Article

Medicare Advantage Plans Brace for Sweeping 2025 CMS Audit and Payment Rule Changes

  • June 11, 2025

CMS Tightens Oversight of Medicare Advantage Plans

In the coming year, the nation’s Medicare Advantage insurers – which cover over 31 million Americans – face an unprecedented wave of regulatory changes and scrutiny. The Centers for Medicare & Medicaid Services (CMS) has quietly ushered in a more aggressive audit regime for Medicare Advantage (MA) plans, alongside significant updates to how these plans are paid for the health risks of their enrollees.

Health plan CEOs, whose organizations collectively received about $455 billion in Medicare payments last year, are now grappling with what these changes mean operationally and financially. Many are preparing for a future in which annual federal audits become a routine part of doing business and risk adjustment rules are rewritten to curb excess payments.

Oversight Intensifies: RADV Audits Expand in 2025

Late this spring, CMS announced a dramatic expansion of its Risk Adjustment Data Validation (RADV) audits – the primary tool for verifying that MA plan payments are justified by members documented health status. Historically, CMS audited only a small sample (around 60) of MA contracts each year, targeting plans suspected of excessive billing. That is changing effective immediately: CMS will audit all eligible Medicare Advantage contracts annually (approximately 550 plans in total)1. In addition, the agency is fast-tracking a backlog of past years’ audits, pledging to complete all outstanding audits for payment years 2018 through 2024 by early 2026. This means health plans could be hit with multiple audit findings in short succession, condensing what might have been a decade of scrutiny into a much shorter window.

“We are committed to crushing fraud, waste and abuse across all federal healthcare programs,” Dr. Mehmet Oz, the CMS Administrator, said in a statement announcing the new audit strategy. While emphasizing the value of Medicare Advantage, Oz underscored that CMS must ensure [plans] are billing the government accurately2.

The RADV audits themselves will also become more intensive. CMS is increasing the sample size of medical records it reviews for each plan from about 35 records to as many as 200 records per plan annually1. By reviewing a larger slice of each plan’s claims, CMS aims to make any identified error rates more credible for extrapolation – a process of projecting the sample’s error rate onto the plan’s entire member population1. CMS finalized a rule in 2023 that, for the first time, allows auditors to extrapolate overpayment findings starting with audits of 2018 claims onward. In the past, if an audit uncovered (for example) $100,000 in improper payments in the sample, the plan would repay that amount; now CMS can multiply that figure across all similar cases in the year – a change that could turn modest audit findings into multimillion-dollar liabilities for plans.

To support this ambitious oversight agenda, CMS is bolstering its audit arsenal. The agency will deploy “enhanced technology” – including advanced data analytics, and potentially artificial intelligence, to flag suspect diagnoses in billing data1. It is also undertaking a massive workforce expansion, increasing its team of medical coders from just 40 to roughly 2,000 by September 2025 to manually review records and confirm unsupported codes2. This 50-foldstaffing surge underscores the scale of CMS’s commitment. All Medicare Advantage plans can now expect an audit each year, a stark departure from an era when many insurers never faced a RADV audit at all1.

For health plans, the immediate implication is a significant operational burden. Insurers will need to respond to ongoing documentation requests, often under tight deadlines, and may find themselves in perpetual audit preparation mode. Some plans are already ramping up their own internal audit teams and processes to mirror CMS’s efforts, aiming to catch and correct errors proactively before federal auditors arrive.

A Revamped Risk Adjustment Model and Policy Changes

Behind the audit crackdown is a broader effort to refine how risk adjustment – the system that pays more for sicker patients – is administered. In 2024, CMS began phasing in a new risk adjustment model (known as “V28”) for Medicare Advantage, the first major overhaul in years. This updated model recalibrates which diagnoses count toward a patient’s risk score and how much they raise payments. Notably, CMS removed over 2,000 diagnosis codes from the model that it deemed prone to being “up-coded” – the practice of documenting extra or more severe conditions to inflate payments3. The goal is to target codes most likely to be abused and ensure that payments better reflect genuine health status.

The transition to the new model is occurring gradually to mitigate disruption. For payment year 2024, risk scores were calculated with a blend (33% new model, 67% old model). By 2025, the balance flips to 67% new model (V28) and 33% old4, and by 2026 the new model will be fully in place. The V28 model introduces 115 condition categories (up from 86 in the previous model) but with a more selective set of diagnosis codes – 7,770 codes mapping to those categories, versus 9,797 codes in the old model4. In practical terms, some diagnoses that used to boost payments will no longer do so, or will do so to a lesser degree. Chronic conditions like diabetes, depression, or vascular disease are among those seeing coding criteria tightened or subdivided to prevent overstating a patient’s illness burden, according to policy analysts.

