AI Predictive Tool for Digital Physical Therapy
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Solution Overview
Problem
Existing digital physical therapy technologies struggle to accurately predict whether a patient will respond positively to therapy, particularly for musculoskeletal conditions, due to the multifactorial nature of pain and the inefficiency of manual data review.
Innovation Solution
An AI-driven predictive tool that utilizes machine learning techniques, including tree-based ensemble classifiers, to analyze data such as range of motion, pain levels, and patient-reported outcomes, flagging potential non-responders and adjusting therapy programs accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data review is used to assess patient response to therapy, then comprehensive analysis of multifactorial pain data can be performed, but the process is inefficient and cannot accurately predict outcomes in a timely manner
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning system that uses tree-based ensemble classifiers to analyze patient data. This substitution enables rapid, accurate prediction of therapy response by automatically processing multifactorial pain data including range of motion, pain levels, and patient-reported outcomes without manual intervention.
2Productivity
If digital physical therapy platforms track patient progress automatically, then monitoring efficiency is improved, but the capability to predict patient response prior to program conclusion is lacking
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously analyzes tracked patient progress data and generates predictions about therapy response. The system uses feedback from intermediate assessment points (range of motion measurements, pain level reports, exercise adherence) to update and refine predictions, enabling reliable outcome forecasting while maintaining automated monitoring efficiency.
Solution Approach 2:
The patent applies preliminary action by using the trained machine learning model to predict patient response before the therapy program concludes. By analyzing data collected during intermediate sessions and applying the pre-trained classifier, the system provides early predictions of therapy outcomes, allowing for timely interventions while maintaining efficient automated monitoring.
3Measurement precision
If comprehensive patient data is collected during digital physical therapy sessions, then prediction accuracy can be improved, but data processing complexity increases
Solution Approach 1:
The patent extracts and isolates the core predictive analysis function into a dedicated machine learning module using tree-based ensemble classifiers. This extracted component specifically processes the comprehensive patient data (range of motion, pain levels, exercise adherence, patient-reported outcomes) separately from the main therapy delivery system, managing data processing complexity while maintaining high prediction accuracy through specialized algorithms.
Data Source
AI summary
Examples described herein relate to an artificial intelligence-driven tool for digital physical therapy. First data indicative of a baseline condition of a first user is accessed. Second data is collected for each of a plurality of sessions of a digital physical therapy program. A first device associated with the first user tracks motion of the first user during each session. Input data based on the first data and the second data is provided to a machine learning classifier to cause generation of output data indicative of a predicted outcome of the digital physical therapy program. The predicted outcome is processed to detect that the first user is predicted not to meet a predetermined improvement threshold. An alert is generated and presented at a second device associated with a second user administering the digital physical therapy program.