Glucose Pattern Detection System for Continuous Analyte Monitoring
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Solution Overview
Problem
Conventional methods for analyzing data from glucose sensors are cumbersome, requiring significant time and expertise, leading to delayed detection of hyperglycemic or hypoglycemic conditions, and often result in users ignoring important alerts due to excessive notifications.
Innovation Solution
A system comprising a display screen, processors, and memory that aggregates and weights sensor data by time of day, highlights significant patterns, and provides a user interface for selecting timeframes, allowing users to visualize glucose trends and receive alerts based on clinically significant events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used, then users can review glucose trend data, but the analysis requires significant time and expertise, leading to delayed detection of glucose conditions
Solution Approach 1:
The system performs preliminary analysis by automatically detecting patterns in sensor data (such as glucose trends, hyperglycemic/hypoglycemic episodes) before user review. This pre-processing identifies clinically significant events and prepares summarized information, eliminating the need for users to manually analyze raw data and enabling timely detection without requiring user time investment.
Solution Approach 2:
The patent introduces an intermediary processing layer between the sensor and the user that automatically analyzes sensor data, detects patterns, and generates clinically relevant alerts. This intermediary system performs the complex analysis work, translating raw sensor data into actionable medical insights without requiring user expertise or time investment.
2Reliability
If conventional alert systems are used, then users receive notifications about glucose events, but excessive alerts cause users to ignore important notifications
Solution Approach 1:
The system applies local quality by providing different types of alerts with varying levels of urgency and detail based on the specific clinical situation. Not all glucose events trigger the same alert intensity - the system tailors alert characteristics (such as urgency, delivery method, and information content) to the local context of each event, ensuring critical alerts stand out while less urgent events receive appropriate but less intrusive notification.
Solution Approach 2:
The patent changes alert parameters dynamically based on pattern detection results. The system adjusts alert thresholds, notification methods, and information content based on the detected glucose patterns and their clinical significance. This parameter adaptation ensures that alerts remain reliable and actionable without becoming excessive or ignoring important events.
3Loss of information
If users manually review sensor data downloads, then they can analyze glucose patterns, but the process is time-consuming and requires expertise to detect problem areas
Solution Approach 1:
The system performs self-service by automatically detecting and analyzing glucose patterns without requiring user intervention. The sensor system autonomously monitors glucose levels, identifies clinically significant events (such as hyperglycemic/hypoglycemic episodes, trends), and generates alerts, freeing users from the burden of manual data review while ensuring complete information is captured and analyzed.
Solution Approach 2:
The patent replaces the mechanical process of manual data review with an automated electronic analysis system. Instead of users physically examining sensor data downloads and interpreting patterns, the system uses computational algorithms to automatically detect glucose patterns, eliminating the need for user time and expertise while maintaining complete information analysis.
Data Source
AI summary
Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.