Readers Write: How AI is Helping Providers Navigate Regulatory Uncertainty

How AI is Helping Providers Navigate Regulatory Uncertainty
By Mindy Fortson

Mindy Fortson is COO of Experian Health.

Healthcare organizations have always had to navigate change. But lately, it may feel like the ground is constantly shifting, with The One Big Beautiful Bill Act (OBBBA) being the latest example.

While its full impact won’t be felt until next year, many providers who were surveyed say that they are not prepared and expect major challenges with eligibility and billing.

For many revenue cycle teams, this uncertainty may be creating real anxiety in day-to-day operations for staff who are already overextended. They must prepare for stricter eligibility checks, increased reporting mandates, and the likelihood of more patients cycling in and out of coverage. Providers are turning to AI not as a futuristic concept, but as a stabilizing force that brings consistency, clarity, and efficiency to increasingly complex operational demands.

Providers are asking these questions about OBBBA, and responsible AI-driven support may help answer them.

How Will Providers Know Which Patients Are About to Lose Coverage?

One of the most immediate concerns surrounding OBBBA is coverage volatility. The Congressional Budget Office anticipates that 11.8 million individuals could lose health insurance over the next decade due to policy changes, including new community engagement requirements and more frequent eligibility reviews. For providers, the challenge is not only that coverage may change, but that it may change quickly and unpredictably, creating instability at the front end of the revenue cycle.

That raises an urgent operational question: How do we identify coverage risk early, before it turns into denied claims or uncompensated care?

Many eligibility workflows are manual and fragmented and were not built for this level of change. Revenue cycle teams are already balancing staffing shortages, rising claim denial rates, and growing payer complexity.

AI solutions can help providers build more consistent operational foundations. They can identify accurate patient information, automate eligibility and insurance discovery checks, flag incomplete documentation, and lessen the burden of manual tasks.

Denials are likely to become a bigger pressure point under OBBBA, especially when coverage status changes between scheduling, registration, and billing. When teams are already stretched thin, even small documentation gaps can quickly turn into delayed reimbursement and rework. Operational consistency will be one of the most important safeguards providers can build in the years ahead.

Are Providers Ready for a Surge in Self-Pay and Uncompensated Care?

OBBBA is expected to increase the number of patients moving into self-pay categories, which is a group that already represents the highest share of bad debt write-offs. Loss of coverage doesn’t mean that patients stop needing care. But it does mean that providers face greater financial unpredictability.

Providers are asking: How do we maintain financial stability while patient responsibility grows?

Many providers need more reliable ways to understand patient populations, anticipate payment challenges, and engage patients with clearer payment options earlier in the process. AI-driven solutions can bring structure to this complexity by analyzing large amounts of patient data, demographic indicators, and billing patterns to support segmentation and reduce guesswork in collection strategies.

These tools can also identify potential charity eligibility and help providers better anticipate which patients may struggle to pay. Many providers need more predictable workflows for both staff and patients in an increasingly uncertain coverage environment.

How Will Providers Navigate Additional Operational Complexities?

OBBBA introduces a new layer of operational complexity. Each state will implement the provisions of this law differently. Providers will need to understand both state and federal rules to ensure compliance. Eligibility may hinge on employment hours, participation in training programs, or exemption status that can change month to month. Documentation may be incomplete, delayed, or interpreted differently across states.

For providers, the question becomes: How do we confirm coverage status with confidence when eligibility itself is more dynamic?

The risk isn’t only that patients lose coverage. It’s that coverage appears active at one point in the process and changes before a claim is adjudicated. That creates exposure to retroactive terminations, denials, and rework that strain staff.

Managing this kind of volatility requires more than manual verification. AI can help monitor eligibility timelines, flag missing or inconsistent documentation, and prompt earlier intervention when redetermination windows approach.

In addition, providers need access to broader, more complete data than a single insurance record. They will need to know the correct order of benefits if a patient has more than one insurance and whether they are likely to qualify for Medicaid if they appear uninsured. Eligibility may increasingly depend on data elements that providers have not traditionally needed to consider, like employment status or volunteer activities and income verification.

AI can help pull together these disparate data points and support more consistent front-end decision-making, especially when eligibility is dynamic and documentation requirements are evolving.

As implementation unfolds, operational consistency will depend on building workflows that can adapt to these requirements without adding unnecessary friction for staff or patients.

Preparing Now Means Building Stability into Core Workflows

Providers don’t need every answer today, but they do need to be asking the right questions:

• Which patients may fall through coverage gaps?
• How will self-pay growth change financial exposure?
• Where are administrative processes most vulnerable?

In a time of constant change, providers are searching for stability and workflows that are clearer, more consistent, and less reactive. AI, applied thoughtfully and responsibly, can help bring that stability into the revenue cycle. This technology is one of the best ways to ease administrative strain and help staff focus on what matters most.