Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review

Abstract

Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients.

Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult ED patients. Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool).

Results: Twenty studies met the inclusion criteria, employing logistic regression and machine learning techniques. Logistic regression was noted for its traditional use and clinical interpretability, whereas machine learning provided enhanced flexibility and potential for better predictive accuracy. Common predictors included patient demographics, triage category, vital signs, and mode of arrival. The area under the curve values for model performance ranged from 0.80 to 0.89, demonstrating strong discriminatory ability. However, external validation was limited, and there was variability in outcome definitions and model generalizability.

Conclusions: Predictive models based on triage data show promise in supporting ED operations by facilitating early predictions of hospital admissions, which could help decrease boarding times and enhance patient flow. Further research is necessary to validate these models in various settings to confirm their applicability and reliability.