Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.

EHR clinical narratives deep learning disambiguation electronic health records human-in-the-loop, feature extraction language modeling machine learning natural language processing rule-based approach short-form expression vector representations word embedding

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
26 Sep 2024
Historique:
received: 28 02 2024
accepted: 25 07 2024
revised: 24 05 2024
medline: 26 9 2024
pubmed: 26 9 2024
entrez: 26 9 2024
Statut: epublish

Résumé

Clinical narratives are essential components of electronic health records. The adoption of electronic health records has increased documentation time for hospital staff, leading to the use of abbreviations and acronyms more frequently. This brevity can potentially hinder comprehension for both professionals and patients. This review aims to provide an overview of the types of short forms found in clinical narratives, as well as the natural language processing (NLP) techniques used for their identification, expansion, and disambiguation. In the databases Web of Science, Embase, MEDLINE, EBMR (Evidence-Based Medicine Reviews), and ACL Anthology, publications that met the inclusion criteria were searched according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for a systematic scoping review. Original, peer-reviewed publications focusing on short-form processing in human clinical narratives were included, covering the period from January 2018 to February 2023. Short-form types were extracted, and multidimensional research methodologies were assigned to each target objective (identification, expansion, and disambiguation). NLP study recommendations and study characteristics were systematically assigned occurrence rates for evaluation. Out of a total of 6639 records, only 19 articles were included in the final analysis. Rule-based approaches were predominantly used for identifying short forms, while string similarity and vector representations were applied for expansion. Embeddings and deep learning approaches were used for disambiguation. The scope and types of what constitutes a clinical short form were often not explicitly defined by the authors. This lack of definition poses challenges for reproducibility and for determining whether specific methodologies are suitable for different types of short forms. Analysis of a subset of NLP recommendations for assessing quality and reproducibility revealed only partial adherence to these recommendations. Single-character abbreviations were underrepresented in studies on clinical narrative processing, as were investigations in languages other than English. Future research should focus on these 2 areas, and each paper should include descriptions of the types of content analyzed.

Sections du résumé

BACKGROUND BACKGROUND
Clinical narratives are essential components of electronic health records. The adoption of electronic health records has increased documentation time for hospital staff, leading to the use of abbreviations and acronyms more frequently. This brevity can potentially hinder comprehension for both professionals and patients.
OBJECTIVE OBJECTIVE
This review aims to provide an overview of the types of short forms found in clinical narratives, as well as the natural language processing (NLP) techniques used for their identification, expansion, and disambiguation.
METHODS METHODS
In the databases Web of Science, Embase, MEDLINE, EBMR (Evidence-Based Medicine Reviews), and ACL Anthology, publications that met the inclusion criteria were searched according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for a systematic scoping review. Original, peer-reviewed publications focusing on short-form processing in human clinical narratives were included, covering the period from January 2018 to February 2023. Short-form types were extracted, and multidimensional research methodologies were assigned to each target objective (identification, expansion, and disambiguation). NLP study recommendations and study characteristics were systematically assigned occurrence rates for evaluation.
RESULTS RESULTS
Out of a total of 6639 records, only 19 articles were included in the final analysis. Rule-based approaches were predominantly used for identifying short forms, while string similarity and vector representations were applied for expansion. Embeddings and deep learning approaches were used for disambiguation.
CONCLUSIONS CONCLUSIONS
The scope and types of what constitutes a clinical short form were often not explicitly defined by the authors. This lack of definition poses challenges for reproducibility and for determining whether specific methodologies are suitable for different types of short forms. Analysis of a subset of NLP recommendations for assessing quality and reproducibility revealed only partial adherence to these recommendations. Single-character abbreviations were underrepresented in studies on clinical narrative processing, as were investigations in languages other than English. Future research should focus on these 2 areas, and each paper should include descriptions of the types of content analyzed.

Identifiants

pubmed: 39325515
pii: v26i1e57852
doi: 10.2196/57852
doi:

Types de publication

Journal Article Systematic Review Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e57852

Informations de copyright

©Amila Kugic, Ingrid Martin, Luise Modersohn, Peter Pallaoro, Markus Kreuzthaler, Stefan Schulz, Martin Boeker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.09.2024.

Auteurs

Amila Kugic (A)

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Ingrid Martin (I)

Institute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.

Luise Modersohn (L)

Institute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.

Peter Pallaoro (P)

Institute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.

Markus Kreuzthaler (M)

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Stefan Schulz (S)

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Martin Boeker (M)

Institute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH