Insulin Pump Pattern Detection for Glucose Management
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Solution Overview
Problem
Current insulin delivery systems face challenges in processing and applying vast amounts of data related to biometric, glucose, and pump operation data to improve user experience and physiological outcomes, such as blood glucose levels, due to data volume and complexity.
Innovation Solution
Systems and methods are developed to detect patterns, trends, anomalies, and abnormalities in insulin delivery pump data and user behavior, allowing for operational adjustments and user prompts to enhance insulin delivery timing and dosing based on exercise, eating patterns, and other factors, using a network of wearable devices and remote computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous glucose monitoring and wearable pumps are used to collect vast amounts of data, then the precision of glucose level monitoring is improved, but the complexity of processing and applying the data increases
Solution Approach 1:
The patent extracts and analyzes specific patterns from the vast amount of collected data, such as identifying temporal patterns in glucose readings, insulin delivery, and user behavior. By focusing on extracting meaningful patterns rather than processing all raw data, the system reduces processing complexity while maintaining monitoring precision.
Solution Approach 2:
The system implements feedback mechanisms where analyzed patterns are used to automatically adjust pump operation parameters and provide user guidance. This closed-loop feedback system transforms complex raw data into actionable insights, simplifying the overall system operation while improving glucose management precision.
2Reliability
If the system processes and applies vast amounts of data to improve user experience, then the effectiveness of insulin dosing is improved, but the time required for data processing and analysis increases
Solution Approach 1:
The system performs preliminary analysis of data patterns during off-peak times or in advance, pre-calculating optimal pump settings and preparing guidance recommendations. This allows the system to respond quickly when real-time adjustments are needed, maintaining dosing effectiveness while reducing real-time processing delays.
Solution Approach 2:
Instead of processing all available data in detail, the system applies partial analysis focusing on the most critical patterns and parameters related to insulin dosing effectiveness. This selective processing approach maintains sufficient dosing accuracy while significantly reducing processing time.
3Ease of operation
If the system adjusts pump operation and insulin dosing based on detected patterns, then the user experience is improved, but the complexity of adjusting operational parameters increases
Solution Approach 1:
The system implements self-service functionality where it automatically adjusts pump operation parameters based on detected patterns without requiring manual intervention. The system analyzes data, identifies patterns, and autonomously modifies dosing parameters, thereby improving user experience while containing the complexity within the automated system rather than requiring user expertise.
Solution Approach 2:
The system manages operational parameter complexity by automatically detecting patterns in glucose data, insulin delivery, and user behavior, then translating these patterns into optimized parameter settings. The system handles the complexity of parameter adjustment internally while presenting simplified, pattern-based recommendations to users.
Data Source
AI summary
Systems and methods are provided for determining patterns, trends, anomalies, and/or abnormalities in data relating to operation of an insulin delivery pump and user outcomes relating to an insulin delivery pump and determining certain actions and/or operational adjustments to improve the user outcomes, such as blood glucose levels. For example, troubleshooting actions may be recommended upon determining a pattern, trend, abnormality, and/or anomaly in blood glucose levels that is undesirable. In another example, operation of the insulin delivery pump may be adjusted based habits and/or behaviors of the user. For example, insulin delivery timing may be adjusted based on exercise, activity and/or eating patterns. Based on the detected patterns, trends, anomalies, and/or abnormalities, a user device such as a mobile phone or smart device may present prompts for more information, alerts, status updates, and other information intended to improve the user experience.