CMS argues these changes will improve payment accuracy and curb excess spending. Agency officials noted that Medicare Advantage plans have been paid billions more than similar patients in traditional Medicare, partly due to aggressive coding practices. Indeed, CMS now estimates MA plans overbill the government by about $17 billion a year through unsupported diagnoses, with some estimates as high as $43 billion. The new risk model, coupled with stepped-up audits, is designed to rein in this overspending. Med PAC, a congressional advisory body, has reported that payments to MA plans in 2024 were on track to be roughly $83 billion higher than they would have been in fee-for-service Medicare for the same enrollees – a gap these policies seek to narrow.

Health plans and providers, however, have voiced concern about the speed and impact of these changes. The industry pushed back hard when the new model was proposed, prompting CMS to adopt the three-year phase-in rather than an immediate switch3. Many insurers and health systems fear the model’s stricter coding could reduce payments for vulnerable patients, potentially affecting benefit offerings. CMS’s own projections suggested that despite the model changes, average plan payments per enrollee would still rise in 2024 and 2025, due to other adjustments. But those increases may be smaller than plans are used to, and impacts will vary byplans3.

The American Medical Group Association, representing provider organizations, cautiously noted that the phase-in gives CMS “an opportunity to refine the plan” if unintended consequences emerge by 2026. In essence, while regulators see the new model as a needed course correction, the industry sees a potential budget cut in disguise, to be fought or at least closely watched.

Operational and Compliance Challenges for Health Plans

For health plan executives, the confluence of comprehensive audits and new risk scoring rules translates into a daunting compliance agenda. Operationally, plans must strengthen their documentation practices and IT systems immediately. Every diagnosis code submitted for payment must be backed by proper medical record evidence – not just to withstand a CMS audit, but to ensure the plan isn’t overstating its risk scores under the refined model. Many insurers are conducting internal RADV-style audits on 2018–2022 data right now, essentially red-flagging any diagnosis in their system that might not hold up to scrutiny. By performing these self-audits and deleting or correcting unsupported codes in CMS’s database, plans can mitigate future penalties4. This proactive approach, encouraged by consultants, aims to “reduce and manage RADV financial exposure” by addressing issues before the government does.

Provider engagement is another critical piece. Medicare Advantage insurers often rely on networks of physicians and hospitals to document diagnoses, and historically some have incentivized providers to code comprehensively. Now the dynamic is shifting: plans are implementing new provider training and education on the V28 coding changes, stressing accurate and only supported diagnoses. Some plans are also revisiting their contracts with providers. Those that share risk with providers (through value-based arrangements or bonus incentives) may insert clauses making providers financially liable for coding errors that lead to audit recoveries. If a CMS extrapolated audit claws back millions of dollars from a plan, the plan doesn’t want to shoulder that alone – it may seek to recover portions from the physician groups whose documentation was found lacking. This is a delicate conversation, but it reflects how seriously plans are treating the new audit risk.

Internally, compliance and audit departments at MA organizations are bracing for a heavier lift. Plan CEOs are evaluating whether their teams have the bandwidth and expertise to handle continuous audit requests, or if they need to enlist outside help (such as specialized auditing firms or consulting partners). The administrative load of responding to RADV audits – pulling hundreds of medical records from archives, coding them, and submitting rebuttal evidence – is significant, especially for smaller regional plans. Plans must also keep pace with evolving guidance: CMS recently issued updated RADV audit dispute and appeal instructions (effective January 2025), clarifying how plans can challenge audit findings through a reconsideration process2. Ensuring the legal team is ready to navigate these appeals, especially when extrapolated sums are on the line, will be crucial.

Finally, IT systems need updates to accommodate the 2025 risk model blend and forthcoming full model transition. Claims and billing software must incorporate the new HCC definitions so that as of January 1, 2025, incoming claims are evaluated under the correct risk adjustment logic. Misalignments here could directly affect revenue projections and compliance. Some plans have had to reconfigure analytics dashboards and retrain their coders and coding vendors on the model’s nuances – for example, which codes no longer map to an HCC (and thus no longer increase payments)4. This system work is technical, but vital to avoid errors in submissions that could trigger audits or payment shortfalls.

Financial Stakes and Industry Response

The financial implications of CMS’s 2025 changes are multifaceted. On one hand, Medicare Advantage insurers might see lower revenue growth per patient as risk scores level off under the tighter model. On the other hand, they face the possibility of paying back substantial sums if audits uncover past overpayments. Even a small error rate can translate into a large liability when extrapolated across tens or hundreds of thousands of members. Past RADV audits (2011–2013) found overpayments in the range of 5% to 8%2. If a similar error rate were found today and extrapolated, a mid-sized plan with $1 billion in annual revenue might have to refund $50–$80 million for a single year – a heavy hit to earnings.

