Hypoglycemic Event Data Capture and Pattern Analysis System
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Solution Overview
Problem
Conventional diabetes management systems fail to provide a clear picture of lifestyle patterns related to hypoglycemic events, as they have limited tagging ability and do not effectively associate blood glucose readings with symptoms, causes, and treatments, leading to inadequate recognition of hypoglycemic events.
Innovation Solution
A system and method that record, analyze, and present hypoglycemic data to raise awareness by prompting users to provide information on symptoms, causes, and treatments associated with low blood glucose levels, using a graphical user interface to identify patterns and provide reports that include a hypo awareness grid and recommended changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional diabetes management systems use limited tagging ability to associate readings with basic attributes, then the system complexity is reduced, but the ability to provide a clear picture of lifestyle patterns related to hypoglycemic events deteriorates
Solution Approach 1:
The patent segments the data tagging system into multiple hierarchical levels: basic attributes (date, time, meal status), detailed lifestyle factors (exercise, stress, sleep), and contextual information (symptoms, treatments). This segmentation allows comprehensive data collection while maintaining manageable system complexity through modular data structures.
Solution Approach 2:
The patent creates a universal data collection framework that can handle multiple types of information (glucose readings, lifestyle factors, symptoms, treatments) through a single integrated system. This multi-functional approach eliminates the need for separate tracking systems, reducing overall complexity while comprehensively capturing lifestyle patterns.
2Ease of operation
If conventional systems do not effectively associate blood glucose readings with symptoms, causes, and treatments, then the ease of operation is improved, but the ability to recognize hypoglycemic events and their patterns deteriorates
Solution Approach 1:
The patent implements preliminary structured templates for data entry that guide users through symptom, cause, and treatment information collection. These pre-configured forms enable comprehensive data association without requiring complex manual input, maintaining ease of operation while ensuring complete information capture for accurate hypoglycemic event identification.
Solution Approach 2:
The system provides feedback mechanisms that automatically analyze entered data to identify hypoglycemic events and their patterns. This automated analysis feedback loop enhances measurement precision by systematically associating readings with symptoms, causes, and treatments, while the user-friendly interface maintains ease of operation.
3Loss of time
If doctors and patients do not record symptoms, causes, and treatments properly, then the time required for data collection is reduced, but the ability to recognize lifestyle patterns that cause hypoglycemic events deteriorates
Solution Approach 1:
The patent implements periodic data collection prompts that systematically request symptom, cause, and treatment information at regular intervals or upon detecting hypoglycemic events. This periodic structured collection ensures comprehensive pattern recognition information is captured over time while minimizing user burden through efficient, repeatable data entry processes.
Solution Approach 2:
The system enables patients to self-record symptoms, causes, and treatments through an intuitive interface that automatically structures the data for pattern analysis. This self-service approach reduces the time investment required while ensuring complete and accurate information capture, as patients document their own experiences in real-time without requiring external assistance.
Data Source
AI summary
Systems, methods, and computer readable storage media manage low blood glucose levels. The system receives blood glucose readings, where each reading includes a blood glucose value and a respective timestamp. The system compares each blood glucose reading to a predefined hypoglycemic event threshold, and identifies hypoglycemic events when a respective blood glucose reading is below the predefined hypoglycemic event threshold. For at least a subset of the identified hypoglycemic events, the system prompts a user for information associated with each hypoglycemic event. The information requested includes one or more of: symptoms associated with the respective hypoglycemic event; perceived causes of the respective hypoglycemic event; and treatment for the respective hypoglycemic event. In response to the prompting, the system obtains feedback about the identified hypoglycemic events and provides a report that includes at least a subset of the obtained feedback.