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Physician Wellness Measures and Clinical Performance on a Critically Ill Simulated Patient: Does a Lack of Well-Being Impact Patient Care?

Abstract

Background/objective

Burnout is common among resident physicians, which has the potential to translate into diagnostic and management errors. Our study investigates the relationship between sleepiness, depression, anxiety, burnout, and lack of professional fulfillment with clinical performance during a critically ill patient simulation. Methods/Approach: Emergency medicine residents were recruited to participate in a high-fidelity simulation case of a critically ill patient. A survey with validated wellbeing measures (National Institutes of Health Patient-Reported Outcomes Measurement Information System (NIH PROMIS), Linzer burnout measure, and professional fulfillment index) was administered prior to the simulation. Each encounter was video-recorded and analyzed by two blinded raters based on a binary critical-actions checklist. Time-to-intubation, management errors, and misdiagnosis rates were assessed.

Results

Twenty residents participated, with most subjects endorsing sleepiness (70%) and less than half reporting depression (40%) and anxiety (45%). Burnout was identified to be in 50% of participants by the Linzer measure and 85% by the professional fulfillment index. No significant difference was found between mean performance scores in sleepy, depressed, and anxious cohorts in comparison to groups without those symptoms. Similarly, burnout and professional fulfillment did not yield any significant difference, nor did comparisons with time to intubation, management errors, and frequency of misdiagnosis.

Conclusion

Resident burnout, depression, anxiety, sleepiness, and lack of professional fulfillment did not appear to have a measurable impact on clinical performance in managing a critically ill patient. There is no evidence from this study that the lack of resident physician well-being adversely impacts patient care by increasing errors in management or misdiagnoses during this high-fidelity simulation.

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