ArticleBMJ health & care informatics2023
A natural language processing approach to categorise contributing factors from patient safety event reports.
Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Fine-tuning and evaluating large language models for patient safety tasks: classification of contributing factors in incident reports.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality.NPJ digital medicine · 2026Article
- Structuring medication safety narratives: development and evaluation of the medication-related incident reports annotation scheme.Frontiers in digital health · 2026Article
- Article
- Automatically Identifying Event Reports of Workplace Violence and Communication Failures using Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Artificial intelligence in healthcare: Opportunities come with landmines.BMJ health & care informatics · 2024Article
- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
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Authors and funding
4 authors.
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Abstract
objectivesThe 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.
methodsWe 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
resultsApplying 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.
conclusionsInformation-rich sentence selection can be incorporated to extract the sentences in free-text event narratives in which the contributing factor information is embedded.
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