Revolutionizing Interoperability

For years, prior authorization improvement efforts have centered on one metric: speed. Faster turnaround times. Shorter queues. Quicker determinations. When backlogs grow, the instinctive response is to push harder, add staff, tighten SLAs, accelerate intake, automate submission.

And yet, despite sustained investment, many health plans find themselves in a familiar place. Requests move faster into the system, but decisions do not come out any cleaner. Appeals rise. Clinical teams feel busier, not better supported. Regulatory scrutiny intensifies.  

The problem isn’t that health plans aren’t moving quickly enough. It’s that they’re optimizing for the wrong outcome.

The Question Leaders Should Be Asking

The critical question facing payer executives today is not how to make prior authorization faster. It is how to make authorization outcomes decision-ready.

In theory, prior authorization is a linear process. A request arrives. Medical necessity is assessed. A decision is rendered and communicated. In practice, speed at the front of the process often exposes fragility downstream. Requests arrive sooner, but incomplete. Data flows faster, but inconsistently. Clinical documentation is attached, but not usable.

What feels like progress—shorter intake cycles, higher submission volumes—often masks a deeper inefficiency: decisions still require the same amount of searching, clarifying, and rework. Sometimes more.

When speed becomes the primary goal, organizations optimize how fast work enters the system, not how effectively it can be resolved.

Why Faster Intake Often Slows Decisions

In our experience working with payer organizations, most delays in prior authorization do not occur because reviewers are slow. They occur because reviewers are forced to reconstruct meaning from poorly prepared inputs.

Requests arrive with missing or mis-keyed information. Clinical notes are uploaded as hundreds of unstructured pages. Policy criteria are technically met, but not clearly demonstrated. Nurses and physicians spend their time hunting for evidence rather than applying judgment.  

A routine imaging authorization, for example, may arrive with a 200-page chart attached—office notes, lab results, historical encounters spanning years. The information needed to approve the request may exist somewhere in the record, but reviewers must sift through dozens of irrelevant pages to find it. The delay isn’t clinical complexity. It’s the effort required to locate and validate the right signal inside too much noise. That friction compounds downstream, creating a clinical review bottleneck where highly trained staff spend their time searching for context instead of making decisions.

Accelerating intake without addressing these issues simply increases the volume of work that is not ready to be decided. Each incomplete request introduces pauses, clarifications, and handoffs. What should have been a single pass through the system becomes multiple touches across multiple teams.

From the outside, this looks like insufficient capacity. From the inside, it is capacity being quietly consumed by avoidable friction. Across the U.S. health care system, administrative burden tied to prior authorization contributes to multi-billion dollar annual costs, reflecting how inefficient processes absorb payer and provider resources long before clinical review begins.¹

This is where many modernization efforts stall. Automation accelerates submission and routing, but PA automation alone does not change the quality of what enters the system. Providers submit more requests because it is easier to do so. Intake teams process them faster. Clinical reviewers inherit the same defects at higher velocity. Speed amplifies whatever already exists—and when work is not decision-ready, it multiplies rework rather than reducing it

What High-Performing Plans Optimize Instead

Organizations that consistently control prior authorization performance focus less on turnaround time and more on decision quality at entry.

They ensure requests arrive complete and structured, reducing manual re-keying and downstream correction. Reflecting this shift, a significant proportion of health plans have already implemented electronic prior authorization systems, signalling both the complexity of modern workflows and the growing emphasis on reducing manual friction.² They normalize data so policy criteria can be evaluated consistently. They surface the specific clinical evidence needed for a decision, rather than forcing reviewers to search entire records. And they treat policy logic as a shared, governed asset—not something interpreted differently by each reviewer.

As a result, their systems move work through once. Appeals decrease because rationales are timely and clear. Clinical teams spend their time applying judgment instead of assembling context. Speed improves, but as a consequence of better design, not as the primary objective.

The shift is subtle but decisive. The goal is no longer faster authorization. It is fewer touches per authorization.

