The hidden cost of denials and how AI is changing the equation

Every day, healthcare providers lose revenue not from poor care or broken technology—but from denials. It's one of the most overlooked drains on financial performance. Denied claims quietly pile up, creating avoidable delays, rework, and revenue leakage. According to CAQH, the cost of reworking denials approaches $20 billion annually in the U.S. alone.

And yet, denial management remains a reactive process in most organizations—manual, fragmented, and increasingly misaligned with today’s payer complexities.

Why denials still go unchecked

Denials aren't a new problem, but the cost of inaction is growing. What used to be a clerical nuisance is now a strategic concern. Payer policies are evolving faster. Medical necessity documentation is more scrutinized. Staffing is stretched thinner. These pressures collide in the revenue cycle, where every day of delay translates into lost dollars.

Here’s what most providers are up against:

• Frequent shifts in payer rules and coverage criteria
• High variability in denial reasons and appeal requirements
• Limited visibility into root causes and trends
• Inconsistent handoffs between coding, billing, and appeals teams

Even when claims are appealed, success rates vary widely, and the burden on staff is high. In most organizations, denial workflows remain siloed and heavily manual, making it difficult to act quickly, track patterns, or standardize improvement.

Where AI can create real impact

AI is not about replacing people. It is about reducing the complexity that slows them down. The right tools bring structure to chaos, helping teams work faster and make more confident decisions with less manual lift.

At The Select Group, we work with healthcare organizations to apply AI in ways that are practical and outcome-focused. The goal is not just automation for its own sake, but targeted support where teams need it most.

Here are four areas where AI is driving results today:

1. Identify root causes early

AI can analyze denial codes, clinical documentation, and payer logic in real time. Instead of reviewing rejections weeks after the fact, teams gain immediate insight into why a claim may be denied and how to correct it.

For instance, if coding and documentation do not align, the issue is flagged before submission. That clarity can eliminate weeks of delay.

2. Strengthen claims before submission

Before a claim is sent, AI checks it against payer rules, medical necessity logic, and past trends. If something is missing or inconsistent, it is flagged immediately.

This leads to fewer denials, less rework, and faster payment cycles.

3. Automate the appeals process

Appeals often stall because they are complex and time-consuming. AI can help assemble accurate, payer-specific responses—pulling the right documentation, applying the right policies, and preparing the justification your team would otherwise have to build manually.

This speeds up appeals without sacrificing accuracy or compliance.

4. Predict denials before they happen

Using historical data, payer patterns, and claim content, AI models can estimate the likelihood of denial before a claim is submitted. Teams can then proactively adjust or escalate high-risk claims, avoiding preventable rejections altogether.

A smarter approach for healthcare leaders

Healthcare organizations that succeed with AI are not asking whether the technology works. They are asking where it creates the most impact.

Start by answering three key questions:

• Which types of denials cost the most?
• Where are teams spending the most manual effort?
• What delays are preventing faster resolution?

From there, leaders can focus AI on high-friction areas where gains will be felt immediately without requiring a full system overhaul.

How to get started

The best AI strategies begin with small, focused use cases that deliver value quickly. Look for pain points that are frequent, repeatable, and expensive.

Some of the most common entry points include:

• Identifying documentation gaps before submission
• Automating high-volume appeal types
• Prioritizing high-risk claims
• Improving submission accuracy at the source

As the system learns and performance improves, these tools can scale across departments and denial types, building a stronger, more adaptive revenue cycle.

If your team is exploring where to begin or how to expand existing efforts, The Select Group can help you identify the highest-impact opportunities and build a roadmap that works in the real world.