Assess and prepare data, talent, and workflows to enable predictive analytics, and ambient and agentic intelligence.
of health AI pilots fail at implementation
when predictive analytics is deployed
of clinicians say ambient AI reduces burnout
of AI projects with a readiness assessment succeed
Evaluate people, processes, and platforms to identify practical steps for AI deployment.
Create the data pipelines and governance guardrails required to operationalize predictions.
Identify high-impact use cases (ED, sepsis, discharge) where ambient AI can reduce workload.
Define policies that ensure model transparency, fairness, and real-world applicability.
Systems see faster, safer AI adoption and measurable clinical and operational improvements.
Clear roadmap enables faster execution
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Clean inputs increase predictive precision
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Design aligned with workflows and context
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Models meet regulatory and ethical standards
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