Purpose Due to the pandemic, we restructured our medical student knot-tying simulation to a virtual format. This study evaluated curriculum feasibility and effectiveness. Methods Over 4 weeks, second-year medical students (n = 229) viewed a video tutorial (task demonstration, errors, scoring) and self-practiced to proficiency (no critical errors, < 2 min) using at-home suture kits (simple interrupted suture, instrument tie, penrose drain model). Optional virtual tutoring sessions were offered. Students submitted video performance for proficiency verification. Two sets of 14 videos were viewed by two surgeons until inter-rater reliability (IRR) was established. Students scoring "needs remediation" attended virtual remediation sessions. Non-parametric statistics were performed using RStudio. Results All 229 medical students completed the curriculum within 1-4 h; 1.3% attended an optional tutorial. All videos were assessed: 4.8% "exceeds expectations", 60.7% "meets expectations", and 34.5% "needs remediation." All 79 needing remediation due to critical errors achieved proficiency during 1-h group sessions. IRR Cohen's κ was 0.69 (initial) and 1.0 (ultimate). Task completion time was 56 (47-68) s (median [IQR]); p < 0.01 between all pairs. Students rated the overall curriculum (79.2%) and overall curriculum and video tutorial effectiveness (92.7%) as "agree" or "strongly agree". No definitive preference emerged regarding virtual versus in-person formats; however, 80.2% affirmed wanting other at-home skills curricula. Comments supported home practice as lower stress; remediation students valued direct formative feedback. Conclusions A completely virtual 1-month knot-tying simulation is feasible and effective in achieving proficiency using video-based assessment and as-needed remediation strategies for a large student class.
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Objectives:
To perform a review of the literature on the role of simulation-based training (SBT) in healthcare-associated infection (HAI) prevention and to highlight the importance of SBT as an educational tool in infection prevention.
Methods:
We reviewed English language publications from PubMed to select original articles that utilized SBT as the primary mode of education for infection prevention efforts in acute-care hospitals.
Results:
Overall, 27 publications utilized SBT as primary mode of education for HAI prevention in acute-care hospitals. Training included the following: hand hygiene in 3 studies (11%), standard precaution in 1 study (4%), disaster preparedness in 4 studies (15%), central-line–associated blood stream infection (CLABSI) prevention in 14 studies (52%), catheter-associated urinary tract infection (CAUTI) prevention in 2 studies (7%), surgical site infection prevention in 2 studies (7%), and ventilatory associated pneumonia prevention in 1 study (4%). SBT improved learner’s sense of competence and confidence, increased knowledge and compliance in infection prevention measures, decreased HAI rates, and reduced healthcare costs.
Conclusion:
SBT can function as a teaching tool in day-to-day infection prevention efforts as well as in disaster preparedness. SBT is underutilized in infection prevention but can serve as a crucial educational tool.
Background Early introduction and distributed learning have been shown to improve student comfort with basic requisite suturing skills. The need for more frequent and directed feedback, however, remains an enduring concern for both remote and in-person training. A previous in-person curriculum for our second-year medical students transitioning to clerkships was adapted to an at-home video-based assessment model due to the social distancing implications of COVID-19. We aimed to develop an Artificial Intelligence (AI) model to perform video-based assessment. Methods Second-year medical students were asked to submit a video of a simple interrupted knot on a penrose drain with instrument tying technique after self-training to proficiency. Proficiency was defined as performing the task under two minutes with no critical errors. All the videos were first manually rated with a pass-fail rating and then subsequently underwent task segmentation. We developed and trained two AI models based on convolutional neural networks to identify errors (instrument holding and knot-tying) and provide automated ratings. Results A total of 229 medical student videos were reviewed (150 pass, 79 fail). Of those who failed, the critical error distribution was 15 knot-tying, 47 instrument-holding, and 17 multiple. A total of 216 videos were used to train the models after excluding the low-quality videos. A k-fold cross-validation (k = 10) was used. The accuracy of the instrument holding model was 89% with an F-1 score of 74%. For the knot-tying model, the accuracy was 91% with an F-1 score of 54%. Conclusions Medical students require assessment and directed feedback to better acquire surgical skill, but this is often time-consuming and inadequately done. AI techniques can instead be employed to perform automated surgical video analysis. Future work will optimize the current model to identify discrete errors in order to supplement video-based rating with specific feedback.
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