2026 Prior Authorization Mandates

Navigating CMS’s 2026 Prior Authorization Turnaround Time Mandates

Health plan leaders are approaching a pivotal deadline in utilization management. Beginning on January 1, 2026, new federal regulations from the Centers for Medicare & Medicaid Services (CMS) will significantly reduce the turnaround times for prior authorization decisions. These changes stem from the CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F), which aims to streamline approval processes and improve timely access to care. Under this rule, Medicare Advantage plans, state Medicaid agencies, managed care plans, CHIP programs, and exchange-based Qualified Health Plans must respond to expedited prior authorization requests within 72 hours and standard requests within seven calendar days.

Historically, prior authorization processes could extend up to 14 days, making this change particularly impactful for health plans. CMS projects these accelerated decisions and electronic processing will save approximately $15 billion over ten years by reducing administrative inefficiencies. Given the magnitude of this regulatory shift, utilization management (UM) directors and chief medical officers need to rapidly adjust their operations, technology infrastructure, staffing, and reporting mechanisms to ensure compliance.

Background and Rationale of the New Requirements

Prior authorization delays have long hindered patient care by postponing necessary treatments. Acknowledging these persistent issues, CMS finalized the 2024 Interoperability and Prior Authorization Rule to promote quicker, more transparent decisions. The rule standardizes response times, sets clear expectations for denial explanations, and mandates annual public reporting of key authorization metrics, including volumes and average decision times.

Simultaneously, CMS is pushing health plans towards broader adoption of electronic prior authorization and interoperability standards, specifically through FHIR-based APIs to be fully implemented by 2027. This digital transformation seeks to eliminate outdated practices like faxed or phone-based authorizations, dramatically modernizing utilization management processes.

Operational Changes in Utilization Management

The new turnaround requirements compel health plans to thoroughly reengineer their prior authorization workflows. Health plans must now swiftly triage incoming authorization requests, classify them as urgent or routine, and ensure rapid, proactive outreach to providers if necessary information is missing. Many health plans are proactively reassessing the necessity of prior authorizations for lower-risk services, thus reducing the total number of requests requiring manual intervention.

Automation has become critical. Leveraging rules-based decision-making tools, health plans can automatically approve routine, evidence-based requests, significantly reducing turnaround times. This digital integration also supports real-time processing, reducing the administrative burden and allowing clinical teams to concentrate on complex or ambiguous cases.

Technology Infrastructure Improvements

Meeting these new CMS requirements is as much a technology challenge as it is operational. Transitioning from legacy systems to fully digital, API-driven solutions is essential. Health plans are rapidly adopting HL7 FHIR standards, enabling electronic submissions and automated responses directly integrated within providers' electronic health records (EHRs).

Implementing these advanced technologies requires significant investments in IT infrastructure and data management capabilities. Health plans must ensure accurate data mapping, consistent clinical documentation, and robust privacy and security controls. Successful technological transitions not only meet compliance but also offer considerable long-term operational efficiencies.

Workforce Implications

These tighter turnaround requirements directly affect staffing strategies within utilization management departments. Health plans may need to increase staffing levels, cross-train employees, or introduce additional shifts, including weekend or holiday coverage, to manage expedited cases effectively. Moreover, clinical staff will need to clearly document and communicate reasons for denials, enhancing transparency and reducing confusion for providers.

Enhanced Reporting and Accountability

Increased transparency is a central aspect of the new regulations. Beginning in 2026, health plans must publicly report comprehensive prior authorization metrics. This unprecedented level of public accountability is expected to drive continuous performance improvement and foster competitive differentiation among health plans based on their efficiency and responsiveness.

Internally, enhanced data reporting capabilities will be crucial. Health plans are developing automated systems to track, report, and analyze prior authorization metrics. Regular analysis of these metrics will help identify performance bottlenecks, inform process adjustments, and improve provider interactions.

Impact on Member and Provider Experience

The anticipated improvements from these regulatory changes promise substantial benefits for patients and providers alike. Faster decisions will reduce delays in essential treatments, and clear explanations for authorization denials will enable more effective clinical decision-making. Providers, integrating authorization processes into their EHR workflows, will find the prior authorization process less burdensome, enhancing overall provider satisfaction and clinical efficiency.

Members will experience improved transparency and predictability, positively influencing their overall healthcare experience. As health plans continue to refine these processes, consumer trust and satisfaction are expected to increase significantly.

