AI-Driven Fax to EHR Conversion via C-CDA Transformation
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Solution Overview
Problem
The manual creation of Consolidated Clinical Document Architecture (C-CDA) documents from unstructured health records is a time-consuming, expensive, and error-prone process, especially when converting faxed or scanned images into electronic health records.
Innovation Solution
The use of Artificial Intelligence (AI) and Machine Learning (ML) to identify demographic labels and values from unstructured images, followed by a transformation engine that converts the data into a structured C-CDA format, enabling secure delivery via Direct/XDR/XDM to certified Electronic Health Records (EHRs), with a specialized portal for handling files exceeding size limits and non-medical content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and data entry is used to create C-CDA documents from received documents, then data accuracy can be maintained through human verification, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces the manual mechanical process of human review and data entry with an automated optical character recognition (OCR) system and transformation engine. The OCR software automatically extracts text from received documents, and the transformation engine converts this text into C-CDA format, eliminating the need for manual typing while maintaining data accuracy through systematic processing.
Solution Approach 2:
The system enables self-service by allowing the intake process to automatically generate C-CDA documents without requiring manual human intervention. The transformation engine autonomously processes received documents, extracts relevant data, and creates standardized C-CDA outputs, making the system self-sufficient for routine document conversion tasks.
2Adaptability or versatility
If different healthcare providers use different systems and formats for storing and exchanging information, then each provider can optimize for their specific needs, but sharing and accessing patient data becomes challenging
Solution Approach 1:
The transformation engine serves multiple functions: it processes various input formats (fax, email, scanned documents), extracts data using OCR, and outputs standardized C-CDA format compatible with different EHR systems. This multi-functional capability allows the system to handle diverse provider formats while delivering a universal standard output that facilitates seamless data sharing across healthcare organizations.
Solution Approach 2:
The C-CDA transformation engine acts as an intermediary between different healthcare information systems. It receives documents in various formats from different providers, processes them through standardized transformation rules, and outputs universally compatible C-CDA documents that can be exchanged between any participating healthcare organizations, thereby mediating the data sharing process.
3Productivity
If automated processing is implemented to reduce manual effort, then productivity increases, but the system complexity and initial cost increase
Solution Approach 1:
The automated system is segmented into distinct functional modules: an OCR module for text extraction, a transformation engine for format conversion, and a C-CDA generation module for output creation. This segmentation allows each component to be independently optimized, maintained, and scaled, reducing overall system complexity while maintaining high productivity through specialized automated processing at each stage.
Data Source
AI summary
A system for preparing documents in a standards compliant electronic health record (EHR). A document intake device is configured to receive an image file transmitted to the device using an image file transport protocol. An artificial intelligence/machine learning (AI/ML) model is configured to extract patient demographic and medical information contained within the image file as a text file with the patient demographic and medical information, and format the text file for use as a EHR by a transformation engine. The transformation engine is configured to convert the text file formatted for use as the EHR into the standards compliant EHR and transmit the standards compliant EHR as a Direct Secure Message to a Health Information Service Provider (HISP). The HISP configured to send the standards compliant EHR as a Direct Secure Message for storage as a patient record if a patient match is found.