Why This Matters Now

Prior authorization sits at the intersection of cost control, access, and regulatory oversight. As CMS and other regulators increasingly expect decisions to be explainable, not just defensible—as reinforced by the CMS prior authorization rule—the cost of prioritizing speed over clarity rises. Under the CMS Interoperability and Prior Authorization final rule (CMS-0057-F), impacted payers must provide prior authorization decisions within 72 hours for urgent requests and seven calendar days for standard requests, and include specific reasons for denials to improve transparency and explainability of decisions.³ The rule shifts expectations away from throughput alone and toward consistency, traceability, and timely rationale.

Systems that rely on heroics and overtime may hit SLAs in the short term, but they accumulate risk. Systems designed for decision readiness scale more predictably and withstand scrutiny more effectively.

What executives experience as utilization management pressure is rarely a failure of effort. It is a signal that the system has been optimized for motion, not resolution.

At Mizzeto, we work with payer organizations to address this exact gap—connecting intake, clinical review, and policy logic so prior authorization decisions can be made efficiently, consistently, and explainably. This is the design philosophy behind Smart Auth, our prior authorization platform—ensuring requests arrive decision-ready, with structured intake, reduced rework, and clinical evidence surfaced in context rather than buried in charts.

Because in modern utilization management, sustained performance isn’t about pushing teams harder. It’s about removing the friction that never needed to be there in the first place.  

If your team is hitting SLAs but appeals keep climbing, let’s talk.

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SOURCES

1. Prior Authorization Statistics Statistics: Market Data Report 2026

2. https://worldmetrics.org/prior-authorization-statistics/  

3. https://content.govdelivery.com/accounts/USCMSMEDICAID/bulletins/3d5c65a

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Prior authorization backlogs are often described as volume problems. They show up as growing queues on operational dashboards, rising turnaround times, and escalating pressure on clinical teams. The explanation, almost reflexively, is that demand arrived faster than expected - too many requests, too little time.

But for most health plans, that explanation doesn’t hold up under scrutiny. Prior authorization backlogs are rarely caused by volume alone. They are caused by friction inside the authorization process itself. Friction that is well known, consistently repeated, and largely predictable.¹

The Question Leaders Should Be Asking

The real question isn’t why prior authorization volume increased. It’s why so many authorization requests cannot move cleanly from intake to decision. In theory, prior auth is straightforward: receive a request, assess medical necessity, render a decision, notify the provider. In practice, the work looks very different.

Requests arrive incomplete. Key fields are missing or entered incorrectly. Clinical documentation is attached as hundreds of unstructured pages. Nurses and physicians spend their time searching for the few sentences that actually matter. Decisions stall because they are clinically complex, but because the information required to make them is fragmented, inconsistent, or buried.

Backlogs form not at the moment of clinical judgment, but long before that judgment can even begin.

Where Prior Authorization Actually Breaks Down

Most prior authorization backlogs are built upstream, during intake. Provider offices submit requests with missing clinical details, outdated codes, or attachments that don’t align to policy requirements.² Internal coordinators re-key information from faxes, portals, or PDFs, introducing small errors that force rework later. Many prior authorization delays stem from manual processes and technology gaps, leading to inefficiency and error-prone workflows.³ Each defect is minor on its own, but together they create a steady drag on throughput.

Downstream, clinical reviewers inherit this friction. Nurses sift through large medical records to reconstruct timelines.⁴ Physicians pause decisions while clarifications are requested. Requests bounce between teams. Appeals increase, not always because the decision was wrong, but because the rationale was delayed or unclear. The backlog grows quietly, one stalled case at a time.

Why This Feels Like “Unexpected Volume”

From a distance, all of this looks like a surge. Executives see more cases aging past SLA. Leaders see staff working harder without visible progress. The conclusion is that volume must be overwhelming capacity. In reality, capacity is being consumed by rework.

Every incomplete intake, every mis-keyed field, every unclear policy reference turns a single request into multiple touches. What should have been a linear process becomes a loop. The backlog isn’t driven by how many requests arrived, it’s driven by how many times each request must be handled before it can be resolved. That multiplier effect is predictable. And yet, it’s rarely modeled.

