Akshat Rana
Pronouns: he/him
Research Mentor(s): Vitaliy Popov
Research Mentor School/College/Department: Department of Learning Health Sciences / Medicine
Program: UROPF
Session: Session 6 (3:40pm – 4:30pm)
Authors: Akshat Rana , Vitaliy Popov
Abstract
Malignant Hyperthermia (MH) is a rare complication of general anesthesia, but any patient may develop MH intraoperatively. Many care providers may be unprepared for early recognition, treatment, and management of MH. Simulation-based instruction has been generally accepted as playing an important role in MH training. Specifically, the best practices in healthcare simulation have emphasized the importance of teaching both clinical management and nontechnical (team-based) skills during team training. Nontechnical skills like communication and leadership are often overlooked in the practice of medicine. However, they are just as crucial as technical knowledge because care providers often need to work together to provide treatment. To develop trainees’ clinical and team-based skills, the Clinical Simulation Center at the University of Michigan Medical School provides an environment where these abilities can be learned and assessed. Video recordings of the MH training have been collected for several years and have been analyzed for this paper. Specifically, we examine how trainees a) apply the algorithm for the treatment of MH with an emphasis on teamwork; b) recognize the patient’s response to appropriate (and inappropriate) management interventions; c) Develop individual and team skills in the management of MH; and d) Allocate tasks efficiently within a group. The research team is currently developing a system for the quantification and interpretation of trainees behaviors using Epistemic Network Analysis (ENA). ENA (http://www.epistemicnetwork.org) is a novel method used to quantify and model the co-occurrence of coded elements in our data (e.g., what our trainees do, attend to, and say) within a certain temporal context. ENA uses statistical and visualization techniques to identify, quantify, and represent connections among coded behaviors as network models.
