Abstract
EAGER: Smart and Connected Communities: Transformative Emergency Dispatch Protocols for a Sixty Second Response
It is estimated that on average, every individual in the United States or Canada will call for emergency assistance at least twice during his or her lifetime. In a 9-1-1 call, the dispatcher first collects critical information (e.g., location, call-back number, what happened), assesses the situation, and then decides whether and what to dispatch. Once first responders (police, fire, medical) are dispatched, it takes anywhere from a few minutes to several tens of minutes before the first responders arrive. How much a person can do during the pre-arrival time is very critical. It can make the difference between rapid vs. prolonged recovery, temporary vs. permanent disability, and ultimately life and death. However, the caller is often left without the benefit of life-saving instructions while waiting for help. Meanwhile, the dispatchers constantly face a 60-second dilemma; that is within 60 seconds, they need to make a complicated but important decision in a very limited period: whether to dispatch and, if so, what to dispatch. We believe that the results of this proposed emergency dispatch protocols will provide directions to shorten the total response time, reduce the volume of 9-1-1 calls, decrease the operating cost of 9-1-1 call centers, and successfully establish a 60-second response mechanism in emergency dispatching. There is a huge interest in Next Generation 9-1-1 (NG 9-1-1) in not only in the academic community, but also in the operational community of 9-1-1 service providers, network application developers, federal agents, and first responders. By closely collaborating with call takers, dispatchers, emergency physicians, standard body (NENA) and NG 9-1-1 architects, we can achieve results that will not only provide a promising approach for emergency medicine but also guide the preparation of real-world practitioners. The developed dispatch protocols can be used for telemedicine applications where the physicians are able to remote media control the sensors in mobile phones and collect information from patients. In turn, these protocols can help nursing homes in suburban areas where access to specialists is limited.
Next-generation (NG) 9-1-1 is being deployed for higher bandwidth and better routing of 9-1-1 calls.in smart and connected communities With mobile voice and video over IP, and remote control functions, dispatchers can get access to different forms of information (e.g., text, images, and video). In particular, dispatchers will be able to utilize sensors such as cameras, microphones, speakerphones, accelerometers, and pressure sensors for accurately assessing a situation. PI plans to use the already developed technologies such as measuring heart rate, respiratory rate, and the depth of CPR compressions from the previous awards. In this proposal, we want to address on how multimedia information can help the dispatchers and improve pre-arrival instructions to the callers to accomplish required emergency tasks (e.g., CPR or operate a fire extinguisher). The proposed research makes several unique contributions: i) developing new services for NG 9-1-1 for supporting callers and dispatchers prior to the arrival of the first responders, one of the most critical yet neglected areas in emergency dispatching, ii) exploring several fundamental research issues (e.g., index of difficulty of tasks, communication media optimization, miscommunication quantification) related to emergency task executions facilitated by using a mobile phone, which have not been examined before and are not well understood, iii) providing a sea change in the understanding of human-machine interface in NG9-1-1 in emergency dispatching, iv) providing an effective and efficient 60-second response mechanism in emergency dispatching, and v) facilitating a revised set of dispatch protocols for accurate identification of needed medical care.
Performance Period: 09/01/2016 - 08/31/2018
Institution: University of North Texas
Sponsor: National Science Foundation
Award Number: 1637291