Realizing the Power of Text Mining and Natural Language Processing for Analyzing Patient Safety Event Narratives: The Challenges and Path Forward.
Citation: Journal of patient safety. 17(8):e834-e836, 2021 12 01.PMID: 34852413Institution: MedStar Institute for InnovationDepartment: National Center for Human Factors in HealthcareForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Natural Language Processing | *Patient Safety | Algorithms | Data Mining | Humans | Machine LearningYear: 2021Local holdings: Available online through MWHC library: March 2005 - presentISSN:- 1549-8417
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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Journal Article | MedStar Authors Catalog | Article | 34852413 | Available | 34852413 |
Available online through MWHC library: March 2005 - present
ABSTRACT: Patient safety event (PSE) reports are a useful lens to understand hazards and patient safety risks in healthcare systems. However, patient safety officers and analysts in healthcare systems and safety organizations are challenged to make sense of the ever-increasing volume of PSE reports, including the free-text narratives. As a result, there is a growing emphasis on applying text mining and natural language processing (NLP) approaches to assist in the processing and understanding of these narratives. Although text mining and NLP in healthcare have advanced significantly over the past decades, the utility of the resulting models, ontologies, and algorithms to analyze PSE narratives are limited given the unique difference and challenges in content and language between PSE narratives and clinical documentation. To promote the application of text mining and NLP for PSE narratives, these unique challenges must be addressed. Improving data access, developing NLP resources to practically use contributing factor taxonomies, and developing and adopting shared specifications for interoperability will help create an infrastructure and environment that unlocks the collaborative potential between patient safety, research, and machine learning communities, in the development of reproducible and generalizable methods and models to better understand and improve patient safety and patient care. Copyright (c) 2021 Wolters Kluwer Health, Inc. All rights reserved.
English