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.

翻译：美国在医疗保健上的支出接近国内生产总值的17%，却仍面临医疗可及性与结果的不均衡。这一在成本、质量与可及性之间众所周知的权衡——即“铁三角”——推动着系统层面的重新设计。本文提出智能医疗生态系统（iHE）：一个集成化、数据驱动的框架，利用生成式人工智能与大语言模型、联邦学习、互操作性标准（FHIR、TEFCA）以及数字孪生技术，旨在提升医疗可及性与质量的同时降低成本。我们回顾了历史支出趋势、资源浪费与国际比较；引入了一个同步优化可及性、质量与成本的价值方程；并综合分析了支撑iHE的关键技术与运营模式的证据。研究方法基于对近期文献与政策报告的叙述性综述。研究结果概述了核心组成部分（人工智能决策支持、互操作性、远程医疗、自动化），并展示了iHE如何能够减少浪费、实现个性化医疗、支持基于价值的支付，同时应对隐私、偏见与采纳挑战。我们认为，协调一致的iHE能够弯曲——即便不能打破——医疗的铁三角，推动整个系统朝着更可及、更可负担、更高质量的医疗服务迈进。