Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System.

ABSTRACT:

Objective

To prospectively compare nurse predictions with a machine learning (ML) model for hospital admissions and evaluate whether adding the nurse prediction to ML outputs enhances predictive performance.

Patients and methods

In this prospective, observational study at 6 hospitals in a large mixed quaternary/community emergency department (ED) system (annual ED census ∼500,000), triage nurses recorded a binary admission prediction for adult patients. These predictions were compared with an ensemble ML model (XGBoost + Bio-Clinical BERT) trained on structured data (demographics, vital signs, and medical history) and triage text. Nurse predictions were similarly analyzed and then integrated with the ML output to assess for improvement in predictive accuracy.

Results

SUBMITTER: Nover J 

PROVIDER: S-EPMC12336817 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature