内容来源：https://news.microsoft.com/source/emea/features/ai-dispatch-system-munich/

内容总结：

慕尼黑消防部门引入AI接线员 优化非紧急医疗转运调度

在德国慕尼黑消防局的调度中心，接线员茱莉亚·福斯每周需要值一个24小时的班。在此期间，电话铃声几乎从不间断。她不仅要为心脏骤停、交通事故等紧急情况派遣救护车，在电话中指导心肺复苏，还需处理大量非紧急医疗转运请求，例如安排病患在不同医院间转诊或出院回家。这种高强度工作状态是许多调度员的日常。

为缓解调度员压力、优化医疗转运流程，慕尼黑消防局与微软合作，开发出一款能够使用自然语言进行多语言交流的AI接线员。该系统专用于处理非紧急转运预约。其核心由多个智能模块构成：通过微软Foundry平台管理和控制聊天机器人的响应逻辑；Azure语音技术赋予其自然的语音语调；AI搜索模块则能核对市政数据库中的地址等关键信息。

该AI系统旨在消除市民拨打预约电话时的等待时间，将调度员从繁琐的非紧急事务中解放出来，使其能更专注于真正的紧急医疗事件和火警。项目技术负责人之一、同时兼具消防员身份的马蒂亚斯·杜恩辛强调：“我们有一句格言：‘机器人提供帮助，人类实施救援。’” 系统始终遵循“人类在环”原则，若AI无法理解对话或来电者需要人工帮助，通话将立即转接至人工调度员。

这一创新离不开消防部门独特的复合型人才队伍。许多像杜恩辛一样的团队成员，既是IT架构师，也是受过专业训练、定期参与一线灭火救援的消防员。另一位核心架构师弗洛里安·达克斯则拥有急救医疗服务和调度员的亲身经历，他将对医院实际运作、护理人员工作压力的深刻理解融入了AI的设计中。

达克斯指出，在许多养老院和小型医院，负责预约转运的护理人员母语并非德语，语言障碍常导致沟通效率低下。AI接线员的多语言能力正是为了突破这一瓶颈。系统预计今年晚些时候在慕尼黑规模最大的LMU大学医院急诊部进行更广泛的真实场景测试。

LMU大学医院急诊科负责人马蒂亚斯·克莱因教授对此表示欢迎。他认为，当前预约转运的过程耗时较长，护理人员常因等待接通而无法处理其他工作。AI助手有望节省护理人员的时间，使其更专注于病患照护，并通过缩短转运等待时间来加速医院床位周转。

慕尼黑消防局强调，对于危及生命的紧急呼叫，必须由人类调度员处理。AI的目标是高效处理所有辅助性任务和低优先级呼叫，从而让应急响应体系更安全、更高效。正如达克斯所言：“在我们的领域，AI能让应急响应工作显著变得更安全。”

中文翻译：

慕尼黑消防部门的AI接线员如何革新非紧急调度

慕尼黑——茱莉亚·福斯身兼调度员与消防员双重职责服务于慕尼黑消防部门。此前六年她曾担任急救员，穿梭于大都会区救护车中处理心脏病发作、救援车祸伤员、协助分娩等紧急情况——如今这些救护车正由她负责调度。

作为两个幼儿的母亲，福斯每周需完成一个24小时轮值班。当值时，她会在配备七块显示屏的调度台前分三段值守，每段三小时。电话铃声永不停歇，压力如影随形，生死往往悬于一线。

"我们时刻在线，"福斯说，"在救援到达前通过电话提供急救指导，在急诊医生到场前教授心肺复苏术，为患病儿童的父母提供建议，同时还要通过电话关怀精神疾病患者。"

与此同时，她和同事们还需负责协调医院间转运及出院患者的接送服务。慕尼黑消防部门亟需一种既能减轻调度员压力，又能简化医护人员协调流程的解决方案，以期缩短医疗机构等待时间，让患者更早返家。此外，许多协调转运的医护人员仅将德语作为第二或第三语言。

破局之道何在？消防部门与微软的IT专家联合开发出一款能使用自然语言处理非紧急呼叫的AI接线员——支持多语种服务。

该AI系统由多个组件协同构建：微软铸造平台负责整合管理聊天机器人的智能核心，调控系统响应模式与权限范围；铸造工具集中的Azure语音服务（高清语音）赋予系统拟人化声线，能根据语境调整语调韵律；平台的Azure AI搜索组件通过市政数据库核验地址信息，确保入口定位等关键细节准确无误。

