Mapping Clinical Documents to the Logical Observation Identifiers, Names and Codes (LOINC) Document Ontology using Electronic Health Record Systems Structured Metadata.

Autor:	Khan H; MU Institute of Data Science and Informatics, University of Missouri-Columbia.; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Mosa ASM; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Paka V; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Rana MKZ; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Mandhadi V; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Islam S; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Xu H; Yale University, New Haven, CT, USA.; OHDSI Consortium, Natural Language Processing Working Group., McClay JC; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia., Sarker S; Department of Electrical and Computer Science, School of Engineering, University of Missouri-Columbia., Rao P; Department of Electrical and Computer Science, School of Engineering, University of Missouri-Columbia., Waitman LR; Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia.
Jazyk:	angličtina
Zdroj:	AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2024 Jan 11; Vol. 2023, pp. 1017-1026. Date of Electronic Publication: 2024 Jan 11 (Print Publication: 2023).
Abstrakt:	As Electronic Health Record (EHR) systems increase in usage, organizations struggle to maintain and categorize clinical documentation so it can be used for clinical care and research. While prior research has often employed natural language processing techniques to categorize free text documents, there are shortcomings relative to computational scalability and the lack of key metadata within notes' text. This study presents a framework that can allow institutions to map their notes to the LOINC document ontology using a Bag of Words approach. After preliminary manual value- set mapping, an automated pipeline that leverages key dimensions of metadata from structured EHR fields aligns the notes with the dimensions of the document ontology. This framework resulted in 73.4% coverage of EHR documents, while also mapping 132 million notes in less than 2 hours; an order of magnitude more efficient than NLP based methods. (©2023 AMIA - All rights reserved.)
Databáze:	MEDLINE
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