The AI-Driven Healthcare Value Framework—Rethinking Traditional Care Models in the Age of Automation

Abstract

Highlights What are the main findings? This study introduces a comprehensive value framework tailored to artificial intelligence in healthcare, extending evaluation beyond narrow cost–outcome ratios. The framework highlights patient, provider, organizational, and system-level impacts as essential dimensions of healthcare value. It shows that conventional healthcare value models often fail to capture equity, trust, explainability, and environmental sustainability when AI and other digital tools are used at scale. The proposed model organizes these considerations into a structured framework that can be applied to AI-enabled healthcare settings. What are the implications of the main findings? The framework can help policymakers and health system leaders judge whether AI investments truly improve population health and reduce inequities. It provides researchers with clear dimensions for developing indicators and evaluation tools for responsible AI use in health services. It supports more people-centered, ethically grounded, and sustainable decision-making in healthcare innovation. It may also guide future implementation and assessment of AI systems in ways that balance effectiveness, fairness, and long-term system value. Abstract Background: The rapid adoption of artificial intelligence (AI) in healthcare and management information systems has posed both opportunities and challenges in assessing value across clinical, operational, governance and societal dimensions. Existing healthcare value models are inadequate for the dynamic, data-rich, and ethically complex nature of AI-enabled care and thus necessitate a broader evaluative framework. Objective: This paper proposes an AI-Augmented Healthcare Value Framework (AI-HVF) designed as a multidimensional evaluative lens for assessing value in AI-enabled healthcare across structural, process, outcome, cost, and governance domains. Methods: A narrative, theory-driven literature review was undertaken of healthcare quality frameworks, value-based healthcare, digital health, AI in medicine and public health to identify limitations of existing models and to derive the required dimensions for an updated framework. The dimensions were synthesized into an integrated conceptual model linking structures, processes, outcomes, costs and governance in AI-enabled healthcare. Results: AI-HVF transforms traditional healthcare value models in a data-rich and automated care era. Structural dimensions include digital infrastructure, workforce readiness, learning health systems, and operational efficiency; process dimensions include AI-supported clinical excellence, patient experience, prevention, and explainability; and outcome dimensions go beyond traditional clinical metrics to include equity, safety, continuity, provider well-being, sustainability, and societal value. It also includes real cost accounting and has governance, ethics and trust as an overarching layer that supports accountability, transparency and fairness across all domains. Conclusions: AI-HVF provides a multi-dimensional framework to assess, plan and govern AI in health care at the patient, organization, and system levels. It is intended to enable retrospective evaluation and prospective implementation. The framework offers a foundation for developing future indicators, pilot testing and for comparative evaluation of ethical, equitable and sustainable AI integration in healthcare.