Why Automation Alone Doesn’t Fix Prior Auth Backlogs

Automation is often applied at the intake layer, with the promise of speed. And it does make submission faster. Providers submit more requests. Intake teams process them more quickly. But if the underlying issues remain - missing information, poor data normalization, unstructured records, automation simply accelerates the arrival of flawed work.

Clinical teams feel this immediately. More cases arrive faster, but with the same defects. Reviewers spend less time waiting and more time searching, clarifying, and escalating.⁵

This is why many health plans modernize prior auth technology and still experience worsening backlogs. Automation has increased flow, but not decision readiness.

What High-Performing Plans Do Differently

Plans that control prior authorization backlogs focus less on speed and more on decision quality at intake.

They invest in ensuring requests arrive complete, structured, and aligned to policy requirements. They reduce manual keying wherever possible. They use technology to surface the right clinical evidence, rather than flooding reviewers with entire charts. And they treat policy interpretation as something that must scale consistently across reviewers, not as tribal knowledge.

Most importantly, they measure where requests stall and why. Backlogs are treated as signals: indicators of where information breaks down, where policy is unclear, or where rework is being introduced.

As a result, their queues are smaller and not because demand disappeared, but because requests move through the system once, instead of three or four times.

The Preventable Nature of Prior Authorization Backlogs

When prior authorization backlogs are framed as staffing or volume problems, they persist. When they are understood as information and workflow problems, they become solvable.

Prior auth backlogs don’t originate in clinical decision-making. They originate in how information enters the system and how much effort it takes to make that information usable.

What executives experience as UM backlogs are almost always prior authorization system outcomes. They reflect whether a health plan has designed prior authorization to support clean, defensible decisions at scale.

At Mizzeto, we work with payer organizations to address this exact gap. Connecting intake, clinical review, and policy logic so prior authorization decisions can be made efficiently, consistently, and explainably. Through Smart Auth, we help plans ensure requests arrive decision-ready: structured intake, reduced manual rework, and clinical evidence surfaced in context rather than buried in charts. Because in modern utilization management, sustained performance isn’t about pushing teams harder. It’s about removing the friction that never needed to be there in the first place.

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SOURCES

1. https://www.ama-assn.org/practice-management/prior-authorization/prior-authorization-delays-care-and-increases-health-care
2. ‍https://www.aha.org/system/files/media/file/2023/10/aha-urges-cms-to-finalize-the-improving-prior-authorization-processes-proposed-rule-letter-10-27-2023.pdf ‍
3. https://www.atlantisrcm.com/knowledge/single/prior-authorization-delays-the-new-billing-bottleneck-in-the-u-s ‍
4. https://www.aha.org/system/files/media/file/2022/10/Addressing-Commercial-Health-Plan-Challenges-to-Ensure-Fair-Coverage-for-Patients-and-Providers.pdf ‍
5. https://blog.nalashaahealth.com/prior-authorization-automation-for-healthplans

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If you're a VP of Configuration, CIO, or COO at a mid-size health plan, you've likely heard the horror stories. A health plan system migration that was supposed to modernize operations instead creates months of claims backlogs. Provider networks revolt over payment delays. Members flood call centers with complaints. The project that promised transformation becomes a fight for survival.

These cautionary tales aren't outliers. According to research from McKinsey and the University of Oxford, large-scale IT projects run an average of 45 percent over budget and 7 percent over time, while delivering 56 percent less value than predicted (McKinsey, 2012). In healthcare specifically, Gartner research indicates that 83 percent of data migration projects either fail outright or don't meet their planned budgets and schedules (Gartner, 2023). For health plans managing complex claims systems like QNXT or Facets, these statistics should be a wake-up call.

The Real Cost of Getting It Wrong

When a health plan system migration fails, the consequences ripple across every corner of your organization. Claims processing grinds to a halt, creating backlogs that can take months to clear. Providers lose confidence when payments are delayed or adjudicated incorrectly, straining relationships you've spent years building. Members experience frustration when their claims are denied in error or their benefits information is inaccessible.

