Health systems’ AI ambitions outpace operational readiness, leaving advanced models underused despite potential to improve outcomes and efficiency. Executives face hurdles including fragmented data, immature governance, poor integration with EHRs and clinician workflows, unclear value metrics, and limited change management capacity. The piece argues that without pragmatic execution—standardized data pipelines, rigorous validation, and cross-functional ownership—models remain pilots rather than scalable tools affecting patient care.
Authors recommend concrete steps: establish AI governance and ROI frameworks, embed AI into clinical workflows with vendor and IT collaboration, measure outcomes, and invest in upskilling and clinician engagement to build trust. They emphasize iterative scaling from validated pilots, accountable leadership, and transparent performance monitoring to translate AI promise into measurable health system impact. Regulators and ethics frameworks should guide safe deployment.