这些组件协同运作，使AI接线员既能保持自然对话状态，又能恪守职责边界并核实关键信息——人类调度员始终掌握最终控制权。AI接线员能消除来电者的等待时间，将调度员从非紧急呼叫中解放出来，让他们更专注于医疗急救与火灾警情，甚至能在通话间隙获得片刻喘息。

懂技术的消防员

"我们有个口号：'机器人协助，人类拯救。'若聊天机器人无法理解对话内容，或护士感到沟通困难，系统会立即转接人工调度员。"慕尼黑消防部门IT架构负责人马蒂亚斯·杜恩辛表示。这位系统创建倡导者同时也是现役消防员。

测试阶段显示AI接线员高效易用，但正如杜恩辛强调："人类始终处于决策闭环中。"部门人员反复明确，AI旨在提升服务效能，无法替代茱莉亚·福斯等调度员的专业判断。

为解决这一难题，消防部门充分发挥其独特优势——消防员与调度员的多重技能组合。杜恩辛拥有电气工程与信息技术学位，平日主导IT项目，每月仍像调度员福斯一样参与消防轮值。解决方案的另一位架构师弗洛里安·达克斯则将学术研究（直至博士学位）聚焦应急响应服务领域，他不仅是学者，更兼具急救员与调度员的实战经验。

"由于我出身急救医疗系统，深谙医院运作机制：诊所面临的困境、护士的日常职责、她们承受的压力……所有这些认知都融入了语音机器人的设计。"2022年加入消防部门技术团队的达克斯解释道。他与杜恩辛及其直属上司、数据中心副主任托比亚斯·埃尔布博士紧密协作，为语音机器人等创新构想奠定了人员与技术基础。这支团队与慕尼黑消防部门信息通信技术负责人克里斯蒂安·施内普夫（同样是在役消防员）协同微软，共同开发出旨在突破语言障碍的语音机器人概念原型。

在许多护理院和小型医院，负责转运协调的护士等多来自东欧或亚洲国家，德语电话沟通仍是巨大挑战。2023年，自然语言AI调度员构想应运而生，计划通过专线处理非紧急患者转运。

达克斯举例说明设计考量："当患者按下紧急呼叫钮时，护士必须立即赶往现场——但有时她正在与我们通电话协调患者转运。此时我们会被迫等待，导致整个流程延误。语音机器人正是为化解这类困境、释放人力而设计。"

今年晚些时候，这款AI接线员将在慕尼黑最大医院LMU医疗中心的急诊科开展更广泛的实际测试。达克斯表示数据隐私严格遵循欧盟法规，消防部门在整个项目周期中持续与数据保护机构保持沟通。他透露二月起将在LMU医疗中心启用真实患者数据进行测试，此前仅使用模拟数据。

医疗走在技术前沿

在LMU医疗中心，时间如同消防部门一样珍贵。该院急诊科负责人兼教授马蒂亚斯·克莱因博士指出："协调转运本身耗时不多，但排队等待常耗费大量时间。"护士在等待调度员接听时几乎无法处理其他事务，因为必须随时准备应答。若急诊科出现紧急状况，护士只得挂断重拨。

"当前流程确实耗时，"克莱因博士坦言，"如今正值技术与医疗结合的关键节点。医疗领域常引领技术革新，而人工智能正在多维度深入医疗场景。"他认为慕尼黑消防部门的AI接线员有望加速病床周转。

"患者完成所有医疗程序准备出院时，转运环节却成为障碍。从预约转运开始就需要时间，随后往往还需等待数小时接送。若延迟至深夜，我们可能不得不取消安排，毕竟不能让患者凌晨一点才抵达目的地。"

从医院视角看，该系统能节省护士时间，使其更专注于病患照护。克莱因博士期待通过缩短等待时间提升转运系统效率，最终加速病床资源循环。

达克斯深刻理解调度员每日承受的情感压力：前一秒可能正在指导惊慌的家长为重症儿童实施心肺复苏，下一秒就要实时处理心脏病发作或癫痫发作的紧急状况。与此同时屏幕指示灯不断闪烁，新的呼叫持续涌入。

