycemia may occur at any time when a patient's blood glucose level is below a preferred target. Appropriate management of blood glucose levels for critically ill patients reduces co-morbidities and is associated with a decrease in infection rates, length of hospital stay, and death. The treatment of hyperglycemia may differ depending on whether or not a patient has been diagnosed with Type 1 diabetes mellitus, Type 2 diabetes mellitus, gestational diabetes mellitus, or non-diabetic stress hyperglycemia. The blood glucose target range BGTR is defined by a lower limit, i.e., a low target BGTRL and an upper limit, i.e., a high target BGTRH.
Diabetes Mellitus has been treated for many years with insulin. Some recurring terms and phrases are described below:
Injection: Administering insulin by means of manual syringe or an insulin “pen,” with a portable syringe named for its resemblance to the familiar writing implement.
Infusion: Administering insulin in a continuous manner by means of an insulin pump for subcutaneous insulin apparatus 123a capable of continuous administration.
Basal-Bolus Therapy: Basal-bolus therapy is a term that collectively refers to any insulin regimen involving basal insulin and boluses of insulin.
Basal Insulin: Insulin that is intended to metabolize the glucose released by a patient's the liver during a fasting state. Basal insulin is administered in such a way that it maintains a background level of insulin in the patient's blood, which is generally steady but may be varied in a programmed manner by an insulin pump 123a. Basal insulin is a slow, relatively continuous supply of insulin throughout the day and night that provides the low, but present, insulin concentration necessary to balance glucose consumption (glucose uptake and oxidation) and glucose production (glucogenolysis and gluconeogenesis). A patient's Basal insulin needs are usually about 10 to 15 mU/kg/hr and account for 30% to 50% of the total daily insulin needs; however, considerable variation occurs based on the patient 10.
Bolus Insulin: Insulin that is administered in discrete doses. There are two main types of boluses, Meal Bolus and Correction Bolus.
Meal Bolus: Taken just before a meal in an amount which is proportional to the anticipated immediate effect of carbohydrates in the meal entering the blood directly from the digestive system. The amounts of the Meal Boluses may be determined and prescribed by a physician 40 for each meal during the day, i.e., breakfast, lunch, and dinner. Alternatively, the Meal Bolus may be calculated in an amount generally proportional to the number of grams of carbohydrates in the meal. The amount of the Meal Bolus is calculated using a proportionality constant, which is a personalized number called the Carbohydrate-to-Insulin Ratio (CIR) and calculated as follows:
Meal Insulin Bolus={grams of carbohydrates in the meal}/CIR (1)
Correction Bolus CB: Injected immediately after a blood glucose measurement; the amount of the correction bolus is proportional to the error in the BG (i.e., the bolus is proportional to the difference between the blood glucose measurement BG and the patient's personalized Target blood glucose BGTarget). The proportionality constant is a personalized number called the Correction Factor, CF. The Correction Bolus is calculated as follows:
CB=(BG−BGTarget)/CF (2)
A Correction Bolus CB is generally administered in a fasting state, after the previously consumed meal has been digested. This often coincides with the time just before the next meal.
In some implementations, blood glucose measurements BG are aggregated using an exponentially-weighted moving average EMAt as a function for each modal day's time interval BG. The EMAt is calculated as follows:
EMAt=α(BGt)+(1−α)EMAt-1, (3)
wherein:
α=2/(n+1),
wherein n is the number of equivalent days averaged. In other embodiments, an arithmetic moving average is utilized that calculates the sum of all BG values in n days divided by a total count (n) of all values associated with the arithmetic average.
There are several kinds of Basal-Bolus insulin therapy including Insulin Pump therapy and Multiple Dose Injection therapy:
Insulin Pump Therapy: An insulin pump 123a is a medical device used for the administration of insulin in the treatment of diabetes mellitus, also known as continuous subcutaneous insulin infusion therapy. The device includes: a pump, a disposable reservoir for insulin, and a disposable infusion set. The pump 123a is an alternative to multiple daily injections of insulin by insulin syringe or an insulin pen and allows for intensive insulin therapy when used in conjunction with blood glucose monitoring and carbohydrate counting. The insulin pump 123a is a battery-powered device about the size of a pager. It contains a cartridge of insulin, and it pumps the insulin into the patient via an “infusion set”, which is a small plastic needle or “canula” fitted with an adhesive patch. Only rapid-acting insulin is used.
Multiple Dose Injection (MDI): MDI involves the subcutaneous manual injection of insulin several times per day using syringes or insulin pens 123b. Meal insulin is supplied by injection of rapid-acting insulin before each meal in an amount proportional to the meal. Basal insulin is provided as a once, twice, or three time daily injection of a dose of long-acting insulin. Other dosage frequencies may be available. Advances continue to be made in developing different types of insulin, many of which are used to great advantage with MDI regimens:
Long-acting insulins are non-peaking and can be injected as infrequently as once per day. These insulins are widely used for Basal Insulin. They are administered in dosages that make them appropriate for the fasting state of the patient, in which the blood glucose is replenished by the liver to maintain a steady minimum blood glucose level.
Rapid-acting insulins act on a time scale shorter than natural insulin. They are appropriate for boluses.
The decision support system 100 includes a glycemic management module 50, an integration module 60, a surveillance module 70, and a reporting module 80. Each module 50, 60, 70, 80 is in communication with the other modules 50, 60, 70, 80 via a network 20. In some examples, the network 20 (discussed below) provides access to cloud computing resources that allows for the performance of services on remote devices instead of the specific modules 50, 60, 70, 80. The glycemic management module 50 executes a program 200 (e.g., an executable instruction set) on a processor 112, 132, 142 or on the cloud computing resources. The integration module 60 allows for the interaction of users 40 and patients 10 with the system 100. The integration module 60 receives information inputted by a user 40 and allows the user 40 to retrieve previously inputted information stored on a storage system (e.g., one or more of cloud storage resources 24, a non-transitory memory 144 of an electronic medical system 140 of a clinic 42 or hospital call center (e.g., Telemedicine facility), a non-transitory memory 114 of the patient device 110, a non-transitory memory 134 of the service provider's system 130, or other non-transitory storage media in communication with the integration module 60). Therefore, the integration module 60 allows for the interaction between the users 40, patients 10, and the system 100 via a display 116, 146. The surveillance module 70 considers patient information 208a received from a user 40 via the integration module 60 and information received from a glucometer 124 that measures a patient's blood glucose value BG and determines if the patient 10 is within a threshold blood glucose value BGTH. In some examples, the surveillance module 70 alerts the user 40 if a patient's blood glucose values BG are not within a threshold blood glucose value BGTH. The surveillance module 70 may be preconfigured to alert the user 40 of other discrepancies between expected values and actual values based on pre-configured parameters (discussed below). For example, when a patient's blood glucose value BG drops below a lower limit of the threshold blood glucose value BGTHL. The reporting module 80 may be in communication with at least one display 116, 146 and provides information to the user 40 determined using the glycemic management module 50, the integration module 60, and/or the surveillance module 70. In some examples, the reporting module 80 provides a report that may be displayed on a display 116, 146 and/or is capable of being printed.
The system 100 is configured to evaluate a glucose level and nutritional intake of a patient 10. Based on the evaluation and analysis of the data, the system 100 calculates an insulin dose, which is administered to the patient 10 to bring and maintain the blood glucose level of the patient 10 into the blood glucose target range BGTR. The system 100 may be applied to various devices, including, but not limited to, subcutaneous insulin infusion pumps 123a, insulin pens 123b, glucometers 124, continuous glucose monitoring systems, and glucose sensors.
In some examples the clinical decision support system 100 includes a network 20, a patient device 110, a dosing controller 160, a service provider 130, and a meter manufacturer provider 190. The patient device 110 may include, but is not limited to, desktop computers 110a or portable electronic device 110b (e.g., cellular phone, smartphone, personal digital assistant, barcode reader, personal computer, or a wireless pad) or any other electronic device capable of sending and receiving information via the network 20. In some implementations, one or more of the patient's glucometer 124, insulin pump 123a, or insulin pen 123b are capable of sending and receiving information via the network 20.
The patient device 110a, 110b includes a data processor 112a, 112b (e.g., a computing device that executes instructions), and non-transitory memory 114a, 114b and a display 116a, 116b (e.g., touch display or non-touch display) in communication with the data processor 112. In some examples, the patient device 110 includes a keyboard 118, speakers 122, microphones, mouse, and a camera.
The glucometer 124, insulin pump 123a, and insulin pen 123b associated with the patient 10 include a data processor 112c, 112d, 112e (e.g., a computing device that executes instructions), and non-transitory memory 114c, 114d, 114e and a display 116c, 116d, 116e (e.g., touch display or non-touch display in communication with the data processor 112c, 112d, 112e.
The meter manufacturer provider 190 may include may include a data processor 192 in communication with non-transitory memory 194. The data processor 192 may execute a proprietary download program 196 for downloading blood glucose BG data from the memory 114c of the patient's glucometer 124. In some implementations, the proprietary download program 196 is implemented on the health care provider's 140 computing device 142 or the patient's 10 device 110a for downloading the BG data from memory 114c. In some examples, the download program 196 exports a BG data file for storage in the non-transitory memory 24, 114, 144. The data processor 192 may further execute a web-based application 198 for receiving and formatting BG data transmitted from one or more of the patient's devices 110a, 110b, 124, 123a, 123b and storing the BG data in non-transitory memory 24, 114, 144.
The service provider 130 may include a data processor 132 in communication with non-transitory memory 134. The service provider 130 provides the patient 10 with a program 200 (see
In some implementations, a health care provider medical record system 140 is located at a doctor's office, clinic 42, or a facility administered by a hospital (such as a hospital call center (HCP)) and includes a data processor 142, a non-transitory memory 144, and a display 146 (e.g., touch display or non-touch display). The non-transitory memory 144 and the display 146 are in communication with the data processor 142. In some examples, the health care provider electronic medical system 140 includes a keyboard 148 in communication with the data processor 142 to allow a user 40 to input data, such as patient information 208a (
The dosing controller 160 is in communication with the glucometer 124, insulin administration device 123a, 123b and includes a computing device 112, 132, 142 and non-transitory memory 114, 134, 144 in communication with the computing device 112, 132, 142. The dosing controller 160 executes the program 200. The dosing controller 160 stores patient related information retrieved from the glucometer 124 to determine insulin doses and dosing parameters based on the received blood glucose measurement BG.
