用智能生态打破医疗成本、质量与可及性的僵局
Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality)
- 构建数据驱动的智能医疗生态，融合生成式AI与联邦学习
- 通过数字孪生等技术降低浪费，提升诊疗个性化水平
- 适合政策制定者与医疗科技从业者参考
美国医疗支出占GDP近17%，却仍面临服务可及性不均和结果差异。本文提出智能医疗生态系统（iHE），通过生成式AI、大语言模型、联邦学习、互操作标准（FHIR、TEFCA）及数字孪生，系统性优化成本、质量与可及性之间的“铁三角”关系。综述历史支出趋势、浪费情况及国际对比，提出联合优化三要素的价值方程，并整合最新技术证据与运营模式。研究表明，iHE可减少资源浪费、实现精准医疗支持价值支付，同时应对隐私、偏见与采纳挑战。协同推进的iHE有望扭转甚至突破铁三角困局，推动医疗向更可及、更经济、更优质的方向发展。
原文摘要 · Abstract (English)
The United States spends nearly 17% of GDP on healthcare yet continues to face uneven access and outcomes. This well-known trade-off among cost, quality, and access - the "iron triangle" - motivates a system-level redesign. This paper proposes an Intelligent Healthcare Ecosystem (iHE): an integrated, data-driven framework that uses generative AI and large language models, federated learning, interoperability standards (FHIR, TEFCA), and digital twins to improve access and quality while lowering cost. We review historical spending trends, waste, and international comparisons; introduce a value equation that jointly optimizes access, quality, and cost; and synthesize evidence on the enabling technologies and operating model for iHE. Methods follow a narrative review of recent literature and policy reports. Results outline core components (AI decision support, interoperability, telehealth, automation) and show how iHE can reduce waste, personalize care, and support value-based payment while addressing privacy, bias, and adoption challenges. We argue that a coordinated iHE can bend - if not break - the iron triangle, moving the system toward care that is more accessible, affordable, and high quality.
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