Tailoring AI solutions for health care needs

为医疗保健需求量身定制人工智能解决方案

The AI market is full of big promises of grand transformation. Health care is a prime target for those promises, beset as it is by financial pressures, labor shortages, and the growing burden of caring for an aging population. AI developers are targeting functions that vary widely, from curing cancer and performing surgery to streamlining routine administrative tasks. The opportunity is genuine, but execution can be difficult. 人工智能市场充斥着关于宏大变革的承诺。医疗保健行业正是这些承诺的主要目标，因为它正受到财务压力、劳动力短缺以及照顾老龄化人口日益沉重的负担的困扰。人工智能开发人员的目标功能非常广泛，从治愈癌症和进行手术，到简化日常行政任务，应有尽有。机遇是真实的，但执行起来却可能困难重重。

Numerous software vendors have tried to “fix” health care challenges but failed because they misunderstood the environment. “Health care is very complex,” says Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, which supports the buildout and deployment of digital solutions for health care companies through data-based insights and expert validation. “Solution developers must have a deep focus on clinical and technical capabilities, and then align their solutions to the relevant business impacts. If they miss any dimension, the solution will not be adopted or drive value.” 许多软件供应商试图“修复”医疗保健领域的挑战，但由于误解了该环境而失败了。梅奥诊所平台（Mayo Clinic Platform）解决方案开发商市场副总裁史蒂夫·贝斯克（Steve Bethke）表示：“医疗保健非常复杂。”该平台通过基于数据的洞察和专家验证，支持医疗保健公司构建和部署数字解决方案。“解决方案开发人员必须深入关注临床和技术能力，然后将他们的解决方案与相关的业务影响相一致。如果他们忽略了任何一个维度，该解决方案就不会被采用，也无法产生价值。”

AI applications for health care are proliferating rapidly. The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. More than half of these were approved in the past three years, with the earliest dating as far back as 1995. Non-radiological applications carry out tasks as diverse as tracking sleep apnea, analyzing heart rhythms, and planning orthopedic surgeries. 医疗保健领域的人工智能应用正在迅速激增。美国食品药品监督管理局（FDA）已批准了超过 1,300 种人工智能医疗设备，其中大部分用于解读诊断影像。这些设备中超过一半是在过去三年内获批的，最早的可以追溯到 1995 年。非放射学应用执行的任务多种多样，包括追踪睡眠呼吸暂停、分析心律以及规划骨科手术等。

AI applications that do not count as medical devices—for example, those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows that are often conventionally managed by whiteboards and sticky notes. Such functions may well outstrip clinical uses in their impact on health systems. A recent survey of technology leaders found that 72% said their top priority for AI was reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity. 不属于医疗设备的人工智能应用（例如处理日程安排和行政任务的应用）更难追踪，但也在迅速增加。人工智能可以帮助协调那些通常依靠白板和便利贴管理的复杂任务和工作流程。这些功能对医疗系统的影响很可能超过临床用途。最近一项针对技术领导者的调查发现，72% 的受访者表示，他们应用人工智能的首要任务是减轻护理人员的负担并提高其满意度，而超过一半（53%）的受访者提到了工作流程效率和生产力。

Any health care-related application can potentially impact patient care, whether directly or indirectly, and AI apps that are poorly designed or inadequately trained and validated can put patients at risk. Providers recognize that risk: In the same survey, 77% said immature AI tools are a significant barrier to adoption. Regulators and lawmakers are also keeping an eye on the risks as development and adoption burgeon, though the U.S. regulatory picture is still in flux, as a 2024 report to Congress on AI in health care observes. 任何与医疗保健相关的应用都可能直接或间接地影响患者护理，而设计不当或训练和验证不足的人工智能应用可能会使患者面临风险。医疗服务提供者意识到了这种风险：在同一项调查中，77% 的人表示不成熟的人工智能工具是采用该技术的主要障碍。随着开发和应用的激增，监管机构和立法者也在密切关注这些风险，尽管正如 2024 年一份提交给国会的关于医疗保健人工智能的报告所指出的那样，美国的监管格局仍处于变动之中。

To tackle some of the technical challenges, many health care providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of health care organizations intend to pursue partnerships with third-party vendors to develop customized generative AI solutions as a primary strategy as opposed to building them in-house or buying off-the-shelf products. But health care-specific AI applications must also be tailored to the nuanced clinical needs of medical providers as well as the complex business and regulatory considerations of the wider sector. 为了应对一些技术挑战，许多医疗服务提供者正在与应用程序开发人员合作构建人工智能解决方案。麦肯锡在最近的一项研究中发现，61% 的医疗保健组织打算将与第三方供应商合作开发定制化生成式人工智能解决方案作为主要战略，而不是内部构建或购买现成产品。但是，针对医疗保健的人工智能应用还必须根据医疗服务提供者的细微临床需求，以及更广泛行业的复杂业务和监管考量进行量身定制。

This is where developers can benefit from working with a partner with a deep understanding of the health care environment to tailor applications to what providers want and need most. Doing so helps to position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment. 这就是开发人员可以从与深入了解医疗保健环境的合作伙伴合作中受益的地方，从而根据医疗服务提供者最想要和最需要的内容来定制应用程序。这样做有助于使人工智能产品实现最大的影响力和价值，并避免医疗保健环境中特有的陷阱。