RALEIGH, N.C., Nov. 2, 2016 /PRNewswire/ -- Roundtable Analytics, Inc., a Software as a Service (SaaS) and consulting company focused on improving the operational and financial performance of Emergency Departments, is leading its partners at the University of North Carolina at Chapel Hill ( UNC) Medical Center in the creation an analytics-driven approach to staffing. Like many, UNC Medical Center's ED faces complex staffing challenges as it manages the variable and unscheduled patient volumes inherent to emergency medicine. By unlocking routinely collected electronic medical record data and making it convenient, accessible and meaningful, UNC has been able to create a novel staffing model tailored to the needs of their ED. "Roundtable Analytics showed me incredibly detailed views of my department's needs and clearly identified the need for a differential staffing model by day of week," said Christian Lawson, UNC's Director of Emergency Services. "The result was my ability to reduce wasted staffing and shift some of these resources elsewhere. This is a win/win where we have a more effective staffing model along with a substantial six-figure savings for my department." Further, Roundtable Analytics has made both understanding and communicating the needs of UNC's behavioral health division much easier for Lawson. "We also face an extremely challenging behavioral health population at UNC and these patients very often crowd our ED. But thanks to Roundtable's data analytics, we confirmed the need for investment in behavioral health and executive hospital leadership acted immediately to add essential resources." About Roundtable Analytics, Inc. Roundtable Analytics, Inc. has developed on-demand and easy to use data analytics, such as site-specific simulation models, that optimize the performance of client Emergency Departments. Securely leveraging each ED's unique data resources, Roundtable Analytics' predictive analytics are delivered via SaaS to ED managers who can quickly simulate the impact of adjusting key management variables under their control. This approach eliminates the risks of suboptimal patient care and financial losses that can occur during costly trial-and-error periods.