Beyond the Hype: Evaluating Agentic AI in Healthcare Claims Processing
This article explores whether agentic AI is truly transformative for healthcare claims management or if the current excitement exceeds practical reality.
This article explores whether agentic AI is truly transformative for healthcare claims management or if the current excitement exceeds practical reality. It breaks down how agentic AI differs from traditional automation, highlights its potential benefits—like smarter claims intake, faster decision support, lower administrative burden, cost savings, and improved fraud detection—and also discusses key limitations such as data quality challenges, integration hurdles, and the need for strong human oversight. The piece offers balanced insights for healthcare leaders evaluating advanced claims management solutions.
Healthcare revenue cycle management costs the industry over $140 billion annually, with nearly 20% of claims denied and 60% of those denials never appealed. Traditional automation helped at the margins, but agentic AI operates differently - it doesn't just advise, it acts autonomously.
Where It's Actually Working
Agentic AI delivers real value in bounded, high-volume processes: accounts receivable follow-up, denials management, and cash posting. Mayo Clinic and Sentara Health are reclaiming thousands of staff hours by automating eligibility verification, prior authorization, and claims-related workflows. McKinsey projects a 30-60% reduction in cost-to-collect for health systems that fully enable their revenue cycle with agentic AI.
But adoption lags hype. Accenture research shows 45% of insurers have deployed Gen AI for claims intake, yet only 12% have scaled it. Most organizations remain stuck between pilot and production - constrained by legacy infrastructure, data fragmentation, and regulatory complexity.
What Actually Matters in Evaluation
Skip vendor promises. Focus on these questions:
- Autonomy design: How does it handle edge cases? The best systems escalate exceptions efficiently while keeping humans in the loop.
- Integration depth: Is it real-time bidirectional data exchange or periodic batch syncs?
- Explainability: Can compliance teams and regulators follow the reasoning behind decisions?
- Denial prevention vs. recovery: Does it catch errors before submission or manage appeals after?
- Outcome measurement: Are they proving denial rate reduction and AR improvement, or just activity metrics?
The Path Forward
Organizations making progress start with the back end - AR follow-up and denials management - where risk is lower and volume is high. This builds trust and creates the data foundation for expanding to front-end workflows.
The ultimate vision is interconnected agents coordinating across the full revenue cycle, with humans managing exceptions and ensuring compliance rather than executing routine tasks.
Agentic AI in claims processing isn't hype. But converting promise into competitive advantage requires rigorous evaluation, not reactive adoption.