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    Why Q3 Is the Most Expensive Quarter to Ignore for Healthcare Payers | Simplify Healthcare

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    Home » Resources » Blogs » Why Q3 Is the Most Expensive Quarter to Ignore for Healthcare Payers

    Why Q3 Is the Most Expensive Quarter to Ignore for Healthcare Payers

    Aug 19, 2026Blogs

    By Kim Collins, GM & SVP, Claims1™

    Every January, the exceptions arrive in a wave nobody scoped, and someone asks what broke in the claims engine. Nothing broke. The engine is doing exactly what it was configured to do. The configuration was wrong in August, and live volume is just the first thing honest enough to say so.

    I have spent most of my career in that seat. The January queue is not a debugging exercise. It is triage, run by the people who should be building the next thing, against defects that were fixable for the cost of a config edit five months earlier.

    That is what makes Q3 the decisive window. The configuration finalized before the Annual Enrollment Period determines how the first claims of the new plan year adjudicate. Fix it in August and it is an edit. Find it in January and it is rework at scale.

    What is configuration debt, and why does it compound

    Benchmarks put strong auto-adjudication near 80 percent.(2) Real first-pass rates sit well below that across most of the market, and the spread has almost nothing to do with engine capability. Two plans on the same core, the same version, can run twenty or more points apart. What separates them is line-of-business mix and configuration maturity, which is to say the spread is a measure of accumulated configuration debt, not technology.

    Configuration debt is the accumulated set of benefit, accumulator, code-edit, and network-data errors that pass quietly through pre-plan-year setup and only surface once claims adjudicate at volume. Like financial debt, it carries interest: the longer it sits undetected, the more it costs to resolve.

    Debt compounds because volume multiplies every defect. An accumulator built against the wrong benefit period passes every test case you write for it, because the test case and the configuration share the same wrong assumption. It surfaces in January, when a member hits a deductible that reset on a date the plan does not recognize, and by then it has already touched every claim in that population. I have spent enough Januaries in a configuration shop to know what the first week looks like. It is not a debugging exercise. It is triage against a queue that is growing faster than the fixes clear, run by the same people who should be building the next thing.

    Today, an estimated 15 to 20% of claims still require manual processing, which adds one to two weeks to each touched claim and pulls skilled staff into rework instead of exceptions.(3) One configuration defect, repeated across a population, becomes thousands of manual touches.

    What does configuration debt actually cost

    An auto-adjudicated claim costs pennies to process. A claim that requires human intervention costs roughly $20 as a blended average, and a pend that routes to a configuration analyst costs several times that once you count the analyst hour, the reprocessing, and the adjustment.(3) When that difference shows up in the administrative cost forecast rather than the operations dashboard, configuration debt stops being an operations problem alone. It becomes a financial liability.

    The industry-wide number is the one worth sitting with. Claims adjudication costs the U.S. system roughly $25.7 billion a year, and about $18 billion of that is considered potentially unnecessary.(4) Seventy percent waste is not an efficiency gap. It is what happens when configuration errors compound through volume, one avoidable denial and one manual touch at a time.

    The table below illustrates how the cost of a configuration error escalates the longer it goes undetected.

    When the error is caughtCost to fixDownstream impactWho absorbs it
    Pre-AEP configuration (Q3)Lowest: a config editContained before go-liveConfiguration team, planned
    AEP go-live (Oct to Dec)Higher: change under loadTesting and rework compete for timeClaims and config teams
    Q1 live volume (Jan)High: rework at scaleManual touches, denials, delayClaims ops, providers, members
    Post-Q1 remediationHighest: retroactive fixesReprocessing, adjustments, abrasionThe whole organization

    Why is fixing it now cheaper than fixing it under AEP load

    Fixing configuration in Q3 is optimal because the work is planned, isolated, and testable before any claim depends on it. Under Annual Enrollment Period load, the same fix competes with live operations, carries higher change risk, and often cannot be fully regression-tested before it ships. And a clean test season is not the same as a clean January. Test environments rarely reproduce full plan-year volume, every benefit combination, and real provider data, which is why volume, not testing, is usually where configuration debt first becomes visible.

    There is also an opportunity cost few teams price in. When skilled configuration analysts spend Q1 firefighting preventable defects, they are not building, testing, or improving anything. Automation already helps the industry avoid an estimated $222 billion in administrative spending each year, a 15% increase over the prior year, which shows how much leverage sits in getting upstream work right rather than reworking it downstream.(5) Readiness is not a cost center. It is the best investment a payer can make.

    How should payers act on the Q3 window

    Most readiness plans already account for benefit changes, new groups, and provider data. Inside the tools most plans have, the highest-leverage step is the one that ties them together: validating the benefit-to-claim handoff end-to-end and regression-testing the new configuration against prior-year adjudication before live volume does it for you. That connection is what separates a January that performs as expected from one defined by avoidable exceptions, manual workarounds, and unnecessary cost.

    Q3 is more than a planning milestone. It is one of the last opportunities to identify and resolve configuration risk before it becomes an operational problem.

    There is a difference between finding configuration errors and not creating them. Most of this market observes plan data after the core has already adjudicated against it, which means the best it can do is tell you what broke. Simplify authors the benefit and provider configuration upstream of the core. When the configuration is authored correctly at the source, the benefit-to-claim gap is closed by construction rather than by inspection.

    Not sure where your greatest exposure lies Our free Claims Operations Self-Assessment takes about ten minutes and flags which phase of the claims lifecycle carries the most risk, before small configuration gaps become large operational challenges.
    About the Author
    Kim CollinsGM & SVP, Claims1™, Simplify HealthcareKim is a healthcare technology leader with more than 25 years of experience helping health plans modernize claims operations. With deep expertise in payer technology and core claims administration, she is focused on applying AI to solve complex claims challenges and drive meaningful operational transformation.


    Sources:
    1. Medicare Annual Enrollment Period dates, U.S. Centers for Medicare & Medicaid Services. medicare.gov
    2. Claims Auto-Adjudication Rate benchmark, OpsDog. opsdog.com
    3. Reducing Costs and Efforts with Auto-Adjudication Software, MDI NetworX. mdinetworx.com
    4. Claims Adjudication Costs Providers $25.7 Billion, Premier Inc. premierinc.com
    5. New CAQH Index Reveals $20B Savings Opportunity, CAQH (2024 Index). caqh.org
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    Skills

    Benefit Accumulator Code-EditClaims Auto-AdjudicationClaims AdministrationArtificial Intelligence

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