Preparing for Successful Implementation

Health plan leaders should:

• Conduct comprehensive assessments of current authorization workflows.

• Accelerate adoption of advanced interoperability and API technologies.

• Invest in adequate staffing and robust training programs.

• Establish strong internal reporting and analytics infrastructure.

• Proactively engage and educate providers about new processes and expectations.

By embracing these strategies, health plans will not only comply with CMS mandates but also position themselves as industry leaders in utilization management.

Conclusion

The CMS 2026 prior authorization regulations mark a substantial shift towards quicker, more transparent, and patient-centered healthcare processes. While the transition poses challenges, the long-term benefits—enhanced efficiency, improved patient care, and stronger provider relationships—make the effort worthwhile. Health plan leaders who proactively adapt and innovate will set new standards in utilization management, ultimately benefiting providers, members, and the broader healthcare community.

Sources

1. CMS Interoperability & Prior Authorization Final Rule (CMS0057F) – details on turnaround timelines, denial rationale requirement, API deadlines CMADocs+15Centers for Medicare & Medicaid Services+15K&L Gates+15MCG Health+6Epstein Becker Green+6CMADocs+6Leavitt Partners An HMA Company+1Centers for Medicare & Medicaid Services+1The Guardian

2. AMA insights and industry pledges – scope reduction, transparency goals, continuity of care commitments American Medical Association+4American Medical Association+4The Guardian+4

Strategic Impact of Federal Medicaid Cuts

Congress recently passed significant Medicaid cuts as part of the "Big Beautiful Bill," reshaping the financial landscape for healthcare providers and health plans nationwide. These funding reductions profoundly impact healthcare stakeholders, affecting Medicaid eligibility, provider reimbursements, and overall healthcare access. This article examines these cuts in detail, highlighting strategic insights for health plan executives.

Breakdown of the Medicaid Cuts

The Congressional Budget Office (CBO), analyzed extensively by KFF, forecasts substantial federal Medicaid spending reductions over the next decade, estimating a nationwide reduction of approximately $500 billion over ten years. Most states will experience an average decrease of about 13%, with some states facing cuts ranging from 7% to as high as 18%. These funding reductions specifically target expansion populations, managed care rates, and provider reimbursement structures.

Impact on Health Plans

Health plans will experience both immediate and long-term effects from these Medicaid cuts. Increased member churn and enrollment volatility are likely outcomes, given that tightened eligibility criteria will cause individuals to frequently cycle in and out of Medicaid. This scenario increases administrative burdens, eligibility verification costs, and outreach expenses aimed at retaining eligible members. Furthermore, maintaining continuity of care and managing member health outcomes amid these disruptions will pose additional challenges.

Financial forecasting and risk management will become even more critical. Health plans must adjust their actuarial assumptions, premium rate-setting, and risk adjustment methodologies to account for potential revenue fluctuations and changing risk profiles. Contingency planning will be essential to navigate financial uncertainties resulting from decreases in enrollment.

Operational efficiencies will also become imperative as budgets tighten. Health plans will need to strategically invest in automation and advanced analytics to reduce administrative overhead and streamline core processes like utilization management (UM) and claims processing. Emphasizing preventive care and chronic disease management programs will also help lower costs associated with emergency and acute care services.

Provider-Side Implications

Providers will encounter significant reimbursement pressures due to reduced Medicaid funding, impacting care delivery and financial stability. Health plans will need to proactively manage potential narrowing of provider networks as reimbursement rates decline, which may increase provider contract negotiations. To mitigate these pressures, innovative value-based payment models and alternative reimbursement structures can incentivize quality outcomes and help stabilize provider networks.

Network adequacy and provider stability will become central concerns, as reimbursement cuts could lead to provider attrition from Medicaid networks, jeopardizing access to care for Medicaid members. The increased demand for essential providers, such as behavioral health specialists and primary care physicians, will further complicate network adequacy. Collaborating closely with providers on shared-risk arrangements can help stabilize networks and ensure continuity of care.

State-by-State Variability

The Medicaid cuts vary significantly across states. High-impact states such as California, Texas, and New York face larger proportional reductions, intensifying pressures on local health systems and requiring more strategic adjustments. In contrast, lower-impact states like Wyoming, Florida, and Alabama may have greater flexibility in transitioning their Medicaid operations. Health plan executives must closely analyze state-specific data to accurately project regional impacts and tailor responses accordingly.

