Design and Feasibility Assessment of a Compact Emergency Unit in Rural and Remote Areas: A Multicenter Analysis of KTAS-Based Triage Data
Approximately 29% of ED visits could be CEU-suitable. Single-modality machine learning is insufficient for safety-critical triage, but a layered ML-plus-vitals screening approach achieves operationally relevant sensitivity. Prospective implementation studies are required before clinical deployment.
Authors
- Jee Yong Lim (ORCID: https://orcid.org/0000-0001-5395-3284)
- Kyungman Cha (ORCID: https://orcid.org/0000-0001-5345-7424)
- Jaekwang Shin
- Sohee Lee
- Youngjin Kim
Institutions
- St. Mary's Hospital (US)
- The Catholic University of Korea St. Vincent's Hospital (KR)
- Korea Testing & Research Institute (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Seokyeong University (KR)
Publication Details
- Journal
- Healthcare
- Published
- 2026-04-20
- DOI
- https://doi.org/10.3390/healthcare14081099
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Seoul St. Mary's Hospital, Catholic University of Korea