Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation

TL;DR

This study develops a decision support system using predictive modeling and discrete-event simulation to estimate hospital length-of-stay for emergency department admissions, aiding resource planning.

Contribution

It introduces a combined approach of length-of-stay prediction and simulation to evaluate hospital resource management strategies.

Findings

Prediction models achieve reasonable accuracy (AUC 0.69)

Feature selection does not improve prediction performance

Models can inform resource allocation decisions

Abstract

Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation. In this study we aim to build a decision support system that predicts hospital length-of-stay for patients admitted to general internal medicine from the emergency department. We conduct an exploratory data analysis and employ feature selection methods to identify the attributes that result in the best predictive performance. We also develop a discrete-event simulation model to assess the performances of the prediction models in a practical setting. Our results show that the recommendation performances of the proposed approaches are generally acceptable and do not benefit from the feature selection. Further, the results indicate that hospital length-of-stay could be predicted with reasonable accuracy (e.g., AUC value for classifying short and long stay patients is 0.69) using patient admission demographics, laboratory test results, diagnostic imaging, vital signs and clinical documentation.