Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.
Home health care
long-term care
natural language processing
nursing informatics
post-acute care
scoping review
Journal
Journal of the American Medical Directors Association
ISSN: 1538-9375
Titre abrégé: J Am Med Dir Assoc
Pays: United States
ID NLM: 100893243
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
received:
29
06
2023
revised:
05
09
2023
accepted:
07
09
2023
pmc-release:
01
01
2025
medline:
5
1
2024
pubmed:
15
10
2023
entrez:
14
10
2023
Statut:
ppublish
Résumé
To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings. Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities). PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised. Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators. The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.
Identifiants
pubmed: 37838000
pii: S1525-8610(23)00791-0
doi: 10.1016/j.jamda.2023.09.006
pmc: PMC10792659
mid: NIHMS1931886
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
69-83Subventions
Organisme : NINR NIH HHS
ID : P30 NR016587
Pays : United States
Organisme : AHRQ HHS
ID : R01 HS027742
Pays : United States
Organisme : NINR NIH HHS
ID : T32 NR007969
Pays : United States
Informations de copyright
Copyright © 2023 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.
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