Mizzeto offers a comprehensive suite of healthcare services designed to streamline payer back office opreations.
Business Process Automation
Optimizing Healthcare Operations with BPO and Automation for Faster, Smarter Service Delivery.
Accelerate results in utilization management, claims configuration, and beyond with BPO solutions designed for payers. Whether you need end-to-end support or targeted assistance, our flexible solutions adapt to your needs without slowing you down.
Accelerate claims adjudication with intelligent automation and expert handling of exceptions—driving accuracy, speed, and savings.
Ensure accurate benefit interpretation with rules-based configuration that minimizes errors and audit risk.
Optimize enrollment and billing with automated, compliant processes that enhance member experience, minimize errors, and accelerate onboarding and revenue capture.
Eliminate claim delays and costly errors with streamlined provider data management and credentialing processes.
Unlock hidden revenue with accurate payment reconciliation and faster provider recovery workflows
Digitize inbound mail with high-accuracy data capture to accelerate processing and streamline workflows.
Deliver high-quality member support through scalable contact center teams trained in payer-specific needs, ensuring personalized service while efficiently managing fluctuating call volumes.
Ensure data accuracy and regulatory compliance with rigorous transactional audits that uphold quality and reduce risk.
Empower strategic decisions with custom reports and interactive dashboards delivering actionable insights.
Our payer solutions provide a range of client benefits including: streamline operations, cost reductions, and overall customer satisfaction. Our global team allows us to enable faster claims processing, accurate data analysis, and enhanced decision-making without increasing client costs.
Mizzeto accelerates prior authorization decisions by up to 40% through intelligent intake, automated data validation, and payer-specific rules configuration. This results in faster treatment initiation and improved provider satisfaction.
Mizzeto improves member enrollment turnaround times by streamlining and fully automating the process. This can be through customized system configuration, reduction in enrollment file errors, and workflow automations.
For every 3% lift in claims auto-adjudication rate, clients have the potential to save up to $250K annualy.
Healthcare operations are complex, fragmented, and buried in manual work. Mizzeto’s automation suite plugs into your existing platforms—like QNXT, Facets, and Epic—without forcing a rip-and-replace.
SLAs aligned to your KPIs, with real-time reporting, performance guarantees, and built-in scalability during peak volumes
Utilize Gen AI to make data-driven decisions and predictions
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Optimize workflows and reduce errors with our automation suite
Manage provider data through automating workfows
Minimize provider overpayments using pre-adjudication auditing
Increase auto-adjudication rates through RPA automation
The rapid acceleration of AI in healthcare has created an unprecedented challenge for payers. Many healthcare organizations are uncertain about how to deploy AI technologies effectively, often fearing unintended ripple effects across their ecosystems. Recognizing this, Mizzeto recently collaborated with a Fortune 25 payer to design comprehensive AI data governance frameworks—helping streamline internal systems and guide third-party vendor selection.
This urgency is backed by industry trends. According to a survey by Define Ventures, over 50% of health plan and health system executives identify AI as an immediate priority, and 73% have already established governance committees.
However, many healthcare organizations struggle to establish clear ownership and accountability for their AI initiatives. Think about it, with different departments implementing AI solutions independently and without coordination, organizations are fragmented and leave themselves open to data breaches, compliance risks, and massive regulatory fines.
AI Data Governance in healthcare, at its core, is a structured approach to managing how AI systems interact with sensitive data, ensuring these powerful tools operate within regulatory boundaries while delivering value.
For payers wrestling with multiple AI implementations across claims processing, member services, and provider data management, proper governance provides the guardrails needed to safely deploy AI. Without it, organizations risk not only regulatory exposure but also the potential for PHI data leakage—leading to hefty fines, reputational damage, and a loss of trust that can take years to rebuild.
Healthcare AI Governance can be boiled down into 3 key principles:
For payers, protecting member data isn’t just about ticking compliance boxes—it’s about earning trust, keeping it, and staying ahead of costly breaches. When AI systems handle Protected Health Information (PHI), security needs to be baked into every layer, leaving no room for gaps.
To start, payers can double down on essentials like end-to-end encryption and role-based access controls (RBAC) to keep unauthorized users at bay. But that’s just the foundation. Real-time anomaly detection and automated audit logs are game-changers, flagging suspicious access patterns before they spiral into full-blown breaches. Meanwhile, differential privacy techniques ensure AI models generate valuable insights without ever exposing individual member identities.
Enter risk tiering—a strategy that categorizes data based on its sensitivity and potential fallout if compromised. This laser-focused approach allows payers to channel their security efforts where they’ll have the biggest impact, tightening defenses where it matters most.
