AI success in healthcare: beyond budgets and deadlines
Hitting the budget and the go-live date doesn't prove your AI will scale or deliver value. That's the core message echoed by HIMSS' Rob Havasy and PMI's Karla Eidem. In healthcare, success is measured by safe outcomes, sustained adoption, and the ability to grow without breaking workflows.
Leaders need to align the right people early, own the outcomes end-to-end, and adapt fast. The plan isn't the product. The product is the result it delivers, reliably, across sites and over time.
Redefine what "success" means
- Clinical impact: Track outcome deltas, reduction in time-to-diagnosis, false positive/negative rates, and time saved per clinician per shift.
- Safety and risk: Define failure modes, drift detection, bias checks, and a near-miss review loop. Document kill-switches and rollback paths.
- Scalability: EHR and data pipeline integration (think FHIR/HL7), latency under load, multi-site configuration management, and version control for models.
- Interoperability and audit: Clear data lineage, audit logs, model cards, and traceable decision rationale for regulators and quality teams.
- Economics that last: Cost per prediction, retraining cadence, labeling costs, monitoring overhead, and vendor exit options to avoid lock-in.
- Adoption signals: Clinician satisfaction, override rates, alarm fatigue trends, and training completion rates by role.
Align stakeholders early (and for real)
Map who decides, who does, and who lives with the outcome. Give clinical leadership a true seat at the table, not a courtesy review at the end.
- Set decision rights and a simple RACI. One accountable owner per outcome.
- Bring compliance, privacy, and InfoSec in before vendor selection.
- Nominate a clinical champion and an integration lead; make them co-owners.
Own the outcome, not just the project plan
Budgets and timelines are constraints. Outcomes are the product. Assign a product owner who is measured on patient safety, clinician experience, and sustained value, not just delivery.
- Pick one metric that matters per use case (e.g., door-to-needle time, readmission rate).
- Tie Service Level Objectives to this metric and publish them. Misses trigger a review, not excuses.
Adapt fast: your operating cadence
- Proof of value in 30-90 days: Small cohort, shadow mode if needed, with pre-agreed kill criteria.
- Canary releases: Start with one unit or site, monitor overrides and safety events, then scale.
- Post-launch monitoring: Drift alerts, periodic revalidation, and a clear change request path for clinicians.
- Red-team the model: Try to break it with edge cases before it meets patients.
Pilot to production without the rework
- Standardize data contracts and feature stores to avoid per-site rewrites.
- Decide make vs. buy with total lifecycle cost, not sticker price.
- Keep humans in the loop where risk is high; design graceful degradation and fallbacks.
- Document who can change the model, how it's validated, and how clinicians are notified.
Governance that scales
Don't improvise governance. Use established frameworks and make them actionable in your environment.
- Anchor policies to the NIST AI Risk Management Framework to structure risk controls, monitoring, and accountability.
- If your tool influences clinical decisions, align with FDA expectations for AI/ML-enabled software and maintain auditable documentation.
Quick start checklist
- Define the problem in clinical terms and pick one metric that matters.
- Assign a single accountable owner for outcomes, not just delivery.
- Stand up a cross-functional core team: clinical, data, IT, compliance, security.
- Run a time-boxed proof of value with explicit safety gates and stop rules.
- Design for scale on day one: data contracts, monitoring, and rollback.
- Publish success criteria and review them monthly with stakeholders.
Why this matters
Healthcare doesn't reward flash-it rewards safe, repeatable results. Budgets and timelines are table stakes. The teams that win are the ones that align people early, measure what actually improves care, and adjust faster than the problems change.
If you're upskilling teams for these roles and responsibilities, see our AI courses by job for practical pathways.
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