Telemedicine System Using Machine Learning for Preventative Action
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Solution Overview
Problem
Current telemedicine systems face challenges in effectively monitoring patient progress and adapting treatment plans remotely, particularly in cardiovascular rehabilitation, due to limitations in physical examination and real-time feedback, leading to inefficiencies and inaccuracies in treatment plan selection.
Innovation Solution
A computer-implemented method and system that uses machine learning models to analyze data from treatment apparatuses and patient interfaces, determining preventative actions and adjusting treatment plans in real-time based on user interactions and health metrics, enabling remote monitoring and personalized rehabilitation protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine systems rely on verbal communication and limited remote observation, then patient accessibility is improved, but measurement precision and treatment monitoring accuracy deteriorate
Solution Approach 1:
The patent introduces an electromechanical machine as an intermediary device between the patient and healthcare provider. This machine includes sensors that objectively measure treatment parameters (such as range of motion, force, velocity) and communicate this data remotely, thereby maintaining patient accessibility while significantly improving measurement precision and monitoring accuracy without requiring direct physical examination
Solution Approach 2:
The patent replaces the manual mechanical assessment method (physical examination by healthcare provider) with an automated electromechanical measurement system. The machine uses sensors and actuators to objectively quantify treatment parameters, substituting the subjective verbal communication and limited observation with precise mechanical measurements that can be transmitted remotely
2Reliability
If treatment plans are adjusted based on real-time data analysis, then treatment effectiveness is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The patent implements a closed-loop feedback system where sensors continuously monitor treatment parameters, the processor analyzes the collected data in real-time, and the system automatically adjusts treatment plan parameters based on this analysis. This feedback mechanism improves treatment effectiveness by enabling dynamic adaptation while managing complexity through automated decision-making algorithms that process data systematically
Solution Approach 2:
The patent employs machine learning models that are pre-trained on historical treatment data to predict optimal treatment adjustments. By performing preliminary training and model preparation in advance, the system reduces the computational complexity required during real-time operation, as the heavy analytical work has already been done during the offline training phase
3Measurement precision
If machine learning models are used to determine preventative actions, then treatment plan accuracy is improved, but loss of time for data processing and decision-making worsens
Solution Approach 1:
The patent trains machine learning models in advance using historical treatment data and patient outcomes. This preliminary training phase allows the models to learn complex patterns and relationships beforehand, so that during actual treatment monitoring, the models can quickly process new data and provide accurate predictions without requiring extensive real-time computation, thus reducing data processing time while maintaining high accuracy
Data Source
AI summary
A computer-implemented method is disclosed. The method includes determining whether one or more messages have been received while a user performs a treatment plan. The one or more messages may pertain to at least one of a user and a usage of the electromechanical machine by the user. The electromechanical machine may be configured to be manipulated by the user while the user is performing a treatment plan. The method also includes, responsive to determining that the one or more messages have not been received, determining, using one or more machine learning models, one or more preventative actions to perform. The method also includes causing the one or more preventative actions to be performed.