Vu, T. N. Diem

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value.

AbstractBackgroundVenous thromboembolism (VTE) chemoprophylaxis in breast surgery remains controversial. In 2012, we instituted a practice change of routine chemoprophylaxis for patients with invasive cancer undergoing mastectomy. Herein, we report the effects of this on rates of VTE and hematoma.MethodsOur 30‐day rates of VTE and hematoma requiring reoperation among patients with mastectomy since the practice change were retrospectively collected from National Surgical Quality Improvement Program (NSQIP). The subsequent 5‐year data (2012‐2017) was compared with historic NSQIP data (2006‐2010). We utilized information from our 30‐day follow‐up databank to assess patients not sampled by NSQIP.ResultsAfter the practice change, the heparin prophylaxis rate rose from 19.5% to 95.6% (P < .001) and the VTE rate fell from 0.8% to 0% (P = .30). There was no significant change in reoperative hematoma rate (P = .39). The majority of the current NSQIP patient population (93.1%) had a Caprini score of 5 or greater. Among 663 patients obtained from 30‐day postoperative follow‐up, there were 2 VTE (0.3%) and 7 (1.1%) reoperations for hematoma.ConclusionsThe practice change resulted in an increase of VTE chemoprophylaxis without significant change in hematoma incidence. Although not statistically significant, VTE incidence decreased. This supports the use of standardized VTE chemoprophylaxis in this population and warrants further study.