Compounding the concern, CMS’s decision to finalize audits from 2018 through 2024 in one burst means some plans could be writing checks for multiple years’ worth of overpayments almost at once. Financial officers are reviewing reserves and worst-case scenarios now. “If CMS identifies and extrapolates overpayments for those years, financial losses due to recoupment will be concentrated over a much shorter time period than under the prior timetable,” the Ropes & Gray analysis cautioned1. In other words, what might have been staggered as a series of smaller repayments over a decade could become a tidal wave of obligations around 2025–2026. This has implications for plan budgeting, dividend plans, and even market valuations – indeed, stock analysts have begun asking public MA insurers about their audit exposures in earnings calls.

Preparing for Change: Mitigation Strategies for Plans

In response to these challenges, savvy health plans are taking a multi-pronged approach to mitigate risk. One key strategy is investing in advanced analytics to identify coding outliers. Plans are leveraging data algorithms to scan claims for patterns – for example, providers who code unusually high rates of certain lucrative diagnoses – and then conducting targeted chart reviews to verify those cases. By doing so, plans can either validate the codes with proper documentation or proactively “unlock” and remove unsupported diagnoses from their submissions, thereby inoculating against future audit findings. This kind of internal cleanup, though potentially reducing payments in the short term, can save a plan from a costly claw-back down the road. Several large insurers have created special RADV task forces for this purpose, blending expertise from compliance, IT, and clinical coding teams.

Education and training are also front and center. Health plan leaders are doubling down on provider education programs to reinforce documentation standards. For example, physicians are being reminded that every chronic condition must be explicitly documented each year in the medical record to count for risk adjustment – and if they add a diagnosis, it should be one actively managed or treated, not just noted in passing. Plans are updating provider handbooks to reflect diagnoses that no longer risk-adjust under the new model, so clinicians don’t waste effort coding conditions that won’t contribute to funding. Some plans are even offering or requiring “documentation integrity” training sessions for network providers, knowing that many audit issues can be prevented at the point of care through better record-keeping.

Another defensive measure is incorporating more stringent audit clauses in vendor contracts. Many health plans use third-party vendors for chart reviews or in-home assessments to help identify additional diagnoses. In the wake of the RADV rule, plans are making sure those vendors attest to the accuracy of codes they submit on the plan’s behalf – and assume liability if codes don’t hold up in an audit. Similarly, plans in risk-sharing arrangements with providers are clarifying how any recovered payments will be handled, as noted earlier. The overarching aim is to align incentives so that everyone – plan, provider, vendor – has “skin in the game” to only report truthful, supportable diagnoses.

From a financial planning perspective, some insurers are bolstering reserves or reinsurance coverage to cushion against possible repayments. Just as importantly, they are scenario-testing the impact of lower risk scores. CFOs are running models on 2025 revenue under various coding intensity assumptions (for instance, if certain common diagnoses drop out of HCC scoring) to guide bids and benefit design for the upcoming plan year. In extreme cases, a few plans have hinted they might need to trim benefits or adjust premiums if the new model significantly undercuts their payments – a move that would likely invite member and political backlash. For now, most are taking a wait-and-see approach, hoping that improved documentation and coding accuracy can blunt the negative financial impacts.

Navigating the Changes with Technology and Support

As Medicare Advantage organizations brace for this new regulatory landscape, many are turning to technology and specialized support services to adapt more effectively. Digital operations platforms and analytics tools are emerging as essential aids in ensuring compliance without overwhelming internal teams. For example, some health plans are deploying AI-driven software to automatically review medical records for any discrepancies between documented conditions and submitted diagnosis codes. These tools can flag potential unsupported diagnoses in real time, allowing plans to correct errors before they are picked up in a CMS audit. Enhanced reporting systems also help plans continuously monitor their risk score trends under the new model and identify areas where scores are dropping due to the V28 changes – insight that can inform provider outreach and member care programs.

Mizzeto’s healthcare digital operations suite is designed to streamline back-office processes for payers, which now include the heavy compliance workloads. For instance, Mizzeto provides audit and compliance assistance, conducting transactional audits to ensure policy compliance and quality control. Such services can take on the labor-intensive task of reviewing claims and medical records for accuracy, effectively augmenting a health plan’s internal audit department. Mizzeto also specializes in claims processing automation and data management, which helps plans keep their billing accurate and up-to-date with the latest rules. By automating routine claims checks and integrating the new risk adjustment logic into claims workflows, these technologies reduce the chance of human error that could lead to audit findings.

Another area where external partners prove valuable is in financial reconciliation and provider recovery efforts. If a plan does end up owing money back to CMS or identifies overpayments made to providers, Mizzeto’s services include analyzing overpayment situations and even helping to recoup excess payments from providers in the plan’s network. This kind of support is critical when plans are processing the results of an audit or adjusting payments post-review. It ensures that once a compliance issue is identified, the plan can resolve it swiftly on the financial side – whether that means correcting claims, retrieving funds, or crediting CMS – all with minimal disruption to operations.

