Artificial Intelligence Finally Solvees a $25.7B Healthcare Problem

For most people, health care feels complicated everywhere but in the exam room. We trust our doctors, we get the care we need, and then the bill comes, often filled with codes and explanations that simply don't explain much. Behind the scenes, the billing process is more complex: dozens of payer portals, ever-changing rules, and thousands of small administrative decisions that determine whether claims are paid quickly, slowly or not at all.

I learned this early on. Growing up, I spent many summers helping my parents (both of whom were healthcare providers) with the billing for their small practice. I watched them fight denials, decipher payer language, and wait hours trying to understand why claims that “should be covered” weren't covered. It's frustrating to me that we've standardized this as a “billing method” even though it clearly doesn't have to be that way.

Today, this pressure continues unabated. The number of rejections has risen for three consecutive years, while administrative requirements have been tightened. Last year, healthcare providers spent more than $25.7 billion responding to claim denials, although 70% of those denials were ultimately overturned. The system is not broken because denial is impossible to win. It's broken because the people trying to fix them are overwhelmed.

The question facing the healthcare industry now—and one that artificial intelligence is finally starting to answer—is simple: What if we could give billers and providers the same types of smart tools that payers have used for years?

Artificial Intelligence is closing the automation gap between providers and payers

Insurers have quietly built sophisticated automated systems over the past decade: instant eligibility checks, automated denials, document scanning and rules engines that can flag even the smallest mismatch.

In the meantime, providers still need to navigate all of this manually.

This imbalance creates a gap: a growing divide between what payers automate and what providers must do manually. This is unsustainable. That’s why artificial intelligence is starting to change the revenue cycle in very real ways.

We see providers using AI to:

1. Understand and resolve denial issues faster – Billers can now instantly get explanations of denial codes, coverage rules, and required documentation without having to dig into PDFs, reducing research time to seconds.
2. Prevent errors before claims are denied – AI can analyze compatibility between CPT and ICD codes, check for missing modifiers, identify prior authorization requirements, and compare submissions to payer policies.
3. Automate repetitive follow-up tasks – The average practice logs into 5-20 payer portals just to track claim status. AI can now automatically monitor these steps, flagging issues early and helping teams prioritize which rejections to address first.
4. Generate payer-ready appeal letters – Since 70% of appeals are successful, speed and consistency are important. AI can now draft structured, compliant letters in minutes, helping teams generate more revenue with less effort.

None of this is hypothetical. This is happening now in practices using AI-driven tools. Suppliers are seeing shorter receivables cycles, fewer rejections, and faster cash flow, driven by one simple fact: They finally have a system that can help them keep up.

Impact on humans: AI doesn’t replace billers, it enhances them

People have a misunderstanding that artificial intelligence is meant to replace humans. In fact, organizations that are successful with AI are using AI to empower their teams, not shrink them.

RCM and billing work requires high skills, but much of the day-to-day work is repetitive: checking status, tracking rules, writing appeal letters. These tasks waste time without adding value.

Artificial intelligence reverses this dynamic. By handling repetitive, rules-based tasks, AI frees up billers to do the strategic work that actually drives revenue:

• Review complex cases
• Analyze trends
• Improve documentation
• Provide advice to providers on how to prevent rejection in the first place

In other words, the best AI doesn’t eliminate billers; It turns them into “super billers,” able to get more done with less burnout.

Why this matters to patients

Billing issues are not just the provider's problem; This is also a patient problem.

Every delayed claim, every mistake, every confusing denial ultimately impacts the person receiving care. The emergence of artificial intelligence can help reduce this friction:

• Solve problems faster – With the right tools, facts that once took weeks to deny can be overturned in days.
• Reduce unexpected bills – Clear answers about upfront coverage mean fewer surprises downstream for patients.
• Greater financial transparency – AI can provide eligibility requirements, coverage limitations, and patient responsibilities before care is provided.
• Reduce supplier burnout – When billing teams aren't drowning in administrative work, they can stop chasing payments and focus on patients.

The result is a smoother experience for providers and patients.

Photo: Crowden Nakagawa, Getty Images

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