Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
From the evolution of public data ecosystems to the evolving horizons of the forward-looking intelligent public data ecosystem empowered by emerging technologies
Public data ecosystems (PDEs) represent complex socio-technical systems crucial for optimizing data use in the public sector and outside it. Recognizing their multifaceted nature, previous research pro-posed a six-genera…
Cloud ComputingSystematic Literature ReviewReal-Time Fish Detection in Indonesian Marine Ecosystems Using Lightweight YOLOv10-nano Architecture
Indonesia's marine ecosystems, part of the globally recognized Coral Triangle, are among the richest in biodiversity, requiring efficient monitoring tools to support conservation. Traditional fish detection methods are t…
Object DetectionRobot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control
The Robot Context Protocol (RCP) is a lightweight, middleware-agnostic communication protocol designed to simplify the complexity of robotic systems and enable seamless interaction between robots, users, and autonomous a…
"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quality, relevance, and utility of transmitte…
Reinforcement LearningAutonomous VehiclesRE-TRIANGLE: Does TRIANGLE Enable Multimodal Alignment Beyond Cosine Similarity in Retrieval?
Multimodal alignment is critical for bridging the semantic gap in information retrieval. However, traditional pairwise strategies introduce a geometric blind spot: while they align anchor modalities (e.g., text) with oth…
Information RetrievalVideo Retrieval