How a healthcare organization built an
AI-powered denial management MVP in
100 days

It's the use of AI models and automation to proactively identify, validate, and resolve claim denials , rather than reacting to them after the fact. Instead of staff manually reviewing each denial, AI agents flag likely denial scenarios, interpret the underlying issue, and recommend or automate corrective action.

Denial management touches claims, clinical documentation, and payer-specific rules all at once, and traditionally requires teams to manually review each denial, extract relevant demographic and charge data, and determine the right corrective path. At enterprise volume, this manual approach can't keep pace with claim volume or catch patterns early enough to prevent repeat denials.

A denial assist agent is trained on historical claims data to recognize patterns associated with specific denial categories, then applies that pattern recognition to new claims in real time. Instead of a person manually reviewing a denial, the agent flags the likely cause, validates it against claims data, and can trigger or recommend the corrective workflow, with coverage typically expanded category by category rather than all at once.

Agentification refers to restructuring an AI use case into modular, reusable agents rather than a single-purpose script. In this success story, the team used agentification to extend existing AI capability into new use cases, like anesthesia claims, without rebuilding the underlying logic from scratch, each new use case reuses components already validated elsewhere.

Timelines depend heavily on data readiness, denial category complexity, and existing systems, but in this success story, the team delivered a production-ready MVP within 100 days and validated outcomes through pilot deployments before scaling further. If you're scoping what a realistic timeline looks like for your organization, consult with our experts to talk through it.

AI models used for denial validation are typically trained on an organization's own historical claims data, so the patterns they learn reflect real payer behavior and claim characteristics rather than generic assumptions. Validation happens through pilot deployment: the model's flagged denials are checked against real outcomes before its coverage is expanded to additional denial categories.

Results depend on which denial categories and workflows are targeted first, but in this success story, the engagement enabled AI-driven coverage across 20 to 30% of key denial categories, improved first-pass claim acceptance through proactive issue identification, and reduced manual intervention across denial workflows. Curious what this could look like across your own denial categories? Consult with our experts to map it out.

The key is designing for reusability from the start: build AI components (like a denial assist agent) as modular pieces that can be pointed at new denial categories or use cases, rather than building a new solution for each one. That's what allowed this engagement to extend from an initial MVP into broader denial coverage and additional use cases like anesthesia claims, without starting over each time.