AI’s Future in Health Care Depends on Data Standardization, Collaboration: Erin Weber, MS

In the second and final part of her interview with The American Journal of Managed Care^® (AJMC^®), Erin Weber, MS, chief policy and research officer at CAQH, discusses her vision for the future of artificial intelligence (AI) in health care, emphasizing the importance of data and process standardization, along with support from policymakers and researchers, in making this a reality.

She explores these topics further in part 1 of the interview and 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

What steps should be taken to standardize data and processes to ensure the successful implementation of AI across the health care industry?

Standardization is essential, and I live and breathe this every day. AI can't work well if it's built on inconsistent or messy data. We need common formats, rules, and processes to ensure the underlying data are usable across different systems.

This is where initiatives like CAQH Core come in, establishing consistent, industry-wide requirements that support interoperability and then scalable innovation. Going forward, we should focus not just on data availability but on data usability so AI tools can generate meaningful insights and action.

What role can policymakers and researchers play in accelerating AI adoption? How can they address provider concerns, such as privacy, bias, and cost?

Policymakers and researchers have a critical role in this process. Policymakers can really set the guardrails, creating clear, enforceable standards for data privacy, security, and fairness. This helps build trust for broader AI adoption.

They can also provide funding or incentives to help smaller practices, rule providers, and those underserved and under-resourced organizations to help them access and implement these technologies.

Researchers also have a critical role in measuring what works, identifying where bias creeps in, and developing strategies to improve AI tools over time. I mean, the last thing we want to do is start adopting technology that has no proven ROI [return on investment].

Together, policymakers and researchers can really make sure AI improves care delivery without introducing new risks or disparities.

Looking ahead, what do you envision for the future of AI in health care? What key actions are needed to make that vision a reality?

The future of AI and health care, I think, depends on 3 things. Number 1, collaboration. Two is trust, and 3 is usability. To get there, we really need to invest in strong data standards, fostering public and private partnerships, and designing tools with input from those who are really living and breathing this everyday: providers, patients, and administrators.

If we do this right, I'm hopeful AI won't replace people; it will empower them. It will simplify the complexity of our system so that every stakeholder, from the clinician to the patient, can really focus on what matters most, which is better care.