ERI: Workload-Informed Operations for Emergency Medical Services and other First-Responder Systems

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This Engineering Research Initiation (ERI) award supports research enabling a fair work system for Emergency Medical Service (EMS) professionals and other first responders. EMS systems aim at saving the lives of patients needing emergency health care outside the hospital by responding to 911 calls. These professionals tend to work long shifts during which they may be exposed to a great variety of situations, including traumatic events.

However, the actual workload experienced by EMS professionals is not well understood, leading to stress, burnout, and high levels of attrition. The resulting shortage of paramedics in the United States endangers the ability to respond to emergency calls and puts lives at risk. This research will assess the workload experienced by EMS crewmembers in real time and incorporate that into operational decision-making, such as call coverage, task and crew switching, within EMS systems.

An improved operating environment is expected to lead to a reduction in work-related attrition among EMS professionals, thereby ensuring that the community receives high-quality emergency out-of-hospital health care while promoting the well-being of first responders. This research will impact the discipline of work systems science by transforming how work one measures and designs work involving highly variable, non-repetitive tasks, which constitute many of today’s occupations in the United States.

This research will study EMS operations from the perspective of frontline personnel by 1) developing new methods to approximate individual workload using routinely collected process data, and 2) defining and modeling new real-time operational strategies to balance workload among crewmembers within a shift. This research will develop and validate a methodology for real-time workload assessment based on trace-driven simulation of observed dispatch patterns augmented with task analysis data.

The workload-balancing strategies to be investigated include intra-shift crew swapping to level crewmembers’ workload using stochastic dynamic programming techniques and a heterogeneous crew configuration strategy based on demand prediction. The project will provide a team of graduate and undergraduate students from the Department of Industrial, Systems and Manufacturing Engineering with the opportunity to collaborate with EMS professionals and decision-makers through data collection, analysis and reporting activities.

The PI is committed to recruiting and mentoring students from diverse backgrounds, particularly women and Hispanic, into the research team. The methods developed in this project will be integrated into Work Systems and Data Mining courses offered by the PI, and will be used to create materials for community outreach activities through collaboration with Wichita State University’s Ulrich Museum of Art.

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.