Consumer Physical Assessment System for Clinical Diagnosis
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Solution Overview
Problem
Telemedicine faces challenges in time efficiency and quality of patient care due to difficulties in assessing and treating patients remotely, and the inaccuracy of consumer-grade devices for clinical diagnosis.
Innovation Solution
A system that uses consumer-grade devices integrated with sensing technologies in wearable consumer electronics to obtain and correlate physical assessment measurements, generating clinical-grade data for accurate medical diagnosis through machine learning and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical grade physical assessment devices are used for accurate online diagnosis, then measurement precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system enables patients to autonomously perform physical assessments using consumer-grade devices with automated guidance. The machine learning model processes measurements automatically, eliminating the need for patients to manually operate complex clinical devices while maintaining diagnostic accuracy.
Solution Approach 2:
A machine learning model acts as an intermediary between consumer-grade devices and clinical diagnosis. The model processes measurements from simple devices (smartphone cameras, microphones, accelerometers) and transforms them into clinically actionable data, bridging the gap between consumer technology and medical-grade precision.
2Ease of operation
If multiple consumer grade devices are used for tracking activities and vitals, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The system merges measurements from multiple consumer-grade devices (smartphone camera, microphone, accelerometer, existing health app data) into a unified assessment. By combining data from these easily operated devices and processing them through a machine learning model, the system achieves clinical-grade precision without requiring patients to handle complex equipment.
3Adaptability or versatility
If telemedicine is used for providing clinical healthcare at a distance, then accessibility is improved, but measurement precision and quality of care worsen
Solution Approach 1:
The machine learning model serves as an intermediary that enables remote telemedicine consultations to achieve clinical-grade measurement precision. It processes data from consumer devices used during virtual visits, allowing physicians to obtain accurate diagnostic information without physical presence, thus maintaining accessibility while ensuring measurement quality.
Data Source
AI summary
A clinical grade consumer physical assessment system generates clinical grade physical assessment information for a patient based on a plurality of consumer grade measurements taken from different body portions of the patient using a consumer device. The system operates to correlate the plurality of consumer grade measurements, which have low resolution and accuracy, to a clinical grade result that is adequate for clinical purposes.