Personalized Visualizations for Treatment Adherence Motivation
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Solution Overview
Problem
Current treatment adherence systems fail to effectively motivate patients to adhere to prescribed treatments, leading to increased medical complications, antibiotic resistance, and public health issues due to nonadherence, which can result in chronic conditions and epidemics.
Innovation Solution
A computer-implemented method using deep generative machine learning models, such as GANs or VAEs, to generate personalized visualizations showing the consequences of treatment adherence and nonadherence, encouraging patients to comply with their treatments by illustrating hypothetical outcomes based on their adherence to prescribed treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional treatment adherence systems are used, then patients receive standard treatment monitoring, but patient motivation and adherence rates remain low
Solution Approach 1:
The system uses color changes in visualizations to represent different adherence states and outcomes. The generated images use varying colors to depict health conditions, making the consequences of adherence or nonadherence visually apparent and emotionally impactful to patients.
Solution Approach 2:
The system changes visual parameters of patient images to show hypothetical future states based on adherence behavior. By modifying image parameters (such as skin condition, vitality indicators) to reflect health outcomes, the system creates compelling visual feedback that motivates adherence.
2Reliability
If personalized visualizations are generated using deep generative models, then patient motivation and adherence improve, but system complexity and computational requirements increase
Solution Approach 1:
The system creates simplified visual copies or representations of patient health states through generated images. Instead of complex medical monitoring systems, it uses image generation to copy and visualize health outcomes, making complex medical information accessible and motivating through simple visual representations.
Solution Approach 2:
The system replaces traditional mechanical or manual adherence monitoring with AI-based image generation. Instead of complex tracking mechanisms, it uses deep learning models to generate motivational visualizations, substituting physical monitoring systems with intelligent software-based solutions.
3Reliability
If visual feedback is provided to motivate adherence, then patient compliance improves, but loss of medical information privacy increases
Solution Approach 1:
The system extracts only the necessary visual features needed for motivation while leaving sensitive medical information separate. It takes out the essential visual elements (appearance changes, health indicators) from the full medical dataset, using only what's needed for visualization while preserving privacy of detailed medical records.
Solution Approach 2:
The system introduces generated images as an intermediary between raw medical data and patient feedback. Instead of showing patients direct medical information, it uses visual representations as a mediator that conveys health status and consequences without exposing sensitive underlying medical data.
Data Source
AI summary
A method includes: receiving, by a computer device, an association of a prescribed treatment to a user; receiving, by the computer device, an image of the user; receiving, by the computer device, an image of treatment adherence by the user; determining, by the computer device, adherence to the prescribed treatment by analyzing the image of treatment adherence; and generating, by the computer device, a personalized visualization illustrating the determined adherence to the prescribed treatment by modifying the image of the user.