Forward-Looking Strategic InsightsTo effectively respond to these Medicaid cuts, health plans should consider several strategic actions. Embracing value-based care models can stabilize revenue streams and improve patient outcomes by reducing reliance on fee-for-service reimbursement structures. Investing in digital transformation, including advanced analytics, automation, and artificial intelligence (AI), will enhance operational efficiencies, streamline UM processes, prior authorization, and claims processing.

Enhancing provider partnerships is another critical strategy. Health plans should explore shared-risk arrangements and provide resources to support providers in navigating financial pressures and improving operational efficiencies. Additionally, implementing robust member engagement and outreach strategies can help mitigate churn by proactively communicating eligibility criteria, renewal processes, and preventive care resources. Enhanced digital tools can further simplify member interactions and improve overall satisfaction.

Conclusion

The Medicaid cuts implemented under the "Big Beautiful Bill" represent a significant shift in the Medicaid landscape, requiring health plan executives to make critical strategic decisions. By proactively addressing enrollment volatility, reimbursement pressures, operational efficiencies, and provider stability, health plans can navigate these reductions effectively and maintain the quality of care delivery.

Health plans that embrace innovation, strengthen provider partnerships, and enhance operational agility will be well-positioned to manage the impact of these Medicaid cuts and emerge stronger in an evolving healthcare market. At Mizzeto, we specialize in helping health plans adapt to market disruptions through smart automation, AI-powered operational tools, and expert staffing solutions. Whether you're reevaluating your UM workflows, navigating reimbursement shifts, or planning for churn-driven outreach, our team is here to help you build resilient, cost-efficient systems.

Sources:

https://www.kff.org/medicaid/issue-brief/allocating-cbos-estimates-of-federal-medicaid-spending-reductions-and-enrollment-loss-across-the-states/?utm_source=chatgpt.com

https://www.investors.com/politics/one-big-beautiful-bill-act-president-trump-tax-cuts-medicaid-spending/?utm_source=chatgpt.com

https://www.wsj.com/health/healthcare/medicaid-cuts-healthcare-trump-bill-7236d5e6?utm_source=chatgpt.com

https://healthjournalism.org/blog/2025/07/trumps-budget-bill-could-cost-lives-put-5-million-in-debt-how-to-cover-the-story/?utm_source=chatgpt.com

https://www.americanprogress.org/article/the-truth-about-the-one-big-beautiful-bill-acts-cuts-to-medicaid-and-medicare/?utm_source=chatgpt.com

https://www.cbo.gov/publication/61469?utm_source=chatgpt.com

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Modernizing UM Intake with OCR Excellence

In healthcare payer operations, utilization management (UM) intake is a mission-critical function—tasked with ingesting, interpreting, and processing prior authorization requests, clinical review documentation, and care determination materials submitted by providers. Yet despite widespread digital transformation across other parts of the healthcare ecosystem, UM intake remains heavily manual. Faxed forms, PDFs, and scanned medical records often arrive via fragmented workflows, requiring health plan staff to manually extract, transcribe, and route essential data to the correct systems and reviewers. This inefficiency adds significant friction to operations, delaying determinations and increasing labor costs. According to the 2023 CAHQ Index¹ only 31% of prior authorization transactions are fully electronic, while 35% still rely on manual submission methods—a persistent mismatch that creates avoidable delays and administrative burden for health plans.

AI-driven OCR and NLP systems in payer settings have achieved very high accuracy in extracting and interpreting data from clinical and administrative documents. For example, a U.S. federal health agency (likely the VA) modernized its claims intake with an AI-based OCR platform: the legacy system’s ~77% accuracy was boosted to over 96% accuracy, while automating 99% of the form processing². These results indicate that modern OCR/NLP solutions can surpass the 95%+ accuracy mark, often corresponding to extremely high classification AUCs. Such accuracy dramatically reduces human error – insurers have seen up to 90% error reduction in document processing when adopting OCR automation³. High precision in data extraction means payers can trust AI outputs for critical processes, minimizing misclassifications or overlooked information.

Enter Optical Character Recognition (OCR), now enhanced by natural language processing (NLP) to transform unstructured documentation into structured, actionable data. Unlike legacy OCR tools that merely digitize text, today’s AI-augmented OCR systems can parse scanned clinical forms and extract key fields—such as diagnosis codes, provider names, dates of service, and member identifiers—with high accuracy. For health plans, this advancement allows manual intake to be replaced—or significantly augmented—by automated workflows that feed critical data directly into UM systems, reducing processing time, ensuring auditability, and accelerating clinical review timelines.

