AI adoption in healthcare: New survey shows providers confident but cautious
AI adoption in healthcare: New survey shows providers confident but cautious
By natalie lima | Published: 2025-12-03 11:00:00 | Source: Healthcare Blog
At a glance
Nearly two-thirds of healthcare providers now use artificial intelligence (AI) in their revenue cycle management (RCM) processes, according to the latest Experian Health survey. Discover key insights into the evolving role of AI in healthcare, including barriers and top use cases.
Main takeaways:
- Providers see eligibility verification and patient access as the most important use cases, but are cautious about using AI to make critical decisions.
- Privacy, security, accuracy, and cost are the biggest barriers to AI adoption.
- Most providers expect the use of AI to continue to grow, but agree that some level of human oversight will remain important.
New statistics Data shared by Experian Health shows that trust in AI is growing steadily among healthcare providers, although many remain cautious about how and where it is used. while 63% of service providers They have already introduced AI into their RCM workflow in some way, and most reserve it for less risky tasks like analysis and automation. Many are still testing the waters to see where AI can add value without compromising accuracy or security.
Experian Health Surveyed 200 healthcare decision makers in October 2025 to measure their feelings toward artificial intelligence. This article summarizes their views on AI adoption in healthcare, including key barriers, top revenue cycle use cases, and predictions for the next few years.
How much do healthcare providers trust AI?
Experian Health Data It suggests that providers are torn between feeling confident about using AI and wary about letting the technology make decisions on its own. around Four out of ten survey participants They say they trust technology mostly or completely. Three in ten describe their level of trust as moderate, while the remaining third say they trust it slightly or not at all.
These confidence levels determine the type of tasks for which AI is used. Most providers are comfortable using AI for automation and data analysis, but are reluctant to rely on it for higher-stakes decisions. Only 5% say They will trust AI to make critical decisions independently.
Interestingly, hospitals seem more confident: Half of this group say they mostly or completely trust AICompared to 28% of other organizations providing services. In the latest consultation response, American Hospital Association He stated that hospitals are already seeing AI making a “significant positive impact” in clinical care, noting that “AI tools hold tremendous potential in helping to transform care delivery and address some of the administrative burdens that drive up costs.” This experience may explain why they feel more comfortable moving forward with using AI in business processes as well.
What are the biggest barriers to adopting AI in healthcare revenue cycle management?
Concerns about data privacy and security are the biggest barrier to AI adoption, he said Half of the survey participants. For 41%, accuracy is a sticking point, making it difficult to fully trust AI results.
Although hospitals tend to be more confident in AI overall, their reasons for hesitation differ from other providers. Hospitals are more concerned about regulatory issues, with 26% having incorporated this into their hospitals Top three concerns Compared to 21% of other organizations. On the other hand, they may have found more cost-effective ways to implement AI. only 23% of hospitals They see cost as a barrier compared to 39% of other providers.
What areas of the revenue cycle will benefit most from AI?
Detect errors early in the revenue cycle
according to Experian Health DataService providers believe that AI has the greatest impact at the front end of the revenue cycle. More than half (52%) Place insurance eligibility and benefits verification in your top three opportunities. Patient scheduling and access track 45%, with 44% referring to patient registration and data collection.
Optimizing front-end operations is exactly what Patient Access Trust (PAC) It was designed to do. Using artificial intelligence and machine learning, it automatically verifies and updates patients’ insurance information with one click. Improving data quality at this early stage reduces errors and delays.
Clarissa Riggins, Product Manager, Experian Healthdiscussed the benefits of implementing AI early in the revenue cycle in an interview with the Journal of Medical Economics: “A lot of (the burden of denial) still comes down to friction in the workflow and incomplete or inaccurate enrollment information at the point of entry,” she says. “.”Fix the problems at the beginning and this should cure the claim situation eventually
Predicting and reducing denials
only 32% of survey participants She mentioned seeing claim submission and denial prevention as the best opportunities for AI. This is somewhat surprising, given that 69% of those who use artificial intelligence reported seeing a reduction in rejections and improved resubmission outcomes, according to Experian Health 2025 Claims Status Survey. This suggests that there may be some untapped potential for AI in rejection management.
Tools like Experian Health Artificial Intelligence feature Leverage advanced analytics and machine learning to identify rejection risks earlier, predict outcomes more accurately, recover revenue more efficiently, and act as a complement to Patient Access Secretary.
Learn how AI Advantage predicts and prevents rejections:
How are healthcare organizations currently using AI?
currently, Nearly two-thirds of providers They say they use artificial intelligence in some way. About 15% of providers have fully integrated it in their RCM operations. On the other end of the spectrum, 24% are still in the exploratory stages.
For organizations not yet at this point, the following resources outline possible outcomes:
How do providers see the use of AI changing over the next few years?
Most healthcare leaders expect AI adoption to continue to grow over the next three to five years. to More than halfThis comes with the caveat that human supervision will still be necessary. A Small number (6%) I think progress may stall due to regulatory or trust issues.
As reliance increases, providers will need to find a balance between independence and oversight. The most effective models will see AI and employees working together, as the technology improves efficiency and gives teams greater ability to handle those complex, high-stakes tasks.
Frequently asked questions
Clarissa Riggins She recommends starting small by targeting specific processes where automation can make an immediate difference, such as eligibility verification or claim adjustments. Running an AI pilot program in a workflow is a good way to help teams build confidence in the technology and measure results before scaling.
Experian Health data suggests that providers see AI as a way to support their teams, not replace them. By taking over repetitive, data-heavy tasks, AI gives employees more time to focus on problem-solving and higher-value work that requires human judgment.
Successful adoption depends on clear management, reliable data, and employee-friendly interfaces. This means choosing tools that complement human decision-making and enhance oversight rather than remove it.
Find out more about how to do this Patient Access Secretary and Artificial intelligence feature Helping service providers use AI to achieve stronger revenue cycle performance.
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