SCC-CIVIC-PG Track B Creating an Integrated Emergency and Crisis Response System

911系统仍然是美国人在面临危机时寻求帮助的主要方式。然而，拨打911电话的多个公共机构仍然断开连接，并依赖于接听电话的人的判断，一些人出于对当局的不信任而“自我解决”紧急情况。这引发了一场关于地方政府如何更好地应对危机局势和避免悲剧性结果的全国性对话。该项目将汇集负责911网络的多个实体，包括亨内平县、亨内平县治安官办公室和明尼阿波利斯市。这个项目将与现有的努力一起，努力发展一种组织上统一和技术上一体化的应急反应，使公众更容易确定向谁求助，并根据情况向现场派遣适当的人员。该项目团队将研究人工智能和机器学习等技术工具，以帮助响应者评估紧急情况，并将其发送给合适的专业人员。其目标是创建一个可在全国复制的模型，将技术和应急响应系统整合在一起，以更适当地应对人们面临的各种危机。项目小组将征求社区对需要应急反应的资源类型和情况多样性的意见，并将与社区伙伴合作，确定应急反应的标准以及每种标准所需的专业人员。第一阶段赠款将评估当前的紧急情况和危机应对格局，绘制其不同部分及其联系水平，与市、县工作人员合作，讨论一个更具适应性的系统可能是什么样子，并让社区成员收集他们的见解，以建立信任并改善精神卫生应急响应，有效满足他们的各种需求，同时最大限度地降低成本并优化资源部署.规划阶段将历时4个月，在此期间，该团队将开发一个试点项目，以便在为期12个月的第二阶段赠款中实施和测试。从技术上讲，该团队将研究现有的人工智能应用程序对城市应急响应的适用性。我们还将开发人工智能和机器学习技术，利用应急响应人员的知识和经验。该团队还将研究共享经济战略，探索如何利用经常被忽视的社区资产应对紧急情况，以确保人们获得所需的服务。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.