Insulin Pump Basal Rate Adaptation for Automated Glycemic Control
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Solution Overview
Problem
Current insulin delivery systems for diabetes management impose a significant cognitive burden on users and healthcare providers, requiring frequent manual adjustments and recalibration, and there is a lack of reliable, safe, and effective automated solutions for glycemic control.
Innovation Solution
A method and system that uses a continuous glucose monitoring system to generate and select insulin delivery profiles based on projected blood glucose values, adjusting insulin delivery rates to minimize differences from target levels, with adaptive adjustments to basal insulin rates and sensitivity factors over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual insulin delivery adjustments are used, then flexibility and adaptability are maintained, but cognitive burden and time consumption increase significantly
Solution Approach 1:
The system enables self-service by having the insulin pump automatically adjust delivery rates based on real-time glucose monitoring data and pre-programmed algorithms, eliminating the need for manual user intervention in routine insulin dosing adjustments
Solution Approach 2:
The system performs preliminary action by pre-programming multiple insulin delivery profiles with different parameters before execution, allowing the system to automatically select and switch between profiles based on predicted glucose trends without requiring real-time manual configuration
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 applies segmentation by dividing the automated delivery control into distinct functional modules: glucose monitoring module, prediction module, profile selection module, and insulin delivery module, each handled by separate components that can be independently optimized and maintained
Solution Approach 2:
The system manages complexity through parameter changes by automatically adjusting key delivery parameters (insulin rate, timing, duration) based on predicted glucose trajectories, while maintaining a structured set of discrete delivery profiles that simplify the control logic
3Measurement precision
If frequent manual recalibration is performed, then glycemic control accuracy is maintained, but productivity and convenience deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring actual glucose levels and comparing them with predicted values, automatically adjusting insulin delivery profiles to correct deviations and maintain target glycemic ranges without requiring manual recalibration
Solution Approach 2:
The system applies dynamics by enabling real-time adaptation of insulin delivery parameters based on changing glucose patterns, activity levels, and patient response, allowing the system to learn and adjust to individual needs over time without manual intervention
4Reliability
If multiple insulin delivery profiles are generated and evaluated, then glycemic control improvement is achieved, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by evaluating only a subset of plausible delivery profiles rather than exhaustively analyzing all possible combinations, using heuristics and constraints to focus computational resources on the most promising options that are likely to achieve target glycemic control
Data Source
AI summary
A method may include delivering insulin, using an insulin pump and a controller, over a first diurnal time period based on a baseline basal insulin rate stored in memory. The controller may receive blood glucose data to control delivery of insulin via the insulin pump in amounts variable from the baseline basal insulin rate to control blood glucose levels for a person with diabetes (PWD). The method may also include modifying the baseline basal insulin rate stored in the memory for a second diurnal time period that is at least 20 hours after the first diurnal period based on an amount of insulin actually delivered during the first diurnal time period.