Hospitals Race to Keep Pace as Payers and AI Reshape Denials 

Hospitals Race to Keep Pace as Payers and AI Reshape Denials 

Denials have become one of the defining pressure points in healthcare finance. Across hospitals and health systems, claims teams are balancing rising volumes, increasingly complex payer behavior, staffing strain, and mounting expectations around cash performance, often simultaneously. 

For years, most organizations responded in familiar ways: adding staff, expanding vendor support, building new workflows, and pushing more accounts through the queue. Those efforts can help teams keep pace in the short term, but they rarely change the underlying nature of the work itself. 

That is the shift many revenue cycle leaders are beginning to confront. Denials are still too often treated as an operational backlog to work through rather than a sign that a process upstream in the revenue cycle needs to be redesigned. As AI and automation become more deeply embedded across claims and revenue cycle operations, the gap between managing denials and actually reducing them is becoming much harder to ignore. 

Faster Isn’t the Same as Better 

Healthcare organizations are investing heavily in AI-enabled workflows across coding, clinical documentation improvement (CDI), utilization management, and denials operations. In many cases, the early impact has been meaningful. Work that once required extensive manual review now moves with far greater speed and visibility, giving teams quicker access to the information they need and a clearer view of emerging denials patterns. 

Even so, faster workflows do not automatically lead to better financial performance. 

Pat DeAngelo, President of CorroHealth, sees many organizations caught in a cycle where technology improves efficiency without changing the underlying dynamics driving denials. Teams can process more accounts and respond more quickly, yet the same payer behaviors continue resurfacing and many of the underlying workflow issues remain unchanged. 

The work moves faster, but the organization is still fighting many of the same battles. 

Mid-revenue cycle leaders, like Advocate Health Vice President of Revenue Cycle Applications Lissa Mann, emphasizes this risk, noting that it’s possible to have phenomenal throughput in areas like authorizations and appeals while still failing to overturn denials if the focus is on speed instead of accuracy and root cause correction. 

That tension is becoming harder to ignore as denials volumes continue climbing. Many revenue cycle teams still devote significant time to piecing together documentation, interpreting payer expectations, and identifying patterns only after problems have already taken hold. AI can ease much of that burden by bringing those issues into focus earlier and helping teams respond with far greater precision. 

The more important shift begins once organizations stop spending so much energy chasing the work itself. 

The Goal is to Reduce the Noise 

One of the more meaningful shifts happening in the revenue cycle today has less to do with automation itself and more to do with how organizations are redirecting human expertise. 

DeAngelo often describes AI as a way to remove the “noise” from denials management by taking on much of the repetitive administrative work that has long consumed skilled teams. As that burden eases, experienced staff can spend less time working queues and more time focused on the issues that actually shape financial performance, from complex denials and payer escalation to the documentation and workflow gaps driving repeat problems upstream. 

That shift is already changing how organizations approach the mid-revenue cycle. AI-assisted pre-bill review solutions such as VISION Clinical Validation Technology® are helping teams identify the claims most likely to require intervention before submission, making it possible to focus attention where it will have the greatest impact rather than attempting to review every encounter manually. At the scale many health systems now operate, that level of manual review is no longer practical. 

Technology can bring the right issues into focus faster, but human judgment still drives the decisions that matter most. That balance becomes increasingly important as payer requirements, documentation expectations, and denials patterns continue to shift. 

According to Keisha Downes, Vice President of Mid-revenue Cycle at Beth Israel Lahey Health, AI is especially powerful when it prioritizes where CDI specialists and coders should focus in a high-volume, high-variation environment, allowing teams to reserve human expertise for gray areas and high-risk encounters instead of spreading it thin across every discharge. 

Revenue Cycle Teams Are Operating in a Constant Adjustment Cycle 

Payers continue to change authorization requirements, documentation expectations, and reimbursement rules, even as workforce pressures reshape how revenue cycle teams operate. At the same time, AI is evolving quickly enough that technology introduced only months ago can already feel outdated. 

Static denials strategies struggle to keep pace in that environment. More organizations are recognizing that revenue cycle redesign is not a one-time effort but an ongoing process of refinement. The strongest teams are approaching AI less as a standalone technology initiative and more as part of an evolving operational strategy. 

