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，处理911电话的多个公共机构仍然断开连接，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“自行解决”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惨后果的全国性讨论。该项目将汇集负责911网络的多个实体，包括亨内平县、亨内平县警长办公室和明尼阿波利斯市。该项目将与现有的努力一起，努力制定一个组织统一和技术综合的应急反应，使公众更容易确定谁需要求助，并根据情况派遣适当的人员前往现场。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。目标是创建一个全国可复制的模型，将技术和紧急响应系统整合在一起，以更恰当地应对人们面临的各种危机。项目小组将就需要紧急反应的资源类型和情况多样性征求社区意见，并将与社区合作伙伴合作，确定有关反应的标准以及每种反应需要哪些专业人员。第一阶段将评估当前的应急和危机应对格局，绘制其不同部分及其联系程度，与市县工作人员合作讨论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，以建立信任并改进心理健康应急响应，以有效满足他们的不同需求，同时最大限度地减少成本和优化资源配置。规划阶段将需要4个月，在此期间，该小组将制定一个试点项目，在12个月的第二阶段赠款中进行实施和测试。从技术上讲，该团队将检查现有的人工智能应用程序是否适用于城市的应急响应。我们还将开发人工智能和机器学习技术，利用应急人员的知识和经验。该团队还将研究共享经济战略，以探索在应对紧急情况时使用经常被忽视的社区资产，以确保人们能够获得他们需要的服务。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.