内容来源：https://www.technologyreview.com/2026/06/02/1137827/rehumanizing-global-health-care-with-agentic-ai/

内容总结：

全球医疗系统引入AI智能体以缓解人力短缺危机

面对日益严峻的医护人员短缺问题，全球医疗机构正将希望寄托于“智能体AI”技术。据毕马威调查，超过三分之二（68%）的医疗机构已将AI智能体纳入工作流程。

世界卫生组织警告，到2030年全球医护人员缺口将达1100万。在此背景下，AI智能体正被用于自动化处理复杂行政事务、协助医疗团队决策甚至进行患者分诊，旨在减轻临床医生负担，提升医疗质量。

与以往数字化手段不同，新型AI智能体具备自主决策能力，可从权威临床资料中检索信息并持续迭代。纽约特种外科医院首席数字与技术官巴拉德博士指出：“AI智能体正在重塑工作流程，使其更高效。”该院已应用AI处理保险索赔，月均完成1100例，申诉处理时间从45分钟缩短至5分钟，成功率从65%提升至100%。

在患者服务领域，该医院与AI开发商Ema合作推出智能分诊系统，提供24小时在线服务。系统通过对话式AI收集患者症状信息，综合地理位置、保险和医生排班等因素，自动匹配最合适的专科医生。针对高风险病例，系统内置人工干预机制，确保所有决策可追溯、可审查。

专家指出，真正的变革在于将AI智能体作为通用技术进行全系统整合。这需要医疗机构建立统一数据战略，打破各科室信息孤岛。以手术室效率指标“手术开始时间”为例，不同医院定义各异，数据碎片化严重制约AI效能发挥。

巴拉德博士预测，未来90%的非临床医疗任务将由AI智能体完成，使医生专注于最复杂、最特殊的病例。“现在医护人员花费大量时间在键盘和电脑前，AI将让医疗回归人性化。”他强调，医疗机构有责任在系统中嵌入必要的安全护栏，确保技术应用不偏离“患者优先”原则。

中文翻译：

赞助内容
用智能体AI重塑全球医疗人性化
面对迫在眉睫的医护人员短缺，AI智能体正自动化处理复杂的行政任务甚至临床决策，让人类能更专注于患者照护。
与Ema联合呈现

全球医疗行业正承受日益沉重的压力。
数十年来长期投资不足与招聘限制，恰逢人口老龄化对服务需求的激增。医疗供给缺口已造成严重影响：就医渠道碎片化、医护人员压力与倦怠率居高不下。而情况还在恶化。世界卫生组织警告，到2030年当前的人员短缺将扩大至1100万。

在急切寻求解决方案的过程中，许多医疗机构正将希望寄托于智能体AI。据毕马威调查，超过三分之二（68%）的机构已将AI智能体纳入其员工队伍。
这项技术正被用于自动化复杂的后台流程、与医疗团队协作，甚至进行患者分诊——所有努力都旨在减轻临床医生的认知负荷，并在人类医护人员供给减少时提升患者照护质量。

一种不同的数字化
迄今为止，医疗领域数字化的益处十分有限。
许多员工指责缓慢或过时的技术反而加重了行政负担，而非减轻。例如，美国患者数据在21世纪初迁移至电子健康记录系统，但这些数据至今仍碎片化且依赖人工输入。

纽约市专注肌骨健康的学术医疗中心——特殊外科医院的数字与科技首席官Ashis Barad医学博士指出，新的远程医疗服务和数字护理工具（如远程监测设备）同样存在短板。他表示，这两项技术虽通过消除地理障碍改善了就医可及性，但未能复制面对面护理的质量，也未能赢得患者信任。

他坚持认为，智能体AI与现有技术截然不同。
AI智能体不依赖人工输入，也无需在遇到框架外的病例时默认转交人类处理，它能应对微妙复杂的场景。它能自主决策、从专业临床资料中检索信息并持续迭代，从而解放临床医生，使其专注于更高层级的患者照护。正如Barad博士所言：“智能体AI能压缩、增强、赋能你的工作流，使其更高效。”

在特殊外科医院，AI智能体已部署于多个领域。它们处理复杂的后台流程，例如此前需数周完成、需医院员工与第三方承包商共同处理的保险理赔。Barad博士说，如今AI智能体每月完成1100件理赔。实施九个月以来，申诉环节从45分钟缩短至5分钟，申诉成功率从65%提升至100%。特殊外科医院现已将所有理赔业务内部处理。

