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A section identification tool: Towards HL7 CDA/CCR standardization in Spanish discharge summaries
Iakes Goenaga1, Xabier Lahuerta1, Aitziber Atutxa2
1HiTZ Basque Center for Language Technology, University of the Basque Country (UPV/EHU), Spain(1); Faculty of Computer Science, P°Manuel Lardizabal, 1 - 20018 Donostia-San Sebastián, Spain.
Journal of Biomedical Informatics
|July 29, 2021
Summary
This study developed methods to automatically standardize Spanish Electronic Discharge Summaries (EDS), achieving high accuracy in section identification. This advances clinical data interoperability and secondary use across healthcare systems.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Clinical Document Standardization
Background:
- Digitalization of healthcare systems generates vast clinical narratives.
- Interoperability and information extraction from these narratives remain significant challenges.
- Standardizing clinical documents is crucial for seamless data exchange.
Purpose of the Study:
- To automatically standardize Spanish Electronic Discharge Summaries (EDS) using HL7 Clinical Document Architecture.
- To address the challenge of section annotation in Spanish EDS.
- To enhance interoperability across diverse healthcare systems and hospitals.
Main Methods:
- Experimentation with three distinct approaches for section annotation.
- Development of a knowledge-based solution using manually constructed rules.
- Application of supervised Machine Learning algorithms, including Perceptron and transfer learning-based Neural Networks.
Main Results:
- Evaluation of three methods across two different hospitals.
- Achieved a 93.03% F-score for section identification with the best system.
- Identified variability in performance across different section types and hospitals.
Conclusions:
- Demonstrated the feasibility of accurate automatic detection and standardization of section blocks in clinical narratives.
- Paved the way for improved interoperability and secondary use of clinical data.
- Highlighted the potential for advancing healthcare informatics through automated document processing.
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