On top of that, data minimization strategies work to reduce unnecessary PHI usage, and automated consent management tools put members in the driver’s seat, letting them control how their data is used in AI-powered processes. Without these layers of protection, payers risk not only regulatory crackdowns but also a devastating hit to their reputation—and worse, a loss of member trust they may never recover.
AI should break down barriers to care, not build new ones. Yet, biased datasets can quietly drive inequities in claims processing, prior authorizations, and risk stratification, leaving certain member groups at a disadvantage. To address this, payers must start with diverse, representative datasets and implement bias detection algorithms that monitor outcomes across all demographics. Synthetic data augmentation can fill demographic gaps, while explainable AI (XAI) tools ensure transparency by showing how decisions are made.
But technology alone isn’t enough. AI Ethics Committees should oversee model development to ensure fairness is embedded from day one. Adversarial testing—where diverse teams push AI systems to their limits—can uncover hidden biases before they become systemic issues. By prioritizing equity, payers can transform AI from a potential liability into a force for inclusion, ensuring decisions support all members fairly. This approach doesn’t just reduce compliance risks—it strengthens trust, improves engagement, and reaffirms the commitment to accessible care for everyone.
AI should go beyond automating workflows—it should reshape healthcare by improving outcomes and optimizing costs. To achieve this, payers must integrate real-time clinical data feeds into AI models, ensuring decisions account for current member needs rather than outdated claims data. Furthermore, predictive analytics can identify at-risk members earlier, paving the way for proactive interventions that enhance health and reduce expenses.
Equally important are closed-loop feedback systems, which validate AI recommendations against real-world results, continuously refining accuracy and effectiveness. At the same time, FHIR-based interoperability enables AI to seamlessly access EHR and provider data, offering a more comprehensive view of member health.
To measure the full impact, payers need robust dashboards tracking key metrics such as cost savings, operational efficiency, and member outcomes. When implemented thoughtfully, AI becomes much more than a tool for automation—it transforms into a driver of personalized, smarter, and more transparent care.
An AI Governance Committee is a necessity for payers focused on deploying AI technologies in their organization. As artificial intelligence becomes embedded in critical functions like claims adjudication, prior authorizations, and member engagement, its influence touches nearly every corner of the organization. Without a central body to oversee these efforts, payers risk a patchwork of disconnected AI initiatives, where decisions made in one department can have unintended ripple effects across others. The stakes are high: fragmented implementation doesn’t just open the door to compliance violations—it undermines member trust, operational efficiency, and the very purpose of deploying AI in healthcare.
To be effective, the committee must bring together expertise from across the organization. Compliance officers ensure alignment with HIPAA and other regulations, while IT and data leaders manage technical integration and security. Clinical and operational stakeholders ensure AI supports better member outcomes, and legal advisors address regulatory risks and vendor agreements. This collective expertise serves as a compass, helping payers harness AI’s transformative potential while protecting their broader healthcare ecosystem.
At Mizzeto, we’ve partnered with a Fortune 25 payer to design and implement advanced AI Data Governance frameworks, addressing both internal systems and third-party vendor selection. Throughout this journey, we’ve found that the key to unlocking the full potential of AI lies in three core principles: Protect People, Prioritize Equity, and Promote Health Value. These principles aren’t just aspirational—they’re the bedrock for creating impactful AI solutions while maintaining the trust of your members.
If your organization is looking to harness the power of AI while ensuring safety, compliance, and meaningful results, let’s connect. At Mizzeto, we’re committed to helping payers navigate the complexities of AI with smarter, safer, and more transformative strategies. Reach out today to see how we can support your journey.
Feb 21, 2024 • 2 min read
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.
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.
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.
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.
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.
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.
For quick reference, successful QNXT implementations address these critical areas:
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.
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.
Jan 30, 2024 • 6 min read
Why utilization management may determine who clears the coming audit wave—and who doesn’t.
CMS doesn’t usually announce a philosophical shift. It signals it. And over the past year, the signals have grown louder: tougher scrutiny of utilization management, more rigorous document reviews, and an expectation that payers show—not simply assert—how they operate. The 2026 audit cycle will be the first real test of this new posture.
For health plans, the question is no longer whether they can survive an audit. It’s whether their operations can withstand a level of transparency CMS is poised to demand.
Behind every audit protocol lies a single question: Does this plan operate in a way that reliably protects members? Historically, payers could answer that question through narrative explanation—clinical notes, supplemental files, post-hoc clarifications. Those days are ending. CMS wants documentation that stands on its own, without interpretation. Decisions must speak for themselves.
That shift lands hardest in utilization management. A UM case is a dense intersection of clinical judgment, policy interpretation, and regulatory timing. A single inconsistency—a rationale that doesn’t match criteria, a letter that doesn’t reflect the case file, a clock mismanaged by a manual workflow—can overshadow an otherwise correct decision.
