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Treffer: Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.

Title:
Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.
Authors:
Liu L; Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW 2052, Australia., Blake V; Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW 2052, Australia., Barman M; Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW 2052, Australia., Gallego B; Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW 2052, Australia., Churches T; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia., Kennedy G; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW 2052, Australia., Ooi SY; School of Clinical Medicine, University of New South Wales, Sydney, NSW 2052, Australia.; Prince of Wales Hospital, Randwick, NSW 2031, Australia., Delaney GP; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW 2052, Australia., Jorm L; Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW 2052, Australia.
Source:
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2026 Feb 01; Vol. 33 (2), pp. 484-499.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE
Imprint Name(s):
Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-
MeSH Terms:
Information Storage and Retrieval*/methods , Data Mining*/methods , Natural Language Processing* , Electronic Health Records* , Registries*, Humans ; Machine Learning
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Grant Information:
MRFFRD000154 Australian Medical Research Futures Fund; Australian Medical Research Futures Fund; MRFFRD000154 Research Data Infrastructure
Contributed Indexing:
Keywords: clinical registries; clinical text; electronic medical records; information extraction; natural language processing
Entry Date(s):
Date Created: 20251015 Date Completed: 20260127 Latest Revision: 20260129
Update Code:
20260130
PubMed Central ID:
PMC12844598
DOI:
10.1093/jamia/ocaf176
PMID:
41093296
Database:
MEDLINE

Weitere Informationen

Objective: Clinical registries advance healthcare by tracking patient outcomes and intervention safety. Manually extracting information from clinical text for registries is labor- and resource-intensive and often inaccurate. Therefore, this systematic review aims to evaluate the use and effectiveness of natural language processing (NLP) methods in extracting information from clinical text for populating clinical registries.
Materials and Methods: PubMed, Embase, Scopus, Web of Science, and ACM Digital Library were systematically searched. Studies were included if they used NLP techniques to populate clinical registries. The extracted data included details of the registry, the clinical text, the registry data elements extracted, the NLP methods used, and how their performance was evaluated.
Results: Fifteen articles were included in the review. Since 2020, the use of NLP methods for extracting information to populate clinical registries has been increasing steadily. Initially, rule-based NLP methods dominated the field, but machine learning-based approaches have gradually gained popularity. However, only one of the included studies employed generative large language models (LLMs). The diversity of clinical text and extracted data elements posed challenges to the generalizability of the NLP methods.
Conclusion: To date, the application of NLP methods to clinical text for populating clinical registries has been limited in both the number of published studies and the scope of implementation. The NLP methods used thus far face significant challenges in effectively managing the complexity and diversity of clinical text and data elements. Moreover, the performance of the NLP methods varied significantly. This review underscores the need for a robust and adaptable NLP framework. Generative LLMs may provide direction for future research, but their use must account for challenges such as accuracy, cost, privacy, and limited supporting evidence.
(© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association.)