Personalized Digital Therapy Recommendations from Patient Events
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Solution Overview
Problem
Existing digital therapy platforms face challenges in providing personalized and timely interactions due to therapist resource constraints, reliance on generic templates, lack of empathy in automated communications, and inadequate processing of complex patient data, leading to suboptimal care and reduced patient satisfaction.
Innovation Solution
A digital therapy platform leveraging AI and machine learning models, particularly large language models (LLMs), analyzes diverse patient data to generate personalized recommendations and feedback, facilitating continuous dialogue between therapists and patients, and adapting therapy programs in real-time to individual needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated systems use generic templates and predefined responses, then system complexity is reduced and ease of operation is improved, but personalization depth and empathy quality deteriorate
Solution Approach 1:
The patent introduces an AI language model as an intermediary between the automated system and patient data. This mediator processes structured patient information and generates personalized, empathetic responses without requiring complex manual configuration. The AI model translates clinical data into natural, context-aware communications that maintain both automation efficiency and personalization quality.
2Reliability
If therapists manually analyze patient data to provide personalized care, then care quality and personalization are improved, but therapist time availability and scalability worsen
Solution Approach 1:
The system enables self-service by allowing the AI language model to autonomously analyze patient data, detect events, and generate personalized recommendations without requiring manual therapist intervention for each patient. This automation maintains high care quality through accurate data processing while significantly improving therapist scalability and reducing workload.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system continuously monitors patient data, detects changes in patient status, and generates real-time recommendations. This closed-loop feedback ensures high care quality by promptly responding to patient needs while maintaining scalability through automated decision-support rather than requiring continuous manual assessment.
3Measurement precision
If the system processes complex patient data from multiple sources, then measurement precision and insight quality are improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the complex data processing task into distinct components: data collection from multiple sources, structured data storage, event detection algorithms, and recommendation generation. This segmentation allows the system to process complex multi-source patient data with high precision while managing system complexity through modular architecture and specialized processing for each data type.
Data Source
AI summary
An example digital therapy platform is disclosed that provides personalized recommendations. Patient data is collected from a plurality of data sources associated with a patient profile of a patient in the digital therapy platform. At least a subset of the patient data is processed to detect a patient event. In response to detecting the patient event, a personalized recommendation is generated. The personalized recommendation is associated with the patient profile and generated using at least one machine learning model. An action is invoked in the digital therapy platform based on the personalized recommendation, and the patient profile is adjusted to reflect the action. The digital therapy platform causes presentation of at least one of a first indication of the action at a first device associated with a therapist assigned to the patient profile or a second indication of the action at a second device associated with the patient.