STTR Phase I: Data-Driven Decision Support Services for Emergency Department Operations

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is very significant. Suboptimal operational decision-making in emergency departments leads to inefficiencies that result in extreme patient wait-times, the diversion of ambulances to other emergency departments, wasted resources and patients who either leave before being treated or against medical advice. By connecting modern analytical approaches, including statistical modeling and systems engineering methods, to real-time data routinely collected in emergency departments, the proposed project promises to result in a tremendously valuable analytics platform that will assist emergency departments in making dozens of operational and staffing decisions each day. This informed decision-making will not only improve emergency department efficiency, it will lead to both healthier and more satisfied patients and simultaneous dramatic increases in revenue and profit. The technology proposed will have the potential to add significant value to the approximately 5,000 emergency departments in the U.S., often on the order of millions of dollars annually. Hospitals and health systems now realize the value of effective analytics, and the analytics platform proposed here will be an obvious investment for any emergency department whose goal is to provide the best care to its patients at lower costs. The proposed project promises to yield a decision-support platform upon which emergency departments will base their decisions each day. Substantial investments by hospitals and health systems on information technology, and in particular, electronic health records, have set the stage for evidence-based, data-driven decisions. These decisions will effectively leverage real-time data along with analytical methods such as statistical forecasting and event-simulation modeling. In particular, this proposed project will create a software platform, based on these analytical methods and linking to real-time data sources, tailored to emergency departments. This project will involve 1) understanding the capacity and real-time availability of data in emergency rooms, 2) developing a statistical and simulation modeling platform that maximizes the potential of these data, in real-time, and specifically reflects emergency departments, and 3) ultimately ensuring that actionable insights are delivered in a timely and intuitive manner to key stakeholders. These actionable insights that derive from the data and sophisticated methods must be delivered to the right decision-maker at the right time and in the right format, but will then have the capacity to substantially improve both the quality and efficiency of care-delivery in an emergency department.