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Design and Feasibility Assessment of a Compact Emergency Unit in Rural and Remote Areas: A Multicenter Analysis of
Kyungman Cha1, Youngjin Kim2, Sohee Lee2
1Department of Emergency Medicine, Suwon St. Vincent Hospital, The Catholic University of Korea, Suwon 16247, Republic of Korea.
Healthcare (Basel, Switzerland)
|May 4, 2026
Summary
Approximately 29% of emergency department (ED) visits may be suitable for a Compact Emergency Unit (CEU). A layered machine learning and vital signs screening approach shows promise for identifying these low-acuity patients safely.
Area of Science:
- Emergency Medicine
- Health Services Research
- Data Science in Healthcare
Background:
- Emergency department (ED) overcrowding is a significant issue, particularly in rural areas, limiting access to timely care.
- The proposed Compact Emergency Unit (CEU) offers a potential solution with remote physician oversight but lacks empirical validation.
- This study investigates the feasibility and operational metrics of CEUs in a multicenter setting.
Purpose of the Study:
- To quantify the proportion of CEU-eligible patients (final KTAS 4-5) in emergency departments.
- To compare operational metrics of CEU-eligible versus non-eligible patients.
- To characterize hourly demand patterns for facility planning.
- To develop machine-learning models for predicting non-discharge within the low-acuity patient stratum.
Main Methods:
- Retrospective analysis of 12 months of data from two Korean university-affiliated EDs.
- Development and comparison of three machine-learning classifiers (logistic regression, random forest, XGBoost) using baseline and vital sign-augmented features.
- Evaluation of model calibration and decision curve analysis.
Main Results:
- 28.6% of 34,544 ED visits were CEU-eligible, with shorter lengths of stay (LOS) and high rates of symptomatic home discharge.
- Nighttime visits constituted 43.7% of CEU-eligible encounters, with peak demand at 20:00.
- A vital sign-augmented random forest model achieved an AUROC of 0.794; XGBoost demonstrated near-perfect calibration.
- A combined machine learning-plus-vital sign screening rule achieved 94.1% sensitivity for non-discharge prediction.
Conclusions:
- A significant proportion of ED visits are potentially suitable for CEU utilization.
- Single-modality machine learning is insufficient for safety-critical triage; a layered approach is necessary.
- Prospective studies are required to validate the clinical deployment of CEUs and associated screening tools.
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