According to Health and Human Services, demand for emergency health care services is rapidly increasing, causing over-crowding and long wait-times in emergency departments (EDs) nationwide. New research from Columbia Business School shows that predictive analytics—that is, using data about ED demand to predict future demand—could help hospitals reduce wait times and improve care by diverting patients away from EDs before they become overcrowded.
Hospital diversions are intended to help patients get care faster by directing them away from overcrowded EDs and toward facilities that can care for them more appropriately and quickly. In current practice, diversion decisions are typically made based solely on information about current congestion—ie, if a maximum threshold is reached, then new patients will be diverted. However, the researchers suggest that by using predictions of when patient congestion is likely to build, hospitals could substantially reduce the wait times of patients seeking medical care from an ED.
"Patients on their way to the emergency room want to know that their emergency is going to be handled as expeditiously as possible," says Professor Carri Chan, coauthor of the study and Sidney Taurel Associate Professor of Business at Columbia Business School. "By using predictive modeling to develop more effective diversion policies, hospitals can reduce wait times for patients by up to 15%, improving care and customer satisfaction while at the same time saving time and money."
The study, titled "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion," coauthored by Chan and Kuang Xu of Stanford University, proposes a new algorithm to predict future emergency arrivals. This algorithm can be then be applied to make decisions about diverting incoming patients.
Chan concludes: "Using predictive analytics is a step towards eliminating the over-crowding and long wait times that plague may of today's emergency rooms, ensuring patients receive the care they need when they need it."
Source: Columbia Business School