Mizzeto offers a comprehensive suite of healthcare services designed to streamline payer back office opreations.
Business Process Automation
Optimizing Healthcare Operations with BPO and Automation for Faster, Smarter Service Delivery.
Accelerate results in utilization management, claims configuration, and beyond with BPO solutions designed for payers. Whether you need end-to-end support or targeted assistance, our flexible solutions adapt to your needs without slowing you down.
Accelerate claims adjudication with intelligent automation and expert handling of exceptions—driving accuracy, speed, and savings.
Ensure accurate benefit interpretation with rules-based configuration that minimizes errors and audit risk.
Optimize enrollment and billing with automated, compliant processes that enhance member experience, minimize errors, and accelerate onboarding and revenue capture.
Eliminate claim delays and costly errors with streamlined provider data management and credentialing processes.
Unlock hidden revenue with accurate payment reconciliation and faster provider recovery workflows
Digitize inbound mail with high-accuracy data capture to accelerate processing and streamline workflows.
Deliver high-quality member support through scalable contact center teams trained in payer-specific needs, ensuring personalized service while efficiently managing fluctuating call volumes.
Ensure data accuracy and regulatory compliance with rigorous transactional audits that uphold quality and reduce risk.
Empower strategic decisions with custom reports and interactive dashboards delivering actionable insights.
Our payer solutions provide a range of client benefits including: streamline operations, cost reductions, and overall customer satisfaction. Our global team allows us to enable faster claims processing, accurate data analysis, and enhanced decision-making without increasing client costs.
Mizzeto accelerates prior authorization decisions by up to 40% through intelligent intake, automated data validation, and payer-specific rules configuration. This results in faster treatment initiation and improved provider satisfaction.
Mizzeto improves member enrollment turnaround times by streamlining and fully automating the process. This can be through customized system configuration, reduction in enrollment file errors, and workflow automations.
For every 3% lift in claims auto-adjudication rate, clients have the potential to save up to $250K annualy.
Healthcare operations are complex, fragmented, and buried in manual work. Mizzeto’s automation suite plugs into your existing platforms—like QNXT, Facets, and Epic—without forcing a rip-and-replace.
SLAs aligned to your KPIs, with real-time reporting, performance guarantees, and built-in scalability during peak volumes
Utilize Gen AI to make data-driven decisions and predictions
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Optimize workflows and reduce errors with our automation suite
Manage provider data through automating workfows
Minimize provider overpayments using pre-adjudication auditing
Increase auto-adjudication rates through RPA automation
The rapid acceleration of AI in healthcare has created an unprecedented challenge for payers. Many healthcare organizations are uncertain about how to deploy AI technologies effectively, often fearing unintended ripple effects across their ecosystems. Recognizing this, Mizzeto recently collaborated with a Fortune 25 payer to design comprehensive AI data governance frameworks—helping streamline internal systems and guide third-party vendor selection.
This urgency is backed by industry trends. According to a survey by Define Ventures, over 50% of health plan and health system executives identify AI as an immediate priority, and 73% have already established governance committees.
However, many healthcare organizations struggle to establish clear ownership and accountability for their AI initiatives. Think about it, with different departments implementing AI solutions independently and without coordination, organizations are fragmented and leave themselves open to data breaches, compliance risks, and massive regulatory fines.
AI Data Governance in healthcare, at its core, is a structured approach to managing how AI systems interact with sensitive data, ensuring these powerful tools operate within regulatory boundaries while delivering value.
For payers wrestling with multiple AI implementations across claims processing, member services, and provider data management, proper governance provides the guardrails needed to safely deploy AI. Without it, organizations risk not only regulatory exposure but also the potential for PHI data leakage—leading to hefty fines, reputational damage, and a loss of trust that can take years to rebuild.
Healthcare AI Governance can be boiled down into 3 key principles:
For payers, protecting member data isn’t just about ticking compliance boxes—it’s about earning trust, keeping it, and staying ahead of costly breaches. When AI systems handle Protected Health Information (PHI), security needs to be baked into every layer, leaving no room for gaps.
