Whole-task Hybrid Simulation Improves Medical Student Competence in Cardiology Clerkship

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 goal of this randomized controlled waitlist trial is to assess the utility of expert tuition with hybrid simulation and repeated peer grading on medical student learning and performance in cardiology long-case examinations. The primary aim of this research is to assess the effects of time, individual teaching with an expert trainer, and repeated peer assessment on students' performance scores in sequential formative long-case examinations in cardiology. The secondary aims are: (a) to assess to what degree performance scores change over time with respect to the intervention group, and (b) to assess for any change in the level of inter-observer variability over time. Participants will be randomized into two groups and undertake three formative long-case examinations in cardiology with a hybrid patient. Each group will have tuition from an expert trainer in a randomized controlled waitlist design. The investigators will compare groups to see if the tuition from a clinical expert has an effect on participants' performance.

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).

Hybrid simulation, Cardiology clerkship, Peer assessment, Whole-task learning, Standardized patient, Wearable technology

Medical students who had early intervention, i.e. teaching from an expert trainer, in the randomized controlled waitlist design.

Medical students who had a late intervention, i.e. teaching from an expert trainer, in the randomized controlled waitlist design.

Each group was initially provided with an online tutorial on the complete physical examination of the cardiovascular system as performed on a standardized patient by the trainer. At the beginning of each session, the trainer performed the examination on a hybrid patient wearing the auscultation vest and provided additional teaching, explanations, and rationale. Following the demonstration, each participant was invited to perform repeated physical examinations in front of their peers with real-time one-to-one feedback from the trainer. After each performance, the participant was encouraged to critique their own performance(s), peers were invited to provide some insights, and the trainer gave some overall feedback. At the end of the session and after each participant had had the opportunity to perform the complete examination at least once, the participants were taken through a range of abnormal signs using the auscultation vest with explanations provided by the trainer.

To assess the effects of time, individual teaching with an expert trainer, and repeated peer assessment on students' performance scores in sequential formative long case examinations in cardiology. Students will undertake three long case examinations. Each participant's total score in each examination will be compared across time.

Students' total scores categorized by intervention group in sequential cardiology long case examinations

Variability in students' performance scores in sequential cardiology long case examinations as assessed by peer and faculty graders

Each long case examination will be assessed by two peers and two faculty graders. To assess inter-observer variability over time using Intraclass Correlation Coefficients.

Inclusion Criteria: were in either Year 2 of the Graduate Entry Medicine (GEM) program (equivalent to Year 3/5 of the Direct Entry Medical Degree Programme), or Year 1 of the Senior Cycle, i.e., Year 4/5 of the Direct Entry Medical Degree Programme, having completed Year 2 of the GEM program in the preceding year; and had successfully completed the standard RCSI module in Clinical Cardiology. Exclusion Criteria: None