基于这一成功，特殊外科医院正通过与AI智能体企业开发者Ema Unlimited的合作，将AI智能体部署于非临床的直面患者场景，推出AI日程安排与分诊服务。该服务通过网页、短信或电话24小时可用。它使用对话式AI向患者询问病情细节，然后综合考虑地点、保险覆盖和医生排班，安排最合适的临床医生接诊。“它完成了整个闭环，”Barad博士说。他补充道，AI智能体基于“我们所有的语境、规则和知识库”训练，让患者能便捷获取世界顶级外科医生高度专业的知识。

鉴于AI智能体被赋予高风险的决策权，分诊服务设有内置安全机制——敏感、复杂或不确定的案例会升级至人类专家处理。AI智能体的每项决策均可审计，人类员工可随时介入。患者数据保持安全，系统基于特殊外科医院所有规程、政策和护理路径训练。Ema表示，通过保持人机协作，其技术在高效自动化、以患者为先的安全性和人类知情决策之间取得了平衡。

Barad博士说，随着这项技术日益普及，医疗机构有责任确保系统中嵌入此类防护机制。在特殊外科医院，所有与技术相关的决策均需通过由Barad博士与一位高级护理高管共同主持的AI小组委员会审核。他解释称，可能涉及患者照护的AI智能体将受到比后台流程严格得多的审查。

AI智能体引发系统级变革
例如，Barad博士计划在纽约市特殊外科医院主院区创建专用AI实验室——此举旨在推动该技术在全机构的普及应用。他解释说，实验室将向所有希望了解或构建AI智能体的员工开放，提供信息课程和一对一培训。“我们要让人人都能用上智能体AI。”这与德勤的研究相呼应，该研究发现医疗领域智能体AI的领先采用者更倾向于选择多智能体解决方案，重新设计端到端工作流程，而非固守狭窄方案或孤立用例。

关键在于，要将AI智能体整合至整个企业，将其视为通用技术。正如Barad博士所说：“把智能体AI局限于用例是错的……它是一种通用技术，就像电一样。”
实践中，这意味着医疗机构需为通过智能体AI创造价值奠定正确基础，包括创建统一的数据战略，整合机构内碎片化的数据源，形成单一、全面的真实数据源。在医疗领域，数据常分散于多个部门和供应商，各自拥有遗留IT系统。

在依赖碎片化数据源的系统中，指标也常缺乏标准化定义。例如，Barad博士说，他工作过的每家医院对“手术开始时间”这一衡量手术室效率的常用指标定义都略有不同。这种碎片化程度阻碍了AI智能体从不同来源或应用程序检索信息，并吸收使其区别于其他技术的隐性知识。

通过提升特殊外科医院数据的互操作性，面向患者的AI智能体能调取患者的临床病史和医生的现有建议，将这些信息与当前症状结合，判断是否需要升级处理，然后通知正确的专科医生并告知患者。

构建更好的结果
对于Barad博士而言，AI智能体彻底改革医疗、缓解当前资源、就医和患者照护压力的潜力巨大。
他设想了一个未来：90%的非临床医疗任务可由AI智能体执行，从而解放临床医生，专注于他所说的“白手套工作”，即最复杂、最专业和最敏感的病例。

大多数医疗机构同样乐观。据毕马威研究，84%的机构已放心将特定流程的决策权交给AI智能体。
“我们现在花了太多时间在键盘和电脑上，反而没做该做的事，”Barad博士说，“这将让医疗重新人性化。”

本内容由MIT科技评论定制内容部门Insights制作，非其编辑团队撰写。内容由人类作者、编辑、分析师和插画师调研、设计并撰写，包括问卷设计与数据收集。可能使用的AI工具仅限于经过严格人工审核的辅助生产流程。