对达克斯和慕尼黑消防部门而言，最重要的是运用一切工具提升生命救援效能。"参与塑造应急服务领域的人工智能对我们至关重要。我们虽属细分领域——没有成千上万的分支机构——但在我们的世界里，AI能显著提升应急响应的安全性。当然，危及生命的呼叫必须由人类处理；而对于所有辅助任务和低优先级呼叫，AI确实能提供实质帮助。"

英文来源：

How the Munich Fire Department’s AI operator is modernizing non-emergency dispatch
MUNICH—Julia Voss works as a dispatcher and firefighter for the Munich Fire Department.
She spent six years before that as a paramedic, riding in the ambulances that she now dispatches throughout the metropolitan area to treat heart attacks, rescue car accident victims and deliver babies, among other things.
A mother of two small children, Voss works one 24-hour shift per week. During that time, she’ll sit at one of the dispatch desks, with its seven computer screens, for three separate three-hour stints. The phone never stops ringing. The pressure is intense; lives are at stake.
“We are constantly on the line,” said Voss. “We provide first aid over the phone until the emergency services arrive, we instruct people in CPR until the emergency doctor is there, we give advice to parents of sick children, we also look after mentally ill people on the phone.”
At the same time, she and the other dispatchers are responsible for arranging transportation for patients who are moving between hospitals or being discharged. The Munich Fire Department sought a way to relieve pressure on the dispatchers while also making the process easier for the nurses and other health-care workers who arrange the transports. The hope was also to shorten waiting times at the hospitals and nursing homes that need the service and to get patients home more quickly. In addition, many of the health-care workers arranging the transports speak German as a second or third language.
“We are constantly on the line. We provide first aid over the phone until the emergency services arrive, we instruct people in CPR until the emergency doctor is there, we give advice to parents of sick children, we also look after mentally ill people on the phone.”
Julia Voss at the Munich Fire Department’s Dispatch Center, where she works one 24-hour shift per week. Photo by Anastasia Pivovarova for Microsoft.
The solution? IT experts from the fire department and Microsoft created an AI operator that could handle non‑emergency calls using natural language—in several languages.
They combined several components to build this AI operator.
Microsoft Foundry is where the chatbot’s intelligence is assembled and governed, helping manage how the system responds and what it is allowed to do. Azure Speech (HD Voice), part of Foundry Tools, gives the system a natural‑sounding voice, adjusting tone and cadence based on the words it is speaking. Foundry’s Azure AI Search validates addresses against a municipal database, confirming details such as the correct entrance or other critical information.
Together, the components ideally allow the AI operator to sound natural, stay within its assigned task and verify important details—while leaving human dispatchers in control.
The AI operator can eliminate wait times for those who call in requests while unburdening the dispatchers of the non-emergency calls—giving them more time for medical emergencies and fires and perhaps giving them a little time to take a few deep breaths between calls.
Firefighters who also know tech
“We came up with a saying, ‘Der Bot hilft, der Mensch rettet,’ it means the bot helps but the human saves. If the chatbot doesn’t understand something, or the nurse gets frustrated, they are connected to a human dispatcher immediately.”
Mathias Duensing who is head of IT-Architecture in the Munich Fire Department’s IT department. He’s also an active firefighter. Photo by Chris Welsch for Microsoft.
In beta testing, the AI operator has been very effective and easy to use, testers have said, but as Mathias Duensing, one of the firefighters who helped create and advocate for the system, said, “there is always a human in the loop.” He and others at the department emphasized the AI operator is there to improve services; it can’t do what Julia Voss and the other dispatchers do.
“We came up with a saying, ‘Der Bot hilft, der Mensch rettet,’ it means the bot helps but the human saves,” said Duensing, who is head of IT-Architecture.
“If the chatbot doesn’t understand something, or the nurse gets frustrated, they are connected to a human dispatcher immediately.”
In the effort to address the problem, the fire department drew on one of its strengths—the multiple skillsets of its firefighters and dispatchers.
Duensing, for example, has a degree in electrical engineering and information technology and works most days on IT projects, but still serves monthly shifts fighting fires, like Julia Voss, the dispatcher. Florian Dax, one of the other architects of the solution, devoted his university studies—through to a Ph.D.—on subjects related to emergency response services, but he’s not just an academic. He worked as a paramedic and as a dispatcher, too.
“Because I originally came from emergency medical services, I also know how hospitals work: what problems clinics face, what a nurse does all day, how much pressure they’re under. All of this knowledge flowed into the voice bot.”
