protocol selection

Emergency medical responders and firefighters are the very first people to arrive at an incident scene to assess and control the situation and assist victims by providing medical care. In medical emergencies, natural disasters, and terrorist attacks, minutes can make difference between life and death. First responders need to process substantial amount of data with different levels of importance and confidence and quickly prioritize available information for situation assessment and response. They may consider circumstances and history of the incident, communications with the command center, other responders, and the victims, and base their actions on this information plus knowledge of established emergency response protocols. In addition, with the rise of Internet of Things, large streams of previously inaccessible data from the wearables, mobile devices, smart buildings, and smart utilities become available to responders and the safety community. Manual collection, aggregation, filtering, and interpretation of such data at the incident scene or control center requires significant human cognitive effort that could be better used to address other incident complexities.

The main objective of this project is to develop a cognitive assistant system that improves situational awareness and safety of emergency responders by real-time collection and analysis of data from incident scene and providing dynamic data-driven feedback to them. The proposed system leverages the responder-worn devices and smart sensors to monitor activities and communications at the incident scene and aggregates this data with static data sources such as protocols and guidelines to generate insights that can assist first responders with effective decision making and taking safe response actions.

I’m working on how to build an intelligent EMS Cognitive Assistant by using pre-collected electronic Patient Care Reports (ePCR).