Building AI-Ready Infrastructure for U.S. Healthcare: A Product Management Perspective

Abstract

U.S. public and safety-net hospitals widely view AI as a path to better outcomes, lighter clinician workload, and lower costs, but most are not yet “AI-ready” due to immature governance, uneven data infrastructure, and chronic resource constraints. This policy perspective outlines a practical roadmap for building AI-ready infrastructure from a product management lens. We synthesize evidence on five pillars: (1) modernizing legacy IT and enforcing interoperability to unlock data liquidity; (2) raising data quality and governance standards to reduce bias and protect privacy; (3) provisioning compute, storage, and resilient networks via hybrid on-prem/cloud architectures; (4) developing an AI-literate workforce and co-design practices that integrate tools into real clinical workflows; and (5) adopting disciplined procurement, validation, and post-deployment monitoring to ensure safety and value. We translate global lessons from the NHS, Canada, and Singapore into actionable steps for U.S. public systems, emphasizing standards like FHIR, privacy-preserving approaches such as federated learning, and guideline-aligned evaluation (e.g., DECIDE-AI). The result is a sequenced, governance-anchored playbook that helps executives and product leaders move from pilot-itis to sustainable scale. Implemented well, this approach can accelerate equitable AI adoption in safety-net settings, reduce clinician burden, and improve patient outcomes while maintaining transparency, accountability, and trust.

Ähnliche Arbeiten