Chronic Disease Trigger Map Visualization System
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Solution Overview
Problem
Current methods for managing chronic disease symptoms lack effective tools to monitor and analyze disease triggers and factors, leading to inadequate patient awareness and control over symptom occurrence.
Innovation Solution
A computer-based system that monitors and collects data on disease symptoms, factors, and triggers, analyzes these data to identify correlations, and provides patients with personalized insights and notifications to manage their triggers and prevent symptoms through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If patients manually keep written or electronic records of disease symptoms and triggers, then patients can track their symptoms over time, but the data remains unanalyzed and patients lack insight into correlations between triggers and symptoms
Solution Approach 1:
The patent introduces an intermediary computational system that sits between the patient's manual data entry and the patient's understanding. This intermediary automatically analyzes the recorded data, identifies correlations between triggers and symptoms, and presents actionable insights to the patient, thereby recovering the lost correlation information without requiring the patient to perform complex analysis themselves
Solution Approach 2:
The system enables self-service by automatically performing the analysis function that would otherwise require external medical intervention. The computational system autonomously processes the recorded symptom and trigger data, identifies patterns and correlations, and generates personalized insights, allowing patients to self-manage their chronic disease understanding without requiring continuous medical provider involvement
2Measurement precision
If no automated analysis system is used, then the system remains simple, but patients cannot identify which disease factors or triggers most likely cause their symptoms
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring and analyzing disease factors and triggers as they occur, rather than waiting for symptom occurrence. This preliminary action identifies potential triggers and calculates risk scores in advance, enabling the system to predict symptom likelihood and provide early warnings to patients before symptoms manifest
Solution Approach 2:
The system implements feedback loops where patient responses to predictions and identified triggers are continuously incorporated into the analysis model. This feedback mechanism refines the accuracy of trigger identification over time, allowing the system to learn from patient confirmations or corrections and improve its predictive capabilities for future symptom episodes
3Loss of information
If the system provides detailed statistical associations and correlations, then patients gain deep insights into their disease triggers, but the information becomes complex and difficult for patients to understand and act upon
Solution Approach 1:
The system applies local quality by tailoring the presentation of information to the individual patient's needs, understanding level, and specific disease pattern. Rather than providing uniform detailed statistical data to all patients, the system adapts the complexity and format of insights based on patient characteristics, ensuring each patient receives information at an appropriate level of detail that is both accurate and actionable for them
Solution Approach 2:
The system uses visual encoding techniques where different colors and visual indicators represent different levels of risk, trigger strength, and symptom likelihood. This transforms complex statistical associations into intuitive visual information that patients can quickly comprehend and act upon, maintaining measurement precision while dramatically improving ease of operation through visual metaphors and color-coded risk indicators
Data Source
AI summary
The disclosed systems and methods include displaying disease trigger icons within a disease trigger map in a GUI, where the disease trigger map corresponds to a particular disease symptom, and where the position and size of a particular trigger icon within the trigger map based one or more of (i) the degree or strength of a statistical association between the trigger icon's corresponding disease trigger and the disease symptom, and (ii) a cumulative frequency and/or amount of exposure to the trigger icon's corresponding disease trigger. Some embodiments also include displaying a patient population disease trigger map one or more relationships between (i) one or more disease triggers and (ii) one or more patients of the patient population. Some embodiments may further include facilitating communication and/or disease trigger information sharing among patients.