An Evaluation of Physician Burnout by EMR Use Characterization and Correlation

Burnout disproportionately affects healthcare workers and continues to rise.  This condition potentially contributes to cost, quality and patient safety risk in an already overburdened United States healthcare system.  While the causes of burnout are complex, evidence exists pointing to Electronic Medical Record use (EMR) as one major contributor due to the increased clerical burden that decreases patient contact time and contributes to disruption for the provider.  The growth and consolidation of large-scale EMR vendors has given rise to enterprise-scale electronic medical records with workflows applied across disparate venues and specialties, further complicating the ability to optimize the physician EMR experience and leading to variability in clinician EMR experience.  Targeted training and optimization efforts are generally deployed one-time at a clinic or specialty level but are challenging to deploy longitudinally and in surveillance mode due to the cost and effort of administering traditional survey instruments.  This proposal, An Evaluation of Physician Burnout by EMR Use Characterization and Correlation, is foundational to future research to predict EMR-related burnout and design and evaluate interventions for improvement.  We propose a pragmatic approach at the University of Missouri Healthcare, an Academic Medical Center, to test the feasibility and reliability of capturing longitudinal physician feedback on burnout through the EMR.  We further propose the use of existing longitudinal event logging data to create an EMR-based, longitudinal platform for understanding how varying EMR use correlates to burnout.  In order to accomplish this, we must first evaluate the utility of the proposed EMR event logging data to discriminate presumed differences in workflow between venues (inpatient, outpatient, and emergency department) and specialty groups (primary care, surgery and non-surgical specialties).  Once this longitudinal platform has been evaluated, we will use it to characterize aggregate variations in workflow that most highly correlate to physician burnout, independent of individual physician characteristics.  

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