How Health Plans Can Prepare for Member Enrollment Season

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

Preparing For Member Enrollment Season

Each fall, as open enrollment begins, health plans find themselves on the front lines of one of the most complex operational efforts in the industry: onboarding new members. For those managing a health plan, the stakes are high. A single misstep during enrollment can cascade into months of backlogs, errors, and missed care — damaging not just reputation, but patient outcomes.

That’s why this season, more health plans are turning to automation.

A Critical Moment for Member Experience

Enrollment is often the first impression a new member has of your health plan. It’s also one of the most resource-intensive processes for healthcare operations teams — requiring precise coordination between eligibility checks, ID card generation, network matching, and regulatory compliance. Last year’s open enrollment season set new records. During the 2024 Open Enrollment Period, a staggering 21.4 million members either selected or were automatically re‑enrolled in Marketplace plans—a 31% increase over the prior year. That includes 16.4 million through HealthCare.gov and 5.1 million via state‐based Marketplaces.

In short: It’s a moment of truth. And getting it right matters.

At Mizzeto, we’ve seen how fragmented systems and manual processes can bog down enrollment efforts, causing slowdowns that frustrate patients and strain staff. That’s why modernizing these workflows isn’t just a tech upgrade — it’s a care upgrade.

‍The Problem: Fragmented Systems Are Failing Member Enrollment

Despite the growing complexity of member enrollment, many health plans are still relying on outdated, manual processes. These inefficiencies introduce friction at every step—from eligibility verification delays and incomplete forms to ID card mailing lags and compliance tracking gaps. In a landscape where digital-first expectations are rising and operational resilience is essential, failing to modernize enrollment workflows can lead to increased churn, compliance risk, and unnecessary administrative spend.

These inefficiencies introduce friction at every step of the enrollment process—creating bottlenecks that frustrate members, drain staff capacity, and increase the risk of downstream errors. Eligibility verification delays can leave members in limbo, unable to access care or benefits while systems catch up. Incomplete or manual form entries lead to data inaccuracies that require costly rework and erode trust. Delays in ID card mailing not only slow access to care but also flood call centers with avoidable support requests. And without automated compliance tracking, plans face serious regulatory exposure—missing disclosures, outdated acknowledgements, or lack of documentation altogether.

In a landscape where digital-first expectations are becoming the norm and operational resilience is more critical than ever, continuing to rely on outdated, siloed systems is no longer viable. The cost isn’t just inefficiency—it’s higher member churn, avoidable compliance violations, and mounting administrative overhead. Modernizing enrollment workflows is no longer a nice-to-have; it’s a strategic necessity.

‍How Automation Transforms Member Enrollment

Automation isn’t just a convenience—it’s the foundation for a modern, resilient member enrollment experience. Leading health plans are replacing fragmented, manual processes with integrated, automated workflows that accelerate onboarding, reduce errors, and improve satisfaction for both members and staff.

It starts with intelligent eligibility verification and smart intake handling. Automated systems connect directly with payer databases to verify eligibility in real time, eliminating the need for back-and-forth with exchanges or internal teams. In parallel, OCR (Optical Character Recognition) technology can extract data from scanned or faxed UM intake forms—automatically digitizing key information and routing it into downstream systems. This dramatically reduces the need for manual re-keying, lowers the risk of transcription errors, and speeds up the review and approval of services tied to enrollment.

Next is instant benefit activation and guided plan selection. Once eligibility is confirmed, members receive their digital ID cards in seconds—no waiting for print-and-mail cycles. Simultaneously, automated plan matching tools can recommend the best-fit providers and plans based on a member’s location, prior care usage, and preferences. This seamless experience not only boosts confidence in the plan but also reduces early churn by ensuring the member is matched to care that fits their needs from day one.

Finally, end-to-end communication and compliance tracking close the loop. Automated reminders, email nudges, and portal prompts guide members through every stage of enrollment, improving completion rates and reducing support tickets. On the compliance side, automation ensures that every disclosure, acknowledgement, and timestamped interaction is captured and logged—helping payers stay audit-ready while reducing risk.

