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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2510.03331 |
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| _version_ | 1866908575038701568 |
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| author | Acharya, Vivek |
| author_facet | Acharya, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality) Acharya, Vivek Computers and Society Artificial Intelligence 68T07, 92C55, 92C60 I.2.1; J.3; H.3.5 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. |
| title | Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality) |
| topic | Computers and Society Artificial Intelligence 68T07, 92C55, 92C60 I.2.1; J.3; H.3.5 |
| url | https://arxiv.org/abs/2510.03331 |