Adaptive Insulin Delivery Using User Override Feedback
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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 existing automated systems fail to adapt to user inputs effectively.
Innovation Solution
A system and method that adjusts insulin delivery parameters based on user-specific inputs, using continuous glucose monitoring and automated algorithms to determine basal rates, carbohydrate-to-insulin ratios, and insulin sensitivity factors, allowing for adaptive control and reducing the need for manual recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated insulin delivery systems are implemented, then glycemic control is improved, but the system fails to adapt to user inputs and overrides effectively
Solution Approach 1:
The system incorporates feedback loops where user overrides of recommended bolus doses are detected and used to adjust future insulin recommendations. The processor monitors whether users consistently accept or modify recommendations and adapts the delivery parameters accordingly, creating a closed-loop system that learns from user behavior patterns.
Solution Approach 2:
The system performs self-adjustment by automatically modifying its own delivery parameters based on observed user behavior. When users consistently override recommendations in certain situations, the system autonomously recalibrates its algorithms to account for these patterns, reducing the need for manual reconfiguration by healthcare providers.
2Adaptability or versatility
If manual insulin dosing adjustments are required, then flexibility is provided, but cognitive burden on patients and caregivers increases significantly
Solution Approach 1:
The system introduces an intelligent intermediary layer between the glucose monitoring data and the insulin delivery decision. The processor analyzes glucose trends, user behavior patterns, and delivery history to generate recommended bolus doses, serving as a cognitive assistant that reduces the mental effort required while preserving user control and flexibility.
Solution Approach 2:
The system performs preliminary analysis of glucose data and user behavior patterns to pre-calculate recommended dosing parameters before the user needs to make a decision. By having recommendations ready in advance based on real-time data analysis, the system reduces the cognitive load during critical dosing moments while maintaining flexibility for user adjustment.
3Measurement precision
If therapeutic parameters are recalibrated episodically, then accuracy is maintained, but the frequency of adjustments creates operational complexity
Solution Approach 1:
The system transitions from episodic recalibration to continuous adaptation by constantly monitoring user responses to insulin deliveries and glucose trends. Rather than periodic manual adjustments, the system continuously refines its parameters based on ongoing data collection, maintaining accuracy while reducing the operational complexity of scheduled recalibrations.
Solution Approach 2:
The system makes delivery parameters dynamic rather than static, allowing automatic adjustment based on real-time glucose data and user behavior patterns. This dynamic approach replaces fixed recalibration schedules with continuous adaptive modification, maintaining precision while simplifying the operational burden on users and providers.
Data Source
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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 a user provided insulin delivery amount, including a user provided insulin delivery amount that varies from a recommended insulin delivery amount.