Medical Instrument Positioning and Audio Signal Detection for Remote Diagnosis
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Solution Overview
Problem
Existing telemedicine solutions do not provide sufficient patient-physician interaction for accurate medical diagnosis, leading to inefficiencies in patient intake, triage, diagnosis, treatment, electronic health record data entry, billing, and patient follow-up, which increases healthcare costs.
Innovation Solution
A computer-assisted medical diagnostic architecture using a medical kiosk or camera system with sensors and deep learning systems to facilitate remote medical intake, triage, diagnosis, and treatment, enabling patients to perform self-diagnosis with guidance from a remotely located healthcare professional.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine solutions use video or audio conferencing with a doctor, then patient access to healthcare providers is improved, but the quality of patient-physician interaction is insufficient for accurate medical diagnosis
Solution Approach 1:
The patent introduces automated diagnostic systems with sensors, image capture devices, and machine learning algorithms as intermediaries between the patient and physician. These systems collect, process, and analyze patient data locally, then present processed information to the remote physician, enhancing the quality of interaction without requiring direct physical presence
Solution Approach 2:
The patent replaces traditional mechanical physical examination methods with electronic sensors, digital imaging, and automated data processing systems. This substitution enables remote collection and analysis of diagnostic data while maintaining or improving measurement precision through consistent, automated measurement protocols
2Productivity
If traditional medical facilities increase doctor to patient throughput, then healthcare efficiency is improved, but the quality of patient care and interaction is reduced
Solution Approach 1:
The patent segments the medical consultation process into distinct phases: automated data collection, local processing and analysis, and physician review of processed information. This segmentation allows physicians to focus on high-level decision-making while automated systems handle routine measurements and initial analysis, maintaining care quality at higher throughput levels
Solution Approach 2:
The patent implements self-service capabilities where patients can perform initial assessments, data collection, and preliminary diagnostics using automated kiosk systems before seeing a physician. This reduces the burden on physicians for routine tasks while maintaining comprehensive care quality
3Loss of energy
If medical facilities reduce healthcare costs through regulation changes and electronic health record changes, then operational efficiency is improved, but the quality of medical examinations and patient interaction is compromised
Solution Approach 1:
The patent replaces manual medical examination procedures with automated sensor-based systems and digital imaging. This substitution reduces costs by eliminating manual labor while improving measurement precision through consistent, programmable measurement protocols and automated data processing
Solution Approach 2:
The patent changes the parameters of medical examination from subjective, manual assessments to objective, quantifiable digital measurements. This enables more precise tracking of health parameters while reducing costs through automated data collection and analysis
Data Source
AI summary
Presented are systems and methods for the accurate acquisition of medical measurement data of a body part of patient. To assist in acquiring accurate medical measurement data, an automated diagnostic and treatment system provides instructions to the patient to allow the patient to precisely position a medical instrument in proximity to a target spot of a body part of patient. For a stethoscope examination, the steps may include utilizing object tracking to determine if the patient has moved the stethoscope to a recording site; utilizing DSP processing to confirm that the stethoscope is in operation, utilizing DSP processing to generate a pre-processed audio sample from a recorded audio signal; using machine learning (ML) to determine if a signal of interest (SOI) is present in the pre-processed sample. If SoI is present, using ML to evaluate characteristics in the signal which indicate the presence of abnormalities in the organ being measured.