Automated Insulin Delivery Personalization Using Back-Filled Basal Segments
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Solution Overview
Problem
Current insulin delivery systems for diabetes management impose a significant cognitive burden on patients and caregivers, requiring frequent manual adjustments and recalibration, and there is a lack of reliable, safe, and simple systems for automatic glycemic control.
Innovation Solution
A system and method that adjusts insulin delivery by determining available insulin delivery segments for a back-fill time, calculating cumulative insulin on board, and adjusting the baseline basal rate based on back-filled insulin delivery segments to achieve improved glycemic control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual insulin dosage adjustments and recalibration are performed frequently, then glycemic control can be maintained, but cognitive burden on patients and caregivers increases significantly
Solution Approach 1:
The insulin delivery system automatically monitors glucose levels, calculates appropriate insulin dosages, and adjusts delivery parameters without requiring manual intervention. The system self-calibrates by monitoring glucose responses to insulin deliveries and automatically adjusts future dosages based on learned patterns, eliminating the need for frequent manual recalibration by patients or caregivers.
Solution Approach 2:
The system continuously monitors glucose levels and uses this feedback to automatically adjust insulin delivery. By tracking the relationship between insulin deliveries and subsequent glucose changes, the system learns individual patient responses and dynamically optimizes dosing parameters, maintaining reliable glycemic control while removing cognitive burden from users.
2Ease of operation
If automated insulin delivery systems are implemented, then cognitive burden is reduced, but system complexity and reliability requirements increase
Solution Approach 1:
The system performs self-calibration by automatically monitoring glucose responses to insulin deliveries and adjusting its control parameters accordingly. This self-learning capability reduces the need for manual intervention and external calibration, thereby improving reliability while maintaining automation.
Solution Approach 2:
The system pre-calculates insulin dosages based on predicted glucose trends and delivers insulin proactively before hyperglycemia occurs. By anticipating glucose excursions and pre-adjusting delivery parameters, the system maintains reliable control while operating automatically without requiring complex real-time decision-making.
3Ease of operation
If simple and safe automated systems are developed, then ease of use improves, but achieving regulatory approval becomes more difficult
Solution Approach 1:
The system automatically adapts to individual patient needs through self-calibration, eliminating the need for complex manual programming and extensive customization. This simplicity in operation, combined with robust automated safety features and continuous monitoring, facilitates regulatory approval by demonstrating both ease of use and reliability.
Data Source
AI summary
The embodiments described herein may relate to methods and systems for adjusting insulin delivery. Some methods and systems may be configured to adjust insulin delivery to personalize automated insulin delivery for a person with diabetes. Such personalization may include adjusting user specific dosage parameters in response to one or more back-filled time segments associated with a diurnal time block.