Crucially, these solutions are not about replacing human expertise but augmenting it. Health plan executives remain at the helm in setting strategy (such as how to respond to CMS rule changes or when to self-audit), but they are leveraging technology and trusted partners to execute those strategies at scale. The result can be a more resilient organization: one that can handle an uptick in audits and shifting payment formulas without sacrificing focus on member care.

Looking ahead, Medicare Advantage plans will continue to refine their approach as real-world data from 2025 rolls in. Early audit results and the first full year of the new risk score model will provide feedback, showing where coding patterns need improvement or which compliance investments yield the best returns. Health plan CEOs are keenly aware that the stakes are high – both in terms of dollar amounts and public trust. Yet, with thorough preparation, the right expertise, and strategic use of technology, plans can navigate these reforms. The overarching goal is aligning Medicare Advantage’s impressive growth with robust accountability. And while the 2025 CMS audit changes pose undeniable challenges, they also present an opportunity: for health plans to demonstrate their commitment to accuracy and quality, strengthening the partnership between the government and private insurers that millions of seniors rely on every day.

1CMS Announces Significant Changes to RADV Auditing Efforts: Considerations and Next Steps for the Medicare Advantage Industry

2CMS Rolls Out Aggressive Strategy to Enhance and Accelerate Medicare Advantage Audits

3Providers, payers press CMS to get rid of Medicare Advantage risk adjustment changes entirely

4Key Areas of Focus for Risk Adjustment as the Calendar Turns to 2025

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AI Data Governance - Mizzeto Collaborates with Fortune 25 Payer

AI Data Governance

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. 

Define Ventures, Payer and Provider Vision for AI Survey

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.  

Principles of AI Data Governance  

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:  

  1. Protect People Ensuring member data privacy, security, and regulatory compliance (HIPAA, GDPR, etc.). 
  1. Prioritize Equity – Mitigating algorithmic bias and ensuring AI models serve diverse populations fairly. 
  1. Promote Health Value - Aligning AI-driven decisions with better member outcomes and cost efficiencies. 

Protect People – Safeguarding Member Data 

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.

Prioritize Equity – Building Fair and Unbiased AI Models 

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.

Promote Health Value – Aligning AI with Better Member Outcomes 

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.

Integrated artificial intelligence compliance
FTI Technology

Importance of an AI Governance Committee

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.

Mizzeto’s Collaboration with a Fortune 25 Payer

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.

February 14, 2025

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Which LLMs Are Best for Healthcare Use?

Not all intelligence is created equal. As health plans race to integrate large language models (LLMs) into clinical documentation, prior authorization, and member servicing, a deceptively simple question looms: Which model actually works best for healthcare?

The answer isn’t about which LLM is newest or largest — it’s about which one is most aligned to the realities of regulated, data-sensitive environments. For payers and providers, the right model must do more than generate text. It must reason within rules, protect privacy, and perform reliably under the weight of medical nuance

Understanding the Core Question

For payers and providers alike, the decision isn’t simply “which LLM performs best,” but “which model can operate safely within healthcare’s regulatory, ethical, and operational constraints.”

Healthcare data is complex — part clinical, part administrative, and deeply contextual. General-purpose LLMs like GPT-4, Claude 3, and Gemini Ultra excel in reasoning and summarization, but their performance on domain-specific medical content still requires rigorous evaluation.1 Meanwhile, emerging healthcare-trained models such as Med-PaLM 2, LLaMA-Med, and BioGPT promise higher clinical accuracy — yet raise questions about transparency, dataset provenance, and deployment control.

Analyzing the Factors That Matter

Evaluating an LLM for healthcare use comes down to five dimensions:

  1. Data Security and Privacy: Models must support on-premise or private cloud deployment, with PHI never leaving the payer’s-controlled environment.
  1. Domain Adaptation: Can the model be fine-tuned or context-trained on medical ontologies, payer workflows, or prior authorization rules?
  1. Explainability: Does it provide confidence scores, citations, or audit logs for generated content — essential for regulatory defense and trust?
  1. Integration Readiness: Can it interact with existing data ecosystems like QNXT, HealthEdge, or EPIC via APIs or orchestration layers?
  1. Cost and Scalability: Beyond performance, can it operate efficiently at enterprise scale without prohibitive inference costs?

The Case for General-Purpose Models

Models like OpenAI’s GPT-4 and Anthropic’s Claude 3 dominate enterprise use because of their versatility, mature APIs, and strong compliance track records. GPT-4, for instance, underpins several FDA-compliant tools for clinical documentation and prior authorization automation.2

Advantages include:

  • Maturity and security: Vendors offer HIPAA-aligned enterprise environments, audit trails, and SOC-2 compliance.
  • Cross-domain adaptability: They integrate easily across payer workflows — intake, summarization, or correspondence.
  • Rapid iteration: Frequent updates and strong partner ecosystems reduce implementation lag.