From Bottlenecks to Breakthroughs: Time and Cost Savings

The operational impact of OCR is significant. Manual UM intake can take 10–15 minutes per case; OCR-capable automation handles them in seconds. When multiplied across thousands of prior authorizations, the downstream effect is profound—faster processing, reduced labor cost, fewer transcription errors, and a more agile payer workflow.

Faster, cleaner intake dramatically improves care delivery velocity. Staff can redirect from data entry to higher-value tasks like complex case review or provider outreach. Moreover, structured intake data enables robust analytics. Health plans can identify bottlenecks, monitor denial rates, and drill into clinical trends—all made possible by reliable, automated data capture.

Safeguarding Accuracy, Compliance, and Privacy

Speed means little without accuracy and control. To ensure trusted OCR deployment, health plans must couple systems with domain-specific training—covering medical terminology, diagnostic codes, and typical UM document formats. Validation layers should automatically flag low-confidence extracts for human review—kept deliberately low, often at under 5% of cases.

From a compliance standpoint, logging every step—from document ingestion to final approval—is non-negotiable. These logs support HIPAA mandates and audit readiness. Strong encryption, access controls, and document tracing help meet CMS and OCR privacy guidelines. OCR accuracy has also been rigorously tested in federally funded studies, where performance varies by layout, font, and data quality, reinforcing the need for process-specific oversight⁴.

The Tangible Benefits of OCR-Enabled UM Intake

Evidence from clinical and administrative implementations highlights the growing maturity of OCR/NLP in healthcare environments. For example, in a federally funded study, hybrid OCR/NLP systems achieved over 99% accuracy when extracting colonoscopy and pathology data across varied clinical report formats (PMC). 

Automating UM intake dramatically accelerates processing and decision times. By digitizing incoming requests and using AI to pre-populate or even auto-adjudicate cases, health plans have cut throughput times by as much as half. Real-world deployments back this up: one regional Medicare contractor implemented an intelligent document processing solution and cut document handling times by over 50% for 35 million Medicare appeal pages, speeding up prior authorization case reviews accordingly⁵. In short, OCR/NLP intake automation enables payers to process UM requests in a fraction of the time previously required.

A Roadmap for Implementation

Deploying OCR at scale begins with Document Profiling. This first step involves identifying the highest-impact document types—typically those that are high-volume and low in variability. Prior authorization forms, structured provider notes, and standardized intake formats are ideal candidates, as they offer consistency that allows OCR engines to perform with optimal accuracy from the outset.

Next comes Pilot Deployment, where a focused OCR/NLP solution is tested on a specific document category. During this phase, health plans should measure throughput, accuracy rates, error types, and the percentage of cases requiring manual review. This controlled environment helps teams fine-tune extraction logic, validate system performance, and establish key performance indicators for scaling.

System Integration follows, ensuring the extracted data flows securely and traceably into utilization management platforms or case management systems. This includes configuring APIs or file-based ingestion pipelines, verifying that data fields map correctly, and implementing audit trails to support traceability and compliance with HIPAA and CMS requirements.

The fourth phase, Scale Strategically, involves expanding OCR capability across a broader range of document types. This may include more complex or unstructured documents such as handwritten physician notes, scanned lab forms, or multi-page clinical summaries. As use cases broaden, it’s critical to continuously refine OCR models and adjust workflows to account for new formats or data variability.

Finally, Governance and Monitoring underpin the long-term success of OCR implementation. Health plans should maintain live dashboards to monitor system accuracy, processing times, and manual intervention rates. Regular audits must be conducted to validate performance, uncover biases or drift, and ensure that all data handling practices meet HIPAA, HITRUST, and other applicable standards. This ensures not only operational excellence but also defensible compliance in a highly regulated industry.

Conclusion: OCR as the Cornerstone of Modern UM

OCR is no longer optional in modern UM workflows—it’s essential. When properly deployed and governed, OCR enables profound improvements: faster decisions, fewer errors, reduced costs, and liberated staff. The shift from bottlenecked intake to seamless, automated data capture represents a measurable leap forward for payer operations.

For health plans facing cost pressures, regulatory scrutiny, and rising expectations, OCR-powered UM intake is one of the most accessible tools for transformation. With accuracy rates routinely exceeding 95%, and ROI often reaching 30%+ in the first year, the business case is compelling.

