Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study
We identified subgroups of patients consistently undertriaged using 2 common triage systems. While the importance of clinical patient characteristics to triage misclassification varies by triage system and location, we found consistent evidence between the two locations that the clinical referral department is the most important variable associated with triage misclassification. Replication of this approach at other centers could help to further improve triage scoring systems and improve patient care worldwide.
Authors
- Dagfinn Lunde Markussen (ORCID: https://orcid.org/0000-0003-3504-8859)
- Marcus Landschulze (ORCID: https://orcid.org/0000-0002-9351-7898)
- Yeneabeba Tilahun Sima (ORCID: https://orcid.org/0000-0003-4909-5314)
- Sage Wyatt (ORCID: https://orcid.org/0000-0003-3922-2316)
- Christopher Martin Sauer (ORCID: https://orcid.org/0000-0002-2388-5919)
- Hauke Bartsch (ORCID: https://orcid.org/0000-0003-1486-4128)
- A. Vestbø
- Mounir Haizoune
- Maria Ilene Sandboe
Institutions
- Norwegian University of Science and Technology (NO)
- Western Norway Regional Health Authority (NO)
- Haukeland University Hospital (NO)
- Bergen Kommune (NO)
- Essen University Hospital (DE)
- Western Norway University of Applied Sciences (NO)
- University of Bergen (NO)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-10-25
- DOI
- https://doi.org/10.2196/56382
- Citations
- 2
- Primary Topic
- Medical Coding and Health Information
- Type
- article
- Field-Weighted Citation Impact
- 1.42