Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality)
This addresses the problem of inefficiency and inequity in healthcare systems, particularly in the U.S., but is incremental as it synthesizes existing technologies into a new framework.
The paper tackles the trade-off among cost, quality, and access in U.S. healthcare by proposing an Intelligent Healthcare Ecosystem (iHE), an integrated framework using generative AI, federated learning, and other technologies to reduce waste and improve care, aiming to make healthcare more accessible, affordable, and high-quality.
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.