But there are caveats. General models sometimes “hallucinate” clinical or regulatory facts, especially when interpreting EHR data. Without domain fine-tuning or strong prompt governance, output quality can drift.

The Case for Healthcare-Specific LLMs

A growing ecosystem of medical-domain LLMs is changing the landscape. Google’s Med-PaLM 2 demonstrated near-clinician accuracy on the MedQA benchmark, outperforming GPT-4 in structured reasoning about medical questions. Open-source options like BioGPT (Microsoft) and ClinicalCamel are being tested for biomedical text mining and claims coding support.

Advantages include:

  • Higher clinical grounding: Trained on PubMed, clinical guidelines, and biomedical literature.
  • Explainability: Some models provide citation-based reasoning or evidence chains.
  • On-premise deployability: Open-source variants allow PHI-safe environments.

Yet, the trade-offs are real:

  • Limited generalization: These models can underperform on administrative or financial text.
  • Resource demands: Fine-tuning and maintenance require specialized infrastructure and talent.
  • Regulatory uncertainty: Validation for real-world payer use remains early-stage.

Synthesizing the Middle Ground

The emerging consensus is hybridization. Many payers and health systems are adopting dual-model architectures:

  • A general-purpose model (e.g., GPT or Claude) for summarization, knowledge extraction, and conversational interfaces.3
  • A domain-specific, internally governed model (often LLaMA or Mistral–based) for compliance-sensitive tasks involving PHI, clinical logic, or audit documentation.

This “governed ensemble” strategy balances innovation and oversight — leveraging the cognitive power of frontier models while preserving control where it matters most.

The key isn’t picking a single best model. It’s building the right model governance stack — version control, prompt audit trails, human-in-the-loop review, and strict access controls. Healthcare’s best LLM is not the one that knows the most, but the one that knows its limits.

The Bottom Line

Choosing an LLM for healthcare isn’t a procurement exercise — it’s a governance decision. Plans should evaluate models the way they would evaluate clinical interventions: by evidence, reliability, and risk tolerance.

The best LLMs for healthcare are those that combine precision, provenance, and privacy — not those that simply perform best in general benchmarks. Success lies in orchestrating intelligence responsibly, not in adopting it blindly.

At Mizzeto, we help payers design AI ecosystems that strike this balance. Our frameworks support multi-model orchestration, secure deployment, and audit-ready oversight — enabling health plans to innovate confidently without compromising compliance or control. Because in healthcare, intelligence isn’t just about what a model can say — it’s about what a plan can trust.

SOURCES

  1. Assessing the use of the novel tool Claude 3 in comparison to ChatGPT 4.0
  2. Use of GPT-4 to analyze medical records of patients with extensive investigations and delayed diagnosis
  3. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Jan 30, 20246 min read

October 24, 2025

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Article

Build or Buy? The Strategic Crossroads for Payer Automation

Every payer today faces the same dilemma: automate or fall behind. But as health plans modernize claims, prior authorization, and member servicing workflows, a harder question emerges — should automation be built in-house, or outsourced to specialized partners?

It’s not a new question, but it’s never been more consequential. The industry’s next wave of competitiveness will hinge not on whether payers automate, but how they do it — and whether their automation strategy aligns with scale, compliance, and differentiation goals.

The Core Question

At its heart, the decision to build or buy automation is a test of strategic identity. Is automation a core capability, something that defines how a plan competes and operates — or is it a commodity, a function that can be standardized and sourced efficiently from outside partners?

For some payers, automation is mission-critical — a differentiator in member experience and operational agility. For others, it’s infrastructure: vital, but not unique. That distinction shapes everything that follows.

The Case for Building In-House

Building automation internally appeals to payers seeking control, customization, and intellectual ownership. It allows them to define workflows in ways that reflect their unique mix of products, regions, and compliance requirements.

Advantages include:

  • Alignment with proprietary processes: In-house development ensures automation mirrors the plan’s rules, data models, and legacy integrations.
  • Data governance and security: Sensitive PHI and analytics stay within the enterprise perimeter.
  • Strategic flexibility: Internal teams can iterate faster and adapt automation to emerging needs without vendor dependency.
  • Institutional learning: Each build deepens internal knowledge of systems, workflows, and decision logic — a long-term competitive asset.

But building comes at a cost. It demands high technical maturity, deep domain expertise, and cross-department coordination.1 Development cycles can stretch months or years, and maintaining the systems consumes scarce IT resources. For many plans, the real bottleneck isn’t willingness — it’s capacity.

The Case for Partnering

Outsourcing automation to experienced partners offers a different calculus — one built on speed, scalability, and proven expertise.

Key advantages:

  • Faster time-to-value: Pre-built frameworks and tested integrations allow quicker deployment.
  • Regulatory assurance: Partners often stay ahead of evolving CMS, HIPAA, and interoperability mandates.2
  • Access to specialized talent: Few payers can sustain teams with expertise in both healthcare operations and advanced automation technologies.
  • Cost predictability: Subscription or managed-service models reduce capital expense and limit the risk of project overruns.

