Big Data Analytics for Mass Casualty Incident (MCI) Situational Awareness

Introduction A variety of big data analytics, techniques and tools including social media analytics, open source visualizations, statistical anomaly detection, use of Application Programming Interfaces (APIs), and geospatial mapping, are used for infectious disease biosurveillance. Using these methodologies, policy makers and practitioners detect and monitor outbreaks across the world near real time, in multiple languages, 24/7. The non-infectious disease community, namely critical care, injury, and trauma stakeholders, currently lack this level of sophistication. To respond to most MCIs like a terrorist bombing, validated, real-time information is typically available via closed radio channels and limited to a specific set of emergency responders. Health care workers, policy makers, and citizens reach for news, radio, and Internet sources to characterize casualties and hazards, and increasingly social media. During the Boston Marathon bombing, witnesses began posting tweets seconds after the bombing and 15 seconds before CNN reported the incident. Current trauma data sets are unhelpful for real time response, including trauma registries that are used for hospital performance after an incident, and disaster databases consist of secondary reporting used for academic research purposes.