GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response


  • M Arif Rahman University of Virginia
  • Sarah M. Preum University of Virginia
  • Ronald Williams University of Virginia
  • Homa Alemzadeh University of Virginia
  • John A. Stankovic University of Virginia



EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.




How to Cite

Rahman, M. A., Preum, S. M., Williams, R., Alemzadeh, H., & Stankovic, J. A. (2020). GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13356-13362.



IAAI Technical Track: Emerging Papers