The trade-off is dependency. Vendor-managed solutions can limit flexibility, especially when plans want unique configurations or when data must flow through external systems.3 Integration complexity and long-term lock-in can also undercut initial savings.

The Hybrid Middle Ground

The best strategies often blend both approaches. Leading payers are moving toward hybrid automation models — building internal frameworks for strategic functions (e.g., utilization management, clinical decisioning) while partnering for standardized tasks (e.g., claims intake, document processing, member correspondence).

This model captures the best of both worlds: retaining control where differentiation matters, outsourcing where scale and efficiency dominate. It also creates optionality — the ability to evolve as organizational maturity, regulatory requirements, or vendor ecosystems shift.

In practical terms:

  • Build when automation defines your strategic advantage or touches sensitive clinical workflows.
  • Buy when automation is repeatable, compliance-driven, or infrastructure-heavy.
  • Blend when speed and learning are equally important.

The Decision Framework

For CEOs and CIOs, the build-vs-buy question is not purely technical — it’s strategic. A sound framework includes:

  1. Mission alignment: Does the automation initiative advance core differentiation or just maintain parity?
  1. Capability audit: Do internal teams have the skill, bandwidth, and governance maturity to sustain it?
  1. Regulatory horizon: Will external vendors adapt faster to rule changes or interoperability mandates?
  1. Cost vs. value timeline: How does total cost of ownership compare across three, five, and seven years?
  1. Data ownership: Who controls the insights, algorithms, and audit trails — and how secure are they?

These questions clarify whether automation should be a center of excellence or a service partnership.

The Bottom Line

Automation is no longer optional. But how payers approach it will separate the efficient from the exceptional. Building offers control; buying offers speed. The smartest plans will use both — designing architectures that evolve with the industry while maintaining ownership of what truly differentiates them.

At Mizzeto, we help payers strike that balance. Our modular automation frameworks integrate with core systems like QNXT, Facets, and HealthEdge, enabling plans to retain strategic control while accelerating execution. Whether building, buying, or blending, we help payers turn automation into a competitive advantage — not just an operational upgrade.

SOURCES

  1. Toolkit: Addressing the Administrative Burden of Prior Authorization
  2. CMS Interoperability and Prior Authorization Final Rule
  3. Building Interoperable Healthcare Systems - One Size Doesn't Fit All

Jan 30, 20246 min read

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min read

Article

From Promise to Proof: Measuring the ROI of Prior Authorization Reforms in 2025–2027

Few issues in healthcare generate as much consensus — and as much frustration — as prior authorization. Providers say it delays care and drives burnout. Patients say it creates barriers and confusion. Payers defend it as a necessary check on cost and safety. For decades, the debate has been stuck in a cycle of promises: that reforms are coming, that automation will help, that balance is possible.

That cycle is beginning to break. Starting in 2025, new CMS rules will tighten prior authorization response times, mandate public reporting of approval data, and require API-based interoperability across Medicare Advantage, Medicaid, CHIP, and ACA exchange plans.1 At the same time, several large payers — including Humana, Cigna, and UnitedHealthcare — have announced major cuts to prior authorization requirements.

The question is no longer if prior authorization will change. It’s how much value those changes will deliver.

For payer CEOs, the core challenge is shifting from promise to proof: measuring whether reforms translate into measurable returns in cost, efficiency, provider satisfaction, and member outcomes.

Where the Value Lies

Prior authorization touches nearly every stakeholder. That’s why ROI must be assessed on multiple fronts:

  • Operational efficiency: Every hour a nurse spends processing prior auth requests is an hour not spent on clinical judgment. Automating intake, routing, and documentation reduces this administrative drag.2
  • Provider satisfaction: According to an AMA survey, 94% of physicians reported care delays due to prior authorization,3 and 30% said it had led to a serious adverse event for a patient. Reforms that cut down unnecessary requests or speed up turnaround times directly improve the provider relationship.
  • Member experience: Delays erode trust. Streamlined prior auth can improve satisfaction scores, reduce appeals, and strengthen retention.
  • Medical cost management: The original purpose of prior authorization was cost containment. Eliminating it wholesale risks overutilization, but smart reforms — especially paired with gold-carding or risk-based contracting — can maintain oversight while cutting waste.

Each of these levers can be measured. The trick is deciding which metrics matter most for executives and regulators alike.

Early Evidence

The industry doesn’t have to speculate. Early experiments in trimming prior authorization already show ROI.

  • Humana announced in 2023 it would remove prior authorization for 1,000 services — nearly 20% of its total requirements.4 The company reported significant reductions in provider complaints and faster turnaround on the cases that still required review.
  • Cigna followed by cutting prior auth on 600 procedures, citing the need to “reduce friction” with providers. Early internal analyses showed reduced processing costs without a spike in utilization.5
  • UnitedHealthcare said it would eliminate PA for 20% of procedures in 2024. Aetna announced similar streamlining.