Perhaps most critically, regulatory compliance can be compromised during a troubled migration. With the CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F) requiring impacted payers to implement non-technical provisions by January 1, 2026, and API requirements by January 1, 2027, the margin for error has never been thinner (CMS, 2024). A botched migration can put your organization at risk of failing to meet these mandates, potentially exposing you to penalties and damaging your reputation with state regulators.

McKinsey's research reveals an even more sobering reality: 17 percent of large IT projects become "black swans"—catastrophic failures with budget overruns exceeding 200 percent that can threaten the very existence of the organization (McKinsey, 2012). For a regional Medicaid MCO or Medicare Advantage plan operating on thin margins, a project of this magnitude going wrong isn't just an inconvenience. It's an existential threat.

What Success Actually Looks Like in a Health Plan System Migration

Too many health plans define migration success narrowly as reaching go-live. But true success extends far beyond flipping the switch. A successful health plan system migration delivers operational stability from day one. Claims auto-adjudication rates remain high. Provider payment cycles stay consistent. Member services teams can access accurate information to resolve inquiries.

Configuration accuracy is equally essential. Your benefit plans, provider contracts, and business rules must translate precisely from the legacy system to the new platform. Even minor configuration errors can cascade into major payment inaccuracies, triggering provider disputes and regulatory scrutiny. According to KLAS Research, network and provider contracts are among the biggest challenges to manage in any claims processing platform, and misconfigurations during migration are a primary source of post-go-live problems (KLAS, 2020).

Staff adoption matters just as much as technical execution. The most elegantly designed system delivers no value if your configuration analysts, claims examiners, and customer service representatives can't use it effectively. Success means your teams feel confident, not overwhelmed, when they log in on day one. Finally, regulatory compliance must be maintained throughout the transition. Whether it's HIPAA data security, state-specific Medicaid requirements, or the looming CMS interoperability mandates, your compliance posture can never take a back seat to project timelines.

Key Phases of a Successful Migration

The foundation of any successful migration is a thorough discovery and assessment phase. This isn't a cursory inventory of your current system—it's a deep dive into how your organization actually operates. Which benefit configurations are standard, and which represent years of accumulated customizations? What undocumented workarounds has your team developed? Where does institutional knowledge live that might not survive the transition? Rushing through discovery virtually guarantees costly surprises later.

Parallel testing is where theory meets reality. Running both systems simultaneously on real-world claim scenarios exposes discrepancies before they become production problems. This phase requires patience and rigor. A regional health plan that recently migrated from a legacy platform discovered during parallel testing that their provider fee schedule translations had subtle rounding errors. Catching this before go-live prevented what would have been thousands of incorrect payments and the administrative nightmare of recoupment.

Data validation cannot be an afterthought. Member eligibility records, provider demographics, historical claims data, and prior authorization information must transfer accurately and completely. HIMSS Analytics research indicates that 78 percent of healthcare organizations have either completed or are in the process of migrating data to new systems, and data compatibility issues remain a top challenge (HIMSS, 2023). Establishing clear validation protocols and acceptance criteria before migration begins gives your team objective measures of success.

Staff training deserves far more attention than most migration plans allocate. Your configuration analysts need hands-on practice with the new system's logic, not just theoretical walkthroughs. Your claims examiners need to understand how familiar processes translate to new workflows. Change management isn't a soft skill—it's a critical success factor. A phased rollout approach reduces risk by allowing you to identify and address issues at manageable scale. Finally, post-go-live stabilization requires dedicated resources and realistic expectations. Even well-executed migrations require weeks of close monitoring and rapid issue resolution.

Common Pitfalls to Avoid

The most dangerous pitfall is underestimating configuration complexity. Health plan configurations are living systems shaped by years of regulatory changes, contract negotiations, and operational refinements. What appears straightforward in documentation often conceals intricate dependencies. Plans that approach migration as a simple lift-and-shift inevitably discover—usually too late—that their new system doesn't behave as expected.

