Abstract
Objectives
Death notification, the verbal communication to a next-of-kin survivor after a patient has died, is a stressful and high-stakes procedure. Trainees in emergency medicine should achieve competency in death notification delivery by the completion of residency, but this has not been previously demonstrated. GRIEV_ING is a validated assessment tool for death notification skills. This study utilized the GRIEV_ING checklist in a high-fidelity simulation environment to assess the performance of emergency medicine residents compared to a minimum passing standard.
Methods
Post-graduate year 1–4 residents participated in a simulation case in which the patient died. Residents were asked to deliver a death notification to a standardized patient playing the role of the patient’s next-of-kin. Resident performance on the GRIEV_ING checklist was scored by the standardized patient and by a faculty member. A minimum passing standard was determined using the Ebel and Hofstee methods. Resident performance was compared to the minimum passing standard.
Results
Agreement between standardized patient and faculty physician raters was good (K = 0.6572; 95% CI 0.6121–0.7024). The minimum passing standard as determined by the Ebel and Hofstee methods was 56.2 and 54%, respectively. Residents did not uniformly meet or exceed the minimum passing standard. There was no statistically significant difference in faculty rated resident performance by post-graduate year on any of the 28 checklist items.
Conclusion
Workplace-based instruction alone is insufficient for all emergency medicine residents to achieve mastery of death notification skills. Future efforts should focus on ensuring resident competency in this critical domain.
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Acknowledgements
The authors would like to thank Natasha B. Wheaton, MD, Rocio Pozzi, MD, Christopher Morrison, MD, and John A. Vozenilek, MD for their contributions to this project.
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Shakeri, N., Fant, A., Gisondi, M.A. et al. Assessment of Death Notification Skills Among Emergency Medicine Residents Using GRIEV_ING in High-Fidelity Patient Simulation. Med.Sci.Educ. 27, 881–886 (2017). https://doi.org/10.1007/s40670-017-0449-6
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DOI: https://doi.org/10.1007/s40670-017-0449-6