Claims analytics and denial automation for a healthcare network

Denied claims piled up faster than the billing team could work them. Each denial required reading payer correspondence, cross-referencing the original claim, finding the root cause, and drafting an appeal: 40 or more minutes of skilled work per claim. With hundreds of denials arriving monthly, the team triaged by dollar value and simply wrote off the long tail.

Leadership had no visibility into why denials happened. The same preventable errors (expired authorizations, mismatched codes, missing modifiers) recurred month after month because nobody had time to analyze patterns, let alone feed fixes back to the front desk and coding staff.

An earlier attempt to outsource denial management had failed: the vendor worked only the high-value claims, quality was inconsistent, and the network's data left its control. The client wanted the capability in-house, but hiring more billers only scaled the cost of the problem.

We started with a two-week audit of the denial workflow, sampling six months of payer correspondence to quantify root causes and appeal outcomes. The audit showed 68% of denials fell into eight repeatable categories, and that appeals in those categories succeeded four times out of five when they were actually filed. That made the business case: the network wasn't losing to hard denials, it was losing to unworked ones.

We then built an AI pipeline that reads each denial as it arrives, classifies its root cause against the eight categories, pulls the matching claim record, and drafts a complete appeal letter with supporting documentation attached. Every draft lands in a review queue where a biller approves, edits, or rejects it. The system never files anything on its own.

Alongside the pipeline we delivered a denial-analytics dashboard that ranks root causes by dollar impact per clinic, per payer, and per procedure code. The revenue-cycle director now runs a monthly review with front-desk and coding leads, working down the top of that list. Prevention, not just recovery.

Rollout was deliberately staged: two clinics for the first month with every AI draft double-checked, then network-wide once the approval rate stabilized above 90%. The billing team was involved from the first workshop, which is why adoption never became a fight.