Digital Home Exercise Plan Risk Prediction for Remote PT Progression
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Solution Overview
Problem
Existing physical therapy methods require frequent in-person visits for exercise plan progression and regression, leading to inefficiencies and variability in patient diagnosis and exercise prescription, with limited remote monitoring and compliance tracking.
Innovation Solution
A digital home exercise plan system utilizing machine learning models to predict patient risk and adjust exercise plans based on patient input, enabling remote monitoring and compliance tracking, and ensuring safe progression or regression of exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical therapists conduct in-person examinations and visual evaluations to ensure accurate diagnosis and exercise prescription, then diagnostic accuracy and treatment safety are improved, but patient time loss and treatment cost increase
Solution Approach 1:
The patent replaces the mechanical system of in-person visual evaluation with an automated image analysis system using machine learning algorithms. The system processes patient images through trained models that automatically detect impairments and evaluate progress, eliminating the need for continuous physical therapist visual assessment while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables patients to perform self-evaluation through automated image analysis. Patients submit images of their condition, and the machine learning system automatically assesses their status, allowing them to monitor their own progress without requiring frequent in-person visits to the physical therapist.
2Reliability
If physical therapists provide personalized exercise prescriptions through verbal communication during in-person visits, then treatment effectiveness is improved, but treatment cost and patient time loss increase
Solution Approach 1:
The patent replaces verbal communication and manual exercise prescription with an automated machine learning system that generates exercise recommendations based on image analysis. The system processes patient data through trained models to automatically prescribe appropriate exercises, eliminating the need for continuous physical therapist involvement while maintaining treatment effectiveness.
Solution Approach 2:
The system creates digital copies of the physical therapist's diagnostic and prescription functions through machine learning models trained on expert data. These digital copies replicate the decision-making process, allowing the system to generate treatment plans that mirror those created by trained physical therapists without requiring their continuous presence.
3Reliability
If physical therapists conduct frequent in-person visits to monitor patient progress, then treatment safety and accuracy are improved, but productivity decreases and treatment cost increases
Solution Approach 1:
The patent replaces the physical therapist's visual monitoring function with an automated image analysis system. The machine learning models continuously assess patient progress through submitted images, detecting impairments and safety concerns without requiring physical therapist time, thereby maintaining treatment safety while significantly improving therapist productivity.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the patient and physical therapist. This intermediary processes patient images and provides objective progress measurements, allowing physical therapists to work more efficiently by reviewing only cases that require their expertise rather than manually evaluating every patient visit.
4Measurement precision
If physical therapists manually evaluate patient progress visually during office visits, then accurate detection of impairments is achieved, but measurement precision and objectivity are limited
Solution Approach 1:
The patent replaces subjective visual evaluation with objective automated image analysis using machine learning. The system processes patient images through trained algorithms that quantitatively measure progress and detect impairments, providing consistent, objective assessments free from human bias while maintaining high measurement precision.
Data Source
AI summary
Systems, methods and computer readable media are provided for determining patient risk of participating in a physical therapy digital home exercise program. The patient risk is generated by one or more artificial intelligence/machine learning (AI/ML) models. Based on the patient risks compared to the benefits, one or more actions may be initiated to create or modify a digital home exercise program for a patient.