What's Actually Inside a 20,000-Claim Pended Queue
When you pull apart a typical Facets exception queue, the same categories recur: coordination of benefits edits, procedure code mismatches, missing referral authorizations, duplicate claim flags. Many of these have deterministic resolution paths that experienced analysts have been applying the same way for years. But because Facets doesn't distinguish between those deterministic cases and genuinely ambiguous ones, everything lands in the same queue. Analysts spend time on claims that could resolve automatically, and the backlog grows. At 15,000–20,000 pended claims per week, even a modest classification improvement has material throughput impact.
How an AI Agent Resolves the Classification Problem
An AI Labor Company agent extracts adjudication exception classification logic from your TriZetto Facets decision histories and Optum ClaimsLogic denial code patterns — building a working model of how your team has historically resolved each exception category. Deployed against incoming pended claims, the agent classifies each claim by root cause, auto-resolves the deterministic categories (those with consistent, rule-based resolution paths), and routes remaining cases to analysts with pre-populated resolution recommendations and relevant policy references. Jira tracks analyst review volumes and decision accuracy, while Tableau surfaces the exception categories driving the most pend volume so you can target configuration improvements at the source.
The Operational and Revenue Impact
Moving auto-adjudication from 72% to above 87% is primarily an efficiency and revenue protection story. Claims paid on time reduce provider abrasion, protect network contracts, and keep you clear of state timely payment requirements. Faster adjudication also means faster member EOB delivery and fewer inbound calls. Teams in this position typically see 65–85% reduction in manual exception handling volume once deterministic categories are automated. The agent reaches production and starts improving auto-adjudication rates in approximately 7 weeks — fast enough to show measurable backlog reduction within the same quarter it goes live.
How does the agent know which exception categories are safe to auto-resolve?
It learns from your Facets decision histories — specifically, which exception types your team has resolved consistently in the same way over time. Categories that show high consistency and low override rates become candidates for auto-resolution. Ambiguous or low-frequency patterns are always routed to analysts.
What's the compliance posture for auto-resolved claims?
Every auto-resolution is logged with the classification rationale and the Facets decision path applied, creating a complete audit trail. This supports state DOI audits and ACA compliance reviews without requiring manual documentation by analysts.
Will this require changes to our Facets configuration?
Not initially. The agent works with your existing Facets setup and ClaimsLogic integration. Over time, it will surface configuration recommendations — exception categories where upstream rule changes could prevent pends from occurring at all — but those are presented for your review, not applied automatically.