ECG Reading Workflow With De-Identification and AI Triage
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Solution Overview
Problem
Existing electrocardiogram measurement devices have low reliability and require unnecessary hospital visits for verification, leading to wasted medical resources and missed early detection of heart diseases due to their inability to accurately diagnose conditions and integrate with medical information systems.
Innovation Solution
A system that uses a neural network model to analyze electrocardiogram data from wearable devices, providing real-time reading results and integrating with hospital systems for accurate diagnosis, with expert review for unclear cases, and ensuring user data privacy through de-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-electrocardiogram measurement devices are used for diagnosis, then convenience and accessibility are improved, but reliability and measurement precision deteriorate due to low positive predictive rate
Solution Approach 1:
The patent merges self-measurement convenience with hospital-based expert diagnosis by integrating personal electrocardiogram data with medical information systems. Users can conveniently measure their own ECG data at home, which is then automatically transmitted to hospital servers for expert analysis, combining the accessibility of self-measurement with the reliability of professional diagnosis.
Solution Approach 2:
The patent introduces an automated intermediary system consisting of servers and neural network models that mediate between self-measured ECG data and expert diagnosis. The system automatically transmits data, performs preliminary analysis using AI models, and facilitates expert review, eliminating the need for users to personally visit hospitals while maintaining diagnostic reliability.
2Measurement precision
If users personally visit hospitals for electrocardiogram tests, then measurement precision and diagnostic accuracy are improved, but loss of time and medical resource waste increase
Solution Approach 1:
The patent performs preliminary ECG measurements at the user's home before hospital visits are needed. Users can conduct self-measurements conveniently, and the system performs automated preliminary analysis using AI models, so that when users do visit hospitals, the diagnostic process is accelerated and more efficient.
Solution Approach 2:
The patent enables users to perform self-service ECG measurements and automated data transmission without requiring hospital staff intervention for data collection. The system automatically manages data transmission, storage, and preliminary analysis, freeing up medical staff to focus on actual diagnosis and treatment.
3Reliability
If electrocardiogram data is transmitted to hospital systems, then diagnostic reliability is improved, but information security and privacy protection become more complex
Solution Approach 1:
The patent introduces de-identification servers as intermediaries that strip personal identifying information from ECG data before transmission to hospital systems. This intermediary layer maintains diagnostic reliability by preserving clinical data while automatically protecting privacy through automated de-identification processes.
Solution Approach 2:
The patent extracts and removes personal identifying information from ECG data through automated de-identification processes. By separating identifiable information from clinical data, the system maintains diagnostic utility while reducing privacy risks and simplifying security management.
Data Source
AI summary
The present disclosure is directed to a system and method for providing an electrocardiogram reading service. The system may include: a user terminal configured to generate electrocardiogram data for a user based on user input, to make an electrocardiogram reading request, and to view electrocardiogram reading result data for the electrocardiogram data; a first server configured to receive the electrocardiogram data, to generate de-identification information by using a de-identification code value for user de-identification processing for the electrocardiogram data, and to provide collaboration request data including the generated de-identification information and the electrocardiogram data; and a second server configured to receive the collaboration request data, and to generate electrocardiogram reading result data for the electrocardiogram data based on the collaboration request data by using a pre-trained neural network model.