Referring to
The network 20 may include any type of network that allows sending and receiving communication signals, such as a wireless telecommunication network, a cellular telephone network, a time division multiple access (TDMA) network, a code division multiple access (CDMA) network, Global system for mobile communications (GSM), a third generation (3G) network, fourth generation (4G) network, a satellite communications network, and other communication networks. The network 20 may include one or more of a Wide Area Network (WAN), a Local Area Network (LAN), and a Personal Area Network (PAN). In some examples, the network 20 includes a combination of data networks, telecommunication networks, and a combination of data and telecommunication networks. The patient device 110, the service provider 130, and the hospital electronic medical record system 140 communicate with each other by sending and receiving signals (wired or wireless) via the network 20. In some examples, the network 20 provides access to cloud computing resources, which may be elastic/on-demand computing and/or storage resources 24 available over the network 20. The term ‘cloud’ services generally refers to a service performed not locally on a user's device, but rather delivered from one or more remote devices accessible via one or more networks 20.
Referring to
In some implementations, before the program 200 begins to receive the parameters, the program 200 may receive a username and a password (e.g., at a login screen displayed on the display 116, 146) to verify that a qualified and trained healthcare professional 40 is initiating the program 200 and entering the correct information that the program 200 needs to accurately administer insulin to the patient 10. The system 100 may customize the login screen to allow a user 40 to reset their password and/or username. Moreover, the system 100 may provide a logout button (not shown) that allows the user 40 to log out of the system 100. The logout button may be displayed on the display 116, 146 at any time during the execution of the program 200.
The decision support system 100 may include an alarm system 120 that alerts a user 40 at the clinic 42 (or hospital call center) when the patient's blood glucose level BG is outside the target range BGTR. The alarm system 120 may produce an audible sound via speaker 122 in the form of a beep or some like audio sounding mechanism. For instance, the alarm system 120 may produce an audible sound via a speaker 122 of the mobile device 110b In some examples, the alarm system 120 displays a warning message or other type of indication on the display 116a-e of the patient device 110 to provide a warning message. The alarm system 120 may also send the audible and/or visual notification via the network 20 to the clinic system 140 (or any other remote station) for display on the display 146 of the clinic system 140 or played through speakers 152 of the clinic system 140.
For commencing a SubQ outpatient process 1800 (
Referring to
TDD=QuickTransitionConstant*MTrans (4A)
where QuickTransitionConstant is usually equal to 1000, and MTrans is the patient's multiplier at the time of initiation of the SubQ transition process. In other implementations, the TDD is calculated by a statistical correlation of TDD as a function of body weight. The following equation is the correlation used:
TDD=0.5*Weight (kg) (4B)
In other implementations, the patient's total daily dose TDD is calculated in accordance with the following equation:
TDD=(BGTarget−K)*(MTrans)*24 (4C)
where MTrans is the patient's multiplier at the time of initiation of the SubQ transition process.
In some implementations, the patient SubQ information 216a is prepopulated with default parameters, which may be adjusted or modified. In some examples, portions of the patient SubQ information 216 are prepopulated with previously entered patient subcutaneous information 216a. The program 200 may prompt the request to the user 40 to enter the SubQ information 216a on the display 116 of the patient device 110. In some implementations, the subcutaneous insulin process 1800 prompts the request on the display 116 for a custom start of new SubQ patients (
The program 200 flows to block 216, where the user 40 enters patient subcutaneous information 216a, such as bolus insulin type, target range, basal insulin type and frequency of distribution (e.g., 1 dose per day, 2 doses per day, 3 doses per day, etc.), patient diabetes status, subcutaneous type ordered for the patient (e.g., Basal/Bolus and correction that is intended for patients on a consistent carbohydrate diet, frequency of patient blood glucose measurements, or any other relevant information. In some implementations, the patient subcutaneous information 216a is prepopulated with default parameters, which may be adjusted or modified. When the user 40 enters the patient subcutaneous information 216a, the user selects the program 200 to execute the SubQ outpatient process 1800 at block 226.
In some implementations, the user 40 selects to initiate a subcutaneous outpatient program 200 (
In some implementations, some functions or processes are used within the SubQ outpatient program 200 (
Referring to
In some examples, the process 700 may determine the total daily dose TDD of insulin once per day, for example, every night at midnight or at the next opening of the given patient's record after midnight. Other times may also be available. In addition, the total daily dose TDD may be calculated more frequently during the day, in some examples, the total daily dose TDD is calculated more frequently and considers the total daily dose TDD within the past 24 hours. The process 700 provides a timer 702, such as a countdown timer 702, where the timer 702 determines the time the process 700 executes. The timer 702 may be a count up timer or any other kind of timer. When the timer 702 reaches its expiration or reaches a certain time (e.g., zero for a countdown timer 702), the timer 702 executes the process 700. The counter 702 is used to determine at what time the process 700, at block 704, calculates the total daily dose TDD. If the counter is set to 24 hours for example, then decision block 704 checks if the time has reached 24 hours, and when it does, then the process 700 calculates the total daily dose TDD of insulin. Block 706 may receive insulin dosing data from a merged database 1906 (
TDD=Sum over previous day (all basal+all meal boluses+all correction boluses) (5A)
For some configurations, the TDD is calculated as the sum of the latest recommended insulin doses:
Alternate TDD=Sum of (latest recommended basal+latest recommended Breakfast Bolus+Latest Recommended Lunch Bolus+Latest Recommended Dinner Bolus) (5B)
After the process 700 determines the total daily dose TDD of insulin at block 706, the process 700 determines a Correction Factor CF immediately thereafter at block 710, using the calculated total daily dose TDD from block 706 and EQ. 5. The correction factor CF is determined using the following equation:
CF=CFR/TDD (6)
where CFR is a configurable constant stored in the non-transitory memory 24, 114, 144 of the system and can be changed from the setup screen (
Once the correction factor CF is determined in EQ. 6, the process 700 determines the correction bolus insulin dose at block 714 using the following equation:
CB=(BG−BGTarget)/CF) (7)
where BG is the blood glucose measurement of a patient 10 retrieved at block 712, BGTarget is the patient's personalized Target blood glucose, and CF is the correction factor. The process 700 returns the correction bolus CB at block 716. Rapid-acting analog insulin is currently used for Correction Boluses because it responds quickly to a high blood glucose BG. Also rapid acting analog insulin is currently used for meal boluses; it is usually taken just before or with a meal (injected or delivered via a pump). Rapid-acting analog insulin acts very quickly to minimize the rise of patient's blood sugar which follows eating.
A Correction Bolus CB is calculated for a blood glucose value BG at any time during the program 200. Pre-meal Correction Boluses CB, are calculated using EQ. 7. In the Pre-meal Correction Bolus equation (7) there is no need to account for Remaining Insulin IRem because sufficient time has passed for almost all of the previous meal bolus to be depleted. However, post-prandial correction boluses (after-meal correction boluses) are employed much sooner after the recent meal bolus and use different calculations that account for remaining insulin IRem that remains in the patient's body after a recent meal bolus. Rapid-acting analog insulin is generally removed by a body's natural mechanisms at a rate proportional to the insulin remaining IRem in the patient's body, causing the remaining insulin IRem in the patient's body to exhibit a negative exponential time-curve. Manufacturers provide data as to the lifetime of their insulin formulations. The data usually includes a half-life or mean lifetime of the rapid-acting analog insulin. The half-life of the rapid-acting analog insulin may be converted to mean lifetime by the conversion formula:
mean lifetime=Half-life*ln(2) (8A)
where ln(2) is the natural logarithm {base e} of two.
In some implementations, the process 700 accounts for post-prandial correction boluses by determining if there is any remaining insulin IRem in the patient's body to exhibit a negative exponential time-curve. At block 718, process 700 initializes a loop for determining IRem by setting IRem equal to zero, and retrieves a next earlier insulin dose (Dprev) and the associated data-time (TDose) at block 720.
The brand of insulin being used is associated with two half-life parameters: the half-life of the insulin activity (HLact) and the half-life of the process of diffusion of the insulin from the injection site into the blood (HLinj). Since the manufacturers and brands of insulin are few, the program 200 maintains the two half-lives of each insulin brand as configurable constants. These configurable constants can be input by a healthcare provider using an input screen of
For a single previous dose of insulin Dprev, given at a time TDose, the insulin remaining in the patient's body at the current time TCurrent refers to the Remaining Insulin IRem. The derivation of the equation for IRem involves a time-dependent two-compartment model of insulin: The insulin in the injection site Iinj(t) and the “active” insulin in the blood and cell membrane, Iact(t). The differential equation for Iinj(t) is:
dIinj/dt=−(0.693/HLinj)*Iinj(t). (8B)
The differential equation for Iact(t) is:
dlact(t)/dt=(0.693/HLinj)*Iinj(t)−(0.693/HLact)*Iact(t) (8C)
Equations 8B and 8C are simultaneous linear first-order differential equations. The solutions must be added together to represent the total insulin remaining, IRem. The final result can be written as a time-dependent factor that can be multiplied by the initial dose Dprev to obtain the time-dependent total remaining insulin IRem.
Process 700 determines, at block 724, IRem by multiplying the previous single dose of insulin Dprev {e.g. a Meal Bolus, Correction Bolus, or combined bolus} times a time-dependent factor as follows:
IRem(single dose)=Dprev*EXP(−(TCurrent−TDose)*0.693/HILinj)+D0*0.693/HLinj/(0.693/HLact−0.693/HLinj)+Dprev*(EXP(−(TCurrent−TDose)*0.693/HLinj)−EXP(−(TCurrent−TDose)*0.693/HLact)) (9A)
The Remaining Insulin IRem may account for multiple previous doses occurring in a time window looking backwards within a lifetime of the insulin being used. For example, IRem may be associated with a configurable constant within the range of 4 to 7 hours that represents the lifetime of rapid analog insulin. For example, IRem may be determined as follows:
IRem=sum of [IRem(single dose) over all doses in the within the lifetime of the insulin being used] (9B)
Process 700 iteratively determines IRem in the loop until, at block 722, the difference between the current time TCurrent and the time at which the bolus was administered TDose is greater than a time related to the lifetime of the insulin used. Thus, when block 722 is “NO”, process 700 calculates, at block 714, a post meal correction bolus CBpost that deducts the remaining insulin IRem in the patient's body as follows:
In some examples, Post Meal Correction doses CBPost (EQ. 10) are taken into consideration only if they are positive (units of insulin), which means a negative value post meal correction bolus CBPost cannot be used to reduce the meal bolus portion of a new combined bolus.
