Healthcare AI Readiness Assessment

Does the organization have a documented AI strategy?

Is there executive sponsorship for AI initiatives?

Are AI use cases tied to measurable business or clinical outcomes?

Is core clinical and operational data accessible and reliable?

Does the organization have data governance standards?

Can data be integrated across EHR, claims, financial, and operational systems?

Does the organization have modern integration capabilities?

Are APIs, cloud, security, and identity controls mature enough for AI?

Can AI tools be embedded into existing workflows?

Do clinicians and staff understand approved AI use?

Is there an AI literacy or training plan?

Are workflows designed around human-in-the-loop validation?

Is there an AI governance committee or review board?

Are AI tools evaluated for accuracy, bias, privacy, and safety?

Is there a shadow-AI policy?

Are use cases prioritized by ROI, risk, workflow fit, and complexity?

Are high-burden / low-risk workflows identified?

Are pilot success metrics defined before purchase?

Does the organization have a vendor evaluation process for AI?

Are vendors reviewed for evidence, references, security, and interoperability?

Are referral or commercial conflicts disclosed?

Can the organization measure baseline cost, time, quality, and access metrics?

Are savings assumptions tied to real operational data?

Is there an owner accountable for realizing benefits?

Are PM, change management, analysts, integration, and clinical champions available?

Is there a plan for training, rollout, and support?

Is post-go-live optimization resourced?

Are post-deployment monitoring processes defined?

Are escalation paths defined for AI errors or safety issues?

Are model performance and user behavior reviewed regularly?