Analyte Episode Detection Software for Excursion Cause Analysis
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Solution Overview
Problem
Existing analyte monitoring systems fail to effectively identify and investigate the root causes of analyte excursions and variability, relying on cumbersome data recording and post-hoc question-and-answer sessions that are prone to inaccuracies and user confusion.
Innovation Solution
A system comprising an in vivo sensor control device, reader device, and monitoring application that utilizes episode investigative software (EIS) to monitor analyte levels, prompt users for relevant information, and analyze data to identify patterns and root causes of analyte excursions and variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data recording is implemented to understand excursion causes, then measurement precision is improved, but device complexity and user burden increase
Solution Approach 1:
The system automatically collects and stores relevant data (analyte readings, user-logged events, metadata) before excursions occur, creating a pre-prepared dataset that simplifies later analysis. This preliminary data gathering eliminates the need for complex real-time recording systems while maintaining comprehensive data availability for root cause investigation.
Solution Approach 2:
The system extracts only the necessary data elements needed for excursion analysis from the broader dataset. By identifying and isolating specific data points (pre-excursion readings, temporal patterns, associated events), the system reduces data complexity while maintaining the precision needed for cause identification, separating essential information from unnecessary data.
2Device complexity
If post-hoc question and answer sessions are used to investigate excursions, then device complexity is reduced, but loss of time and measurement precision increase
Solution Approach 1:
The system automatically analyzes collected data and provides immediate feedback about potential excursion causes through pattern recognition algorithms. This automated feedback loop eliminates the need for manual post-hoc questioning, reducing investigation time while maintaining simplicity. The system continuously monitors data for patterns that indicate excursion causes and provides timely insights without requiring complex user interaction.
Solution Approach 2:
The system performs self-analysis of the collected data to identify excursion patterns and potential causes without requiring external human investigation. The automated algorithms independently process the dataset, detect patterns, and generate insights, eliminating the need for time-consuming manual review while keeping the system simple. This self-service capability allows the system to investigate excursions autonomously and efficiently.
3Device complexity
If manual data collection is required from users, then device complexity is reduced, but loss of information and reliability decrease
Solution Approach 1:
The system automatically collects and stores relevant data without requiring user intervention. The automated data collection mechanism ensures consistent, reliable data capture while maintaining simplicity. By having the system itself gather the necessary information (analyte readings, timestamps, user-logged events), the system eliminates the variability and errors associated with manual data collection while avoiding complex data gathering mechanisms.
4Measurement precision
If extensive data analysis is performed to identify patterns, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system extracts and focuses analysis only on the most relevant data patterns and features that indicate excursion causes. By identifying and isolating specific temporal patterns, data combinations, and contextual factors, the system achieves high measurement precision in pattern identification while avoiding the computational burden of analyzing all data comprehensively. This targeted extraction approach maintains productivity by limiting analysis scope to essential patterns only.
Data Source
AI summary
Systems, devices, and methods are provided that allow detection of episodes in analyte measurement, prompting a patient to self-report possible causes for the episodes. Correlation of possible causes with detected episodes assists patient behavior modification to reduce the occurrence of episodes.