Chronic Disease Management System Integrating Analyte Data
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Solution Overview
Problem
Current glucose meters provide challenging data for patients to interpret, leading to frustration and reduced compliance in managing diabetes, as they lack actionable suggestions for behavior modification.
Innovation Solution
A system that integrates data from glucose monitors and patient trackers to provide customizable, actionable suggestions for behavior change, device settings, and prompts for patients and healthcare professionals, leveraging data analysis to improve understanding and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If glucose testing data is provided to patients, then patients can monitor their blood glucose levels, but the data is difficult for patients to interpret and understand
Solution Approach 1:
The patent introduces an intermediary system (mobile device with processing algorithms) that receives raw glucose data from meters, analyzes it against patient profiles and behavior patterns, and translates it into actionable insights. This intermediary bridges the gap between complex medical data and patient comprehension, providing personalized recommendations without requiring patients to directly interpret raw glucose numbers.
Solution Approach 2:
The system transforms glucose data from its original numerical form into multiple derived parameters including trend indicators, risk assessments, and behavior correlations. By changing the parameters from raw glucose values to meaningful categories and actionable insights, the system makes the data interpretable while maintaining ease of operation for patients.
2Quantity of substance
If detailed glucose testing data is provided to patients, then complete monitoring information is available, but review of data becomes burdensome for patients
Solution Approach 1:
The system extracts only the most relevant and actionable information from the complete glucose dataset, separating critical insights from redundant details. It identifies and presents key patterns, anomalies, and behavior correlations while omitting unnecessary raw data points, thereby reducing patient burden while maintaining monitoring completeness.
Solution Approach 2:
The patent segments glucose data into organized categories such as temporal trends, behavioral correlations, risk assessments, and actionable recommendations. This segmentation allows patients to review specific aspects of their data without being overwhelmed by the complete dataset, reducing cognitive load while preserving comprehensive monitoring capabilities.
3Device complexity
If glucose meters provide only raw data, then device complexity is minimized, but actionable suggestions for behavior modification are lacking
Solution Approach 1:
The patent merges the simple glucose meter with a intelligent processing system (mobile device or server) to create a hybrid solution. The meter itself remains simple and low-cost, while the combined system provides comprehensive analysis and actionable insights. This merging allows the retention of meter simplicity while eliminating the loss of actionable information through integrated data processing.
Data Source
AI summary
Devices, systems, and methods herein relate to managing a chronic condition such as diabetes. These systems and methods may obtain patient data from a plurality of devices, integrate the data for analysis of trends that may be presented to the patient and/or health care professional along with an actionable suggestion. In some variations, a method may include the steps of receiving analyte data generated by an analyte measurement device and patient data generated by a patient measurement device. One or more data trends may be generated by analyzing the analyte data against the patient data using a computing device. The device settings of one or more of the analyte measurement device and the computing device may be modified in response to one or more of the data trends.