Industry 4.0 principles for AI-ready information systems in health contexts: A framework for digital strategy

Digital transformation projects in public and clinical health expose persistent data governance and information architecture gaps that limit AI adoption and measurable impact. This paper examines how Industry 4.0 principles can be applied to design artificial intelligence (AI)-ready information systems in health contexts, using (i) upstream food systems and (ii) downstream clinical workflows as exemplars. Although Industry 4.0 concepts such as interoperability, real-time analytics, decentralized decision-making are well established in manufacturing for AI adoption, their systematic application to public health and healthcare information architecture remains underexplored. To address this gap, we develop a conceptual and methodological framework linking Industry 4.0 design choices to AI readiness and health outcomes. We study literature across health informatics, digital food supply chains, and systems engineering, and operationalize the framework through Theory of Change and systems thinking, presenting two simplified causal loop diagrams to visualize complex interdependencies and identify intervention points. The analysis highlights the role of systems integration, information sharing, and cross-sector collaboration in leveraging (i) digital platforms such as Internet of Things-enabled supply chains and (ii) AI-augmented health information systems. This transdisciplinary conceptual paper supports the co-design of digital interventions and articulates testable propositions to guide prospective evaluations and digital strategy. We conclude with a call to action for policymakers, providers, and vendors to align open standards, data governance, and investment roadmaps with the proposed framework to enable credible AI-driven interventions in both public health and care delivery.