Mizzeto brings deep healthcare domain knowledge, process design expertise, and governance frameworks tailored for payer use. If your organization is ready to eliminate intake inefficiencies, improve accuracy, and secure your UM pipeline, reach out to Mizzeto. Let us help you deploy AI-enhanced OCR responsibly, effectively, and with demonstrable results.

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Sources Cited

¹ CAHQ Index

² SPEEDING UP CLAIMS PROCESSING

‍³ Top Benefits of OCR in Insurance

⁴ Extracting Medical Information from Paper COVID-19 Assessment Forms

⁵ Amazon Web Services

Governance at Scale

As artificial intelligence reshapes healthcare operations—from member outreach to risk adjustment—health plans face a pivotal question: how to harness large language models (LLMs) safely and strategically. The answer lies in robust governance that tiers each model based on its capacity, accuracy, bias risk, and regulatory exposure.

The Landscape: Capabilities Without Guarantees

Large language models have emerged as versatile tools capable of generating fluent, contextually rich content and responding to queries across a wide spectrum of domains. Some models excel in conversational fluency, while others focus on delivering traceable, source-backed answers. However, fluency and technical metrics like perplexity—which measure how well a model predicts the next word in a sequence—do not guarantee factual reliability, safety, or fairness.

While these models demonstrate strong baseline performance, they can generate incomplete, outdated, or hallucinated content. A 2023 Stanford study found that some models hallucinated in over 20% of healthcare-related outputs, particularly when asked to summarize or recommend treatments¹. These shortcomings make rigorous evaluation and governance essential when applying LLMs in healthcare, where the stakes include patient safety, regulatory compliance, and operational integrity.

Strengths and Limitations in a Healthcare Setting

Clinical studies and operational evaluations suggest that general-purpose LLMs show promising results in areas like patient communication, decision support, and knowledge synthesis. However, assessments also reveal inconsistencies in accuracy, response variability, and hallucination of data or references. A Mayo Clinic review found that only 59% of model-generated clinical advice aligned with actual medical guidelines when left unchecked ². Models often struggle with nuance in medical context or decision-making logic, and may underperform in real-world clinical alignment.

These limitations reinforce a critical truth: even the most sophisticated LLMs must be carefully validated and monitored, particularly when integrated into healthcare workflows that impact diagnoses, treatments, or member experiences.

A Regulatory Horizon: LLMs as High-Risk Medical Tools

Governance is becoming non-negotiable. The FDA’s AI/ML Action Plan calls for lifecycle monitoring, model versioning, and real-world performance auditing. The European Union’s AI Act classifies healthcare-related AI as "high-risk," and evolving HIPAA interpretations increasingly cover algorithmic transparency and data traceability.

A Deloitte report from 2023 found that 71% of healthcare executives believe AI regulations will significantly affect future digital strategies, particularly around LLM use³. For health plans, this means implementing a rigorous framework that risk-tiers LLMs based on their application, capability, and potential for harm.

A Four-Tier Risk Framework for Health Plan LLMs

Mizzeto proposes a structured tiering model aligned with payer priorities in compliance, automation, and member impact.

Tier 1: Advisory or Information Retrieval

Tier 1 includes models used for non-clinical functions such as internal knowledge bases, FAQ bots, and general education. These applications typically present minimal risk, as they do not influence care decisions or involve sensitive data handling. The primary concerns here are outdated content and potential inaccuracies, which can usually be mitigated with well-defined content review cycles.

Governance strategies at this level should focus on basic controls: logging user interactions, conducting periodic accuracy audits, and performing Privacy Impact Checks (PICs) to ensure no protected health information (PHI) is inadvertently introduced. These models are well suited for provider self-service portals, employee onboarding, and low-risk internal search applications.

Tier 2: Administrative Automation

Tier 2 applies to models assisting with operational workflows such as claims triage, prior authorization support, and provider communications. These models play a more active role in administrative decision-making, which introduces a higher risk of downstream impact. Errors at this level could lead to incorrect approvals, delays in processing, or provider dissatisfaction.

Due to this elevated risk, governance must include human-in-the-loop oversight for high-stakes outputs. Logs should capture both prompts and model responses, and performance monitoring should track error rates, bias, and hallucination frequency. Following NIST-aligned frameworks, health plans should incorporate calibration tests to measure overconfidence in outputs and reduce automation bias.