Florian Dax is one of the chief architects of the AI operator that will be handling non-emergency transport calls for the Munich Fire Department. Photo by Nur Bayraktepe for Microsoft.
He became part of the fire department’s tech team in 2022. “The overlap with IT came mainly through Mathias Duensing, who said: ‘We’re missing someone who can bring IT knowledge into practice—someone who understands what the control center needs, where the pain points are,’” he said.
Duensing and his immediate supervisor, Dr. Tobias Erb, who is also the deputy head of the data center, worked closely together as a team to establish both the staff and technical foundation needed to implement innovative ideas such as the speech bot. Together with Christian Schnepf, the head of the Information and Communication Technology Department at the Munich Fire Department—and, like Duensing and Erb, an active firefighter—the team collaborated with Microsoft to develop the concept of a speech bot, initially aimed at overcoming language barriers.
At many nursing homes and smaller hospitals, the nurses and others handling transport calls come from Eastern European or Asian countries and speaking German on the phone could still be challenging. In 2023, the idea of a natural language AI dispatcher emerged, with the idea of handling non-emergency patient transports through a special line.
“Because I originally came from emergency medical services, I also know how hospitals work: what problems clinics face, what a nurse does all day, how much pressure they’re under,” Dax said. “All of this knowledge flowed into the voice bot.
“For example, when a patient presses the emergency button, the nurse has to run there immediately—but sometimes she’s on the phone with us ordering a patient transport. She then puts us on hold, which slows down everything. These delays cost us a lot of time. So, the voice bot is designed to handle exactly those cases and free up capacity.”
The AI operator will move into broader, real-life testing later this year in the emergency department at LMU Klinikum, Munich’s biggest hospital. Data privacy is governed by European law, and the fire department has been in contact with the data protection authorities throughout the project, Dax said. The fire department will begin working with real patient data at LMU Klinikum in February and soon be able to work with real patient data, he said. Until now it has been using only made-up patient data.
‘Medicine at the forefront of technology’
There, as at the fire department, time is a precious commodity. The clinicians at LMU Klinikum have been collaborating on the development of the AI operator.
The call to arrange the transport “might not take that long, but the waiting line often takes a lot of time,” said Dr. Matthias Klein, who leads the hospital’s emergency ward and is also a professor. While nurses are waiting for the dispatcher to take the call, they can’t do much else, he said, because they must be ready when someone eventually picks up. If there is an urgent situation on the emergency ward, the nurse will have to hang up and try again later.
“It’s really a time-consuming process at the moment,” he said. “And it’s really an important moment right now with technology and medicine. Medicine is often at the forefront of technology, but AI is really coming in a lot of ways right now.”
He said the AI operator at the Munich fire department has the potential to help open hospital beds more quickly.
“We have the patient ready [to leave the hospital], we did everything that’s necessary medically, but then we have this obstacle—the transport,” he said. “That starts with ordering the transport, which already takes some time, and then we often have to wait for the pickup. And that can take several hours. And if it stretches into the evening, we might have to cancel because we don’t want the patient to arrive at the final destination at 1 a.m.”
Klein said that from the hospital’s perspective, it will save nurses’ time that can better be devoted to patient care. He hopes it will eventually help improve the efficiency of the transport system, to open hospital beds more quickly by cutting wait times.
Florian Dax said that it’s hard to understand the emotional stress that the dispatchers face each day.
One minute they might be trying to explain to a panicked parent how to perform CPR on a critically ill child. The next dealing with someone’s heart attack or seizure in real time. Meanwhile the lights are flashing on their screens, and another call is coming in.
For Dax and the Munich Fire Department, the most important thing is using any tool to improve its life-saving role.
“It’s important for us to be part of shaping AI in emergency services. We’re a niche—we don’t have thousands of branches—but in our world, AI can make emergency response significantly safer. Not for life‑threatening calls; those must always be handled by humans. But for all the support tasks and low‑priority calls, AI can really help.”
Florian Dax is one of the chief architects of the AI operator that will take non-emergency transport calls for the Munich Fire Department. Photo by Anastasia Pivovarova for Microsoft.