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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".