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英文来源：

Sponsored
Rehumanizing global health care with agentic AI
As health-care providers face looming staff shortages, AI agents are automating complex administrative tasks and even clinical decisions so humans can focus more on patient care.
In partnership withEma
The global health care sector is under increasing strain.
Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse. The World Health Organization has warned that current shortfalls will increase to 11 million workers by 2030.
In their urgent hunt for a solution, many health-care providers are now pinning their hopes on agentic AI, with more than two-thirds (68%) having already adopted AI agents into their workforce, according to KPMG.
The technology is being deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients, all in a bid to reduce the cognitive load on clinicians and improve quality of care for patients as the supply of human health-care workers dwindles.
A different type of digitalization
Until now, the benefits of digitalization within health care have been limited.
Many staff have blamed slow or outdated technology for adding to the administrative burden rather than alleviating it. For example, U.S. patient data was migrated to electronic health records (EHRs) in the early 2000s, but this data remains fragmented and reliant on manual inputs.
New telehealth services and digital care tools, like remote monitors, have had similar shortcomings, says Ashis Barad, MD, chief digital and technology officer at Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health. Both technologies have helped improve access to health care by removing geographical barriers, he says, but they’ve failed to replicate the quality of in-person care or win trust from patients.
Agentic AI is different from these existing technologies, he insists.
Rather than relying on manual inputs or defaulting to human workers for any case that sits slightly outside a rigid framework, AI agents can handle nuanced, complex scenarios. They can make autonomous decisions, retrieve information from expert clinical sources, and iterate over time, freeing clinicians to focus on higher-level patient care. As Dr. Barad puts it: “Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.”
At HSS, AI agents have already been deployed in multiple areas. They handle complex backend processes, such as insurance claims that previously took several weeks to complete and involved both HSS staff and a third-party contractor to handle the volume. Now, says Dr. Barad, AI agents complete 1,100 claims per month. They’ve reduced the appeals stage from 45 minutes to five and improved the success rate of those appeals from 65% to 100% in the nine months since implementation. HSS now handles all claims in-house.
Building on that success, HSS is now deploying AI agents in non-clinical patient-facing settings with an AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then books appointments with the most appropriate clinician, factoring in location, insurance coverage, and physician availability. “It completes the whole loop,” says Dr. Barad. The AI agent is trained on “all of our context, all of our rules, and all of our knowledge base,” he adds, providing patients with streamlined access to highly specialist knowledge from world-leading surgeons.
Given the high-stakes decisions delegated to AI agents, the triage service has built-in safeguards—sensitive, complex, or uncertain scenarios are escalated to human specialists. Every decision made by the AI agent is auditable and human staff can step in at any point. Patient data is kept secure and the system is trained on all HSS protocols, policies, and care pathways. By keeping humans in the loop, Ema says its technology strikes the balance between efficient automation, patient-first safety, and human-informed decision making.
As the technology becomes more prolific, it will be incumbent on providers to ensure they have these sorts of guardrails embedded into systems, says Dr. Barad. At HSS all decisions around the technology are filtered through an AI subcommittee that Dr. Barad co-chairs alongside a senior nursing executive. AI agents that may touch on patient care will be scrutinized with far more rigor than, say, backend processes, he explains.
AI agents prompt systems-level change
For example, Dr. Barad has plans to create a dedicated AI lab at the HSS main campus in New York City—a move that aims to democratize access to the technology across the organization. It will be open to all staff looking to understand or build AI agents, he explains, with informative classes and one-on-one training. “We’re getting agentic AI into everybody's hands,” he says. This echoes research by Deloitte, which found that leading agentic AI adopters in health care were far more likely to have opted for multiagent solutions, redesigning end-to-end workflows rather than sticking to narrow solutions or individual use cases.
The key, it appears, is to integrate AI agents across the entire enterprise, treating them as a general-purpose technology. As Dr. Barad puts it: “It’s wrong to think of agentic AI in use cases… It’s a general-purpose technology, analogous to electricity.”
In practice, this means health-care providers need to set the right foundation to achieve value with agentic AI. This includes creating a unified data strategy, one that integrates fragmented data sources across an organization to create a single, comprehensive source of truth. In health care, data is often split across multiple departments and providers, each with their own legacy IT system.
In systems that rely on fragmented data sources, metrics often lack standardized definitions too. For example, Dr. Barad says that each hospital he’s worked in has had a slightly different definition for “time to start surgery,” a metric commonly used to gauge operating room efficiency. This level of fragmentation impedes AI agents from retrieving information from different sources or applications and assimilating the tacit knowledge that differentiates them from other technologies.
By creating greater interoperability of data at HSS, patient-facing AI agents can draw from a patient’s clinical care history and existing recommendations from their clinician, combine this information with current symptoms, and decide whether a situation requires escalation before notifying the correct specialist and informing the patient.
Building better outcomes
For Dr. Barad, the potential for AI agents to overhaul health care and alleviate the current pressures on resources, access, and patient care is huge.
He envisions a future in which 90% of non-clinical health-care tasks could be administered by AI agents, freeing clinicians up for what he calls white-glove work, meaning the most complex, specialized, and sensitive cases.
Most health-care providers seem equally optimistic. According to research by KPMG, 84% of providers are already comfortable handing decision making about specific processes over to AI agents.
“We’re spending so much time on keyboards and computers right now that we're actually not doing what we should be doing,” says Dr. Barad. “This is going to rehumanize health care.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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