Referring to
At block 802, the adjustment factor process 800 receives the Governing Glucose value BGgov from non-transitory memory 24, 114, 144, since the adjustment factor AF is determined using the Governing Glucose value BGgov. To determine the adjustment factor AF, the adjustment factor process 800 considers the blood glucose target range BGTR (within which Basal doses and Meal Boluses, are not changed), which is defined by a lower limit, i.e., a low target BGTRL and an upper limit, i.e., a high target BGTRH. As previously discussed, the target range BGTR is determined by a doctor 40 and entered manually (e.g., using the patient device 110 or the medical record system 140, via, for example, a drop down menu list displayed on the display 116, 146). Each target range BGTR is associated with a set of configurable constants including a first constant BGAFL, a second constant BGAFH1, and a third constant BGAFH2 shown in the below table.
The adjustment factor process 800 determines, at block 804, if the Governing Glucose value BGgov is less than or equal to the first constant BGAFL (BGgov<=BGAFL), if so then at block 806, the adjustment factor process 800 assigns the adjustment factor AF to a first pre-configured adjustment factor AF1 shown in Table 2.
If, at block 804, the Governing Glucose value BGgov is not less than the first constant BGAFL, (i.e., BGgov≥BGAFL), then at block 808, the adjustment factor process 800 determines if the Governing Glucose value BGgov is greater than or equal to the first constant BGAFL and less than the low target BGTRL of the target range BGTR (BGAFL≤BGgov<BGTRL). If so, then the adjustment factor process 800 assigns the adjustment factor AF to a second pre-configured adjustment factor AF2, at block 810. If not, then at block 812, the adjustment factor process 800 determines if the Governing Glucose value BGgov is greater than or equal to the low target BGTRL of the target range BGTR and less than the high target level BGTRH of the target range BGTR (BGTRL≤BGgov<BGTRH). If so, then the adjustment factor process 800 assigns the adjustment factor AF to a third pre-configured adjustment factor AF3, at block 814. If not, then at block 816, the adjustment factor process 800 determines if the Governing Glucose value BGgov is greater than or equal to the high target level BGTRH of the target range BGTR and less than the second constant BGAFH1 (BGTRH≤BGgov<BGAFH1). If so, then the adjustment factor process 800 assigns the adjustment factor AF to a fourth pre-configured adjustment factor AF4, at block 818. If not, then at block 820, the adjustment process 800 determines if the Governing Glucose value BGgov is greater than or equal to the second constant BGAFH1 and less than the third constant BGAFH2 (BGAFH1≤BGgov<BGAFH2). If so, then the adjustment factor process 800 assigns the adjustment factor AF to a fifth pre-configured adjustment factor AF5, at block 822. If not, then at block 824, the adjustment process 800 determines that the Governing Glucose value BGgov is greater than or equal to the third constant BGAFH2 (BGgov≥BGAFH2); and the adjustment factor process 800 assigns the adjustment factor AF to a sixth pre-configured adjustment factor AF6, at block 826. After assigning a value to AF the adjustment factor process 800 returns the adjustment factor AF to the process requesting the adjustment factor AF at block 828 (e.g., the SubQ outpatient process 1800 (
Referring to
Basal insulin is for the fasting insulin-needs of a patient's body. Therefore, the best indicator of the effectiveness of the basal dose is the value of the blood glucose BG after the patient 10 has fasted for a period of time. Meal Boluses are for the short-term needs of a patient's body following a carbohydrate-containing meal. Therefore, the best indicator of the effectiveness of the Meal Bolus is a blood glucose measurement BG tested about one mean insulin-lifetime iLifeRapid after the Meal Bolus, where the lifetime is for the currently-used insulin type. For rapid-acting analog insulin the lifetime is conveniently similar to the time between meals.
Referring to
In some implementations, the glucometer 124 may not be configured to display the BGtype selections as shown at block 1806, and instead, determines if the time at which the blood glucose BG was measured BGtime falls within one of a number of scheduled blood glucose time buckets, that are contiguous so as to cover the entire day with no gaps. Further, the BGtypes are provided with Ideal BG Time Intervals, where each ideal scheduled time is associated with an interval configured with a start time margin (MStart) and an end time margin (MEnd). Moreover, each interval may be associated with a corresponding BGtype: pre-breakfast blood glucose measurement, a pre-lunch blood glucose measurement, a pre-dinner blood glucose measurement, a bedtime blood glucose measurement, or a midsleep blood glucose measurement
Referring to
CBBreakfast=(BGBreakfast−BGTarget)/CF (11)
Additionally or alternatively, the Correction Dose may be determined using the Correction Dose Function of process 700 (
The SubQ outpatient process 1800b (
Referring back to block 1806, in some implementations, the SubQ outpatient process 1800a, 1800b provides the blood glucose BG data, including the recent correction dose CD, the blood glucose measurement BG, the BGtype, and the BGTIME, in real time to a web-based application 198 of the manufacturer of the glucometer 124 at block 1814, and in turn, the web-based application 198 of the manufacturer via the network 20 may format a data file of the BG data for storage in the non-transitory memory 24, 114, 144 at block 1816. The glucometer 124 may sync the BG data with a mobile device, such as the smart phone 110b, to wirelessly transmit the BG data to the web-based application 198 at block 1814. The computing devices 132, 142 of the dosing controller 160 may retrieve the exported BD data file for calculating a next recommended insulin dose and a new value for the Correction Factor (CF) for the patient 10 at entry point Q. The next recommended insulin doses for adjusting the basal and the CF may be input to entry point Q using a basal adjustment process 2300 (
The SubQ outpatient process 1800a, 1800b transmits via the network 20 the next recommended insulin dose and the new value for the CF for the patient 10 calculated at 1816 to the web-based application 198 of the meter manufacturer at block 1818, wherein the web-based application 198 of the meter manufacturer formats the next recommended insulin dose and the new value for the CF for the glucometer 124 to receive via the network 20. In some examples, the web-based application 198 transmits the next recommended dose and the new value for the CF to a mobile device, such as the smart phone 110b, via the network 20 the mobile device 110b syncs with the glucometer 124 (and/or smart pen 123b) to provide the next recommended dose and the new value for the CF to the glucometer 124 (and/or the smart pen 123b). For instance, the number of insulin units associated with the recommended dose may be automatically measured by the smart pen 123b or smart pump 123a. Next, the SubQ outpatient process 1800 displays the next recommended insulin dose for the breakfast meal bolus on a Meter Screen via display 116c at block 1820.
After the patient self-administers the insulin dose (or the dosing controller 160 executing the SubQ outpatient process 1800a, 1800b causes the doser 223a, 223b to administer the insulin dose), at block 1824, the process 1800a, 1800b determines that the patient 10 has selected a “Dose Entry” to record the administered dose. The SubQ outpatient Process 1800a, 1800b then prompts the patient 10 to select the insulin dose type on a Meter Screen via display 116c at block 1826. The Meter Screen permits the patient to simultaneously select “Correction” and “Meal Bolus” for when the patient 10 has administered a combined dose that the patient 10 would like to record. The selection of “Basal” may not be selected simultaneously with another selection but is operative to cancel out other selections. In response to the patient's 10 selection, the SubQ outpatient process 1800a, 1800b, at block 1828, presents an insulin drop down menu of populated insulin doses on a Meter Screen via the display 116c. Here, the patient 10 may select the number of units of insulin recently administered by the patient 10.
In some implementations, as shown in
In some implementations, the data flow process 1900a sends the BG data in real-time via a first data transfer path from the mobile device 110b at block 1902. The first data transfer path may always be available provided the mobile device 110b is able to connect to the network 20 or cellular service. In some scenarios, the data flow process 1900a, at block 1902, sends the BG data in real-time via the first data transfer path from the mobile device 110b to the computing device 192 of the service provider 130. Thereafter, the data flow process 1900a transmits the BG data from the first data transfer path, at block 1904, to a merged database within the non-transitory memory 24, 134, 144 at block 1906.
In other implementations, the data flow process 1900a executes a batch process for downloading the BG data from the memory 114c of the glucometer 124 at the patient device 110a or other computing device connecting to the network 20 at block 1908, and then, transmits the BG data from the patient device 110a via a second data transfer path to a web-based application 198 of the manufacturer of the glucometer 124 at block 1910. In some examples, the batch process downloads all BG data stored on the memory 114c of the glucometer 124 for a configurable time period. In other examples, the batch process downloads all BG data stored on the memory 114c of the glucometer 124 since an immediately previous download session. The web-based application 198 may format a data file (e.g., merged database) of the BG data for storage in the non-transitory memory 24, 114, 144 at block 1906.
In other implementations, the data flow process 1900a executes a batch process for downloading the BG data from the memory 114c of the glucometer 124 at the health care provider computing device 142 via a third data transfer path at block 1912. For instance, the patient 10 or health care professional 40 may connect the glucometer 124 to the computing device 142 when the patient 10 visits a clinic 42 associated with a hospital call center during a regular check-up. In some examples, the computing device 142 executes a proprietary download program 196 provided by the manufacturer of the glucometer 124 to download the BG data from the memory 114c of the glucometer 124. The BG data transfer may be facilitated by connecting the glucometer 124 to the computing device 142 using Bluetooth, Infrared, near field communication (NFC), USB, or serial cable, depending upon the configuration of the glucometer 124. In some examples, the BG data downloaded at block 1912 may be displayed via display 146 for the health care professional to view. The data flow process 1900a receives a user 40 input to load the downloaded BG data (e.g., via a button on display 146), and exports the BG data downloaded by the proprietary download program 196 as a formatted BG data file for storage within the non-transitory 24, 114, 144 at block 1916. For example, the exported BG data file may be a CVS file or JSON file. In some examples, the batch process downloads all BG data stored on the memory 114c of the glucometer 124 for a configurable time period. In other examples, the batch process downloads all BG data stored on the memory 114c of the glucometer 124 since an immediately previous download session during a previous clinic visit by the patient 10.
