Improving preparedness for mass casualty incidents in hospitals: insights from a large-scale simulation exercise with geotracking and validated questionnaires

BACKGROUND: Mass casualty incidents (MCIs) rapidly exceed routine hospital capacity. Full-scale exercises are essential for preparedness, but systematic, multidimensional evaluations remain scarce. This study aimed to evaluate overall team performance in triage accuracy, workflow, and individual workload throughout a MCI exercise. METHODS: In a prospective observational study at Heidelberg University Hospital (Germany), healthcare professionals managed 91 simulated casualties using a two-stage triage process. Patients and staff carried location tags enabling continuous spatiotemporal tracking. Objective outcomes included triage accuracy, triage duration, patient flow, and staff–patient contact frequency. Subjective workload and teamwork were assessed using the NASA Task Load Index (NASA-TLX) and the Team Emergency Assessment Measure (TEAM), respectively. RESULTS: Overall triage accuracy was 75.4%. Undertriage occurred in 11.6% of category I and 10.1% of category II cases; overtriage was infrequent (2.9%). Mean triage times differed significantly by category: ‘red’ 59 ± 25 s, ‘yellow’ 173 ± 74 s, ‘green’ 205 ± 100 s (p < 0.0001). Geotracking demonstrated consistent patient flow without detectable bottlenecks and a mean of 7.1 ± 5.7 patient contacts per staff member. NASA-TLX scores indicated high temporal demand but low frustration with an overall workload of 66.7 ± 16; specialists and staff with greater professional experience reported significantly lower perceived workload (p < 0.05). TEAM ratings were homogeneously good across all participants (79.8%). CONCLUSIONS: This study provides reproducible benchmark data on simulated hospital MCI response. The integration of geotracking with subjective measures, enables a comprehensive evaluation of hospital disaster preparedness. Moreover, the ability to compare different exercises and collect reliable longitudinal data may further enhance hospital disaster response.