Meal Bolus Modeling With Glucose Feedback for Insulin Dosing
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Solution Overview
Problem
Patients face challenges in managing mealtime insulin therapy due to the need for estimating carbohydrate intake and insulin dosage, leading to potential hypoglycemic or hyperglycemic states, which are risky and detrimental to health.
Innovation Solution
A system utilizing meal models that reduce user involvement by automatically determining insulin dosage based on meal size, composition, and timing, adjusting delivery parameters, and incorporating blood glucose feedback for personalized insulin delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually estimate carbohydrate intake and insulin dosage, then user control and customization are maintained, but the complexity of the management process increases and safety risks arise from estimation errors
Solution Approach 1:
The system automatically determines insulin dosage without requiring user estimation of carbohydrate intake. The processor receives information about meal ingestion and autonomously calculates the appropriate bolus insulin dose based on the meal model and blood glucose data, eliminating the need for users to manually estimate and reducing safety risks from estimation errors.
Solution Approach 2:
The manual estimation process is replaced with an automated computational system. The processor uses algorithms to determine insulin dosage based on received meal information and blood glucose measurements, substituting the mechanical estimation process with an automated computational approach that improves reliability.
2Productivity
If users predict and preemptively deliver insulin before carbohydrate absorption, then blood glucose control is improved, but the precision of timing and dosage estimation becomes critical and increases user burden
Solution Approach 1:
The system automatically determines and delivers the insulin bolus without requiring user prediction or involvement in the timing and dosage calculation. The processor receives meal ingestion information and autonomously determines the appropriate insulin dose and timing, eliminating the need for users to predict carbohydrate absorption patterns.
Solution Approach 2:
The system uses blood glucose measurement values received within a predetermined time range of meal ingestion to inform the insulin dosage determination. This feedback mechanism allows the system to adjust the bolus calculation based on actual glucose levels, improving control efficiency without increasing user burden.
3Ease of operation
If a closed loop system reduces user engagement, then ease of operation is improved, but the system complexity and automation requirements increase
Solution Approach 1:
The system performs automated bolus determination without requiring user engagement in the calculation process. The processor autonomously receives meal information, selects appropriate meal models, determines dosage settings, and calculates the insulin bolus, achieving minimal user engagement while maintaining appropriate automation levels.
Solution Approach 2:
The system adjusts various parameters including delay parameter, extend parameter, and delivery constraint based on meal characteristics. These parameter changes enable the automated system to adapt to different meal types and timing scenarios, managing system complexity through structured parameter adjustment rather than complex decision logic.
Data Source
AI summary
Disclosed are examples that include receiving information related to ingestion of a meal. The information may include a coarse indication of a size of the meal, a relative time of ingestion of the meal, and a general composition indication of the meal. A blood glucose measurement value received within a predetermined time range of a relative time of ingestion of the meal may be identified. Settings for a delay and an extend parameter and a delivery constraint may be determined. A meal model may be modified using the determined settings for the delay parameter, the extend parameter, and the delivery constraint. The modified meal model may be used to determine a dose of insulin to be delivered in response to the received information. An instruction indicates a determined dose of insulin to be delivered may be output for delivery to a drug delivery device.