In some examples, the non-transitory memory 24, 114, 144 includes a database for storing the BG data of the patient 10 received from any one of the first, second, or third data transfer paths. The database may store the BG data in a designated file associated with the patient 10 and identifiable with a patient identifier associated with the patient 10. The BG data within the database of the non-transitory memory 24, 114, 144 may be retrieved by the dosing controller 160 for determining or adjusting doses of insulin for the patient 10 to administer. Block 1906 may send the data within the merged database to Entry point T for routing to other processes, including a Time Limits of Data for Adjustment process (
Moreover, block 1906 may provide the data within the merged database to the patient's mobile device 110a, 110b, 124, 123a, 123b at block 1902. For instance, block 1922 may determine if the mobile device includes a self-sufficient application capable of sharing the merged database. If block 1922 is a “YES” indicating that the mobile device includes the self-sufficient application, block 1920 provides the merged database to block 1924 for sharing with the mobile device. Thereafter, block 1924 may provide an adjusted basal dose (from process 2300 of
Referring to
Blood glucose measurements may be aggregated or flagged according to their associated blood glucose type BGtype or blood glucose time BG time interval to determine a mean or median blood glucose value (EQ. 3) for each BGtype that may be used to determine or adjust recommended doses of insulin (e.g., bolus and/or basal). Referring to
If the time of a BG is outside of the bucket indicated by its flag by more than a configurable margin (FlagMargin) then the flag is changed to reflect the BGtype indicated by the actual time of the BG. The loop uses some dummy variables that are re-used, so they are initialized at the start at 2904. The start of the loop at 2906 starts at the present and retrieves each BG moving backward in time. If the date/time of the BG being checked at 2908 is earlier than the DataStartDateTime, then the loop is stopped, if not then the time of the BG is checked at 2912 to see if it is outside the bucket for which it is flagged. If so then the flag is changed at 2914 to indicate the bucket actually inhabited by the BG. The loop ends when a BG is found earlier that the DataStarteDateTime, and all the data in the acceptable date-time range are sent to Entry Point V for use by the BG aggregation processes 2200a, 2200b.
In some examples, the BG measurements are transmitted from the patient's 10 portable device 110a, 110b, 124, 123a, 123b and stored within the non-transitory memory 24, 134, 144. For instance, the BG measurements obtained by the glucometer 124 may be communicated and stored within the non-transitory memory 24, 134, 144 by using the data flow process 1900a, as described above with reference to
Referring to
Referring back to
Referring to
At block 2204, the aggregation process 2200a determines a ratio of the DayBuckets containing BG measurements to DayBuckets in the associated bucket (e.g., NDayBucketsWBG/NdaysBedtime) and compares the ratio to a configurable set point (Kndays). The value of Kndays is presently configured at 0.5. If the ratio is less than Kndays, the aggregation process 2200a prevents, at block 2206, the dosing controller 160 from adjusting the dose governed by the associated time-bucket (e.g., Bedtime BG time-bucket). For example, when the aggregation process 2200a aggregates BG measurements for the Bedtime BG time-bucket, block 2206 prevents the adjustment of the Dinner meal bolus when the ratio of NDayBucketsWBG/NdaysBedtime is less than Kndays indicating that the Bedtime BG time-bucket does not contain enough BG measurements. Block 2206 provides the determination that prevents adjusting the dose governed by the associated time-bucket to Entry Point S for use by processes 2300, 2400, 2500 of
The aggregation process 2200a of
Referring back to
The parameter MMtype is associated with a “mean or median type” that controls a choice of the aggregation method applied to the results of the DayBucket aggregations, i.e. mean or median. The Modal Day Scatter Chart 502 (
Referring to
If, however, block 2264a determines that filter2514 is applying a filter to the Bedtime BG time-bucket (e.g., block 2264a is “NO”), then the subroutine process 2260a determines, at block 2268a, whether the selected filter applied by filter2514 includes the “Ideal Mealtimes” filter. If filter 2514 is applying the “Ideal Mealtimes” filter (e.g., block 2268a is “YES”), then the subroutine process 2260a sets the BG value in the nth DayBucket, BGbedtimeDB(n) equal to the selected aggregate method AgMeth applied, at block 2270a to the union of all BG measurements flagged “bedtime” in the DayBucket together with all non-flagged BG measurements within the Ideal Mealtimes filter. Thereafter, the subroutine process 2260a routes BGbedtimeDB(n) back to block 2212 of the aggregation process 2200a (
On the other hand, if filter2514 is not applying the “Ideal Mealtimes” filter (e.g., block 2268a is “NO”), then the subroutine process 2260a determines, at block 2272a, whether the selected filter applied by filter2514 includes the “All” filter corresponding to the use of all BG measurements within the associated time-bucket (e.g., Bedtime BG time-bucket). When filter2514 includes the “All” filter (e.g., block 2272a is “YES”), the subroutine process 2260a sets the BG value in the nth DayBucket, BGbedtimeDB(n) equal to the selected aggregate method AgMeth applied at block 2274a to the union of all BG measurements flagged “bedtime” in the DayBucket together with all non-flagged BG measurements within the entire Bedtime DayBucket. Thereafter, the subroutine process 2260a routes the BGbedtimeDB(n) back to block 2212 of the aggregation process 2200a (
Referring to
On the other hand, if filter2514 is not applying the “Ideal Mealtimes” filter (e.g., block 2284a is “NO”), then the subroutine process 2280a determines, at block 2288a, whether the selected filter applied by filter2514 includes the “All” filter corresponding to the use of all BG measurements within the associated time-bucket (e.g., Bedtime BG time-bucket). If filter2514 is applying the “All” filter (e.g., block 2288a is “YES”), then the subroutine process 2280a sets, at block 2290a, the BG value in the nth DayBucket, BGbedtimeDB(n) equal to the selected aggregate method AgMeth applied to all non-flagged BG measurements within the “bedtime” DayBucket. Thereafter, the subroutine process 2280a routes the BGbedtimeDB(n) back to block 2214 of the aggregation process 2200a (
Referring to
At block 2234, the aggregation process 2200b determines a ratio of the DayBuckets containing BG measurements to DayBuckets in the associated bucket (e.g., NDayBucketsWBG/NdaysBreakfast) and compares the ratio to a configurable set point (Kndays). The value of Kndays is presently configured at 0.5. If the ratio is less than Kndays, the aggregation process 2200b prevents, at block 2236, the dosing controller 160 from adjusting the dose governed by the associated time-bucket (e.g., Breakfast BG time-bucket). For example, when the aggregation process 2200b aggregates BG measurements for the Breakfast BG time-bucket, block 2236 prevents the adjustment of the basal dose when the ratio of NDayBucketsWBG/NdaysBreakfast is less than Kndays indicating that the Breakfast BG time-bucket does not contain enough BG measurements. With respect to the Lunch BG time-bucket, block 2236 would prevent the adjustment of the Breakfast meal bolus when the ratio of NDayBucketsWBG/NdaysLunch is less than Kndays. Similarly, when the ratio of NDayBucketsWBG/NdaysDinner is less than Kndays, block 2236 would prevent the adjustment of the Lunch meal bolus. Block 2236 provides the determination that prevents adjusting the dose governed by the associated time-bucket to Entry Point S for use by processes 2300, 2400, 2500 of
The aggregation process 2200b for the meal BG time-buckets (e.g., Breakfast, Lunch, and Dinner) executes a loop at block 2238 when block 2234 determines that the ratio of NDayBucketsWBG/NdaysBreakfast is greater than or equal to Kndays. Specifically, the aggregation process 2200b examines, at block 2238, all the DayBuckets in the associated time-bucket (e.g., Breakfast BG time-bucket) back to the DataStartDateTime based on the filter selections 512, 514 of the Modal Day Scatter Chart (
As set forth above in the aggregation process 2200a (
Referring to
If, however, block 2264b determines that filter2514 is applying a filter to the Breakfast BG time-bucket (e.g., block 2264b is “NO”), then the subroutine process 2260b determines, at block 2268b, whether the selected filter applied by filter 2514 includes the Pre-Meal Bolus “PreMealBol” filter. If filter 2514 is applying the “Pre-Meal Bolus” filter (e.g., block 2268b is “YES”), then the subroutine process 2260a at block 2270b, sets the aggregate value of the BG's in the nth DayBucket of the Breakfast bucket, BGBreakfastDB(n) to the selected aggregate method AgMeth applied to the union of the set of BG measurements flagged “breakfast” in the DayBucket together with the set of all non-flagged BG measurements having times earlier than a time of the breakfast meal bolus (TimeMealBolus). Thereafter, the subroutine process 2260b routes BGBreakfastDB(n) back to block 2242 of the aggregation process 2200b (
At block 2272b, the subroutine process 2260b determines whether the selected filter applied by filter2514 includes the “Ideal Mealtimes” filter. If filter2514 is applying the “Ideal Mealtimes” filter (e.g., block 2272b is “YES”), then the subroutine process 2260b at block 2274b, sets BGBreakfastDB(n) to the selected aggregate method AgMeth applied to the union of the set of BG measurements flagged “breakfast” in the DayBucket together with the set of non-flagged BG measurements within the Ideal Mealtimes filter for breakfast. Thereafter, the subroutine process 2260b routes BGBreakfastDB(n) back to block 2242 of the aggregation process 2200b (
On the other hand, if filter2514 is not applying the “Ideal Mealtimes” filter (e.g., block 2272b is “NO”), then the subroutine process 2260b determines, at block 2275b, whether the selected filter applied by filter2514 includes the “Pre-MealBolus OR IdealMealtime” filter, which passes a union of the sets of BG's that meet the Pre-Meal Bolus filter criteria or Ideal Mealtimes filter criteria. If filter2514 is applying the “Pre-MealBolus OR IdealMealtime” filter (e.g., block 2275b is “YES”), then the subroutine process 2260b, at block 2276b, sets BGBreakfastDB(n) to the selected aggregate method AgMeth applied to the union of the set of BG measurements flagged “breakfast” in the DayBucket together with the set of all non-flagged BG measurements having times earlier than TimeMealBolus for breakfast together with the set of non-flagged BG measurements within the Ideal Mealtime interval for breakfast. Thereafter, the subroutine process 2260b routes BGBreakfastDB(n) back to block 2242 of the aggregation process 2200b (
