Automated Backup Insulin Protocol Generation
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Solution Overview
Problem
Current blood glucose control systems lack the ability to autonomously generate backup therapy protocols and effectively track user modifications to control parameters, leading to potential disruptions in therapy delivery when the primary system is unavailable or when physiological changes occur.
Innovation Solution
An automated blood glucose control system that generates backup therapy protocols based on autonomously determined insulin doses and tracks user modifications to control parameters, allowing for continuous and personalized therapy delivery even when the primary system is not functioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the automated blood glucose control system generates backup therapy protocols and tracks user modifications, then therapy continuity and reliability are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by generating backup therapy protocols in advance before the primary system fails. The processor creates and stores alternative insulin dosing schedules based on historical data and current physiological parameters, ensuring that backup protocols are ready for immediate implementation when needed, thus maintaining therapy continuity without requiring complex real-time decision-making during failures.
Solution Approach 2:
The system creates simplified copies of the primary control logic in the backup protocol. The backup protocol replicates essential dosing algorithms and parameters in a more robust, fail-safe format that can operate with reduced computational resources, allowing the system to maintain therapy continuity while managing complexity through standardized protocol templates.
2Adaptability or versatility
If the system tracks user modifications to control parameters, then adaptability and personalization are improved, but data processing requirements and device complexity increase
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor user modifications to control parameters such as insulin sensitivity factors, carb ratios, and baseline rates. The processor analyzes these modifications patterns over time and automatically adjusts therapy recommendations, enabling personalization through iterative learning without requiring complex artificial intelligence, as the feedback loops use straightforward statistical analysis of user preferences and physiological responses.
Data Source
AI summary
An automated blood glucose control system is configured to generate a backup therapy protocol comprising insulin therapy instructions derived from autonomously determined doses of insulin. The system generates a dose control signal using a control algorithm configured to autonomously determine doses of insulin to be infused into a subject for the purpose of controlling blood glucose of the subject based at least in part on a glucose level signal received from a glucose sensor. The system can track insulin therapy administered to the subject over a tracking period, including storing an indication of the autonomously determined doses of insulin delivered to the subject as basal insulin, as correction boluses of insulin, or as mealtime boluses of insulin. The system can generate a backup injection therapy protocol or a backup pump therapy protocol with insulin therapy instructions based at least in part on the insulin therapy administered to the subject over the tracking period.