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The “Push and Pull” of 2026: Why Healthcare AI Is Entering Its Most Dynamic Year

by Dr. Heather Bassett, Chief Medical Officer with Xsolis 02/05/2026 Leave a Comment

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The Future of AI in Healthcare: Best Practices for Responsible Innovation
Dr. Heather Bassett, Chief Medical Officer with Xsolis

As AI continues to permeate the healthcare industry, 2026 will be a time of push and pull: well-designed and well-implemented AI scripts will pull the industry forward, while some stakeholders push back against the technology for a variety of reasons. The convergence of these trends should make for a dynamic year for payers, providers, and patients alike.

Expansion of AI for legal compliance in healthcare

Regulatory compliance at the state and federal level is a familiar concern to every hospital, health system, and private practice. Even if obtaining patient consent is a frictionless process, different states might have different laws about what requires consent. And those laws might change from year to year.

For these and other reasons, some providers are strictly applying AI to low-risk, administrative tasks. Others have pressed the accelerator with patient-facing, higher-risk scenarios. Within this complex landscape, with many potential barriers to adoption, AI solutions for regulatory compliance are an obvious domino waiting to fall. This tech is already broadly available, but 2026 will see the outliers — often small or rural hospitals — continue to weave AI compliance solutions into their EMR systems.

Washington might also take another stab at removing some healthcare regulatory powers from states as it concerns AI. That would only add to the need for digital compliance solutions that can keep up with rapid change.

Growing pains in clinical settings

Physicians have access to GenAI platforms (such as OpenEvidence) that hold promise for improving clinical decision-making and diagnoses. Not all providers are aggressively implementing these toolsets, however.

Bridging the gap between promise and results is an ongoing process, and these diagnostic toolsets all have some margin for error. It’s dependent on the vendor to put as many guardrails in place to minimize mistakes. Of course, mistakes take time to resolve, and 2026 will see more frustration as hospitals and health systems sort the best AI tools from the worst. Some will continue to resist AI adoption because of skepticism around its efficacy.

Other clinicians will resist AI adoption for another reason: the fear of being “replaced.” Although this is a common fear in many industries, healthcare is generally understaffed. Much of its AI adoption to this point has focused on assisting already-overworked units, not depleting the talent pool. AI will take longer to supplant jobs in healthcare compared to other industries. 

Whether the threat is real or perceived, administrators need to make a concerted effort around change management in 2026. Changing peoples’ minds will remain as much of a job as changing technology.

Patient pushback

As patients interact more with AI tools in their daily lives, they become less resistant to AI tools in their interactions with the healthcare system. What might be true on a population-wide level might not be true in every provider’s office.

A pediatrician asking for consent from new parents (usually people in their 20s and 30s) will probably encounter less resistance to the use of AI than, for example, a gerontologist working with mostly elderly patients. Middle-aged patients (Generation X and older Millennials) encountered the personal computer and internet revolutions early in their lives, and are perhaps best-equipped to make another big transition — this time, to AI-assisted technologies.

These are important factors for private practices to consider when adopting any new AI toolsets. Do you specialize in geriatric medicine or pediatrics? How should demographics dictate your AI rollout? How can you bring in patients as part of testing a product, then take their feedback and implement quality controls?

AI and prior authorization 

The CMS “Final Rule” goes into effect in 2026. Impacted payers — Medicare Advantage, Medicaid, CHIP, and certain exchange plans — must respond to non‑urgent prior authorization requests within seven calendar days, and to expedited or urgent requests within 72 hours.

Beginning in 2026, payers must provide a specific reason for denials (e.g., referencing coverage criteria, explaining missing documentation) to aid resubmissions or appeals.

Insurance providers were already aiming for more efficiency in prior authorization by layering in AI tools. Those who have already optimized their prior authorization processes with AI hold an advantage catching up to speed by Jan. 1.

Traditional Medicare has historically limited prior authorization requirements, but CMS is launching a pilot model (Wasteful and Inappropriate Service Reduction, or WISeR) in six states (New Jersey, Ohio, Oklahoma, Texas, Arizona, Washington) beginning Jan. 1, which will require prior authorization for 17 selected outpatient services that CMS considers vulnerable to overuse, waste, or fraud. The pilot will allow use of technology and AI-assisted review, though final decisions will rest with clinicians.

Working toward a set of best industry-wide best practices

In October, representatives from the government, healthcare, academic, and AI sectors convened to discuss best practices for regulating AI in the healthcare space at the annual “CHAI on the Hill” conference. Stakeholders from each industry recognize the value in collaborating to lessen each other’s pain points, rather than operating in silos.

The summit comes on the heels of CHAI and the Joint Commission’s 8-page missive on “The Responsible Use of AI in Healthcare,” which outlined industry-wide best practices “to establish a shared view on what responsible AI use in healthcare should look like.”

These issues will continue to take center stage as the industry moves from high-level philosophical debates around the best uses of AI in healthcare settings to ground-level rollout efforts — now featuring broader buy-in than ever.


About Dr. Heather Bassett 

Dr. Heather Bassett is the Chief Medical Officer with Xsolis, the AI-driven health technology company with a human-centered approach. With more than 20 years experience in healthcare, Dr. Bassett provides oversight of Xsolis’ data science team, denials management team and its physician advisor program. She is board-certified in internal medicine.   

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