ENA & VRM

In healthcare settings, poor communication between physicians and nurses is one of the most common causes of adverse events. This study used Epistemic Network Analysis to help identify communication patterns in physician-nurse dyad interactions. We used existing video data where physicians made patient care rounds on two oncology patient units at a large academic medical center, and video recordings captured conversations physicians had with nurses on the plan of care. All data was transcribed, segmented and annotated using the Verbal Response Mode (VRM) taxonomy. The results showed that the relationship between Edification and Disclosure was strongest for the dyads that reached a shared understanding, suggesting the importance of these two modes to reaching shared understanding during patient care rounds. Reflection and Interpretation were the least used VRM codes, and this might be one possible area for intervention development. This pilot study provided new insight into how to improve communication between physicians and nurses using ENA coupled with VRM taxonomy.

Comparison epistemic network analysis models showing the mean network locations (colored squares) and 95% confidence intervals (dashed boxes) for the physician-nurse dyads who reached a shared understanding (blue, left) and the dyads who did not reach shared understanding (red, right) along with their respective mean network graphs.

TEAM MEMBERS: Milisa Manojlovich, Ph.D., RN, FAAN, Professor in the Department of Systems, Populations and Leadership at the University of Michigan School of Nursing, Luke Granberg, undergraduate student Taylor Jones, undergraduate student Kiara Turvey, undergraduate student Raeleen Sobetski, undergraduate student