Assessing the Utility of Hybrid Simulation With Wearable Technology and Repeated Peer Assessment on Medical Student Learning and Performance in Cardiology Long Case Examinations - the ASSIMILATE ExCELLENCE Study

The long case examination in medicine is regarded as an authentic test of clinical competence; however, it has been shown to have low reliability and validity due to the variability in the patients used and subjective examiner grading. In this study, the investigators hypothesized that expert tuition with hybrid simulation using a standardized patient wearing a novel auscultation vest, i.e., a hybrid patient, and repeated peer grading would improve student learning and performance in sequential cardiology long case examinations. Furthermore, they hypothesized that the format's validity would improve through the use of less subjective quantitative scoring checklists.

Scripted histories and scoring checklists for three clinical scenarios in cardiology were co-created and refined through iterative consensus by a panel of clinical experts; these were then paired with recordings of auscultatory findings from three real patients with known valvular heart disease. A wearable vest with embedded pressure-sensitive panel speakers was developed to transmit these recordings when examined at the anatomically standard auscultation points. Students in the Graduate Entry Medicine degree program at RCSI were invited to enroll in the study and undertake a series of three long case examinations in cardiology (LC1 - LC3) using hybrid simulation. Each participant's performance was recorded and graded using the novel scoring checklist by two peer participants and two RCSI examiners. In addition, participants were randomized into two groups: Group 1 received individual and small-group teaching with a hybrid patient from an expert trainer between LC1 and LC2; those in Group 2 received the same intervention between LC2 and LC3 (a randomized controlled waitlist design). Participants completed a pre- and post-study questionnaire. Data are presented as number (%), mean ± standard deviation, or median (interquartile range). Group comparisons were made using either the unpaired t-test or the chi-squared (χ2) test. A p-value < .05 was considered statistically significant. Multivariate analysis was undertaken using general linear mixed modeling and multiple logistic regression. Inter-observer variability was assessed using the Intraclass Correlation Coefficient (ICC).