The emerging audit philosophy is clear: If the documentation doesn’t prove the decision, CMS assumes the decision cannot be trusted.
Auditors are increasingly zeroing in on UM because it sits at the exact point where member impact is felt: the determination of whether care moves forward. And yet the UM environment inside most plans is astonishingly fragile.
Case files exist across platforms. Reviewer notes vary widely in depth and style. Criteria are applied consistently in theory but documented inconsistently in practice. Timeframes live in spreadsheets or side systems. Letter templates multiply to meet state and line-of-business requirements, and each variation introduces new chances for error.
Delegated entities add another degree of variation. AI tools introduce sophistication—but also opacity. And UM letters, already the last mile, turn into the site of the most findings. The audit findings from recent years reveal the same weak points over and over: documentation mismatches, missing citations, unclear rationales, inadequate notice language, or timing failures that stem not from malice but from operational drift.
CMS sees all of this as symptomatic of one problem: fragmentation.
To CMS, consistency is fairness. If two reviewers evaluating the same procedure cannot produce the same rationale, use the same criteria, or generate the same clarity in their letters, then members cannot rely on the decisions they receive. From the regulator’s perspective, this isn’t about paperwork—it’s about equity. Documentation is the proof that similar members receive similar decisions under similar circumstances.
Health plans know this in theory. But the internal pressures—volume, staffing variability, outdated systems, multiple point solutions, off-platform decisions, peer-to-peer nuances—make uniformity nearly impossible. CMS’s response is simple: Technical difficulty is not an excuse. Variation is a governance failure.
This is why the agency is preparing to scrutinize AI tools with the same rigor as human reviewers. Automation that produces variable results, or outputs that do not exactly match the case file, is no different from human inconsistency.
CMS is not anti-AI. It is anti-opaque-AI.
Plans that will succeed in 2026 are building something different: a coherent operating system that eliminates guesswork. In these models, the case file becomes a single source of truth. Clinical summaries, criteria references, rationales, and letter text are drawn from the same structured data—so the letter is a natural extension of the decision, not a separate narrative created afterward.
Delegated entities operate under unified templates, shared quality rules, and real-time oversight rather than annual check-ins. AI is governed like a medical policy: with defined behaviour, monitoring, version control, and auditable outputs. And timeframes are treated with claims-like precision, not as deadlines managed by human vigilance.
This is not just modernization—it is a philosophical shift. A move from “reviewers record what happened” to “the system records what is true.”
The path forward isn’t mysterious; it’s disciplined. Plans need to invest the next year in cleaning up documentation, consolidating UM data flows, reducing template drift, tightening delegation oversight, and putting governance around every automated tool in the UM pipeline. The plans that do this will walk into audits with confidence. The plans that don’t will rely on explanations CMS is increasingly unwilling to accept.
The 2026 CMS audit cycle isn’t a compliance event—it’s an operational reckoning. CMS is asking payers to demonstrate integrity, not describe it. And utilization management will be the proving ground. The strongest plans are already acting. The others will be forced to.
At Mizzeto, we help health plans build the documentation, automation, and governance foundation needed for a world where every UM decision must be instantly explainable. Because in the next audit cycle, clarity isn’t optional—it’s compliance.
Jan 30, 2024 • 6 min read
In the age of AI-driven utilization management (UM), one paper trail still refuses to move at the speed of automation: the UM letter.
Whether it’s an approval, denial, or request for additional information, these letters remain the last mile of every UM decision, and too often, the slowest. Despite sophisticated review platforms and integrated medical policy engines, many health plans still rely on legacy templates, fragmented data sources, and manual QA loops to generate what regulators consider a fundamental compliance artifact. UM letters are not just a formality; they are a legal requirement. Under CMS rules, plans must issue timely, adequate notice of adverse benefit determinations, explaining both the rationale and appeal rights to members.
The irony is hard to miss: while decisions are made in seconds, the documentation that justifies them can take days.
The issue isn’t simply that UM letters take time. It’s why they take time, and what that delay reveals about deeper system inefficiencies.
For health plans, the question isn’t “How can we make letters faster?” It’s “Why are they so hard to get right in the first place?”
A single UM letter must synthesize clinical reasoning, regulatory precision, and plain-language clarity all aligned with CMS, NCQA, and state-specific notice requirements. The challenge is not in the writing, but in orchestrating inputs from multiple systems: clinical review notes, policy citations, benefit text, and provider data.
When those inputs don’t talk to each other, letter generation becomes a bottleneck that slows down turnaround times, increases error risk, and erodes member trust.
