Legacy Clinical Document Metadata Augmentation for XDS Interoperability
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Solution Overview
Problem
Legacy clinical documents generated before healthcare information standards were developed are not compliant with current sharing standards, making them inaccessible through modern healthcare information systems.
Innovation Solution
A system and technique that captures metadata from legacy documents using optical character recognition and other methods, maps unstructured data to required standards, and integrates with external systems to provide missing attributes, enabling legacy documents to be made compliant with Cross-Enterprise Document Sharing (XDS) infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy clinical documents are integrated into modern healthcare systems, then information sharing and interoperability are improved, but compliance with current sharing standards deteriorates
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between legacy clinical documents and modern healthcare systems. This intermediary captures metadata from legacy documents using optical character recognition and other methods, maps unstructured data to required standards, and integrates with external systems to provide missing attributes, thereby enabling compliance without modifying the original legacy documents
Solution Approach 2:
The patent changes the parameters of legacy documents by capturing and augmenting their metadata. Through optical character recognition, data mining, and external system integration, the system transforms unstructured legacy document data into structured metadata that conforms to current sharing standards, allowing the documents to be properly integrated into modern systems
2Manufacturing precision
If metadata is captured and augmented from legacy documents, then compliance with sharing standards is improved, but system complexity increases
Solution Approach 1:
The patent segments the metadata capture and augmentation process into distinct functional components: optical character recognition for extracting text from legacy documents, data mining for retrieving additional information, external system integration for obtaining missing attributes, and mapping mechanisms for translating data to required standards. This segmentation makes the complex process more manageable and maintainable
3Loss of information
If optical character recognition and data mining are used to capture metadata, then information completeness is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing legacy documents to extract and store metadata in advance. Optical character recognition and data mining are executed beforehand to capture as much information as possible from the legacy documents, reducing the need for time-consuming processing when the documents need to be accessed or shared
Data Source
AI summary
A healthcare information infrastructure stores and registers clinical documents. The infrastructure requires that the stored documents be registered using certain metadata. Metadata associated with legacy documents is reviewed to determine whether any required metadata is missing. Any required metadata that is missing is obtained. The metadata is augmented with the obtained metadata so that the document can be stored and registered in the healthcare information infrastructure.