Incorporating Machine Learning Driven Factors in the Design of Electronic-triggers to Detect Diagnostic Errors in the Emergency Department
Utilizing machine learning to refine e-triggers slightly improves the identification of diagnostic errors, as evidenced by an increase in PPV values. We identified new potential factors contributing to ED diagnostic errors. These findings open new avenues to construct or modify more accurate e-triggers for diagnostic error identification.
Authors
- Fernanda Bellolio (ORCID: https://orcid.org/0000-0002-1632-4750)
- Sarah J. Parker (ORCID: https://orcid.org/0000-0001-6007-5803)
- Todd Huschka
- Daniel Cabrera (ORCID: https://orcid.org/0000-0002-5891-0459)
- Prashant Mahajan (ORCID: https://orcid.org/0000-0003-4278-3560)
- Moein Enayati (ORCID: https://orcid.org/0000-0001-7391-774X)
- Kalyan S. Pasupathy (ORCID: https://orcid.org/0000-0002-4760-2805)
- Mahsa Khalili (ORCID: https://orcid.org/0000-0002-0510-2554)
- Shrinath Patel
Institutions
- Mayo Clinic (US)
- University of British Columbia (CA)
- University of Michigan (US)
- University of Illinois Chicago (US)
- Michigan Medicine (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Journal of Patient Safety
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1097/pts.0000000000001409
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 0.00