Insufficient user acceptance testing is equally perilous. Under pressure to meet deadlines, organizations often truncate UAT cycles or limit testing to sunny-day scenarios. But edge cases and exception handling are where migrations most frequently fail. The claim that adjudicates perfectly in testing may error when it encounters an unusual modifier combination or a retroactive eligibility change. Comprehensive UAT requires time, realistic test data, and involvement from the staff who will actually use the system.

Inadequate change management rounds out the most common failure modes. Technical excellence means nothing if your organization isn't prepared to adopt new ways of working. Resistance from staff who feel blindsided or unsupported can undermine even the best implementations. The Standish Group's CHAOS Report consistently identifies lack of executive support and user involvement as primary drivers of project failure (Standish Group, 2020).

The Role of Experienced Partners

Health plan system migrations are not the time for on-the-job learning. The complexity of claims configurations, the stakes of regulatory compliance, and the operational risks involved demand expertise that comes from hands-on experience across multiple implementations. Partners who have configured QNXT, Facets, or other major platforms bring pattern recognition that internal teams simply cannot develop from a single migration.

Specialized consultants can identify configuration pitfalls before they become problems, validate data migration completeness, and provide the supplemental staffing that allows your core team to maintain operational continuity during the transition. They bring objectivity to project planning, helping executives set realistic timelines and budgets based on actual experience rather than optimistic projections. For mid-size health plans without dedicated implementation teams, external expertise isn't a luxury—it's often the difference between success and costly failure.

Modernization as Competitive Advantage

The health plans that navigate system migrations successfully don't just survive—they emerge stronger. Modern core administration platforms enable the operational agility that today's healthcare environment demands. They position organizations to meet CMS interoperability requirements not as a compliance burden but as an opportunity to improve member and provider experiences. They create the foundation for AI-powered automation, real-time analytics, and the kind of operational efficiency that translates directly to competitive advantage.

The question isn't whether your health plan will eventually need to modernize its systems. The question is whether you'll do it on your terms, with careful planning and expert support, or be forced into a reactive scramble when legacy platforms can no longer keep pace with regulatory and market demands.

Partner with Mizzeto for Your System Migration

At Mizzeto Healthcare Technology Consulting, we specialize in helping mid-size health plans navigate the complexities of system migrations. Our consultants bring deep, hands-on experience with QNXT, Facets, and other leading claims platforms. We understand the configuration intricacies that can derail a migration, the regulatory requirements that can't be compromised, and the operational realities of keeping a health plan running while transforming its technology foundation.

Whether you're planning a migration to meet CMS 2026 mandates, evaluating new core administration platforms, or recovering from a troubled implementation, Mizzeto can help. We offer migration readiness assessments, configuration validation, staff augmentation, and the specialized expertise that turns high-risk projects into successful transformations.

Contact Mizzeto today for a free migration readiness assessment. Let's discuss how we can help your health plan modernize with confidence.

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References

CMS. (2024). CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F). Centers for Medicare & Medicaid Services. https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-and-prior-authorization-final-rule-cms-0057-f

Gartner. (2023). Data Migration Project Failure Statistics. Referenced in Barcelona Health Hub analysis.

HIMSS Analytics. (2023). Healthcare Data Migration Survey Report.

KLAS Research. (2020). Payer Core Administration Platforms: New Decisions and New Life. https://klasresearch.com/report/payer-core-administration-platforms-2020

McKinsey & Company. (2012). Delivering Large-Scale IT Projects on Time, on Budget, and on Value. McKinsey Digital.

Standish Group. (2020). CHAOS Report: Beyond Infinity. The Standish Group International.

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Few initiatives test a health plan's operational resilience like a core claims system implementation. According to research from McKinsey and the University of Oxford, 66% of enterprise software projects experience cost overruns, and 17% go so badly they threaten the organization's existence.^¹ For health plans implementing QNXT, the stakes include regulatory compliance, provider relationships, and member satisfaction—all at risk if the project goes sideways.