Tier 3: Clinical-Support Applications

This tier includes use cases that directly assist clinical staff or members in understanding care options, interpreting medical information, or identifying risk factors. These models often influence—but do not finalize—care decisions. Because they operate in a high-stakes domain, even small inaccuracies or biases can disproportionately affect health outcomes or erode trust.

Effective governance in Tier 3 requires multiple layers of human review, ideally involving clinicians who can assess content accuracy and relevance. Models should be stress-tested using adversarial techniques to detect vulnerabilities such as data poisoning or performance degradation over time. Additionally, governance must track model provenance, enforce version control, and implement audit trails aligned with FDA and NIST guidelines.

Tier 4: Regulated Diagnostic or Therapeutic Support

The highest tier is reserved for models that directly assist with diagnosis, treatment planning, or other regulated medical functions. These systems are considered Software as a Medical Device (SaMD) and must comply with FDA clearance pathways, such as 510(k) or De Novo classifications. They are subject to the highest scrutiny due to their potential to directly impact patient care.

Governance in Tier 4 must be rigorous and comprehensive. This includes validated performance benchmarks, adherence to GxP practices, explainability standards, and the ability to override model recommendations in real time. These systems also require continuous real-world monitoring to ensure safety and effectiveness, as well as extensive bias testing to ensure equitable performance across diverse populations. Only models that have met these stringent requirements should be deployed in high-impact diagnostic or therapeutic environments.

Why Tiering Matters for Health Plans

A tiered governance model offers multiple strategic advantages. It enables fast rollout of low-risk tools while dedicating due diligence to high-risk applications. It ensures compliance with regulatory bodies like the FDA and aligns with global standards such as the EU AI Act. Most importantly, it focuses oversight where it matters most—on applications where errors can cause harm.

Health plans can operationalize this framework by cataloging LLM use cases and mapping them to the appropriate tier. Governance committees—spanning compliance, clinical, and IT—can establish playbooks, monitoring protocols, and update cadences. Dashboards tracking hallucination rates, bias drift, and PHI leakage support transparency and continuous improvement. This governance strategy dovetails with Mizzeto’s core philosophy: Protect People, Prioritize Equity, and Promote Health Value.

Additionally, implementing this model encourages a culture of responsible innovation. It gives organizations a structured way to experiment with new LLM applications while minimizing exposure to risk. Teams across legal, compliance, product, and data science can speak a common governance language, ensuring that development velocity doesn’t outpace safety and trust requirements.

Mizzeto has already begun implementing this governance model at scale for a Fortune 500 healthcare company, supporting LLM deployment across multiple departments including claims operations, care coordination, and digital member services. By embedding tiered oversight into AI adoption, Mizzeto has helped this client reduce operational risk, meet regulatory expectations, and confidently scale their use of generative AI while keeping patient safety and data integrity at the forefront.

The Road Ahead

As LLM adoption accelerates, governance frameworks must evolve. Explainable AI is essential for clinician trust. Bias detection mechanisms are critical for fair outcomes. Guardrails against data poisoning and alignment with NIST/WHO guidelines will future-proof these systems.

Notably, a McKinsey report found that 60% of healthcare leaders plan to expand generative AI initiatives in 2024, but only 21% have implemented formal governance structures to manage associated risks⁴. These gaps underscore the need for structured oversight like the tiering approach outlined here.

Health plans are at a turning point. Poorly governed AI can result in clinical missteps, regulatory fines, or reputational harm. Smart governance, on the other hand, transforms risk into strategic advantage. By stratifying LLMs into risk-aligned tiers, Mizzeto empowers health plans to deploy AI responsibly, drive innovation, and safeguard patient trust. Governance isn’t just compliance—it’s the infrastructure for sustainable, scalable AI success in healthcare. 

If your organization is navigating the complexities of LLM deployment and seeking a structured, proven approach to governance, Mizzeto is here to help. With deep experience implementing tiered risk models for Fortune 500 healthcare clients, we understand how to balance innovation with compliance, safety, and ROI. Whether you're exploring administrative use cases or deploying LLMs in clinical environments, our team can guide you through every step of responsible integration. Please reach out to Mizzeto to learn how we can help you properly risk-tier your LLMs and deploy them with confidence.

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¹AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries

²Medical Hallucinations in Foundation Models and Their Impact on Healthcare

³About 40% of health execs say generative AI pays off, Deloitte finds

⁴Generative AI in healthcare: Current trends and future outlook