Glucose Monitoring Event Pattern Segmentation for Physician Interpretation
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Solution Overview
Problem
Physicians face challenges in interpreting and incorporating the vast amount of data from continuous glucose monitoring in insulin infusion pumps, leading to a burden in providing meaningful assistance for patient outcomes due to overwhelming automated reports.
Innovation Solution
A processor-implemented method and system that identifies event patterns in glucose monitoring data, generating a snapshot graphical user interface display with graphical representations and recommended therapeutic actions to mitigate these patterns, allowing for automated reprogramming of the medical device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated reports are generated based on continuous glucose monitoring data, then the quantity of information provided to physicians is improved, but the ease of operation deteriorates due to difficulty in parsing and interpreting the overwhelming amount of data
Solution Approach 1:
The patent segments the continuous glucose monitoring data into discrete event patterns (such as high glucose events, low glucose events, and variability events) that occur at specific times. This segmentation transforms the overwhelming continuous data stream into manageable, categorized events that physicians can easily interpret and prioritize.
2Measurement precision
If continuous monitoring is implemented to provide greater understanding of patient condition, then the measurement precision is improved, but the device complexity increases due to the burden of adapting to and incorporating the data
Solution Approach 1:
The system performs self-service by automatically analyzing the continuous glucose monitoring data, identifying event patterns, and generating prioritized reports without requiring physician intervention for data processing. This automation reduces the complexity burden on physicians while maintaining the precision benefits of continuous monitoring.
3Reliability
If automated reports are generated with comprehensive data, then the reliability of patient monitoring is improved, but the loss of time increases due to the time required to parse and interpret the data
Solution Approach 1:
The system performs preliminary action by pre-processing the continuous glucose data, identifying event patterns, and organizing them into prioritized reports before presenting to the physician. This preliminary analysis eliminates the time-consuming data parsing step during physician review while maintaining the reliability of the monitoring data.
Data Source
AI summary
Methods, systems, and media for event pattern treatment recommendations are provided. In some embodiments, a method involve identifying a plurality of event patterns within a plurality of monitoring periods based on measurement values of a physiological condition. The techniques may involve causing display of a snapshot graphical user interface display, wherein the snapshot graphical user interface display comprises a graph overlay region and an event detection region, the graph overlay region comprises a graphical representation of the measurement values, and the event detection region comprises a pattern guidance display for at least a subset of the plurality of event patterns, wherein the pattern guidance display for at least one event pattern includes a graphical representation of a recommended therapeutic remedial action that comprises therapy parameters, to be used by a medical device during a subsequent time period, configured to mitigate occurrence of the at least one event pattern.