Redesigning Emergency Department Clinician Shift Scheduling to Improve Well-being and Patient Outcomes (EDSHIFT)

Clemson University, Clemson SC

Investigators

Abstract

PROJECT SUMMARY Clinician burnout is a significant concern in Emergency Departments, and with increasing workload, shift work requirements, staffing shortages, and other stress-inducing activities, clinician well-being can be significantly compromised. Burnout is usually preceded or accompanied by prolonged periods of chronic stress, which can impair cognitive function, increase risk of heart disease, diabetes, sleep disruptions, insomnia, increase risk for suicide and substance abuse. Beyond personal well-being, burnout has harmful consequences on patient care and safety, including less time spent between clinician and patient, increased miscommunications, and a potential for increased handoffs. Most researchers have focused on identifying the factors leading to clinician burnout and its impact on clinician well-being, patients, health systems, community, and society. However, there is a lack of research on monitoring clinician well-being and using a mixed-methods approach to develop system- level interventions to prevent burnout. Moreover, there is a research opportunity to employ mathematical modeling for planning clinician shift schedules that consider clinician preferences, clinician well-being, patient safety, patient flow, and other constraints of ED departmental staffing. The purpose of this proposal is to create a framework for redesigning ED nurse and physician shift schedules, integrating clinician preferences and physiological parameters to improve clinician well-being and patient outcomes. The first aim “Analyzing staffing schedules, collecting clinician preferences and monitoring well-being and its indicators” will inform how current schedule planning can be improved. The team will gather and analyze current schedules, operational and safety data, clinician shift preferences, and well-being (burnout and fatigue) using validated surveys (e.g., burnout) and physiological markers (e.g., cortisol) that will both be collected on-site. This multimodal data will help identify associations between clinician stress, fatigue, workload, well-being, patient safety, and patient flow in the ED. These relationships will help inform the importance of specific shift design parameters when developing clinician shift schedules in the ED. The second aim “Incorporating operational factors, clinician preferences, and indicators of well-being into a modeling framework of ED shift schedule redesign” will reduce perceived workflow imbalance and will lower stress levels at the planning stage. By incorporating data sources and relationships between each factor, the shift model can generate clinician functional schedules while aiming to improve clinician well-being, patient flow, and patient safety. To benchmark the effectiveness of proposed staffing schedules, the team will use their recently developed computer-based simulation model representative of the ED at the partner hospital to assess how clinician well-being, patient flow, and patient safety are affected. Additionally, we will gather feedback from clinical leadership and ED providers across the health system to comprehend their likes and dislikes.