Building AI-Ready Infrastructure for U.S. Healthcare: A Product Management Perspective
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".