Assessing levels of support for residents following adverse outcomes: A national survey of anesthesia residency programs in the United States

Abstract

Aim: The impact of physician burnout is becoming apparent in the medical community, especially among anesthesiologists and young physicians. Anesthesia residents will experience emotionally charged adverse events during their training. The objective was to determine the prevalence, efficacy and utilization of support structures in place for residents following adverse events in order to develop a best-practices model of resident support.

Methods: We sent an anonymous internet-based survey to program directors (PDs) at all American College of Graduate Medical Education (ACGME)-approved Anesthesia Residency programs, with an optional secondary survey of residents.

Results: There was a 53% response rate among PDs with most reporting resource availability. The strongest support programs were the anesthesia support programs, morbidity and mortality (M&M) conference, and “individual meetings with department leadership.” These results were mirrored in the responses of residents at the 32 programs who opted to participate in the secondary survey. Both also cited M&M conference as being highly utilized, but not efficacious. The strongest support programs had a low prevalence.

Conclusions: These data suggest that a best-practices model of resident support entails establishing a department-based support program, facilitating comfortable meetings with departmental leadership and optimizing M&M conference.

Acknowledgements

I sincerely thank all of the program directors and residents who participated in this survey. This support initiative and study would not have been possible without the moral support of the Department of Anesthesia, Critical Care & Pain Medicine at Beth Israel Deaconess Medical Center as well as in the form of access to the online survey tool. None of the authors have any potential conflicts of interest. I had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. In addition to the authors listed, we would like to thank Vanessa Wong who is the research assistant to Dr. John Mitchell for her work on data synthesis and Dr. Bozena Jachna for manuscript guidance.

Declaration of interest: The authors report no declarations or conflicts of interest.