Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures

Dinesh R. Pai, Balaraman Rajan, Puneet Jairath, Stephen M. Rosito

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). Methods: We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians’ diagnoses (post-model). Results: Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920–0.956], post-model = 0.983 [0.974–0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. Conclusions: Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.

Original languageEnglish (US)
Pages (from-to)219-227
Number of pages9
JournalInternal and Emergency Medicine
Volume18
Issue number1
DOIs
StatePublished - Jan 2023

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Emergency Medicine

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