Predicting Inpatient Admissions From Emergency Department Triage Using Machine Learning: A Systematic Review
Ethan Williams, Daniel Huynh, Mohamed Estai, Toshi Sinha, Matthew Summerscales, Yogesan Kanagasingam
Abstract
This study aimed to evaluate the quality of evidence for using machine learning models to predict inpatient admissions from emergency department triage data, ultimately aiming to improve patient flow management. A comprehensive literature search was conducted according to the PRISMA guidelines across 5 databases, PubMed, Embase, Web of Science, Scopus, and CINAHL, on August 1, 2024, for English-language studies published between August 1, 2014, and August 1, 2024. This yielded 700 articles, of which 66 were screened in full, and 31 met the inclusion and exclusion criteria. Model quality was assessed using the PROBAST appraisal tool and a modified TRIPOD+AI framework, alongside reported model performance metrics. Seven studies demonstrated rigorous methodology and promising in silico performance, with an area under the receiver operating characteristic ranging from 0.81 to 0.93. However, further performance analysis was limited by heterogeneity in model development and an unclear-to-high risk of bias and applicability concerns in the remaining 24 articles, as evaluated by the PROBAST tool. The current literature demonstrates a good degree of in silico accuracy in predicting inpatient admission from triage data alone. Future research should emphasize transparent model development and reporting, temporal validation, concept drift analysis, exploration of emerging artificial intelligence techniques, and analysis of real-world patient flow metrics to comprehensively assess the usefulness of these models.