Rehumanizing global health care with agentic AI

用智能体 AI 重塑全球医疗服务的人文关怀

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.

全球医疗行业正承受着日益沉重的压力。数十年来长期的投入不足和招聘限制，恰逢老龄化人口对医疗服务需求的激增。医疗供给的缺口已经造成了严重后果，表现为医疗资源获取碎片化，以及医护人员极高的压力和职业倦怠率。情况还在恶化：世界卫生组织警告称，到 2030 年，全球医疗人员缺口将扩大至 1100 万人。

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.

在迫切寻求解决方案的过程中，许多医疗服务提供商将希望寄托于智能体 AI（Agentic AI）。根据毕马威（KPMG）的数据，超过三分之二（68%）的机构已将 AI 智能体引入工作流程。这项技术正被用于自动化处理复杂的后台流程、协助医疗团队工作，甚至进行患者分诊。所有这些努力旨在减轻临床医生的认知负担，并在人类医护人员供给减少的情况下，提升患者的护理质量。

A different type of digitalization

一种不同以往的数字化

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 Many staff have blamed slow or outdated technology for adding to the administrative burden rather than alleviating it.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.

迄今为止，医疗领域的数字化红利依然有限。许多医护人员抱怨缓慢或过时的技术不仅没有减轻行政负担，反而增加了负担。例如，美国在 21 世纪初将患者数据迁移至电子健康记录（EHR）系统，但这些数据至今仍处于碎片化状态，且高度依赖人工录入。纽约特种外科医院（HSS，一家专注于肌肉骨骼健康的学术医疗中心）的首席数字与技术官 Ashis Barad 医学博士表示，远程医疗服务和远程监控等数字护理工具也存在类似的缺陷。

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.

他指出，虽然这两种技术通过消除地理障碍改善了医疗服务的可及性，但它们未能复制面对面诊疗的质量，也未能赢得患者的充分信任。他强调，智能体 AI 与这些现有技术截然不同。AI 智能体不再依赖人工录入，也不再对超出僵化框架的案例束手无策，而是能够处理细微且复杂的场景。它们可以自主决策、从专业临床资源中检索信息并不断迭代，从而让临床医生能够专注于更高层次的患者护理。

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.

正如 Barad 博士所言：“智能体 AI 能够重构、增强并强化你的工作流程，使其表现更出色。”在 HSS，AI 智能体已在多个领域投入使用。它们处理复杂的后台流程，例如保险理赔——过去这需要数周时间，且涉及 HSS 员工和第三方承包商共同处理。Barad 博士表示，现在 AI 智能体每月可完成 1100 份理赔。自实施以来的九个月内，它们将申诉环节的处理时间从 45 分钟缩短至 5 分钟，并将申诉成功率从 65% 提升至 100%。目前，HSS 已实现所有理赔的内部自主处理。

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.

基于这一成功，HSS 正与企业级智能体 AI 开发商 Ema Unlimited 合作，将 AI 智能体部署在非临床的患者交互场景中，提供 AI 预约和分诊服务。该服务通过网页、短信或电话全天候（24/7）可用。它利用对话式 AI 向患者询问病情细节，并综合考虑地理位置、保险覆盖范围和医生排班情况，为患者预约最合适的临床医生。“它形成了一个完整的闭环，”Barad 博士说道。

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.

他补充说，该 AI 智能体接受了“我们所有背景信息、规则和知识库”的训练，为患者提供了获取世界顶尖外科医生专业知识的便捷渠道。鉴于 AI 智能体承担着高风险决策，该分诊服务内置了安全保障机制——对于敏感、复杂或不确定的场景，系统会自动升级至人工专家处理。AI 智能体做出的每一项决策都是可审计的，且医护人员随时可以介入。

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.

患者数据得到妥善保护，系统基于 HSS 所有的协议、政策和护理路径进行训练。Ema 表示，通过保持“人在回路”（human-in-the-loop），其技术在高效自动化、患者至上的安全性和基于人类经验的决策之间取得了平衡。Barad 博士认为，随着该技术的普及，医疗服务提供商有责任确保系统中嵌入此类护栏。在 HSS，所有关于该技术的决策都必须经过一个 AI 小组委员会的审核，该委员会由 Barad 博士与一位资深护理高管共同主持。他解释说，涉及患者护理的 AI 智能体将比后台流程受到更严格的审查。

AI agents prompt systems-level change

AI 智能体推动系统级变革

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.

例如，Barad 博士计划在 HSS 位于纽约市的主院区建立一个专门的 AI 实验室，旨在让组织内的每个人都能平等地接触到这项技术。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，并提供信息丰富的课程和一对一培训。“我们正在让智能体 AI 触手可及，”他说。

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.”

这与德勤（Deloitte）的研究结果不谋而合：医疗行业中领先的智能体 AI 采用者更倾向于选择多智能体解决方案，通过重新设计端到端的工作流程，而非局限于狭窄的解决方案或单一用例。关键似乎在于将 AI 智能体整合到整个企业中，将其视为一种通用技术。正如 Barad 博士所言：“将智能体 AI 仅仅视为一个个用例是错误的……它是一种通用技术，类似于电力。”

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…

在实践中，这意味着医疗服务提供商需要打好基础，才能实现智能体 AI 的价值。这包括制定统一的数据战略，整合组织内碎片化的数据源，从而建立单一、全面的“事实来源”。在医疗领域，数据往往分散在多个部门和提供商之间，每个部门都有各自的遗留 IT 系统。在依赖碎片化数据的系统中……