, Professor, Metropolitan Design Center
The 911 system remains the primary way in which Americans seek help when facing a crisis. However, the multiple public agencies that field 911 calls remain disconnected and dependent upon the judgment of the person answering the call, with some individuals "self-resolving" emergencies out of a distrust of authorities. This has led to a national conversation about how local government can better address crisis situations and avoid tragic outcomes. This project will bring together multiple entities responsible for the 911 network, including Hennepin County, the Hennepin County Sheriff's Office, and the City of Minneapolis. Together and in conjunction with existing efforts, this project will work to develop an organizationally unified and technologically integrated emergency response that makes it easier for the public to identify who to call for help and for the appropriate personnel to be dispatched to the scene, depending on the situation. The project team will examine technology tools, like Artificial Intelligence and Machine Learning, to help responders assess an emergency situation and route it to the right professionals.
The goal is to create a nationally replicable model that integrates technology and emergency response systems to respond more appropriately to the diversity of crises that people face. The project team will seek community input regarding the types of resources and diversity of situations that require emergency response and will work with the community partners to define standards around responses and what professionals are required for each. The Stage I grant will assess the current emergency and crisis response landscape, map its diverse parts and their level of connection, work with city and county staff members to discuss what a more adaptable system might look like, and engage community members to gather their insights in order to build trust and improve upon mental health emergency response that effectively meets their diverse needs while minimizing cost and optimizing resource deployment. The planning phase will take 4 months, during which the team will develop a pilot project for implementation and testing in a 12-month Stage II grant. Technically, the team will examine existing AI applications for their applicability to emergency response in cities. We will also develop AI and Machine-Learning technology that leverages the knowledge and experience of emergency responders. The team will look, as well, at sharing-economy strategies to explore the use of often-overlooked community assets in responding to emergencies to ensure that people have access to the services they need.
- Project number: 2021046
- Start date: 02/2021
- Project status: Active
- Research area: Planning and Economy