At the same time, automation is showing measurable impact. Plans deploying AI-assisted intake have reported reductions of 50–70% in manual review time, according to case studies published by AHIP.6

Together, these reforms point to a clear ROI pathway: fewer requests → lower admin burden → happier providers → equal or better utilization control.

Measuring What Matters

To move beyond anecdotes, payers need a measurement framework. CEOs should ask their teams:

  • How much administrative time have we saved? (Nurse hours, FTE cost equivalents, processing turnaround).
  • How has provider satisfaction shifted? (Net promoter scores, complaint volumes, participation rates).
  • What’s the member impact? (Grievances filed, appeal rates, CAHPS scores).
  • Are medical costs stable? (Utilization trends in services with PA removed vs. those retained).
  • What’s the compliance dividend? (Alignment with CMS’s transparency reporting requirements, reduced audit risk).

By tracking these measures over time, plans can prove whether reforms deliver more than good headlines.

The Strategic Risks

Of course, cutting prior authorization is not risk-free.

  • Overutilization creep: Without oversight, services like imaging or specialty drugs may see cost spikes.
  • Uneven execution: If PA cuts are applied inconsistently, providers may still face confusion — and complain even louder.
  • Regulatory mismatch: CMS requires reporting on all PA activity, even as payers reduce requirements. Plans must ensure they still have the infrastructure to measure what’s left.

The risk is not in reform itself, but in reform without data discipline.

From Compliance to Advantage

The true opportunity lies in harmonizing reforms with technology. CMS’s interoperability rule requires plans to build FHIR APIs and expose prior authorization metrics publicly. Instead of treating that as a reporting burden, payers can use the same infrastructure to create real-time dashboards for providers, track ROI metrics internally, and demonstrate performance externally.

Done right, this flips prior authorization from a compliance headache to a competitive differentiator. A plan that can show regulators, providers, and members that reforms improved experience and held costs steady will win trust in a way that rules alone can’t mandate.

The Bottom Line

The era of promises is ending. Between CMS mandates and payer-led reforms, prior authorization is undergoing its most significant transformation in decades. The real test is not whether requirements are reduced or APIs built — it’s whether these changes deliver measurable ROI in efficiency, satisfaction, and outcomes.

For CEOs, the call to action is clear: build the measurement framework now, so when reforms hit full stride in 2025–2027, you’ll have proof — not just promises — to show regulators, providers, and members alike.

At Mizzeto, we help health plans design and implement these measurement frameworks, from integrating API data feeds to creating dashboards that track ROI across operations. Reform is inevitable. Proof is optional. The plans that can show it will lead.

SOURCES

  1. CMS Interoperability & Prior Authorization Final Rule
  1. Fixing Prior Auth - American Medical Association
  1. AMA Survey Indicates Prior Authorization Wreaks Havoc On Patient Care
  1. Humana Accelerates Efforts to Eliminate Prior Authorization Requirements
  1. Cigna Healthcare Removes 25 Percent of Medical Services From Prior Authorization
  1. Improving Prior Authorization for Patients & Providers - AHIP

Jan 30, 20246 min read

October 9, 2025

2

min read

Article

The Challenges of Implementing and Upgrading Core Claims Systems

Every few years, health plan executives face the same question: stick with their core claims platform they know, or invest in the upgrade that promises better performance, new compliance capabilities, and future-proof scalability.  

On paper, upgrading a core claims systems seem straightforward. In practice, it is anything but. Behind every upgrade lies a tangle of operational disruption, hidden costs, and strategic decisions about whether incremental improvements are enough — or whether the organization needs a bigger rethink of its core system.

For CEOs, the issue is no longer whether their core systems can process claims reliably — it can. The real question is how to navigate the complexity of implementation and upgrades in a way that preserves agility, controls cost and positions the plan for a fast-changing regulatory environment.

The Implementation Challenge

Core claims systems are designed as a flexible, rules-driven platform that can accommodate the diverse needs of Medicaid, Medicare Advantage, and commercial lines of business. That flexibility is its strength — and its weakness.

Each new implementation or upgrade requires an enormous degree of configuration, testing, and integration. Payers must align their latest version of their claims systems with legacy systems (eligibility, prior authorization, provider directories, member portals), and each integration point introduces risk. A single misalignment in provider contracting rules or claims adjudication logic can cascade into payment errors, member dissatisfaction, or regulatory exposure.  

Moreover, because most core systems are often deeply customized during initial deployment, upgrades rarely feel like “plug and play.” They often require re-engineering workflows, re-validating interfaces, and retraining staff. What should be a version change can feel like a mini-implementation.