The good news: most implementation failures are preventable. Understanding where projects typically break down allows health plans to plan proactively and avoid the most common pitfalls.

Data Migration and Conversion Complexity

Every QNXT implementation begins with a deceptively simple question: how do we move our data? The answer is never straightforward. Legacy claims systems store member information, provider records, and historical claims in formats that rarely align with QNXT's data model. Mapping decades of accumulated data—complete with inconsistencies, duplicates, and outdated codes—requires meticulous planning.

The risks are significant. Incomplete member histories create gaps in care coordination. Misaligned provider data leads to incorrect reimbursements. Claims history errors trigger audit findings and compliance exposure.

What works: Successful migrations follow a phased approach. Extract and profile legacy data early to understand its quality and structure. Build robust mapping rules with input from both technical staff and business users who understand the data's context. Validate extensively in parallel testing environments before cutover—identifying discrepancies in a test environment costs far less than fixing them in production. Budget adequate time for data cleansing; it almost always takes longer than planned.

Benefit Configuration Complexity

QNXT's flexibility is both its greatest strength and its most significant implementation hurdle. Configuring benefits correctly requires understanding the interplay between plan-level and product-level settings, accumulator logic, coordination of benefits rules, and state-specific requirements for Medicaid and Medicare Advantage populations.

Configuration errors rarely surface immediately. They emerge weeks or months later as claims adjudicate incorrectly, members receive wrong explanations of benefits, or accumulators fail to track properly toward deductibles and out-of-pocket maximums. By then, the remediation effort compounds exponentially.

What works: Prioritize your highest-volume, highest-risk benefit configurations for early testing. Build comprehensive test case libraries that cover edge cases—not just the happy path. Document configuration decisions as you make them; institutional knowledge disappears quickly when team members move on. Engage business analysts who understand both the regulatory requirements and QNXT's configuration nuances. For Medicaid and Medicare Advantage plans, involve compliance staff early to ensure configurations align with CMS requirements.

Auto-Adjudication Rate Optimization

Go-live is just the beginning. Many health plans discover that their auto-adjudication rates plummet after implementing QNXT. The industry standard benchmark for auto-adjudication hovers around 80%, with best practice targets above 85%.^² Yet many organizations fall short, with first-pass rates ranging from 10% to 70%.^³

The financial impact is substantial. An auto-adjudicated claim costs health insurers cents on the dollar, while one requiring human intervention costs approximately $20.^⁴ Every claim that falls out of auto-adjudication strains examiner capacity and extends turnaround times.

Low auto-adjudication rates typically stem from a few root causes: overly conservative editing rules, incomplete provider data, poorly configured fee schedules, or business rules that don't account for real-world claim variations. The system works as configured—the configuration simply doesn't reflect operational reality.

What works: Analyze pend patterns weekly in the months following go-live. Identify which edits generate the most fallout and assess whether they're truly necessary or just overly cautious defaults. Tune provider matching logic to reduce false pends from minor data discrepancies. Refine authorization integration so valid authorizations are properly recognized. Establish a continuous improvement cycle rather than treating go-live as the finish line.

Integration with Your Existing Ecosystem

QNXT doesn't operate in isolation. It must connect with EDI gateways for 837, 835, 834, and 270/271 transactions. It needs interfaces to provider portals, member platforms, care management systems, and payment integrity vendors. Each integration point introduces complexity—and potential failure modes.

The challenge intensifies when health plans operate hybrid environments during transition periods. Data must flow correctly between legacy and new systems without duplication, loss, or timing mismatches. Real-time authorization lookups must perform at production scale. Provider directories must stay synchronized across platforms.

Research shows that 51% of companies experience operational disruptions when going live with new enterprise systems, often due to integration failures.^⁵

What works: Start integration testing earlier than you think necessary. Build end-to-end test scenarios that simulate production volumes and edge cases. Document every interface specification and establish clear ownership for each connection. Consider middleware layers to buffer complexity, but account for the latency and additional failure points they introduce. Plan for a parallel processing period where both old and new systems run simultaneously, allowing you to validate results before fully cutting over.

