Predicting Patients Likely to Overstay in Hospitals

Abstract

Patients that remain in the hospital system longer than necessary (overstay patients) represent a sizeable operational cost and contribute to hospital waiting times and bed shortages. Patient data from four hospitals were analyzed in order to build a classifier that would identity patients that are likely to overstay. The patients that overstay often require special assistance, such as nursing home placement or home care arrangements, and need to be identified early in admission so as to schedule a timely discharge from the hospital. Age, co-morbidity and activities of daily living scores (such as ability to dress and feed oneself) were the major factors in determining if a patient is likely to overstay while waiting special dispensation. The aim of the research is to develop a decision support system using machine learning strategies. A decision tree classifier achieved F-Measure of 0.826 identifying overstay patients from a tertiary teaching hospital and an F-Measure of 0.784 at a community hospital.