Predicting Need for Hospital Beds to Reduce Emergency Department Boarding

Introduction Accurately determining the supply and demand of hospital beds for new admissions can help prevent adverse patient outcomes. Quantitative analysis of modern electronic medical record data can help predict supply and demand for unoccupied staffed hospital beds (SEDs) and aid in eliminating human approximations, standardizing daily work through concrete and objective data. The purpose of this study was to reduce variability and human error in predicting the number of SEDs needed. Methods In this study,the authors analyzed bed calculator data from a medium-sized, suburban medical center to evaluate the efficacy of a unique bed calculator prediction tool to determine the need for SEDs. The calculator aggregates multiple key reference factors available through the bed calculator system into a cohesive linear regression model. Results Compared with human estimation, the authors found that the bed calculator is able to predict the number of SEDs needed more effectively. That being said, there was no significant difference in the average boarding times pre- and postintervention, indicating that the bed calculator did not result in decreased boarding times for patients. Discussion These findings establish the efficacy of the bed calculator and its ability to align bed supply and demand. Because patient boarding times depend on the system's patient flow management, future studies should focus on how to improve various streams of communication and coordination.