OBJECTIVE: The Covid-19 pandemic resulted in a shift in communication of difficult, emotionally charged topics from almost entirely in-person to virtual mediated communication (VMC) methods due to restrictions on visitation for safety. The objective was to train residents in VMC and assess performance across multiple specialties and institutions. DESIGN: The authors designed a teaching program including asynchronous preparation with videos, case simulation experiences with standardized patients (SPs), and coaching from a trained faculty member. Three topics were included - breaking bad news (BBN), goals of care / health care decision making (GOC), and disclosure of medical error (DOME). A performance evaluation was created and used by the coaches and standardized patients to assess the learners. Trends in performance between simulations and sessions were assessed. SETTING: Four academic university hospitals - Virginia Commonwealth University Medical Center in Richmond, Virginia, The Ohio State University Wexner Medical Center in Columbus, Ohio, Baylor University Medical Center in Dallas, Texas and The University of Cincinnati in Cincinnati, Ohio- participated. PARTICIPANTS: Learners totaled 34 including 21 emergency medicine interns, 9 general surgery interns and 4 medical students entering surgical training. Learner participation was voluntary. Recruitment was done via emails sent by program directors and study coordinators. RESULTS: A statistically significant improvement in mean performance on the second compared to the first simulation was observed for teaching communication skills for BBN using VMC. There was also a small but statistically significant mean improvement in performance from the first to the second simulation for the training overall. CONCLUSIONS: This work suggests that a deliberate practice model can be effective for teaching VMC and that a performance evaluation can be used to measure improvement. Further study is needed to optimize the teaching and evaluation of these skills as well as to define minimal acceptable levels of competency.
