Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study
Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.
Authors
- Kristen Miller (ORCID: https://orcid.org/0000-0002-8991-2342)
- Alberta Tran (ORCID: https://orcid.org/0000-0002-9091-7036)
- Laura Schubel (ORCID: https://orcid.org/0000-0001-5258-1407)
- Sonita Bennett
- Mary A. Hill (ORCID: https://orcid.org/0000-0003-0352-2237)
- William S. Weintraub (ORCID: https://orcid.org/0000-0002-5384-5571)
- Azade Tabaie (ORCID: https://orcid.org/0000-0003-1869-5923)
- Arianna Milicia (ORCID: https://orcid.org/0000-0002-5139-1254)
- Kelly Michelle Smith (ORCID: https://orcid.org/0000-0002-9483-5118)
- William Gallagher (ORCID: https://orcid.org/0009-0006-3659-9811)
- John Yosaitis
- Tony Calabria (ORCID: https://orcid.org/0009-0003-4491-8171)
Institutions
- Georgetown University (US)
- University of Toronto (CA)
- Washington Hospital (US)
- Toronto East General Hospital (CA)
- MedStar Georgetown University Hospital (US)
- MedStar Health (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-08-26
- DOI
- https://doi.org/10.2196/50935
- Citations
- 6
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 3.02