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Healthcare AI is full of promise — but adoption is messy. From skeptical clinicians to a regulatory "wild west," experts at a Healthcare Dive event outlined the four biggest hurdles health systems face when rolling out AI tools. The bottom line: governance, trust, and ROI clarity are non-negotiable before hitting go.
Healthcare AI is full of promise — but turning that promise into practice is a whole different story. Experts speaking at a Healthcare Dive virtual event on Nov. 4 laid out the real-world friction points slowing AI adoption across payers and providers, from clinician skepticism to murky cost savings to a fragmented regulatory landscape.
One consistent theme: clinicians trust AI most when they help build it. At Mayo Clinic, a nurse-built handoff summary tool saw strong adoption precisely because nurses owned the process from concept to rollout. Meanwhile, cash-strapped rural hospitals were advised to prioritize AI for prior authorizations and revenue cycle management — areas with the clearest return on investment.
On the cost question, the jury's still out. Some payers argue AI is actually driving up costs by enabling providers to code for more conditions. And without a federal regulatory framework, health systems operating across multiple states are navigating a patchwork of state laws that experts called "unsustainable."
Key Takeaways:
Why it matters: As AI becomes embedded in clinical and administrative workflows, health systems that don't proactively address governance, trust, and ROI risk costly missteps — or missing out on the technology's long-term benefits entirely.