At block 2277b, the subroutine process 2260b determines whether the selected filter applied by filter2514 includes the “All” filter corresponding to the use of all BG measurements within the associated time-bucket (e.g., Breakfast BG time-bucket). At block 2278b, the subroutine process 2260b sets BGBreakfastDB(n) to the the selected aggregate method AGMeth applied to the union of the set of BG measurements flagged “breakfast” in the DayBucket together with the set of all non-flagged BG measurements within the entire Breakfast Daybucket. Thereafter, the subroutine process 2260b routes BGBreakfastDB(n) back to block 2242 of the aggregation process 2200b (
Referring to
At block 2288b, the subroutine process 2280b determines whether the selected filter applied by filter2514 includes the “Ideal Mealtimes” filter. If filter2514 is applying the “Ideal Mealtimes” filter (e.g., block 2288b is “YES”), then the subroutine process 2280b, at block 2290b, sets BGBreakfastDB(n) to the selected aggregate method AgMeth applied to all BG measurements within the Ideal Mealtimes interval (e.g., ideal time filter) for breakfast. Thereafter, the subroutine process 2280b routes BGBreakfastDB(n) back to block 2244 of the aggregation process 2200b (
On the other hand, if filter2514 is not applying the “Ideal Mealtimes” filter (e.g., block 2288b is “NO”), then the subroutine process 2280b determines, at block 2292b, whether the selected filter applied by filter2514 includes the “Pre-MealBolus OR Ideal Mealtimes” filter, which passes the BG's that pass either the Pre Meal Bolus filter or the Ideal Mealtimes filter. If filter2514 is applying the “Both” filter (e.g., block 2292b is “YES”), then the subroutine process 2280b, at block 2294b, sets BGBreakfastDB(n) to the selected aggregate method AgMeth applied to the union of the set of all BG measurements having times earlier than TimeMealBolus for breakfast together with the set of all BG measurements within the Ideal Mealtime interval for breakfast. Thereafter, the subroutine process 2280b routes BGBreakfastDB(n) back to block 2244 of the aggregation process 2200b (
At block 2296b, the subroutine process 2280b determines whether the selected filter applied by filter2514 includes the “All” filter corresponding to the use of all BG measurements within the associated time-bucket (e.g., Breakfast BG time-bucket). At block 2298b, the subroutine process 2280b sets BGBreakfastDB(n) to the selected aggregate method AGMeth applied to all BG measurements within the entire Breakfast DayBucket. Thereafter, the subroutine process 2280b routes BGBreakfastDB(n) back to block 2244 of the aggregation process 2200b (
RecomBasal=(previous RecomBasal)*AF (12)
wherein the previous RecomBasal is provided from block 2312. The basal adjustment process 2300 transmits, at block 2310, the next recommended basal adjustment RecomBasal to the web-based application 198 of the manufacturer of the glucometer 124 or mobile device 110b via Entry Point Q of the SubQ outpatient process 1800 (
Referring to
For calculating the next recommended breakfast bolus (block 2402), the meal bolus adjustment process 2400 receives, at block 2410, the BG measurement (e.g., BGlunch) that occurs after the breakfast meal bolus via Entry Point H of the aggregation process 2200b (
RecomBreakBol=(previous RecomBreakBol)*AF (15A)
wherein the previous RecomBreakBol is provided from block 2408. Block 2408 may obtain the previous RecomBreakBol from block 2442 associated with the merged database 1906 (
For calculating the next recommended lunch bolus (block 2404), the meal bolus adjustment process 2400 receives, at block 2416, the BG measurement (e.g., BGdinner) that occurs after the lunch meal bolus via Entry Point H of the aggregation process 2200b (
RecomLunchBol=(previous RecomLunchBol)*AF (15B)
wherein the previous RecomLunchBol is provided from block 2414. Block 2414 may obtain the previous RecomLunchBol from block 2442 associated with the merged database 1906 (
For calculating the next recommended dinner bolus (block 2406), the meal bolus adjustment process 2400 receives, at block 2422, the blood glucose (BG) measurement (e.g., BGbedtime) that occurs after the dinner meal bolus via Entry Point G of the non-meal aggregation process 2200a (
RecomDinnerBol=(previous RecomDinnerBol)*AF, (15C)
wherein the previous RecomDinnerBol is provided from block 2420. Block 2420 may obtain the previous RecomDinnerBol from block 2442 associated with the merged database 1906 (
In some implementations, the adjusted meal boluses set forth above may be calculated using the grams of carbohydrate consumed by the patient 10 and the Carbohydrate-to-Insulin Ratio CIR where the Recommended Breakfast, Lunch and Dinner Boluses may be calculated as follows:
RecomLunchBolus=(Carbohydrate gms in Lunch)/CIR (16A)
RecomDinnerBol=(Carbohydrate gms in Dinner)/CIR (16B)
RecBreakfastBol=(Carbohydrate gms in Breakfast)/CIR (16C)
Referring to
For calculating the next recommended breakfast CIR (block 2502), the CIR adjustment process 2500 receives, at block 2510, the BG measurement (e.g., BGlunch) that occurs after the breakfast meal bolus via Entry Point H of the aggregation process 2200b (
RecomBreakCIR=(previous RecomBreakCIR)/AF, (17A)
wherein the previous RecomBreakCIR is provided from block 2508. Block 2508 may obtain the previous RecomBreakCIR from block 2542 associated with the merged database 1906 (
For calculating the next recommended lunch CIR (block 2504), the CIR adjustment process 2500 receives, at block 2516, the BG measurement (e.g., BGdinner) that occurs after the lunch meal bolus via Entry Point H of the aggregation process 2200b (
RecomLunchCIR=(previous RecomLunchCIR)/AF, (17B)
wherein the previous RecomLunchCIR is provided from block 2514. Block 2514 may obtain the previous RecomLunchCIR from block 2542 associated with the merged database 1906 (
For calculating the next recommended CIR dinner bolus (block 2506), the CIR adjustment process 2500 receives, at block 2522, the blood glucose (BG) measurement (e.g., BGbedtime) that occurs after the dinner meal bolus via Entry Point G of the non-meal aggregation process 2200a (
RecomDinnerCIR=(previous RecomDinnerCIR)/AF (17C)
wherein the previous RecomDinnerCIR is provided from block 2520. Block 2520 may obtain the previous RecomDinnerCIR from block 2542 associated with the merged database 1906 (
In some implementations, upon the glucometer 124 determining a blood glucose measurement, the glucometer 124 transmits the blood glucose measurement to the smart phone 110b. The smart phone 110b may render the blood glucose measurement upon the display 116b and permit the patient 10 to select the BGtype associated with the blood glucose measurement (e.g., blocks 1804 and 1806 of
In some implementations, recommended meal boluses may be determined by the dosing controller 160 and sent to the smart phone 110b during each adjustment transmission and stored within the memory 114b. For example, upon the patient 10 selecting the BG type for a given blood glucose measurement, the mobile application 1198 executing on the smartphone may determine the meal bolus (e.g., breakfast, lunch, or dinner) based upon the BG type without using carb counting for the current meal. In some configurations, the mobile application 1198 executing on the smart phone 110b executes all functionality of the dosing controller 160, thereby eliminating the need for communications over the network. In some examples, when the BG measurement requires the correction bolus, the mobile application 1198 calculates a total bolus (e.g., meal bolus+correction bolus) and transmits the total bolus to the smart pen 123b. In some examples, the smart pen 123b (using the administration computing device 112e, automatically dials in the total bolus for the doser 223b to administer. In some examples, the smart pen 123b receives a recommended total bolus dose from the smart phone 110b transmitted from the computing device 142 of the dosing controller 160 via the network 20. In some examples, upon administration of an insulin dose by the smart pen 123b, the smart pen 123b transmits the value of the administered dose to the smart phone 110b for storage within memory 114a along with the associated BG measurement.
In some examples, the patient 10 may enter a number of carbohydrates for a current meal into the glucometer 124 for transmission to the smart phone 110b or directly into the smart phone 110b when a blood glucose measurement is received. Using a carbohydrate-to-insulin ratio (CIR) transmitted from the dosing controller 160 to the smart phone 110b, the mobile application 1198 executing on the smart phone may calculate the recommended meal bolus (e.g., breakfast, lunch or dinner) using one of the EQ. 16A-16C. In some examples, the CIR and CF are adjusted each time a BG measurement is received at the dosing controller 160 from the glucometer 124 using the smart phone 110b to facilitate the transmission thru the network 20. In other examples, the CIR and CF are adjusted when all the dosing parameters are adjusted (e.g., via the batch download process) and transmitted to the smart phone 110b for storage within the memory 114b.
Referring to
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Moreover, subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them. The terms “data processing apparatus”, “computing device” and “computing processor” encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus.
A computer program (also known as an application, program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
One or more aspects of the disclosure can be implemented in a computing system that includes a backend component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a frontend component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such backend, middleware, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.
While this specification contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular implementations of the disclosure. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multi-tasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results.
