Glucose Meter Wireless Time Sync and Trend Forecasting
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Solution Overview
Problem
Current glucose monitors rely on users to correctly set the time, which can lead to inaccurate data collection due to user error, especially among older or technologically less savvy individuals. Additionally, these devices only provide snapshot data, lacking the dynamic information needed for accurate treatment decisions.
Innovation Solution
The integration of wireless time data reception and processing allows the glucose meter to automatically and accurately set its internal clock to local time, enabling the collection of accurate historical data and facilitating glucose level forecasting. This feature enhances user convenience, caregiver confidence in data accuracy, and the ability to monitor trends and adjust treatments accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually set the time on glucose meters, then the device can collect timestamped data, but user error leads to inaccurate timekeeping and unreliable data
Solution Approach 1:
The glucose meter automatically receives time data from wireless communication sources (radio stations, cell phones, PDAs) and sets its internal clock without user intervention. This self-service approach eliminates manual time-setting errors while maintaining accurate timestamping of glucose measurements.
Solution Approach 2:
The patent introduces wireless communication infrastructure (radio stations, cell phones, PDAs) as intermediaries to transmit time data to the glucose meter. This intermediary system provides accurate timekeeping without requiring direct user interaction with the device's time-setting functions.
2Reliability
If glucose meters only provide snapshot data, then the device structure remains simple, but treatment decisions lack dynamic information for accuracy
Solution Approach 1:
The glucose meter performs preliminary analysis of glucose trends by calculating rate-of-change values and predicting future glucose levels before the user makes treatment decisions. This advance prediction provides dynamic information that improves treatment accuracy without requiring complex real-time processing.
Solution Approach 2:
The patent replaces simple snapshot data collection with automated computational analysis that calculates glucose trends, rates of change, and predictions. This substitution of mechanical data collection with intelligent data processing enhances treatment decision accuracy while managing device complexity through software-based solutions.
3Reliability
If the device collects extensive historical data for forecasting, then glucose trend prediction improves, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts only the essential time-related features from historical glucose data (timestamps and glucose values) needed for forecasting, rather than storing and processing all possible measurement parameters. This extraction approach maintains forecasting accuracy while minimizing data storage requirements.
Data Source
AI summary
A medical diagnostic device includes a wirelessly transmitted time data receiver and processor. Associated devices, methods and functionality are also described.