This SBIR Phase I project aims at optimizing emergency department staffing decisions. Direct patient care staffing costs consume nearly 50% of an average hospital's operating revenues. As hospitals adapt to a rapidly changing healthcare market, hospital management often seek to reduce staffing costs to increase operational viability. These cost-cutting initiatives introduce significant risk exposure, with studies showing that staffing a unit below the target level is associated with increased mortality and other adverse patient events. Inadequate staffing causes staff to feel overworked and leads to burnout, which costs hospitals over $9 billion annually, in part because of turnover. Optimizing clinical workforce staffing is especially critical in the emergency department, which must provide on-demand availability of staff to meet the needs of rapidly changing patient populations without significant delay. With nearly half of all medical care in the United States occurring in emergency departments and visits to the emergency department increasing 44% over the past decade, it is essential for U.S. public health to optimize emergency department staffing to ensure quality patient care and clinical outcomes, while also delivering better financial performance. No comprehensive technology exists for optimizing emergency department staffing decisions.
This SBIR Phase I project will be used to: i) develop an automated emergency department patient demand predictive engine and create a software platform that automates the predictive model recalibration process; ii) develop a staff flexing recommender and decision support engine, which allows for user input and clinical judgment before a final staffing decisions is made, iii) integrate the recommender engine with real-time patient volume feed, and iv) develop human-centered design interface for the decision support engine that is appropriate for the dynamic clinical setting of the emergency department. The engine and its user interface will be developed in collaboration with three beta sites. Upon successful completion of the project, the engine will be used for cost-benefit analysis of data driven staffing decision making at the proposed beta sites.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