This U.S. patent application is a continuation of, and claims priority under 35 U.S.C. § 120 from, U.S. patent application Ser. No. 16/520,936, filed on Jul. 24, 2019, which is a continuation of U.S. patent application Ser. No. 16/124,026, filed on Sep. 6, 2018, which is a continuation of U.S. patent application Ser. No. 15/855,315, filed on Dec. 27, 2017, which is a continuation of U.S. patent application Ser. No. 14/922,763, filed on Oct. 26, 2015, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application 62/069,195, filed Oct. 27, 2014. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 561422 | Minnis | Jun 1896 | A |
| 4055175 | Clemens et al. | Oct 1977 | A |
| 4151845 | Clemens | May 1979 | A |
| 4206755 | Klein | Jun 1980 | A |
| 4403984 | Ash et al. | Sep 1983 | A |
| 4464170 | Clemens et al. | Aug 1984 | A |
| 4731726 | Allen, III | Mar 1988 | A |
| 4850959 | Findl | Jul 1989 | A |
| 4911168 | Davis | Mar 1990 | A |
| 4947845 | Davis | Aug 1990 | A |
| 4981779 | Wagner | Jan 1991 | A |
| 5091190 | Kuczynski et al. | Feb 1992 | A |
| 5216597 | Beckers | Jun 1993 | A |
| 5251126 | Kahn et al. | Oct 1993 | A |
| 5822715 | Worthington et al. | Oct 1998 | A |
| 5956501 | Brown | Sep 1999 | A |
| 5998363 | Forse et al. | Dec 1999 | A |
| 6024699 | Surwit et al. | Feb 2000 | A |
| 6186145 | Brown | Feb 2001 | B1 |
| 6233539 | Brown | May 2001 | B1 |
| 6379301 | Worthington et al. | Apr 2002 | B1 |
| 6428825 | Sharma et al. | Aug 2002 | B2 |
| 6472366 | Kishino et al. | Oct 2002 | B2 |
| 6540672 | Simonsen et al. | Apr 2003 | B1 |
| 6544212 | Galley et al. | Apr 2003 | B2 |
| 6560471 | Heller et al. | May 2003 | B1 |
| 6572542 | Houben et al. | Jun 2003 | B1 |
| 6575905 | Knobbe et al. | Jun 2003 | B2 |
| 6605039 | Houben et al. | Aug 2003 | B2 |
| 6615081 | Boveja | Sep 2003 | B1 |
| 6669663 | Thompson | Dec 2003 | B1 |
| 6740072 | Starkweather et al. | May 2004 | B2 |
| 6808703 | Park et al. | Oct 2004 | B2 |
| 6890568 | Pierce et al. | May 2005 | B2 |
| 6927246 | Noronha et al. | Aug 2005 | B2 |
| 7025425 | Kovatchev et al. | Apr 2006 | B2 |
| 7039560 | Kawatahara et al. | May 2006 | B2 |
| 7060059 | Keith et al. | Jun 2006 | B2 |
| 7108680 | Rohr et al. | Sep 2006 | B2 |
| 7137951 | Pilarski | Nov 2006 | B2 |
| 7167818 | Brown | Jan 2007 | B2 |
| 7204823 | Estes et al. | Apr 2007 | B2 |
| 7267665 | Steil et al. | Sep 2007 | B2 |
| 7282029 | Poulsen et al. | Oct 2007 | B1 |
| 7291107 | Hellwig et al. | Nov 2007 | B2 |
| 7404796 | Ginsberg | Jul 2008 | B2 |
| 7498318 | Stahl et al. | Mar 2009 | B1 |
| 7509156 | Flanders | Mar 2009 | B2 |
| 7553281 | Hellwig et al. | Jun 2009 | B2 |
| 7651845 | Doyle, III et al. | Jan 2010 | B2 |
| 7704226 | Mueller, Jr. et al. | Apr 2010 | B2 |
| 7734323 | Blomquist et al. | Jun 2010 | B2 |
| 7806886 | Kanderian, Jr. et al. | Oct 2010 | B2 |
| 7824333 | Otto et al. | Nov 2010 | B2 |
| 7837622 | Itoh et al. | Nov 2010 | B2 |
| 7853455 | Brown | Dec 2010 | B2 |
| 7877271 | Brown | Jan 2011 | B2 |
| 7901625 | Brown | Mar 2011 | B2 |
| 7904310 | Brown | Mar 2011 | B2 |
| 7912688 | Brown | Mar 2011 | B2 |
| 7920998 | Brown | Apr 2011 | B2 |
| 7949507 | Brown | May 2011 | B2 |
| 7985848 | Woo et al. | Jul 2011 | B2 |
| 8088731 | Knudsen et al. | Jan 2012 | B2 |
| 8117020 | Abensour et al. | Feb 2012 | B2 |
| 8185412 | Harpale | May 2012 | B1 |
| 8198320 | Liang et al. | Jun 2012 | B2 |
| 8204729 | Sher | Jun 2012 | B2 |
| 8206340 | Arefieg | Jun 2012 | B2 |
| 8257300 | Budiman et al. | Sep 2012 | B2 |
| 8257735 | Lau et al. | Sep 2012 | B2 |
| 8318221 | Miller et al. | Nov 2012 | B2 |
| 8329232 | Cheng et al. | Dec 2012 | B2 |
| 8333752 | Veit et al. | Dec 2012 | B2 |
| 8370077 | Bashan et al. | Feb 2013 | B2 |
| 8398616 | Budiman | Mar 2013 | B2 |
| 8420125 | Webster et al. | Apr 2013 | B2 |
| 8420621 | Lai et al. | Apr 2013 | B2 |
| 8457901 | Beshan et al. | Jun 2013 | B2 |
| 8527208 | Prud'homme et al. | Sep 2013 | B2 |
| 8532933 | Duke et al. | Sep 2013 | B2 |
| 8548544 | Kircher, Jr. et al. | Oct 2013 | B2 |
| 8571801 | Anfinsen et al. | Oct 2013 | B2 |
| 8579879 | Palerm et al. | Nov 2013 | B2 |
| 8600682 | Bashan et al. | Dec 2013 | B2 |
| 8635054 | Brown | Jan 2014 | B2 |
| 8679016 | Mastrototaro et al. | Mar 2014 | B2 |
| 8690934 | Boyden et al. | Apr 2014 | B2 |
| 8700161 | Harel et al. | Apr 2014 | B2 |
| 8703183 | Lara | Apr 2014 | B2 |
| 8718949 | Blomquist et al. | May 2014 | B2 |
| 8755938 | Weinert et al. | Jun 2014 | B2 |
| 8766803 | Bousamra et al. | Jul 2014 | B2 |
| 8828390 | Herrera et al. | Sep 2014 | B2 |
| 8834367 | Laan et al. | Sep 2014 | B2 |
| 8870807 | Mantri et al. | Oct 2014 | B2 |
| 8911367 | Brister et al. | Dec 2014 | B2 |
| 8919180 | Gottlieb et al. | Dec 2014 | B2 |
| 8992464 | Bashan et al. | Mar 2015 | B2 |
| 20010002269 | Zhao | May 2001 | A1 |
| 20030028089 | Galley et al. | Feb 2003 | A1 |
| 20030050621 | Lebel et al. | Mar 2003 | A1 |
| 20030199445 | Knudsen et al. | Oct 2003 | A1 |
| 20030208110 | Mault et al. | Nov 2003 | A1 |
| 20040042272 | Kurata | Mar 2004 | A1 |
| 20040044272 | Moerman et al. | Mar 2004 | A1 |
| 20040054263 | Moerman et al. | Mar 2004 | A1 |
| 20050020681 | Takayama et al. | Jan 2005 | A1 |
| 20050049179 | Davidson et al. | Mar 2005 | A1 |
| 20050054818 | Brader et al. | Mar 2005 | A1 |
| 20050055010 | Pettis et al. | Mar 2005 | A1 |
| 20050096637 | Heruth | May 2005 | A1 |
| 20050171503 | Van Den Berghe et al. | Aug 2005 | A1 |
| 20050176621 | Brader et al. | Aug 2005 | A1 |
| 20050177398 | Watanabe et al. | Aug 2005 | A1 |
| 20050187749 | Singley | Aug 2005 | A1 |
| 20050192494 | Ginsberg | Sep 2005 | A1 |
| 20050192557 | Brauker et al. | Sep 2005 | A1 |
| 20050197621 | Poulsen et al. | Sep 2005 | A1 |
| 20050267195 | Mikoshiba et al. | Dec 2005 | A1 |
| 20050272640 | Doyle et al. | Dec 2005 | A1 |
| 20060040003 | Needleman et al. | Feb 2006 | A1 |
| 20060078593 | Strozier et al. | Apr 2006 | A1 |
| 20060160722 | Green et al. | Jul 2006 | A1 |
| 20060173260 | Gaoni et al. | Aug 2006 | A1 |
| 20060188995 | Ryan et al. | Aug 2006 | A1 |
| 20060224109 | Steil et al. | Oct 2006 | A1 |
| 20060264895 | Flanders | Nov 2006 | A1 |
| 20070036872 | Tsuboi et al. | Feb 2007 | A1 |
| 20070060796 | Kim | Mar 2007 | A1 |
| 20070078314 | Grounsell et al. | Apr 2007 | A1 |
| 20070078818 | Zivitz et al. | Apr 2007 | A1 |
| 20070160678 | Guimberteau et al. | Jul 2007 | A1 |
| 20070168224 | Letzt et al. | Jul 2007 | A1 |
| 20070249916 | Pesach et al. | Oct 2007 | A1 |
| 20070282186 | Gilmore | Dec 2007 | A1 |
| 20070293742 | Simonsen et al. | Dec 2007 | A1 |
| 20080097289 | Steil et al. | Apr 2008 | A1 |
| 20080119421 | Tuszynski et al. | May 2008 | A1 |
| 20080119705 | Patel et al. | May 2008 | A1 |
| 20080139511 | Friesen | Jun 2008 | A1 |
| 20080139907 | Rao et al. | Jun 2008 | A1 |
| 20080172030 | Blomquist | Jul 2008 | A1 |
| 20080188796 | Steil et al. | Aug 2008 | A1 |
| 20080214919 | Harmon et al. | Sep 2008 | A1 |
