Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 150732

150732 - Developing Workload-Informed Crew Configuration Recommendations for Emergency Medical Services (Using 911 Dispatch Data) 

Introduction  

Advanced life support (ALS) in emergency medical services (EMS) assures a minimum of one paramedic in each ambulance. Consequently, the ambulance crews are commonly comprised of an emergency medical technician (EMT) and a paramedic (heterogeneous crew), and less commonly by two paramedics (homogeneous crew). In a homogeneous crew, the paramedics will alternate the lead for the sequence of calls.  This rotation ensures a more balanced workload among them. However, in a heterogeneous crew, where a paramedic is paired with an EMT, the leadership responsibilities are assigned based on the acuity of the patient’s condition. For lower acuity patients, the EMT takes the lead, allowing them to handle less complex cases. Conversely, the paramedic assumes leadership for patients requiring higher levels of assessment and treatment, ensuring that critical care needs are met by the highly trained professional. Thus, within the crew, there may exist a workload differential that common workload metrics neglect. Therefore, the goal of this research was as follows: (1) to investigate whether there are significant differences in workload among members of the same crew, taking into consideration their level of certification (whether they are paramedics or EMTs), leadership roles (whether they are leader of the crew or no), shift time, and station assignment; and (2) to determine if this information can be used to inform crew configuration plans to balance workload.  

Contribution 

This study serves as a starting point for addressing the gap in research on the impact of operational strategies on workload balance among ambulance crew members with a focus on the influence of crew configurations. This paper's main contribution was to provide insight into the workload distribution among members in ALS crews in ambulances with at least two members and at least one paramedic. We also showed that most of the data that would be needed for workload studies is already embedded in most EMS systems. Our method was to label the dispatch data with Visual, Auditory, Cognitive, and Psychomotor (VACP) workload assessments.  
 

Methods  

We used a combination of analytical and computational approaches. We mapped one year of an EMS system’s dispatch data to crew members’ workload estimates assessed using the VACP approach. We then compared the workload estimates at different stations and shifts, considering the level of professional certification as well as the lead or support roles that members often assume. 

Results  

We found that lead crew members (lead paramedics) experience higher workload levels compared to support members (paramedics or EMTs) in both types of crews. Neither configuration had consistently lower workload than the other, but differences varied for different shifts and stations. These results informed crew configuration recommendations for stations and shifts in the collaborating system, and in terms of more generalizable variables. Minimum number of staffed crews, half-half shift type (covering both day and night hours), and 30-day frequency of calls with priority P7 most significantly impact the recommended crew configurations.  

Conclusion  

The workload of ambulance crew members depends on crew configuration, station assignment, priority stratified call volume, type of shift, and minimum number of staffed crews. Ambulance service administrators and operations management personnel should consider these factors when making assignment decisions to inform a more refined approach in support of clinicians’ well-being. 

Presenting Author: Setareh Darvishi Wichita State University

Presenting Author Biography: Setareh Darvishi is a dedicated full-time PhD student and Graduate Research Assistant at Wichita State University. With a robust academic foundation, she holds both a master's and a bachelor's degree in industrial engineering. Her current research is centered on the application of Optimization and Data Analytics in the fields of Manufacturing and Healthcare.

Authors:

Setareh Darvishi Wichita State University
Laila Cure Wichita State University
Paul Misasi Sedgwick County EMS and Kansas College of Osteopathic Medicine