UM letter templates are not just administrative artifacts; they are regulatory documents. Under Centers for Medicare & Medicaid Services (CMS) rules, letters providing notice of adverse benefit determinations must meet detailed content and timing standards. For example, the regulation at 42 CFR § 438.404 mandates that notices be in writing and explain the reasons for denial, reference the medical necessity criteria or other processes used, provide the enrollee’s rights to copies of evidence and appeal, and outline procedures for expedited review.1
In practice, this means letter templates must include:
Failure to incorporate these elements or to issue the notice within required timeframes can expose plans to audit findings, grievances, and regulatory penalties. The tighter the regulatory lens becomes, the less room there is for “good enough” templates. Each health plan must view letter-generation not as a clerical task but as a compliance checkpoint. And beyond the regulatory content itself, many programs require that UM notices be written in plain, accessible language at the 6th-8th grade level, to ensure members can understand their rights and the basis for a decision.
Every health plan faces variations of the same problem, but the underlying breakdowns tend to cluster around five recurring fault lines:
Each of these friction points compounds the next, creating a cycle of rework, delay, and compliance exposure even in otherwise modernized UM environments.
The operational burden of slow UM letters goes far beyond staff productivity. It directly affects regulatory performance, provider satisfaction, and member experience.
Delayed or inconsistent notices can:
The cost is not just administrative, it’s reputational. Every late or unclear letter represents a breakdown in transparency at the very point where payers are most visible to members and regulators alike.5
Leading plans are tackling the problem not with more templates, but with smarter orchestration.
The most effective UM letter modernization strategies share three principles:
The goal isn’t to remove people, it’s to remove friction. Automation should serve precision, not replace it.
When designed correctly, next-generation letter systems can cut turnaround time by 50–70%, reduce rework, and strengthen audit readiness while making communications clearer for both providers and members.
UM letters may seem administrative, but they are where compliance, communication, and care converge. If denials are the visible output of your UM program, letters are the proof of its integrity.
For payers, the question isn’t whether letters can be automated, it’s whether they can be governed with the same rigor as the decisions they document.
At Mizzeto, we help health plans modernize UM letter workflows, integrating automation, policy governance, and compliance intelligence into one seamless ecosystem.
Jan 30, 2024 • 6 min read
In utilization management (UM), few metrics speak louder—or cut deeper—than overturn rates. When a significant share of denied claims are later approved on appeal, it’s rarely just about an individual decision. It’s a reflection of something bigger: inconsistent policy interpretation, reviewer variability, documentation breakdowns, or outdated clinical criteria.
Regulators have taken notice. CMS and NCQA increasingly treat appeal outcomes as a diagnostic lens into whether a payer’s UM program is both fair and clinically grounded.1 High overturn rates now raise questions not just about accuracy, but about governance.
In Medicare Advantage alone, more than 80 % of appealed denials were overturned in 2023 — a statistic that underscores how often first-pass decisions fail to hold up under scrutiny.2 The smartest health plans have started to listen. They’re treating appeals not as administrative noise—but as signals.
Every overturned denial tells a story. It asks, implicitly: Was the original UM decision appropriate, consistent, and well-supported?
Patterns in appeal outcomes can expose weaknesses that internal audits often miss. For example:
These trends mirror national data showing that many initial denials are overturned once additional clinical details are provided, highlighting communication—not medical necessity—as the core failure.3 The takeaway is simple but powerful: Appeal data is feedback—from providers, from regulators, and from your own operations—about how well your UM program is working in the real world.
When you look beyond the surface, overturned denials trace back to five systemic fault lines common across payer organizations:
Federal oversight agencies have long flagged this issue: an OIG review found that Medicare Advantage plans overturned roughly three-quarters of their own prior authorization denials, suggesting systemic review flaws and weak first-pass decision integrity.4
Leading payers are reframing appeals from a reactive function to a proactive improvement system.
They’re building analytics that transform overturn data into actionable intelligence:
This approach turns what was once a compliance burden into a continuous-learning advantage.
High overturn rates are not just a symptom—they’re an opportunity. Each reversed denial offers a data point that, aggregated and analyzed, can make UM programs more consistent, more transparent, and more clinically aligned.
The goal isn’t to eliminate appeals. It’s to make sure every appeal teaches the organization something useful—about process integrity, provider behavior, and the evolution of clinical practice.
When health plans start to see appeals as mirrors rather than metrics, UM stops being a gatekeeping exercise and becomes a governance discipline.
Overturned denials aren’t administrative noise—they’re operational intelligence. They show where your policies, people, and processes are misaligned, and where trust between payer and provider is breaking down.
For forward-thinking plans, this is the moment to reimagine UM as a learning system.
At Mizzeto, we help health plans turn appeal data into strategic insight—linking overturned-denial analytics to reviewer training, policy governance, and compliance reporting. Because in utilization management, every reversal has a lesson—and the best programs are the ones that listen.
Jan 30, 2024 • 6 min read
12414 James Madison Lane, Glenn Dale, MD 20769