The Upgrade Bottleneck

Most payer CEOs hear the same refrain from their operations and IT leaders: “The upgrade will pay for itself in efficiency.” In theory, yes. New versions introduce better automation, compliance updates, and reporting tools. However, large-scale payer platforms were not designed in an era of real-time interoperability. Many of their core workflows still rely on batch processing, extensive customization, and legacy integration patterns. As a result, upgrades are rarely simple. Migrations can stretch across months, often introducing new bugs or defects that disrupt daily operations. The bottleneck is in execution.1

  • Downtime risk: Even short disruptions in claims processing create reputational and financial exposure. A day of delays can ripple into member grievances and provider abrasion.
  • Testing burden: Because payers often maintain highly customized rule sets, regression testing is complex and resource-intensive. IT teams must simulate thousands of claims scenarios before a go-live.
  • Cost creep: What starts as a “standard upgrade” can balloon into a multi-million-dollar initiative once consulting, testing, downtime mitigation, and staff retraining are factored in.

For CEOs, the bottleneck isn’t simply technical. It’s strategic: How many resources should be spent on making the old platform incrementally better, versus rethinking whether a next-generation solution is needed?

Regulatory Pressures

Upgrading a core claims system cannot be deferred indefinitely. Many core claims, enrollment, and utilization management systems remain siloed, limiting real-time insights and slowing operations. Regulatory requirements—like CMS’s interoperability and prior authorization rules (CMS-0057-F), network adequacy reporting, and transparency mandates—further raise the stakes, demanding standardized APIs, automated reporting, and faster turnaround.2 Without modernization, payers risk inefficiency, compliance gaps, and the inability to respond rapidly to operational pressures.

The compliance bar is also moving faster than typical upgrade cycles. A system refreshed every three to five years may not keep pace with annual regulatory changes. This creates a structural tension: the need for compliance agility versus the slow, heavy cadence of traditional upgrades.

The Organizational Strain

When implementations succeed, they can streamline claims workflows significantly. But these gains are not automatic, upgrades also test organizational resilience. Claims staff must learn new interfaces. Clinical teams relying on UM and PA modules must adapt workflows. The transition phase often requires running parallel systems, and custom integration work with provider portals, EHRs, and third-party vendors. Finance leaders face budget overruns. And executives must explain to boards why a platform upgrade is consuming so much capital and time.

For many plans, this strain is amplified by workforce realities. IT and operations teams are already lean. Pulling them into months of testing and implementation work diverts attention from member experience, provider relations, and innovation. The opportunity cost is real.

Making the Strategic Choice

Here is where CEOs must step back and ask the bigger question: Is the goal to modernize your core claims system, or to modernize the enterprise?

  • Incremental approach: Continue upgrading, absorb the disruption, and bolt on compliance tools as needed. This preserves continuity but risks technical debt and operational fatigue.
  • Transformational approach: Use the upgrade decision as a pivot point to evaluate alternative platforms, cloud-native solutions, or modular architectures that align with where the industry is headed.

Neither path is inherently right or wrong. What matters is clarity: knowing the true costs of incrementalism versus transformation and aligning the decision with the payer’s broader strategy.

Toward a Smarter Upgrade Strategy

So how should CEOs approach the next round of implementation or upgrade? A few guiding principles stand out:

  1. Treat upgrades as enterprise projects, not IT projects. The impact crosses claims, UM, provider relations, finance, and compliance. Governance must reflect that.
  1. Model total cost of ownership. Factor in not just licensing and consulting, but also downtime, retraining, and opportunity cost.
  1. Benchmark against regulatory timelines. Ask whether the upgrade cycle will keep pace with CMS mandates, or whether external modules will still be needed.
  1. Invest in interoperability first. Whether sticking with your current claims system or moving beyond it, APIs, FHIR compliance, and real-time data exchange should be the non-negotiable foundation.
  1. Build for flexibility. The real risk is not just being behind today but being unable to adapt tomorrow.

The Bottom Line

For payer CEOs, the question is not whether the platform can do the job. It can — and does, for millions of members nationwide. The real issue is whether the cost, complexity, and cadence of implementation and upgrades align with the demands of a regulatory environment that moves faster than traditional IT cycles.

Compliance is non-negotiable. Execution at speed and scale is existential.

At Mizzeto, we help health plans navigate the challenges of implementing and upgrading their core claims systems, turning complex technology transitions into smooth, high-impact changes. Our services streamline operations, modernize claims, and promote connectivity between disparate systems. By breaking down silos, automating data exchange, and delivering real-time operational insights, we help plans turn upgrades into a foundation for resilience, compliance, and measurable ROI.

Upgrading a claims system is more than a technical project—it’s a test of whether a health plan can translate technology into agility, compliance, and measurable impact.

SOURCES

  1. Payer Claims & Administration Platforms 2023 Vendor Performance in a Segmented Market
  2. CMS Interoperability & Prior Authorization Final Rule

Jan 30, 20246 min read

October 2, 2025

2

min read