Unsupervised clustering analysis of trauma/non-trauma centers using hospital features including surgical care
Unsupervised machine learning identified surgical care delivery patterns that explained variation beyond level designation. This research provides insights into how systems leaders could optimize the level allocation for TCs/non-TCs in a mature trauma system by better understanding the distribution of care in the system.
Authors
- Rebecca Maine (ORCID: https://orcid.org/0000-0003-4327-8742)
- Charles Mock (ORCID: https://orcid.org/0000-0002-0564-568X)
- Monica S. Vavilala (ORCID: https://orcid.org/0000-0002-2354-5279)
- Eileen M. Bulger (ORCID: https://orcid.org/0000-0002-7938-155X)
- Shan Liu (ORCID: https://orcid.org/0000-0003-0012-7282)
- Xiaonan Sun (ORCID: https://orcid.org/0000-0003-3589-9100)
Institutions
- University of Washington (US)
- Harborview Medical Center (US)
- Harborview Injury Prevention and Research Center (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-08-22
- DOI
- https://doi.org/10.1371/journal.pone.0306299
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Association for the Surgery of Trauma
- University of Washington