In today’s environment of staffing shortages and rising digital expectations, automation isn’t a luxury—it’s a strategic imperative. These integrated solutions not only streamline enrollment operations, they also lay the groundwork for stronger member relationships and long-term retention.

Benefits of Streamlined Enrollment Automation

When member enrollment workflows are automated, health plans see measurable improvements across the board. First impressions become lasting trust as members experience smooth onboarding from day one. Operational resilience increases as real-time systems prevent delays, misrouted claims, and network mismatches. Compliance becomes proactive instead of reactive, with automation flagging missing documentation and capturing disclosures automatically. And staff morale gets a boost as tedious manual tasks are replaced with meaningful, high-value work—improving retention and productivity at once

Today’s members expect digital ease and instant access—not paperwork and phone tag. With more than 21 million people navigating enrollment annually, even small inefficiencies can scale into major issues. As member expectations evolve, enrollment must evolve with them. That means modernizing not just the tech, but the entire experience—from eligibility and activation to communication and care coordination. Member enrollment isn’t just a task to complete; it’s the foundation for lasting engagement and plan loyalty.

Why It’s Worth the Investment

Beyond speed and accuracy, automation reduces burnout. Fewer manual entries mean fewer mistakes. Fewer mistakes mean fewer member complaints. And fewer complaints mean happier teams and healthier retention.

In the end, enrollment isn’t just about signing up members. It’s about setting the tone for how care will be delivered — efficiently, personally, and reliably.

Enrollment is no longer just an administrative necessity—it’s a strategic differentiator. A fast, seamless enrollment experience sets the tone for the entire member relationship. With automation, health plan payers don’t just survive enrollment—they own it.

Ready to turn enrollment into a competitive advantage? Book a meeting with Mizzeto to see how our solutions can transform your member experience from day one.

Breaking Bottlenecks in Utilization Management

Utilization Management (UM) remains a fundamental component of health plan operations—ensuring that care is medically necessary and delivered efficiently. However, legacy systems and manual processes continue to impede decision speed, inflate administrative costs, and undermine provider and member satisfaction. Health plan executives are under mounting pressure to modernize these workflows. This article examines two key chokepoints in UM and outlines how automation and artificial intelligence (AI) can reengineer the process for better outcomes.

Fragmented Intake Channels: An Unresolved Legacy

Despite significant investments in digital infrastructure, most prior authorization (PA) requests still arrive through outdated, unstructured channels—faxes, phone calls, emails, scanned PDFs, or even smartphone photographs. These formats demand manual transcription and interpretation, driving up labor costs and introducing errors.

According to the CAQH, only 31 percent of prior authorization transactions were processed fully electronically via ASC X12N278, while 37 percent remained fully manual—processed by phone, fax, mail, or email¹. Manual processing is considerably more expensive and time-consuming. The CAQH Index shows payer-side costs average $3.50 per manual PA, compared to just $0.05 for fully electronic transactions. On the provider side, each manual submission consumes approximately $10–11 in staff effort².

This fragmentation affects the downstream UM workflow. Staff must sort through entries, clarify ambiguities, and reconcile incomplete information—all of which extend turnaround times. Providers frequently complain of submitting faxes or emails only to receive phone calls requesting additional details days later. For health plans, this creates backlogs, missed performance targets, and strained provider relations.

Documentation Overload: Reviewing the Irrelevant Along with the Relevant

Once intake is complete, UM nurses face another critical challenge: the volume of clinical documentation submitted in support of authorization requests. Providers often send extensive electronic medical record printouts, diagnostic reports, test results, and specialist notes—sometimes totalling hundreds of pages per case.

Reviewers must manually scan these documents, identify relevant facts, cross-check coverage guidelines, and reach a clinical determination. The process varies significantly in duration, often ranging from 30 minutes to several hours per case. In workloads of 20+ cases per day, this becomes a considerable staff burden.

From the health plan perspective, these delays translate into higher appeal volumes and compliance risks. When documentation is inconsistent or unnecessarily voluminous, decision-making becomes harder to standardize, resulting in variance across reviewers and potential errors that attract regulatory attention.

