Where There's a War, There's a Way: A Brief Report on Tactical Combat Casualty Care Training in a Multinational Environment.

BACKGROUND: With most combat deaths occurring in prehospital settings, the US Armed Forces focuses on life-threatening conditions at or near the point of injury. Tactical Combat Casualty Care (TCCC) guidelines are required for all US Servicemembers. Multinational militaries lack this requirement, and international partner forces often have limited prehospital medical training. METHODS: From November 2019 to March 2020, military members assigned to the Role 2E at the Hamid Kazai International Airport (HKIA) North Atlantic Treaty Organization (NATO) base conducted multinational TCCC training. The standardized Joint Trauma System (JTS) TCCC curriculum consisted of two-day classroom instruction and situational training exercises. Competency was assessed through verbalized and demonstrated knowledge. After Action Reviews (AAR) were completed. RESULTS: Twelve multinational TCCC training courses trained 590 military Servicemembers and civilians from 10 countries, ranging from 16 to 62 participants (avg class size = 35). Portugal and Turkey represented the two largest participating nations with 219 and 133, respectively. Student feedback determined optimal group ratios for instruction. AARs were reviewed to categorize best practices. CONCLUSION: Multinational TCCC standardization will save lives. Most nations lack TCCC training requirements. Thus, providing opportunities for standardized training for HKIA residents helped established a multinational baseline of medical interoperability. Utilizing this curriculum in multinational environments can replicate these results. International adoption of TCCC is dynamic and ongoing and should be promulgated to reduce preventable deaths.

Competency-Based Assessments: Leveraging Artificial Intelligence to Predict Subcompetency Content.

PURPOSE: Faculty feedback on trainees is critical to guiding trainee progress in a competency-based medical education framework. The authors aimed to develop and evaluate a Natural Language Processing (NLP) algorithm that automatically categorizes narrative feedback into corresponding Accreditation Council for Graduate Medical Education Milestone 2.0 subcompetencies. METHOD: Ten academic anesthesiologists analyzed 5,935 narrative evaluations on anesthesiology trainees at 4 graduate medical education (GME) programs between July 1, 2019, and June 30, 2021. Each sentence (n = 25,714) was labeled with the Milestone 2.0 subcompetency that best captured its content or was labeled as demographic or not useful. Inter-rater agreement was assessed by Fleiss' Kappa. The authors trained an NLP model to predict feedback subcompetencies using data from 3 sites and evaluated its performance at a fourth site. Performance metrics included area under the receiver operating characteristic curve (AUC), positive predictive value, sensitivity, F1, and calibration curves. The model was implemented at 1 site in a self-assessment exercise. RESULTS: Fleiss' Kappa for subcompetency agreement was moderate (0.44). Model performance was good for professionalism, interpersonal and communication skills, and practice-based learning and improvement (AUC 0.79, 0.79, and 0.75, respectively). Subcompetencies within medical knowledge and patient care ranged from fair to excellent (AUC 0.66-0.84 and 0.63-0.88, respectively). Performance for systems-based practice was poor (AUC 0.59). Performances for demographic and not useful categories were excellent (AUC 0.87 for both). In approximately 1 minute, the model interpreted several hundred evaluations and produced individual trainee reports with organized feedback to guide a self-assessment exercise. The model was built into a web-based application. CONCLUSIONS: The authors developed an NLP model that recognized the feedback language of anesthesiologists across multiple GME programs. The model was operationalized in a self-assessment exercise. It is a powerful tool which rapidly organizes large amounts of narrative feedback.