Lavoie-Gagne Ophelie, Woo Joshua J, Williams Riley J, Nwachukwu Benedict U, Kunze Kyle N, Ramkumar Prem N
Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Warren Alpert Brown School of Medicine, Providence, Rhode Island.
Arthroscopy. 2025 Aug;41(8):3270-3275. doi: 10.1016/j.arthro.2025.02.038. Epub 2025 Mar 20.
Despite spending $4.3 trillion annually on health care with $3.4 trillion ($10,193 per capita) attributed to care delivery, the United States still experiences the worst health outcomes among high-income countries. Administrative costs are the second largest contributor, with $353 billion ($1,055 per capita) spent annually. Addressing clinical and administrative fragmentation can reduce annual costs by up to $265 million and increase health care productivity, both of which contribute to care delivery that is necessary, effective, equitable, and fiscally responsible. In the advent of electronic health records, big data, and artificial intelligence (AI), there is an unprecedented opportunity to leverage these tools to drive meaningful improvements in high-value care delivery and reduce both clinical and nonclinical administrative costs. Physician engagement to develop comprehensive musculoskeletal data management systems is a critical precursor to the subsequent application of AI analytics. The incorporation of AI tools developed from these data systems both within organizations and seismically across the health care system can (1) promote transparency via payer/provider data-sharing platforms; (2) automate routine, evidence-based care to reduce ineffective, inefficient, and inconsistent medical decisions; (3) align incentives of key stakeholders by incorporating epidemiologic informatic insights and individual patient-centered value quantification to inform physician-patient decision making; (4) mitigate care delays from prior authorization and claims processing via centralized digital claims clearinghouses; (5) guide payment model evolution to accurately and transparently reflect costs of care for patients with different risk profiles; (6) harmonize quality control reporting for comparability; (7) simplify and standardize prior authorization processes to reduce administrative complexity; and (8) automate nonclinical repetitive work (credentialing, quality assurance, and so on). Adoption of these tools can eliminate $168 billion in annual administrative costs. Although no single solution will perfectly transform health care, the strategic and responsible use of AI technologies could lead to transcendent improvements in delivery of health care that is patient centered, equitable, efficient, and fiscally responsible.
尽管美国每年在医疗保健上花费4.3万亿美元,其中3.4万亿美元(人均10,193美元)用于医疗服务提供,但在高收入国家中,美国的健康状况仍然最差。行政成本是第二大支出项目,每年花费3530亿美元(人均1055美元)。解决临床和行政碎片化问题可使年度成本降低多达2.65亿美元,并提高医疗保健效率,这两者都有助于实现必要、有效、公平且符合财政责任的医疗服务提供。在电子健康记录、大数据和人工智能(AI)出现的背景下,利用这些工具推动高价值医疗服务提供取得有意义的改善并降低临床和非临床行政成本,迎来了前所未有的机遇。医生参与开发全面的肌肉骨骼数据管理系统是后续应用AI分析的关键前提。将从这些数据系统开发的AI工具纳入组织内部并在整个医疗保健系统中进行重大推广,可(1)通过支付方/提供方数据共享平台提高透明度;(2)使常规的循证医疗自动化,以减少无效、低效和不一致的医疗决策;(3)通过纳入流行病学信息学见解和以个体患者为中心的价值量化来调整关键利益相关者的激励措施,为医患决策提供信息;(4)通过集中式数字理赔结算所减少预先授权和理赔处理导致的护理延误;(5)引导支付模式演变,以准确、透明地反映不同风险状况患者的护理成本;(6)协调质量控制报告以实现可比性;(7)简化并规范预先授权流程,以降低行政复杂性;(8)使非临床重复性工作(资格认证、质量保证等)自动化。采用这些工具可消除每年1680亿美元的行政成本。虽然没有单一解决方案能完美变革医疗保健,但战略性且负责任地使用AI技术可带来以患者为中心、公平、高效且符合财政责任的医疗服务提供的卓越改善。