The ultimate goal of medical disaster management must be to predictably orchestrate transition from “standard of care” to “sufficiency of care” using evidence-based methods. However, neither descriptive reports of disaster responses nor epidemiological studies investigating disaster risk factors have been able to provide validated outcome measures as to what constitutes a “good” disaster response. Moreover, it either has been considered impossible, ethically inappropriate, or both, to identify experimental and control groups essential for hypothesis testing for the conduct of scientific randomized controlled clinical trials.

The aim of this study was to identify a number of performance and outcome indicators and define optimal disaster response and management decision-making for various disaster scenarios using simulation optimization.

A system model of medical disaster management was designed, and victim models and performance and outcome indicators were developed. Various mass-casualty and large-scale disaster scenarios were developed, including: (1) a hospital emergency incident/disaster; (2) a CBRNE incident; (3) an airplane crash and airport disaster; (4) a mass gathering; and (5) a military battlefield mass casualty. Using “Discrete Event Driven Simulation”, multiple replications were made for different decision-making modalities, different resource allocations, and different disaster response procedures. Statistical analysis and optimization techniques were applied to achieve the best available setting of parameters of the simulation model. In such a way, the “Medical Disaster Management Simulator” runs the “missing experimental studies” in a simplified artificial simulated disaster environment.

Simulation optimization is an adequate tool for judging and evaluating the effectiveness and adequacy of health and relief services provided during disaster medical response. Evidence-based recommendations and codes of best practice were formulated for optimal medical disaster and military battlefield management in different large-scale event scenarios as well as for teaching, training, and research in medical disaster management.