Claims Processing VisibilitySee What Happens After the Happy Path Ends
Your auto-adjudication rate tells you how many claims go straight through. Operon shows you what happens to the ones that don't - the fall-out, the rework, the leakage, and the cost of every exception.
Built for health plans and the clinical operations teams running them.
The Problem
Auto-adjudication fall-out is a black box.
Every claim that drops out of auto-adj costs $20+ to process manually vs. under $1 auto-adjudicated. But you can't see which fall-out reasons dominate, which provider groups generate the most fall-out, or whether your configuration rules are pending clean claims unnecessarily. The gap between your auto-adj rate and best practice (85%+) is invisible cost.
Pended claims queues grow silently.
Claims pend for dozens of reasons - high-dollar thresholds, COB verification (which alone takes up to 45 days), missing auth references, regulatory holds. Nobody has a unified view of why claims are sitting, how long they've been sitting, and which queues are growing. Pend aging is tracked through weekly Excel pulls, not live dashboards.
Denial-rework loops are a margin killer.
Each rework loop costs $25-$181 in labor. Worse, 35-60% of denied claims are never resubmitted - revenue that simply disappears. You can't see which denial reasons generate the most rework, which claim types loop the most, or the total cost of rework by category.
Unit economics don't exist.
What does it actually cost to process a claim - by type, by resource, by automation level? The spreadsheet that attempts to answer this takes weeks and arrives outdated. Without cost-per-touch visibility, you can't make informed staffing, outsourcing, or automation decisions.
Why This Can't Wait
Labor cost gap: $12+ manual vs. $4 electronic per transaction
Average denial rework costs $48-$64 depending on line of business. These costs compound silently - plans without unit economics visibility can't quantify the bleed.
50%+ of health plans now use AI in administrative workflows
Plans still processing exceptions manually face both competitive and margin pressure as peers automate.
CMS clean claims payment rules benchmark automated processing
42 CFR 447.45 benchmarks payer performance against automated processing standards. Plans without visibility into their exception-handling pipeline can't demonstrate compliance.
What Operon Shows You
Operon reads from your claims adjudication and workflow systems to build a live view of what happens after auto-adjudication. Fall-out patterns, pend aging, denial-driven rework, and cost-per-touch - visible in 2-4 weeks, from your actual claims data.
Friction Matrix (Claims)
Auto-adj handles the happy path. The Friction Matrix shows what happens to the rest - which stages accumulate the most fall-out, where exceptions concentrate, and whether your configuration rules are pending clean claims that should have auto-adjudicated.
Loop Atlas (Claims)
Each denial-rework loop costs $25-$181 in labor, and 35-60% of denied claims are never resubmitted. Loop Atlas traces every rework cycle end to end - how many times claims revisit stages, the labor cost of each loop, and which denial types generate the most waste.
Pend Aging View
Pend aging is tracked through weekly Excel pulls. This view replaces that with a live dashboard - pended claims by reason code, queue, and age. See which queues are growing, which pend reasons dominate, and which claims have been sitting for 45+ days waiting on COB verification.
Resource Leaderboard (Claims)
What does it actually cost to process a claim? The Leaderboard ranks every claims resource - human and automated - by throughput, handle time, and cost per touch. Compare RPA performance against human processors and make informed staffing decisions.
How It Works
Connect
Lightweight, read-only integrations with your claims adjudication and workflow systems. 3-5 data sources, minimal IT involvement.
See
Claims processing dashboards live in 2-4 weeks. Fall-out analysis, pend aging, rework quantification, and resource scorecards from day one.
Act
Target the fall-out reasons and denial types that cost the most. Reduce pend aging. Reallocate resources to exception-heavy queues. Measure the true ROI of your automation investments.
From ThinkSpace
The Real Cost of Claims Fall-Out: What Health Plans Do Not Measure
Let's See If We Can Help
No pitch deck. No pressure. Just a conversation about what you're trying to solve and whether we're a fit.
Good fit if you're:
- Running clinical workflows and can't see where cases stall or why
- Managing multiple vendors and teams with no unified performance view
- Trying to answer 'what does a case cost?' without a 3-week spreadsheet
- Running AI in production but can't prove the ROI
Book a Call
- 30 minutes with our founding team
- Discovery, not a demo
- Honest assessment of fit
Or email us at sales@operon.cloud