Evidence map›Paper›PMID 39325515›Full record

ArticleJournal of medical Internet research2024

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

Amila Kugic, Ingrid Martin, Luise Modersohn, Peter Pallaoro, Markus Kreuzthaler, Stefan Schulz, Martin Boeker

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amila KugicInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.ORCID 0000-0003-4674-0146
Ingrid MartinInstitute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.ORCID 0000-0001-8009-1368
Luise ModersohnInstitute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.ORCID 0000-0002-8258-4366
Peter PallaoroInstitute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.ORCID 0000-0003-4808-0700
Markus KreuzthalerInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.ORCID 0000-0001-9824-9004
Stefan SchulzInstitute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.ORCID 0000-0001-7222-3287
Martin BoekerInstitute for AI and Informatics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.ORCID 0000-0003-2972-2042

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical 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.

objectiveThis 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.

methodsIn 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.

resultsOut 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.

conclusionsThe 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.

Indexed as

Electronic Health RecordsNarrationNatural Language ProcessingHumansclinical narrativesdeep learningdisambiguationEHRelectronic health recordshuman-in-the-loop, feature extractionlanguage modelingmachine learningnatural language processingrule-based approachshort-form expressionvector representationsword embedding

Identifiers

PMID39325515
PMCPMC11467596

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.