| 20080228056 | Blomquist et al. | Sep 2008 | A1 |
| 20080234943 | Ray et al. | Sep 2008 | A1 |
| 20080255707 | Hebblewhite et al. | Oct 2008 | A1 |
| 20080269585 | Ginsberg | Oct 2008 | A1 |
| 20080299079 | Meezan et al. | Dec 2008 | A1 |
| 20080306353 | Douglas et al. | Dec 2008 | A1 |
| 20090029933 | Velloso et al. | Jan 2009 | A1 |
| 20090036753 | King | Feb 2009 | A1 |
| 20090054753 | Robinson et al. | Feb 2009 | A1 |
| 20090069636 | Zivitz et al. | Mar 2009 | A1 |
| 20090099438 | Flanders | Apr 2009 | A1 |
| 20090110752 | Shang et al. | Apr 2009 | A1 |
| 20090157430 | Rule et al. | Jun 2009 | A1 |
| 20090214511 | Tran et al. | Aug 2009 | A1 |
| 20090227514 | Oben | Sep 2009 | A1 |
| 20090239944 | D'orazio et al. | Sep 2009 | A1 |
| 20090240127 | Ray | Sep 2009 | A1 |
| 20090242399 | Kamath et al. | Oct 2009 | A1 |
| 20090247982 | Krulevitch et al. | Oct 2009 | A1 |
| 20090253970 | Bashan et al. | Oct 2009 | A1 |
| 20090253973 | Bashan et al. | Oct 2009 | A1 |
| 20090281519 | Rao et al. | Nov 2009 | A1 |
| 20090299152 | Taub et al. | Dec 2009 | A1 |
| 20090312250 | Ryu et al. | Dec 2009 | A1 |
| 20100016700 | Sieh et al. | Jan 2010 | A1 |
| 20100035795 | Boss et al. | Feb 2010 | A1 |
| 20100137788 | Braithwaite et al. | Jun 2010 | A1 |
| 20100145725 | Alferness et al. | Jun 2010 | A1 |
| 20100160740 | Cohen et al. | Jun 2010 | A1 |
| 20100161236 | Cohen et al. | Jun 2010 | A1 |
| 20100161346 | Getschmann et al. | Jun 2010 | A1 |
| 20100168660 | Galley et al. | Jul 2010 | A1 |
| 20100198142 | Sloan et al. | Aug 2010 | A1 |
| 20100256047 | Sieh et al. | Oct 2010 | A1 |
| 20100262434 | Shaya | Oct 2010 | A1 |
| 20100286601 | Yodfat et al. | Nov 2010 | A1 |
| 20100305545 | Kanderian, Jr. et al. | Dec 2010 | A1 |
| 20100324382 | Cantwell et al. | Dec 2010 | A1 |
| 20100331652 | Groll et al. | Dec 2010 | A1 |
| 20100331654 | Jerdonek et al. | Dec 2010 | A1 |
| 20100332142 | Shadforth et al. | Dec 2010 | A1 |
| 20110021894 | Mohanty et al. | Jan 2011 | A1 |
| 20110071365 | Lee et al. | Mar 2011 | A1 |
| 20110071464 | Palerm | Mar 2011 | A1 |
| 20110098548 | Budiman et al. | Apr 2011 | A1 |
| 20110115894 | Burnett | May 2011 | A1 |
| 20110119081 | Vespasiani | May 2011 | A1 |
| 20110152830 | Ruchti et al. | Jun 2011 | A1 |
| 20110178008 | Arai et al. | Jul 2011 | A1 |
| 20110213332 | Mozayeny | Sep 2011 | A1 |
| 20110217396 | Oldani | Sep 2011 | A1 |
| 20110218489 | Mastrototaro et al. | Sep 2011 | A1 |
| 20110229602 | Aymard et al. | Sep 2011 | A1 |
| 20110286984 | Huang | Nov 2011 | A1 |
| 20110305771 | Sampalis | Dec 2011 | A1 |
| 20110313674 | Duke et al. | Dec 2011 | A1 |
| 20110319322 | Bashan et al. | Dec 2011 | A1 |
| 20120003339 | Minacapelli | Jan 2012 | A1 |
| 20120022353 | Bashan et al. | Jan 2012 | A1 |
| 20120046606 | Arefieg | Feb 2012 | A1 |
| 20120053222 | Gorrell et al. | Mar 2012 | A1 |
| 20120058942 | Dupre | Mar 2012 | A1 |
| 20120065482 | Robinson et al. | Mar 2012 | A1 |
| 20120095311 | Ramey et al. | Apr 2012 | A1 |
| 20120123234 | Atlas et al. | May 2012 | A1 |
| 20120197358 | Prescott | Aug 2012 | A1 |
| 20120213886 | Gannon et al. | Aug 2012 | A1 |
| 20120227737 | Mastrototaro et al. | Sep 2012 | A1 |
| 20120232519 | Georgiou et al. | Sep 2012 | A1 |
| 20120232520 | Sloan et al. | Sep 2012 | A1 |
| 20120238853 | Arefieg | Sep 2012 | A1 |
| 20120244096 | Xie et al. | Sep 2012 | A1 |
| 20120295985 | Miller et al. | Nov 2012 | A1 |
| 20130022592 | Vaughn et al. | Jan 2013 | A1 |
| 20130030358 | Yodfat et al. | Jan 2013 | A1 |
| 20130052285 | Song et al. | Feb 2013 | A1 |
| 20130109620 | Riis et al. | May 2013 | A1 |
| 20130144283 | Barman | Jun 2013 | A1 |
| 20130158503 | Kanderian, Jr. et al. | Jun 2013 | A1 |
| 20130165901 | Ruchti et al. | Jun 2013 | A1 |
| 20130172707 | Galley et al. | Jul 2013 | A1 |
| 20130190583 | Grosman et al. | Jul 2013 | A1 |
| 20130225683 | Gagnon et al. | Aug 2013 | A1 |
| 20130233727 | Tsai et al. | Sep 2013 | A1 |
| 20130245547 | El-Khatib et al. | Sep 2013 | A1 |
| 20130267796 | Enric Monte Moreno | Oct 2013 | A1 |
| 20130281796 | Pan | Oct 2013 | A1 |
| 20130282301 | Rush | Oct 2013 | A1 |
| 20130289883 | Bashan et al. | Oct 2013 | A1 |
| 20130309750 | Tajima et al. | Nov 2013 | A1 |
| 20130316029 | Pan et al. | Nov 2013 | A1 |
| 20130317316 | Kandeel | Nov 2013 | A1 |
| 20130331323 | Wu et al. | Dec 2013 | A1 |
| 20130338209 | Gambhire et al. | Dec 2013 | A1 |
| 20130338630 | Agrawal et al. | Dec 2013 | A1 |
| 20130345664 | Beck et al. | Dec 2013 | A1 |
| 20140000338 | Luo et al. | Jan 2014 | A1 |
| 20140004211 | Choi et al. | Jan 2014 | A1 |
| 20140024907 | Howell et al. | Jan 2014 | A1 |
| 20140037749 | Shea et al. | Feb 2014 | A1 |
| 20140057331 | Tajima et al. | Feb 2014 | A1 |
| 20140066735 | Engelhardt et al. | Mar 2014 | A1 |
| 20140066888 | Parikh et al. | Mar 2014 | A1 |
| 20140081196 | Chen | Mar 2014 | A1 |
| 20140128706 | Roy | May 2014 | A1 |
| 20140170123 | Alam et al. | Jun 2014 | A1 |
| 20140178509 | Jia | Jun 2014 | A1 |
| 20140179629 | Hamaker et al. | Jun 2014 | A1 |
| 20140194788 | Muehlbauer et al. | Jul 2014 | A1 |
| 20140213963 | Wu et al. | Jul 2014 | A1 |
| 20140296943 | Maxik et al. | Oct 2014 | A1 |
| 20140303466 | Fitzpatrick et al. | Oct 2014 | A1 |
| 20140303552 | Kanderian, Jr. et al. | Oct 2014 | A1 |
| 20140337041 | Madden et al. | Nov 2014 | A1 |
| 20140347491 | Connor | Nov 2014 | A1 |
| 20140349256 | Connor | Nov 2014 | A1 |
| 20140349257 | Connor | Nov 2014 | A1 |
| 20140356420 | Huang | Dec 2014 | A1 |
| 20140363794 | Angelides | Dec 2014 | A1 |
| 20140365534 | Bousamra et al. | Dec 2014 | A1 |
| 20140378381 | Chen et al. | Dec 2014 | A1 |
| 20140378793 | Kamath et al. | Dec 2014 | A1 |
| 20150018633 | Kovachev et al. | Jan 2015 | A1 |
| 20150025496 | Imran | Jan 2015 | A1 |
| 20150025903 | Mueller-Wolf | Jan 2015 | A1 |
| 20150031053 | Moerman | Jan 2015 | A1 |
| 20150037406 | Bernabeu Martinez et al. | Feb 2015 | A1 |
| Number | Date | Country |
|---|---|---|
| 199460325 | Aug 1994 | AU |
| 2009283013 | Feb 2010 | AU |
| 2010330746 | Jul 2012 | AU |
| 2519249 | Oct 2004 | CA |
| 2670512 | Jul 2008 | CA |
| 2720302 | Dec 2009 | CA |
| 2720304 | Dec 2009 | CA |
| 2733593 | Feb 2010 | CA |
| 2752637 | Sep 2010 | CA |
| 2761647 | Dec 2010 | CA |
| 2766944 | Jan 2011 | CA |
| 2784143 | Jun 2011 | CA |
| 102016855 | Apr 2011 | CN |
| 102016906 | Apr 2011 | CN |
| 102300501 | Dec 2011 | CN |
| 102395310 | Mar 2012 | CN |
| 102481101 | May 2012 | CN |
| 102946804 | Feb 2013 | CN |
| 1082412 | Oct 2001 | DE |
| 461207 | Dec 1991 | EP |
| 483595 | May 1992 | EP |
| 557350 | Sep 1993 | EP |
| 573499 | Dec 1993 | EP |
| 768043 | Apr 1997 | EP |
| 862648 | Sep 1998 | EP |
| 910578 | Apr 1999 | EP |
| 925792 | Jun 1999 | EP |
| 1017414 | Jul 2000 | EP |
| 1030557 | Aug 2000 | EP |
| 1051141 | Nov 2000 | EP |
| 1067925 | Jan 2001 | EP |
| 1082412 | Mar 2001 | EP |
| 1115389 | Jul 2001 | EP |
| 483595 | Dec 2001 | EP |
| 1173482 | Jan 2002 | EP |
| 1185321 | Mar 2002 | EP |
| 1196445 | Apr 2002 | EP |
| 1214596 | Jun 2002 | EP |
| 1305018 | May 2003 | EP |
| 1317190 | Jun 2003 | EP |
| 1382363 | Jan 2004 | EP |
| 1424074 | Jun 2004 | EP |
| 1482919 | Dec 2004 | EP |
| 1581095 | Oct 2005 | EP |
| 1610758 | Jan 2006 | EP |
| 1679009 | Jul 2006 | EP |
| 1698898 | Sep 2006 | EP |
| 1773860 | Apr 2007 | EP |
| 1846002 | Oct 2007 | EP |
| 1885392 | Feb 2008 | EP |
| 1915171 | Apr 2008 | EP |
| 1921981 | May 2008 | EP |
| 2114491 | Nov 2009 | EP |
| 2129277 | Dec 2009 | EP |
| 2139393 | Jan 2010 | EP |
| 2170430 | Apr 2010 | EP |
| 2257218 | Dec 2010 | EP |
| 2260423 | Dec 2010 | EP |
| 2260462 | Dec 2010 | EP |
| 2276405 | Jan 2011 | EP |
| 2300046 | Mar 2011 | EP |
| 2328608 | Jun 2011 | EP |
| 2352456 | Aug 2011 | EP |
| 2355669 | Aug 2011 | EP |
| 2377465 | Oct 2011 | EP |
| 2384750 | Nov 2011 | EP |
| 2393419 | Dec 2011 | EP |
| 2400882 | Jan 2012 | EP |
| 2418972 | Feb 2012 | EP |
| 2442719 | Apr 2012 | EP |
| 2448432 | May 2012 | EP |
| 2448468 | May 2012 | EP |
| 2448469 | May 2012 | EP |
| 2482712 | Aug 2012 | EP |
| 2516671 | Oct 2012 | EP |
| 2518655 | Oct 2012 | EP |
| 2525863 | Nov 2012 | EP |
| 2535831 | Dec 2012 | EP |
| 2552313 | Feb 2013 | EP |
| 2582297 | Apr 2013 | EP |
| 2585133 | May 2013 | EP |
| 2590559 | May 2013 | EP |
| 2596448 | May 2013 | EP |
| 2603133 | Jun 2013 | EP |
| 2605819 | Jun 2013 | EP |
| 2640373 | Sep 2013 | EP |
| 2641084 | Sep 2013 | EP |
| 2644088 | Oct 2013 | EP |
| 2654777 | Oct 2013 | EP |
| 2659407 | Nov 2013 | EP |
| 2666369 | Nov 2013 | EP |
| 2685895 | Jan 2014 | EP |
| 2720713 | Apr 2014 | EP |
| 2736404 | Jun 2014 | EP |
| 2742447 | Jun 2014 | EP |
| 2742449 | Jun 2014 | EP |
| 2745225 | Jun 2014 | EP |
| 2760335 | Aug 2014 | EP |
| 2763722 | Aug 2014 | EP |
| 2798548 | Nov 2014 | EP |
| 2822647 | Jan 2015 | EP |
| 2004024699 | Jan 2004 | JP |
| 2008524591 | Jul 2008 | JP |
| 4594731 | Dec 2010 | JP |
| 04800928 | Oct 2011 | JP |
| 2012210366 | Nov 2012 | JP |
| 100527154 | Nov 2005 | KR |
| 1020090095073 | Sep 2009 | KR |
| 2011052664 | May 2011 | KR |
| 2012047841 | May 2012 | KR |
| 2011109016 | Sep 2012 | RU |
| 1992019260 | Nov 1992 | WO |
| 1996009823 | Apr 1996 | WO |
| 1999044496 | Sep 1999 | WO |
| 1999063101 | Dec 1999 | WO |
| 2002036139 | May 2002 | WO |
| 2003024468 | Mar 2003 | WO |
| 2003077895 | Sep 2003 | WO |
| 2003094927 | Nov 2003 | WO |
| 2004084820 | Oct 2004 | WO |
| 2005041022 | May 2005 | WO |
| 2005081119 | Sep 2005 | WO |
| 2005081170 | Sep 2005 | WO |
| 2005081171 | Sep 2005 | WO |
| 2005081173 | Sep 2005 | WO |
| 2005110222 | Nov 2005 | WO |
| 2006022619 | Mar 2006 | WO |
| 2006022629 | Mar 2006 | WO |
| 2006022633 | Mar 2006 | WO |
| 2006022634 | Mar 2006 | WO |
| 2006022636 | Mar 2006 | WO |
| 2006022638 | Mar 2006 | WO |
| 2006044556 | Apr 2006 | WO |
| 2003101177 | Jul 2006 | WO |
| 2006079124 | Jul 2006 | WO |
| 2006091918 | Aug 2006 | WO |
| 2006130901 | Dec 2006 | WO |
| 2007116226 | Oct 2007 | WO |
| 2007149533 | Dec 2007 | WO |
| 2008005761 | Jan 2008 | WO |
| 2008013324 | Jan 2008 | WO |
| 2008057213 | May 2008 | WO |
| 2008057384 | May 2008 | WO |
| 2008067245 | Jun 2008 | WO |
| 2008088490 | Jul 2008 | WO |
| 2008112078 | Sep 2008 | WO |
| 2008124478 | Oct 2008 | WO |
| 2009002455 | Dec 2008 | WO |
| 2009005960 | Jan 2009 | WO |
| 2009075925 | Jun 2009 | WO |
| 2009139846 | Nov 2009 | WO |
| 2009146119 | Dec 2009 | WO |
| 2009146121 | Dec 2009 | WO |
| 2010021879 | Feb 2010 | WO |
| 2010056718 | May 2010 | WO |
| 2010075350 | Jul 2010 | WO |
| 2010089304 | Aug 2010 | WO |
| 2010089305 | Aug 2010 | WO |
| 2010089306 | Aug 2010 | WO |
| 2010089307 | Aug 2010 | WO |
| 2010091102 | Aug 2010 | WO |
| 2010097796 | Sep 2010 | WO |
| 2010135646 | Nov 2010 | WO |
| 2010147659 | Dec 2010 | WO |
| 2011008520 | Jan 2011 | WO |
| 2011037607 | Mar 2011 | WO |
| 2011075687 | Jun 2011 | WO |
| 2011089600 | Jul 2011 | WO |
| 2011094352 | Aug 2011 | WO |
| 2011157402 | Dec 2011 | WO |
| 2012023964 | Feb 2012 | WO |
| 2012047800 | Apr 2012 | WO |
| 2012065556 | May 2012 | WO |
| 2012097064 | Jul 2012 | WO |
| 2012122520 | Sep 2012 | WO |
| 2012148252 | Nov 2012 | WO |
| 2012161670 | Nov 2012 | WO |
| 2012177963 | Dec 2012 | WO |
| 2013040712 | Mar 2013 | WO |
| 2013050309 | Apr 2013 | WO |
| 2013086372 | Jun 2013 | WO |
| 2013096769 | Jun 2013 | WO |
| 2013108262 | Jul 2013 | WO |
| 2013134548 | Sep 2013 | WO |
| 2013172833 | Nov 2013 | WO |
| 2013177565 | Nov 2013 | WO |
| 2014011488 | Jan 2014 | WO |
| 2014012084 | Jan 2014 | WO |
| 2014023834 | Feb 2014 | WO |
| 2014024201 | Feb 2014 | WO |
| 2014028607 | Feb 2014 | WO |
| 2014068007 | May 2014 | WO |
| 2014075135 | May 2014 | WO |
| 2014075135 | May 2014 | WO |
| 2014099829 | Jun 2014 | WO |
| 2014099829 | Jun 2014 | WO |
| 2014106263 | Jul 2014 | WO |
| 2014145049 | Sep 2014 | WO |
| 2014149535 | Sep 2014 | WO |
| 2014149535 | Sep 2014 | WO |
| 2014149781 | Sep 2014 | WO |
| 2014152704 | Sep 2014 | WO |
| 2014162549 | Oct 2014 | WO |
| 2014162549 | Oct 2014 | WO |
| 2014164226 | Oct 2014 | WO |
| 2014179171 | Nov 2014 | WO |
| 2014187812 | Nov 2014 | WO |
| 2014190231 | Nov 2014 | WO |
| 2014202024 | Dec 2014 | WO |
| 2014209630 | Dec 2014 | WO |
| 2014209634 | Dec 2014 | WO |
| Entry |
|---|
| Kim, Sarah et al., Hyperglycemia control of the Nil per os patient in the intensive care unit: Introduction of a simple subcutaneous insulin algorithm, Journal of Diabetes Science and Technology, Nov. 2012, vol. 6, Issue 6, pp. 1413-1419. |
| Vaidya, Anand et al., “Improving the management of diabetes in hospitalized patients: The result of a computer-based house staff training program”, Diabetes Technology & Therapeutics, 2012, vol. 14, No. 7, pp. 610-618. |
| Lee, Joshua et al., “Indication-based ordering: A new paradigm for glycemic control in hospitalized inpatients”, Journal of Diabetes Science and Technology, May 2008, vol. 2, Issue 3, pp. 349-356. |
| Nau, Konrad C. et al, “Glycemic Control in hospitalized patients not in intensive care: Beyond sliding-scale insulin”, American Family Physician, May 1, 2010, vol. 81, No. 9, pp. 1130-1133. |
| International Search Report and Written Opinion for Application No. PCT/US2015/011559 dated Apr. 29, 2015. |
| International Search Report and Written Opinion for Application No. PCT/US2015/011086 dated Apr. 29, 2015. |
| International Search Report and Written Opinion for Application No. PCT/US2015/011574 dated Apr. 24, 2015. |
| International Search Report and Written Opinion for Application No. PCT/US2016/047806 dated Nov. 25, 2016. |
| Japanese Office Action for the related Application No. 2016-549782 dated May 16, 2019. |
| Baghdadi et al., “Controlling Blood Glucose Levels in Diabetics by Neural Network Predictor” Annual International Conference IEEE EMBS 2007;2007:3216-9. doi: 10.1109/IEMBS.2007.4353014. PMID: 18002680. |
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