To start, payers can double down on essentials like end-to-end encryption and role-based access controls (RBAC) to keep unauthorized users at bay. But that’s just the foundation. Real-time anomaly detection and automated audit logs are game-changers, flagging suspicious access patterns before they spiral into full-blown breaches. Meanwhile, differential privacy techniques ensure AI models generate valuable insights without ever exposing individual member identities.
Enter risk tiering—a strategy that categorizes data based on its sensitivity and potential fallout if compromised. This laser-focused approach allows payers to channel their security efforts where they’ll have the biggest impact, tightening defenses where it matters most.
On top of that, data minimization strategies work to reduce unnecessary PHI usage, and automated consent management tools put members in the driver’s seat, letting them control how their data is used in AI-powered processes. Without these layers of protection, payers risk not only regulatory crackdowns but also a devastating hit to their reputation—and worse, a loss of member trust they may never recover.
AI should break down barriers to care, not build new ones. Yet, biased datasets can quietly drive inequities in claims processing, prior authorizations, and risk stratification, leaving certain member groups at a disadvantage. To address this, payers must start with diverse, representative datasets and implement bias detection algorithms that monitor outcomes across all demographics. Synthetic data augmentation can fill demographic gaps, while explainable AI (XAI) tools ensure transparency by showing how decisions are made.
But technology alone isn’t enough. AI Ethics Committees should oversee model development to ensure fairness is embedded from day one. Adversarial testing—where diverse teams push AI systems to their limits—can uncover hidden biases before they become systemic issues. By prioritizing equity, payers can transform AI from a potential liability into a force for inclusion, ensuring decisions support all members fairly. This approach doesn’t just reduce compliance risks—it strengthens trust, improves engagement, and reaffirms the commitment to accessible care for everyone.
AI should go beyond automating workflows—it should reshape healthcare by improving outcomes and optimizing costs. To achieve this, payers must integrate real-time clinical data feeds into AI models, ensuring decisions account for current member needs rather than outdated claims data. Furthermore, predictive analytics can identify at-risk members earlier, paving the way for proactive interventions that enhance health and reduce expenses.
Equally important are closed-loop feedback systems, which validate AI recommendations against real-world results, continuously refining accuracy and effectiveness. At the same time, FHIR-based interoperability enables AI to seamlessly access EHR and provider data, offering a more comprehensive view of member health.
To measure the full impact, payers need robust dashboards tracking key metrics such as cost savings, operational efficiency, and member outcomes. When implemented thoughtfully, AI becomes much more than a tool for automation—it transforms into a driver of personalized, smarter, and more transparent care.
An AI Governance Committee is a necessity for payers focused on deploying AI technologies in their organization. As artificial intelligence becomes embedded in critical functions like claims adjudication, prior authorizations, and member engagement, its influence touches nearly every corner of the organization. Without a central body to oversee these efforts, payers risk a patchwork of disconnected AI initiatives, where decisions made in one department can have unintended ripple effects across others. The stakes are high: fragmented implementation doesn’t just open the door to compliance violations—it undermines member trust, operational efficiency, and the very purpose of deploying AI in healthcare.
To be effective, the committee must bring together expertise from across the organization. Compliance officers ensure alignment with HIPAA and other regulations, while IT and data leaders manage technical integration and security. Clinical and operational stakeholders ensure AI supports better member outcomes, and legal advisors address regulatory risks and vendor agreements. This collective expertise serves as a compass, helping payers harness AI’s transformative potential while protecting their broader healthcare ecosystem.
At Mizzeto, we’ve partnered with a Fortune 25 payer to design and implement advanced AI Data Governance frameworks, addressing both internal systems and third-party vendor selection. Throughout this journey, we’ve found that the key to unlocking the full potential of AI lies in three core principles: Protect People, Prioritize Equity, and Promote Health Value. These principles aren’t just aspirational—they’re the bedrock for creating impactful AI solutions while maintaining the trust of your members.