Training, Change Management, and Staffing Gaps

Even a perfectly configured QNXT instance fails if your people can't use it effectively. Research indicates that up to 75% of the financial benefits from new enterprise systems are directly linked to effective organizational change management—yet many organizations allocate less than 10% of their total project budget to this critical area.^⁶

Implementation partners eventually leave. Institutional knowledge walks out the door. Claims examiners, configuration analysts, and IT staff must internalize new workflows, screens, and processes—often while maintaining production on legacy systems.

The training gap is particularly acute for configuration roles. QNXT benefit configuration requires specialized expertise that takes months to develop. Many health plans underestimate this learning curve and find themselves dependent on external consultants long after go-live.

What works: Build knowledge transfer into implementation contracts from day one. Document configuration decisions and create runbooks for common scenarios. Identify internal staff for intensive mentorship during the project—not just attendance at training sessions, but hands-on involvement in configuration work. Plan for productivity dips in the months following go-live and staff accordingly. Consider whether supplemental staffing can bridge capability gaps during the transition period rather than burning out your core team.

The Five Core QNXT Implementation Challenges

For quick reference, successful QNXT implementations address these critical areas:

1. Data migration and validation — ensuring complete, accurate conversion from legacy systems through phased extraction, robust mapping, and extensive parallel testing

2. Benefit configuration — methodical setup with comprehensive testing across all lines of business, with early compliance involvement for government programs

3. Auto-adjudication optimization — continuous tuning post-go-live to maximize straight-through processing and reduce costly manual intervention

4. System integration — reliable connections to EDI, portals, and downstream vendors, tested at production scale before cutover

5. Training and change management — building internal expertise through hands-on involvement, not just classroom training, with realistic productivity expectations

Moving Forward

QNXT implementations are complex, but complexity doesn't have to mean chaos. Health plans that approach these projects with realistic timelines, thorough testing protocols, and genuine investment in their people consistently outperform those who underestimate the effort involved.

The patterns of failure are well-documented. So are the patterns of success. The difference usually comes down to preparation, honest assessment of internal capabilities, and willingness to invest in the areas—like change management and post-go-live optimization—that don't appear on the software license invoice but determine whether the project delivers value.

About Mizzeto

At Mizzeto, we help health plans navigate high-stakes platform transitions with the same rigor they apply to clinical and regulatory decisions. Our teams support QNXT implementations and optimization across Medicare, Medicaid, Exchange, and specialty lines of business—bridging strategy, configuration, and operational execution. The goal isn’t just a successful go-live, but durable performance: higher auto-adjudication, cleaner integrations, and internal teams equipped to govern the system long after consultants exit.

If your organization is preparing for a QNXT implementation—or working to stabilize and optimize one already in production—we’re always open to a thoughtful conversation.

Sources

1. McKinsey & Company and BT Centre for Major Program Management at the University of Oxford. "Delivering Large-Scale IT Projects On Time, On Budget, and On Value." https://www.forecast.app/blog/66-of-enterprise-software-projects-have-cost-overruns

2. Healthcare Finance News. "Claims processing is in dire need of improvement, but new approaches are helping." https://www.healthcarefinancenews.com/news/claims-processing-dire-need-improvement-new-approaches-are-helping

3. HealthCare Information Management. "Understanding Auto Adjudication." https://hcim.com/understanding-auto-adjudication/

4. Healthcare Finance News. "Claims processing is in dire need of improvement, but new approaches are helping." https://www.healthcarefinancenews.com/news/claims-processing-dire-need-improvement-new-approaches-are-helping

5. RubinBrown ERP Advisory Services. "Top ERP Insights & Statistics." https://kpcteam.com/kpposts/top-erp-statistics-trends

6. Sci-Tech-Today. "Enterprise Resource Planning (ERP) Software Statistics." https://www.sci-tech-today.com/stats/enterprise-resource-planning-erp-software-statistics/

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