Abstract
Due to logistical scheduling challenges, social training of conflict resolution skills with healthcare professionals is a difficult task. To overcome these challenges, we used virtual humans to fill in as surgical teammates and train conflict resolution skills in a surgical scenario. Surgical technologists were recruited at a United States teaching hospital to interact with a virtual nurse, virtual surgeon, and virtual anesthesiologist in a team training exercise. Leveraging social learning theory, the virtual nurse on the team modeled one of two conflict resolution strategies, either best practices or bad practices, during an important decision moment in the exercise. In a second important decision moment, we assessed if surgical technologists demonstrated the conflict resolution model they observed. We found human participants were successfully able to demonstrate the ideal conflict resolution strategy after observing the virtual nurse model best practices. While we found participants were positively influenced by the best practices model, we also found that conversely, the bad practices model negatively influenced participants’ conflict resolution behavior. If humans can be positively influenced by virtual humans, this form of social training could transform medical team training, empowering more healthcare professionals to speak up, and potentially decreasing the chances of patient morbidity or death in the OR.
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Acknowledgements
The authors would like to acknowledge Dave Lizdas, Drew Gonsalves, and Terry Sullivan for study setup and support. The authors would also like to acknowledge Jim Martindale for helping with the statistical analysis, and the surgical technologists who participated in the study. This work was supported in part by NSF Grant 1161491.
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Cordar, A., Robb, A., Wendling, A., Lampotang, S., White, C., Lok, B. (2015). Virtual Role-Models: Using Virtual Humans to Train Best Communication Practices for Healthcare Teams. In: Brinkman, WP., Broekens, J., Heylen, D. (eds) Intelligent Virtual Agents. IVA 2015. Lecture Notes in Computer Science(), vol 9238. Springer, Cham. https://doi.org/10.1007/978-3-319-21996-7_23
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DOI: https://doi.org/10.1007/978-3-319-21996-7_23
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