SBIR Phase II: DATA-DRIVEN DECISION SUPPORT FOR EFFICIENT PATIENT PROGRESSION

The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase II is to reduce the patient harm and financial burden created by the intensifying problem of emergency department (ED) crowding. ED crowding is a threat to patient safety for high acuity patients and has been associated with avoidable morbidity and mortality across many conditions. Concurrently, EDs are challenged to efficiently manage large volumes of low-acuity patients visiting with non-emergency conditions. The proposed electronic health record (EHR) integrated technology deploys novel machine learning algorithms that predict clinical events at actionable time-points in patients care pathways. Characterizing the ED as a flow system, these decision support tools will concentrate on the root causes that exist at ED inflow and hospital outflow. This new foresight is expected to enable innovative hospital operational models that expedite patient progression (minimize patient waiting), improve patient safety, and directly translate to measurable cost-savings and/or revenue generation. The SBIR Phase II project should result in higher yield and scalable decision support technology while promoting the value of data-enabled science and engineering in healthcare.



The proposed project objective is to greatly reduce ED crowding by advancing the data-science, decision-science, and operations research that underpins our decision support technology. This new technology is based upon a novel combination of data normalization, feature selection, and supervised machine learning methods to predict clinical events that drives clear action to optimize hospital resources. This includes a decision support tool that functions at ED triage (inflow) to predict risk of critical events to empower safe separation of service streams for acutely ill and non-urgent patients. A complementary tool functions near hospital discharge (outflow) to predict expected discharge time enabling hospital-wide prioritization of resources required to expedite discharge. This unlocks downstream capacity and removes a major ED outflow bottleneck that creates prolonged waiting. The proposed technology is innovative by design to be scaled, yet adaptive to hospitals individual patient populations, operational objectives, and risk tolerances. The technology is expected to further advance learning with providers about how to consume new predictive and explanatory information for decision support. The specific Phase II objectives are to continue the development of the technology by: (1) integrating feedback into prediction output, (2) developing performance monitoring capabilities, and (3) driving systems-based management of patient progression.