Why Claims Denials Remain a Persistent Problem, and How AI Can Help

Few things frustrate more than having to redo work you’ve already done — unless you add delays in getting paid for that work to the mix as well. That’s what we’re talking about when we talk about denials; it’s your team having to expend more time and energy on a claim that could’ve, should’ve been correct the first time. Some denials are beyond your control, but too many times, they’re the result of mistakes along the way, often early in the process.

What’s behind the persistent denial issues? And what can the clinic do to try and turn the tide on the problem and cut claims denials to a minimum? Let’s consider where the problems start. 

Denials tend to have the same root causes.

While the details can change from claim to claim, denials ultimately stem from the same problem spots: you’re submitting with incorrect patient information, missing authorization checks, or running into documentation gaps. What connects these issues is a broader underlying challenge facing many clinics today: the struggle to find enough time to fully focus on these details. 

Turnover and staffing at the front desk are things many clinics have to wrestle with, but even the most seasoned front office pro is going to struggle to manage scheduling, intake, benefits checks, and payments with the number of patients your practice is seeing. The same goes for providers; completing notes already bleeds over into their evenings, and that time crunch leaves less time for review and opens the door for oversights.   

What makes the problem so pernicious is that the cost of these mistakes is more time out of the day for your staff: time your billers spend tracking down the right information, correcting the claims, and resubmitting, all while still trying to manage every other claim going out the door.

Solving for it requires shifting checks to AI—not as a decision-making tool, but as a hyper-efficient reviewer able to surface potential issues for your team to address.

Catch issues before claims are submitted.

Too many clinics are learning about incomplete or inaccurate information from intake forms only when the claim comes back denied.  AI offers the ability to shift that work upstream and catch mistakes before they become actual problems. 

Ensure consistent documentation that supports continued care and coding.

Defensible documentation is an essential element of care, and also one that can be hard to get right. Across evaluations, daily notes, progress reports, and discharge summaries, providers need to demonstrate both the value and necessity of their intervention to a third party who has only those records to go on, making what you say and how you say it invaluable. 

Where AI adds value is that it has been trained on countless compliant notes, so it understands what elements are common across approved claims. It can help reduce denials tied to medical necessity by:

• Highlighting missing required elements before notes are finalized
• Encouraging consistency between goals, progress, and billed services
• Reducing repetitive documentation so clinicians can focus on skilled reasoning

Improve front-end accuracy and reduce authorization-related denials.

Too many denials begin as simple mistakes at the front desk, like incorrect insurance information or incomplete demographics on intake forms, which persist across every form and claim. 

AI can support front office teams by:

• Flagging missing or inconsistent intake information
• Reducing manual review during eligibility and benefits checks
• Helping standardize high-volume intake tasks

One key area where front desks are getting caught up is in managing patient authorizations, or failing to do so. Authorization-related denials remain one of the most preventable categories in rehab therapy.

Using AI-enabled workflows supports your team’s authorization management by:

• Flagging missing authorizations before the first visit
• Alerting teams when visit limits are approaching
• Highlighting expiring authorizations mid-plan of care
• Prioritizing follow-up based on upcoming visits and revenue risk

AI still requires strong processes—and a smart staff.

Adding AI to your practice can raise anxiety without the right conversations in advance; as we saw in last year’s State of Rehab Therapy report, some staff might wonder whether AI is an eventual replacement, and certain providers have concerns about AI’s accuracy in clinical settings. 

While AI’s capabilities have the potential to greatly improve processes in your practice, it won’t eliminate the need for a highly capable team of front office staff, clinicians, and billers — nor should it. Instead, AI should enable them to make greater use of their expertise by sifting through the mountains of data to identify items that require their attention. 

If we look at denial management workflows, for example, an AI integrated into those workflows would:

• Prioritize denials based on likelihood of recovery and dollar impact
• Surface payer-specific denial trends
• Reduce time spent on low-value manual sorting
• Support more consistent follow-up

AI alone isn’t going to fix broken processes, either. It can surface missing information or potential problems, but fixing those gaps falls on your team.  For AI to be at its most effective, it needs to be integrated into well-defined workflows and to have clear ownership across front office, clinical, and billing teams. 

Get ahead of denials with AI.

Denials have a knock-on effect in your practice: your billers have to work that much harder, your team’s focus is pulled away from other work, and payments are delayed in hitting your accounts, adding to the financial pressures your practice might be feeling. Attacking denials at the root cause with AI assistance along the way reduces your risk, improves your overall proactive efficiency, and protects your practice revenue so you can focus on growth, not chasing dollars you’re owed.