Automation at Intake: Converting Chaos into Standardized Data

The advent of Intelligent Document Processing (IDP) and Natural Language Processing (NLP) transforms how unstructured intake is handled. These tools can extract structured data from faxes, PDFs, and images, identifying key fields—member demographics, diagnosis codes, CPT codes, dates of service—and automatically populating intake systems.

Phone-based submissions can be converted to text via speech-to-text and NLP solutions. The value is not in eliminating humans, but in creating a single, reliable digital intake stream.

Health plans that implement these tools report dramatic improvements. One regional insurer processed over 200,000 authorizations annually through automated systems, achieving 90 percent first-pass accuracy and reducing data-entry burden by 40 percent. These gains support compliance with evolving CMS mandates on electronic prior authorization standards³.

AI-Infused Clinical Review: Enabling Smarter Decision-Making

Automation’s benefits extend into the clinical review phase when AI-driven tools analyze and summarize documentation. Models trained on medical language and entitlement policies can identify prescribed treatments, prior interventions, labs, and imaging outcomes relevant to PA criteria.

This allows for a triaged review model: routine, low-complexity requests may be auto-approved; ambiguous or high-risk requests are flagged for clinical review. Clinicians are presented with summaries and highlighted evidence, eliminating the need to browse hundreds of pages manually.

Health plans deploying these tools report up to a 50 percent reduction in average case review time. AI-assisted systems enhance consistency and reduce cognitive overload for UM staff, while preserving human oversight for critical decisions.

Regulatory Alignment: Meeting CMS Requirements Efficiently

The CMS Interoperability and Prior Authorization Final Rule (CMS‑0057‑F), effective January 1, 2026 (with APIs required by 2027), imposes strict requirements: seven‑calendar‑day turnaround for standard requests, 72‑hour turnaround for expedited requests, public reporting of authorization metrics, and standardized API-based communication⁴.

Automation is essential for compliance. Without it, health plans risk missing deadlines, misreporting metrics, and exposing themselves to regulatory sanctions. Automated intake and AI-supported review systems facilitate meeting timeliness standards, improve denial rationale transparency, and generate structured data required for public disclosures.

Operational and Strategic Returns

Adopting automation and AI in UM workflows delivers measurable operational and strategic advantages:

• Faster turnaround times, supporting regulatory compliance and enhanced provider/member experience
• Lower administrative costs, with per-case cost reductions from dollars to cents
• Improved decision consistency, reducing variability and appeal risk
• Better provider relationships, fostering collaboration and satisfaction
• Scalable operations, capable of handling volume without linear staffing growth

According to CAQH projections, universal adoption of electronic PA could save the healthcare system nearly $500 million annually.

Governance and Change Management

Implementing automation successfully requires more than technology. Integration with core UM systems such as QNXT or Facets is a prerequisite. Oversight mechanisms must be in place to audit automated decisions and ensure human review for denied cases. Clinician training is essential to shift from manual workflows to supervisory roles. Transparency—through AI outputs aligned to policy rationale—is critical for provider acceptance.

Monitoring key performance indicators—including intake accuracy, review time, first-pass approval rates, and provider satisfaction—is essential for evaluating ROI and guiding continuous improvement.

Conclusion

In today's healthcare environment, utilization management processes demand urgent modernization. Fragmented intake channels and documentation overload threaten decision efficiency, care quality, and compliance. Automation and AI offer pragmatic, scalable solutions that improve accuracy, reduce administrative friction, and enhance both provider and member experience—all while supporting regulatory alignment.

For health plan executives, investment in intelligent UM is not optional; it is a strategic imperative. At Mizzeto, we partner with plans to deploy integrated technology solutions that optimize intake, augment clinical review, and embed rigorous governance. To explore a tailored blueprint for digitally transforming your UM operations, contact us today.

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Sources Cited

¹2023 CAQH Index Report

²Administrative Transaction Costs by Provider Specialty

³Navigating The CMS Prior Authorization Final Rule: What Health Plans Need to Know

⁴CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F)

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