Collaboration Is Key to Expanding AI Use in Health Care: Erin Weber, MS

In part 1 of an interview with The American Journal of Managed Care^® (AJMC^®), Erin Weber, MS, chief policy and research officer at The Council for Affordable Quality Healthcare (CAQH), highlights that artificial intelligence (AI) adoption in health care remains limited due to concerns about privacy, bias, and cost. While current use is largely focused on administrative tasks, she emphasizes that expanding AI to areas like eligibility verification and prior authorization will require greater collaboration among stakeholders.

Weber explores this topic further in her commentary, "AI in Health Care: Closing the Revenue Cycle Gap," published in this month's issue of AJMC.

This transcript has been lightly edited; captions were auto-generated.

Transcript

Despite significant advancements, AI adoption in health care remains relatively low. What are the primary barriers preventing providers from fully embracing AI? How can these be overcome?

The providers are understandably cautious when it comes to AI; so am I. Concerns about data, privacy, bias, and costs are all valid, but beyond that, there's often a disconnect between what AI tools are designed to do and what providers actually need in their daily workflows.

As our research shows, most AI adoption is focused on administrative tasks, like eligibility checks or documentation, which are really valuable starting points. These use cases can reduce errors and save time, but they don't yet impact patient care directly. Expanding AI into clinical areas will require overcoming probably deeper trust barriers.

To make real progress, AI tools are going to need to be user-friendly, solve meaningful problems, and be built with transparency and security at their core. Most importantly, and this is a theme at CAQH, success depends on collaboration. Providers, health plans, and technology partners really have to work together to make AI not just available but truly useful.

In your commentary, you emphasized the importance of collaboration between providers and health plans. How can these stakeholders work together to extend AI's benefits beyond administrative tasks to improve the revenue cycle?

When providers and health plans collaborate early, and that's sharing data or insights, they can co-develop AI tools that improve the entire revenue cycle rather than just fixing individual pain points. There are several industry-driven initiatives working on this right now. This kind of partnership builds trust and ensures that the AI solutions align with both clinical workflows and payment processes.

The result is, ideally, a smoother experience for patients, fewer billing surprises, less confusion, and quicker access to care. As the trust and collaboration deepen, I'm hopeful that so will the comfort with using AI beyond those administrative tasks, which then opens the door to more impactful applications across the entire system.

Since AI is already being widely used for administrative tasks, what areas of health care services should be prioritized next for AI integration?

Despite all the buzz, AI adoption in health care remains relatively low. The 2023 CAQH index data found that only 19% of medical providers and 12% of dental providers reported using AI for any activity, and those were primarily administrative tasks. Many organizations are still in that exploration and evaluation phase. Adoption has increased since our initial data collection, no doubt, but the overall trend remains that, even when implemented, AI is not yet widely integrated into broader health care workflows.

I think to move the needle on this, we should really focus on some of those high-friction areas, like eligibility verification, prior authorization, and claims correction, because these are ongoing pain points for both providers and patients. If we can automate these processes using AI, we can reduce that administrative burden, save time, and, ideally, help patients access care more quickly and efficiently.