AI adoption in healthcare revenue cycle is accelerating, but a widening trust gap is preventing organizations from moving beyond pilots.
Despite hype surrounding AI in the revenue cycle, industry leaders remain cautious. A recent survey by Experian of healthcare finance and revenue cycle professionals found that while nearly two-thirds of organizations are using AI in some capacity, only 15% have it fully integrated into their standard operations.
For revenue cycle leaders, the hesitation stems from concerns around data privacy, implementation costs, and a fundamental lack of trust in the technology. To bridge the gap between AI's promise and its practical application, leaders must focus on targeted use cases, establish clear oversight, and demand algorithm transparency.
The Front-End Imperative
Providers see the greatest immediate benefit at the front end of the revenue cycle, according to survey results. Respondents ranked insurance eligibility, patient scheduling, and patient registration as their top opportunities for AI deployment.
Shannon Ducat, associate vice president of patient access at ProMedica, recently spoke with HealthLeaders about redesign of their font-end to address these same challenges.
As part of its patient access redesign, ProMedica established a fully remote pre-registration team that calls patients five to ten days before their appointments to complete most tasks typically done day-of-service.
While the approach streamlines patient visits, there has been some resistance to the change. For instance, patients used to traditional patient access models may be reluctant to share personal health or payment information over the phone.
“It’s something new,” Ducat said. “I think some patients have a little bit of a trust issue with just the legitimacy of the phone call.”
Of course, resistance to change also comes from internal sources. While many clinicians are excited about the potential to improve patient engagement, some prefer the level of control afforded to them in under traditional models.
Navigating the "Black Box"
As Ducat noted above, despite potential benefits, many respondents cited a lack of trust in AI accuracy as a primary barrier to adoption. Revenue cycle leaders are cautious about what happens inside the algorithmic "black box.” The propensity for large language models to hallucinate is a major concern when dealing with complex regulations around reimbursement and patient privacy.
To build internal trust, health systems must adopt models that provide transparency into how decisions are made, such as utilizing confidence scores. This allows human staff to review the underlying data, decide whether to accept an AI platform’s conclusions, and intervene when a decision requires nuanced, human validation.
Cutting Through the Noise with Governance
A persistent challenge for revenue cycle leaders is sifting through vendor noise. Many recall that early promises around robotic process automation failed to live up to the hype, which has left many executives skeptical of modern AI claims.
The most successful health systems are navigating this skepticism by establishing formal AI governance committees. The most effective oversight structures feature strong alignment across the organization, bringing together revenue cycle leaders, technology teams, and legal and compliance experts to evaluate tools collaboratively.
At Baptist Health, this meant establishing an "AI Institute" to manage a growing portfolio of new technologies.
For automation projects to move forward, they must have clearly defined goals and receive approval from the AI Institute.
This governance model has a direct impact on contracting, Steven Kos, Senior Director of Revenue Cycle Application Support at Baptist Health recently told us.
Solutions are not approved unless there are strong contracts supporting Service Level Agreements. Crucially, Baptist Health insists on contract clauses that allow them to exit if the projected ROI is not achieved.
Pilot programs must demonstrate potential for ROI, Kos says. And then “we have to take it back to AI governance and show that metrics have been met before we allow the pilot to become a final contract.”
Ultimately, revenue cycle leaders must avoid adopting technology simply for the sake of having AI.
Luke Gale is the revenue cycle editor for HealthLeaders.
KEY TAKEAWAYS
Prioritizing AI for tasks like eligibility and registration prevents costly downstream denials, protecting hospital margins before a claim is ever generated.
To overcome skepticism, organizations must demand transparency from algorithms and keep humans in the loop to validate high-stakes financial decisions.
Successful AI deployment requires structured governance committees that align revenue cycle, IT, legal, and compliance teams to measure actual ROI.