Introduction: Entrustable Professional Activities (EPAs) 9 and 13 are to “collaborate as a member of an interprofessional team” and to “identify system failures thereby contributing to a culture of safety and improvement.” Addressing EPA 9, an interprofessional initiative was begun using a project team between two university programs: medical education and health systems engineering. Addressing EPA 13, this team set out to provide diagnostic analytics for Length of Stay (LOS) delays in the Emergency Department (ED).
Methods: This project was performed in 2018 at an ED with 42 beds, an annual census of 70,000, and a 38% admission rate. Two healthcare systems engineering students and a medical student performed on-site observations to identify specific bottlenecks that could contribute to ED LOS. This data and data generated from the electronic medical record were analyzed and correlated with observations. Factors (44) that affect ED processes were analyzed, including time interval metrics such as arrival to triage, arrival to admit, disposition to departure, and bed request to admit.
Results: Patients had an average LOS of 5.9 hours. A total of 4,940 adult, non-psychiatric cases presented; 1,599 (32.4%) of these were admitted. Process evaluation (Figure, mean and median minutes) showed differences between day (7a-7p) and night (7p-7a) flow patterns. These quantitative results (EPA 13) were determined by the interprofessional collaborative work efforts of the students (qualitatively, the outcome of EPA 9).This project demonstrated a synergistic educational experience that allowed the blending of medical education with process engineering, ultimately improving knowledge gaps of both. This unique process allowed for diagnostics to be performed that were necessary for the ED and simultaneously provided a stronger foundation for QI undertakings for both engineering and medical students.
Conclusion: Medical students can benefit from working alongside systems engineers, allowing them to see the value of using tools (simulation modeling, statistical analysis, process flow mapping, etc.) to uncover evidence-based improvements to a variety of medical processes. Healthcare systems engineering students can gain valuable experience in a complex medical environment. Looking for solutions to the disparity between flow during the day and night is an opportunity for future study.
