A natural language processing approach to categorise contributing factors from patient safety event reports.
Electronic Health Records
Machine Learning
Journal
BMJ health & care informatics
ISSN: 2632-1009
Titre abrégé: BMJ Health Care Inform
Pays: England
ID NLM: 101745500
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
29
12
2022
accepted:
12
05
2023
medline:
2
6
2023
pubmed:
1
6
2023
entrez:
31
5
2023
Statut:
ppublish
Résumé
The objective of this study was to explore the use of natural language processing (NLP) algorithm to categorise contributing factors from patient safety event (PSE). Contributing factors are elements in the healthcare process (eg, communication failures) that instigate an event or allow an event to occur. Contributing factors can be used to further investigate why safety events occurred. We used 10 years of self-reported PSE reports from a multihospital healthcare system in the USA. Reports were first selected by event date. We calculated χ Applying the information-rich sentence selection algorithm boosted the contributing factor categorisation performance. Comparing the AUPRCs, the proposed NLP approach improved the categorisation performance of two and achieved comparable results with baseline in categorising three contributing factors. Information-rich sentence selection can be incorporated to extract the sentences in free-text event narratives in which the contributing factor information is embedded.
Identifiants
pubmed: 37257922
pii: bmjhci-2022-100731
doi: 10.1136/bmjhci-2022-100731
pmc: PMC10254979
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
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