Personalized Physiological Modeling for Timely Disease Management
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Solution Overview
Problem
Existing disease management systems lack personalized and timely treatment recommendations due to reliance on outdated patient data and limited human resources, failing to account for individual patient dynamics and requiring proactive patient participation.
Innovation Solution
A system that collects patient data through biometric and behavioral inputs, generates a personalized modified model, performs statistical analysis to detect data excursions, and develops targeted treatment recommendations using a physiological model to ameliorate negative effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human-based case management services are provided to patients, then personalized attention and treatment guidance can be delivered, but enormous resources are required and timely patient data is unavailable due to limited case manager capacity
Solution Approach 1:
The system enables patients to self-monitor and input their own health data (blood glucose levels, carbohydrate intake, medication doses, physical activity) directly into the database, eliminating the need for case managers to manually collect this information. This self-service approach allows continuous data collection without additional human resources while maintaining personalized treatment guidance through automated model execution.
Solution Approach 2:
The patent replaces the mechanical system of human case managers manually collecting and analyzing patient data with an automated computer-based system. The executable model automatically processes patient inputs, generates treatment recommendations, and updates the modified model without human intervention, thereby eliminating the productivity bottleneck of case managers while preserving personalized care.
2Adaptability or versatility
If general population formulas are applied for disease management, then treatment recommendations can be provided to any patient, but individual patient dynamics and optimal personalized treatment cannot be achieved
Solution Approach 1:
The system transitions from universal treatment formulas to locally optimized recommendations by executing a modified model specific to each patient. The model incorporates individual patient parameters (carbohydrate intake sensitivity, medication response, activity level, blood glucose patterns) to generate personalized treatment guidance tailored to that specific patient's physiological characteristics and current state, rather than applying generic population-based formulas.
Solution Approach 2:
The patent implements a dynamic modified model that continuously adapts to each patient's changing condition. The model parameters are updated based on new patient inputs (blood glucose readings, dietary changes, medication adjustments, physical activity), allowing treatment recommendations to evolve in real-time with the patient's physiology rather than relying on static general population formulas.
3Measurement precision
If proactive patient participation is required for data collection, then personalized treatment data can be obtained, but the system fails when patients are not conscientious or proactive
Solution Approach 1:
The system incorporates automated feedback mechanisms that guide patients through data collection. The interface provides structured prompts and reminders for patients to input their blood glucose readings, carbohydrate intake, medication doses, and physical activity. This feedback loop ensures consistent data collection by guiding patients through the process rather than relying solely on their inherent conscientiousness, thereby improving both data accuracy and system reliability.
Data Source
AI summary
Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.