Speed Over Safety: AI Action Plan Risks Patient Privacy and Deepens Health Inequities

Healthcare AI needs trust built on guardrails, not speed

Recent praise for the U.S. administration's AI Action Plan highlights real advantages: faster innovation in diagnostics and treatment, stronger public-private collaboration, and a push for interoperability. Those gains matter. But the current draft introduces risks that will land hardest on the patients with the fewest resources and the least recourse.

Three gaps stand out: weak privacy protections around unified health records, vague standards paired with a punitive posture on state rules, and a retreat from bias safeguards that will widen disparities. Healthcare leaders should press for changes now - and adopt internal guardrails regardless of federal timelines.

1) Privacy risks of unified health records

The plan champions seamless sharing of personal health information across providers. The tradeoff: centralizing diagnoses, prescriptions, and lab results in systems that are prime targets for threat actors. A single breach could expose millions of patients at once - far worse than an incident at an individual practice.

Patients served by community health centers are most exposed. These organizations often have fewer cybersecurity resources, while their patients face higher stakes from health-based discrimination if sensitive data (mental health, genetic results) leaks. Current rules were not built for AI-scale aggregation and analytics.

What's missing are baseline security expectations and consequences. Stronger encryption standards, clear breach notification timelines under the HIPAA Breach Notification Rule, and explicit protections for PHI in AI workflows should be non-negotiable.

• Act now: Implement zero-trust access, encrypt PHI at rest and in transit, segment AI workloads, and require vendors to meet minimum security certifications and incident SLAs.
• Minimize data: Use the least data necessary for a use case; log and justify every PHI attribute used by an AI system.
• Simulate failure: Run red-team exercises focused on unified record scenarios to quantify breach blast radius and response readiness.

2) Vague standards and a punitive approach to state protections

Clear, consistent governance would help healthcare AI. But the plan's push to remove "onerous" regulations - without defining the term - and to penalize states for stricter rules prioritizes speed over safety. That posture is amplified by a stated "Build, Baby, Build" mentality.

Healthcare is not an industry where you learn by breaking. The plan also fails to require post-deployment monitoring, even though models drift and can introduce new errors over time. That leaves patients - especially in under-resourced communities - as unwitting test subjects.

• Act now: Stand up an internal AI governance policy: approval gates, model registries, change logs, and clinical sign-off before use in care.
• Monitor in the wild: Track calibration, false positives/negatives, and error distribution by demographic groups; trigger rollback thresholds.
• Contract for accountability: Bake performance, monitoring access, and audit rights into vendor agreements; require timely disclosure of model updates.

3) Bias that widens disparities

Removing diversity, equity, and inclusion safeguards from oversight frameworks ignores reality: bias in healthcare AI is a patient safety problem. A widely cited study on racial bias in healthcare algorithms showed that models can systemically under-serve Black patients. In breast cancer risk, models trained largely on data from white patients have underestimated risk for Black women, reducing follow-ups and delaying treatment.

This pattern repeats across pain assessment, cardiology, and triage. Without requirements to test across diverse populations and report performance by subgroup, these errors will spread as adoption scales.

• Act now: Set representativeness thresholds for training and validation cohorts; block deployment if thresholds aren't met.
• Test for fairness: Evaluate sensitivity, specificity, and calibration by race, ethnicity, sex, age, language, and disability status; publish results internally.
• Build patient recourse: Provide clear appeal paths for AI-informed decisions; require clinician override capability with documented rationale.

Policy fixes that protect patients and keep innovation moving

• Privacy and security: Define minimum encryption, identity, logging, and segmentation standards for unified records; mandate fast breach disclosure and penalties that scale with harm.
• Keep state guardrails: Do not penalize states for stronger health protections. Define "burdensome" through transparent, clinically grounded criteria.
• Post-market oversight: Require lifecycle monitoring, periodic revalidation, and public reporting of material model changes and performance drift.
• Transparency: Standardize model fact sheets: intended use, training data sources, limitations, and subgroup performance.
• Equity by design: Mandate diverse validation, bias mitigation plans, and independent audits before and after deployment.

The bottom line for healthcare leaders

The plan gets the momentum right but the mechanics wrong. Without stronger privacy safeguards, defined standards, and enforceable bias protections, healthcare AI will increase risk and widen gaps in care.

Don't wait for Washington. Build internal controls, demand accountability from vendors, and validate performance for the patients you serve - especially those with the most to lose.

Helpful next steps

• Inventory all AI use cases that touch PHI; assign clinical owners and monitoring metrics.
• Establish a cross-functional AI review board (clinical, compliance, security, patient safety).
• Upskill teams on AI risk, bias testing, and model governance. If you need curated options, see role-based courses at Complete AI Training.