July 25, 2014
Emergency departments (EDs) are under growing pressure; while the number of ED visits have sharply increased, the number of EDs serving this need has actually decreased. According to a report from Rand Corporation, ED doctors are increasingly becoming the decision-makers regarding hospital admissions. Today, nearly half of all non-obstetrical hospital admissions occur through the ED. With the adoption of the Affordable Care Act, it is expected the number of ED visits will continue to rise. ED staffs are, therefore, looking for ways to make effective decisions to make their departments more efficient.
A group of researchers from the University of Florida and the Statistical and Applied Mathematical Sciences Institute (SAMSI) have created an online simulator to help hospital ED administrators understand how analytics and simulation can be used to inform decisions in the ED. In particular, the simulator reveals how various factors or decisions affect the flow of patients through the ED. The group includes, Kenneth Lopiano, SAMSI; Joshua Hurwitz, Jo Ann Lee, Scott McKinley, James Keesling, University of Florida Department of Mathematics; and Joseph Tyndall, University of Florida Department of Emergency Medicine.
The simulator is freely available on the web at http://spark.rstudio.com/klopiano/EDsimulation/. On the website doctors or administrators can change several different variables to best mimic the conditions in their particular ED. For example, one can change the number of beds, number of doctors, number of nurses for various hours of the day, or number of patients entering the ED at different times of the day.
Lopiano, who was a postdoctoral fellow at SAMSI during this past year’s Data-Driven Decisions in Healthcare research program, learned about the power of simulation in healthcare through SAMSI-sponsored working groups. It was during a visit to his alma mater, the University of Florida, to discuss his SAMSI experiences when Lopiano learned of lead author Joshua Hurwitz’s efforts. There Lopiano connected with former SAMSI postdoctoral fellow and assistant professor Scott McKinley who introduced Lopiano to Hurwitz. Realizing their common research interests, the core research group was formed which led ultimately to the online simulator, principally developed by Lopiano and Hurwitz. The online simulator has seen substantial increases in traffic since the publication of their research paper in BMC Medical Informatics and Decision Making.
The simulator recognizes that the causes of ED crowding are variable and require site-specific solutions. For example, in a nationally average ED, provider availability can cause bottlenecks in patient flow while investments in other resources may not have the positive impact an administrator would expect. Further, the simulator recognizes that by reallocating resources and creating alternate care pathways, some EDs can dramatically expedite care for lower acuity patients without delaying care for higher acuity patients.
Lopiano, co-founder and principal collaborator of Roundtable Analytics, a healthcare analytics company based in Raleigh, North Carolina, said, “A simulator is very effective because it is risky for health systems to implement overhauls in their care-delivery systems. By using a simulator, administrators are able to evaluate many different scenarios without making these costly and time-consuming changes. Most importantly, administrators can understand the consequences of operational decisions, both intended and unintended.”
The Statistical and Applied Mathematical Sciences Institute (SAMSI) is one of eight mathematical institutes funded by the NSF’s Division of Mathematical Sciences, but is the only one that focuses on statistics and applied mathematics. Its mission is to forge a new synthesis of the statistical and applied mathematical sciences with disciplinary sciences to confront important data- and model-driven scientific challenges. It is based in Research Triangle Park, North Carolina. SAMSI was founded in 2002.
SAMSI is a partnership of the National Science Foundation with a consortium of Duke University, North Carolina State University, the University of North Carolina at Chapel Hill, and the National Institute of Statistical Sciences. You can find more information at www.samsi.info, @NISSSAMSI.