That requires a clearer view into denials trends and a sharper understanding of where automation is truly improving outcomes versus simply increasing throughput. 

DeAngelo describes today’s landscape as a constant cat-and-mouse dynamic between payers and providers. Health systems improve workflows, payers adjust, and organizations respond again. Without strong data visibility and flexible operations, teams can quickly fall back into reactive work. 

Mann frames this shift as big as ‘going from paper to the EMR,’ urging organizations to treat AI not as a one-time project but as a continuous cycle of experimentation, learning, and redesign. 

Human Judgment Still Carries the Hardest Work 

Much of the broader AI conversation still frames automation as a replacement for human labor, but revenue cycle leaders are increasingly describing something more layered. Technology is exceptionally good at handling high volumes of repetitive work and surfacing patterns that might otherwise go unnoticed. The more nuanced areas of revenue cycle operations, however, still depend heavily on human judgment, particularly when conversations become sensitive, ambiguous or complex. 

That distinction becomes especially important inpatient financial interactions. Automated outreach can be highly effective for routine balances and reminder communications, but more difficult financial situations often require a level of empathy and flexibility that technology cannot fully replicate. DeAngelo points to areas like children’s hospitals, where financial discussions are often tied to emotionally challenging clinical circumstances. In those moments, the quality and sensitivity of the interaction matter just as much as operational efficiency. 

The same dynamic is beginning to reshape revenue cycle teams internally. As AI absorbs more lower-complexity work, the remaining responsibilities become more specialized and more strategic. Organizations increasingly need staff who can navigate nuanced denials, interpret gray areas in coding and documentation guidance, and recognize broader operational patterns that contribute to recurring issues. In that environment, traditional productivity measures centered on simple throughput become less meaningful. A staff member working through fewer claims may still be driving significantly greater financial impact if the work involves high-risk payer disputes or complex inpatient denials. 

Downes notes that as lower-skill, repetitive work is automated, benchmarks themselves must evolve. As a result, teams may handle fewer encounters numerically, but each case carries greater complexity and strategic importance. 

Financial Performance Still Tells the Truth 

For all the excitement surrounding AI, revenue cycle leaders still need clear ways to determine whether their strategies are actually improving performance. In many cases, the warning signs are easy to recognize: accounts receivable continues aging, cash collections remain flat, overturn rates show little movement, and denials patterns persist despite new automation investments. When those metrics remain stagnant, organizations may simply be moving through the same ineffective processes more quickly. 

That is why many leaders are shifting their attention away from implementation milestones alone and focusing more closely on operational outcomes. Technology adoption matters far less than whether avoidable denials begin to decline over time. Faster workflows offer limited value if payer behavior remains unchanged or recovery rates fail to improve. 

The organizations making the most meaningful progress tend to take a more targeted approach. Rather than deploying AI as broadly as possible, they focus on the operational friction points creating the greatest financial strain and continue refining workflows as those patterns evolve. Over time, the goal becomes less about processing denials faster and more about reducing how often avoidable denials appear in the first place. 

For healthcare leaders like Mann, that means starting with the hardest problems teams are desperate to solve and using AI to attack those first, while building feedback loops that quickly surface when a solution is drifting or failing to deliver the expected accuracy. 

The Organizations Pulling Ahead Are Rethinking the Entire Workflow 

Denials prevention is becoming less about any single technology and more about how revenue cycle work is designed overall. The strongest organizations are bringing automation, operational oversight, and clinical expertise together in ways that help teams spot patterns earlier, reduce administrative burden, and focus experienced staff where they can have the greatest impact. 

That shift changes the role denials play inside the organization. Instead of serving primarily as downstream cleanup work, denials become a signal that an issue upstream needs attention, whether that involves documentation gaps or changing payer behavior. Organizations that respond to those signals thoughtfully are often able to reduce avoidable denials over time, rather than staying stuck managing the same recurring problems. 

As the technology continues to evolve, the bigger differentiator may be how willing organizations are to rethink the work around it. Leaders pulling ahead are not those deploying the most solutions, but those pairing strong governance and human-centered design with a willingness to fail fast, pivot quickly, and continually reimagine how revenue cycle work gets done.