Research Report: AI Adoption in the Value-Based Era

When smartphones ushered in a new era of technology in the mid-2000s, one phrase summed up the wondrous new array of tools: “There’s an app for that.”

Today, the burgeoning list of artificial intelligence use cases across healthcare might have us updating it slightly: “There’s an AI for that.” In some larger organizations, it has spurred the addition of “Chief AI Officer” to the C-suite ranks.

But these leaps forward aren’t being made at every medical practice, and the journey away from legacy technological systems can be a slow one for many organizations. For AI adopters, the seemingly transformative solutions largely focus on reducing mundane, repetitive administrative burdens — to keep delivering care the same way it had been before, just faster, more cost effective and with less clinician burden.

In many respects, this gradual and uneven technological evolution is akin to the ongoing shift from fee-for-service (FFS) care to value-based care arrangements, which begs another question: Is AI accelerating the shift to value or just making healthcare better at FFS?

MGMA and Humana partnered in June and July 2024 to survey healthcare leaders about their perceptions and implementation of AI solutions in their organizations, specifically:

• The motivations for and barriers to AI adoption
• Governance of AI use, including policies and policy review
• Important factors for organizational buy-in.

The research found:

1. AI adoption is limited but growing, focused largely on speech recognition or clinical diagnostic tools.
2. Process efficiency motivates, while costs and lack of training are barriers.
3. Formal AI governance structures and policy review are sparse.
4. Slower adopters need to see the ROI before they build or buy.
5. Revenue cycle AI is part of the future — so is additional technical training.
6. AI is showing results enough to be seen as a long-term strategy.