If your organization is looking to harness the power of AI while ensuring safety, compliance, and meaningful results, let’s connect. At Mizzeto, we’re committed to helping payers navigate the complexities of AI with smarter, safer, and more transformative strategies. Reach out today to see how we can support your journey.
Feb 21, 2024 • 2 min read
For health plans seeking to modernize utilization management (UM), streamline operations, and meet the evolving expectations of regulators and providers alike, one core issue remains persistently overlooked: the absence of integrated, real-time validation within the UM intake process.
Each day, thousands of prior authorization (PA) requests arrive via fax, web portals, and clearinghouses. Before a nurse or decision engine can determine whether a service is medically necessary, the system must first answer a more fundamental question: Is this member eligible for coverage — right now, for this service, from this provider?
Surprisingly, this foundational step is still often handled manually, inconsistently, or retroactively. The result? Delays, denials, and widespread frustration across the healthcare ecosystem.
Intake is not a back-office task. It is the gateway to care. Without embedding real-time validation at this crucial entry point, the rest of the UM process — no matter how advanced — remains inefficient, error-prone, and reactive.
On paper, eligibility verification should be simple. Health plans maintain detailed member rosters and benefit files. Providers know their patients and the services they’re requesting. Yet in practice, the data submitted — often via scanned faxes or portal uploads — frequently doesn’t match what’s on file.
Typos, outdated coverage, incomplete fields, and mismatched provider information are routine. When a request arrives with a missing member ID or an out-of-network provider, the workflow stalls. Intake staff are forced to search across multiple systems or call provider offices for clarification. What should take seconds can drag into days.
The problem is compounded by the fact that many health plans treat eligibility and provider validation as a downstream function — something checked only after clinical review has begun. This leads to wasted clinical resources, avoidable denials, and costly rework that strains provider relationships.
The impact is significant. Delays in eligibility confirmation are a leading cause of pended or returned prior authorization requests. When plans can’t confirm who the member is, what their benefits include, or whether the provider is in-network, decisions are delayed or denied entirely. This drives up call center volume, inflates administrative costs, and erodes trust with both providers and members.
With prior authorization already under scrutiny for creating access barriers, these avoidable intake failures represent a growing risk — operationally, financially, and reputationally.
The solution begins by reimagining intake as more than just document collection. It must become a real-time eligibility engine.
As faxes, PDFs, and form submissions are received, their data should be immediately digitized, validated, and cross-referenced against the plan’s enrollment and provider systems — before clinical review begins.
The first step is intelligent data capture. Using tools like optical character recognition (OCR), AI-powered form parsers, or integrated EDI feeds, intake systems can now reliably extract key fields such as member name, ID, date of birth, service requested, and provider NPI. Any uncertainty or inconsistency should automatically trigger flagging or human review.
Once extracted, real-time eligibility and provider validation can occur. The system checks whether the member is active for the requested date of service, whether the provider is in-network, whether the benefit covers the requested service, and whether prior authorization is required at all.
Done well, this validation happens in seconds. Clean, accurate requests are routed directly to clinical review — or even automatically approved based on rules. Errors are flagged for correction or returned to the provider with clear guidance, eliminating long-cycle rework.
Leading health plans are beginning to treat these validations not as post-intake audits, but as real-time filters. This ensures that only actionable, member-matched requests move forward. Intake teams and nurses spend less time correcting data, and more time making decisions. Turnaround times improve, and providers experience fewer frustrating delays.
The business value is clear. Plans that have implemented real-time validation of member, provider, and benefit information report a 30–50% reduction in pended requests due to data errors, a 25% improvement in turnaround times, and significantly lower call center burden. These changes directly improve provider satisfaction and help meet regulatory expectations — especially in states adopting prior authorization transparency laws.
Strategically, embedding validation checks at intake reduces the total cost of ownership for UM systems. With fewer unnecessary clinical reviews and resubmissions, plans can achieve more with leaner staffing while maintaining or increasing throughput. That’s not just operational efficiency — it’s defensible ROI.
For health plan executives, the takeaway is simple: validating member, provider, and benefit data is no longer a task for the call center. It’s a critical enabler of modern utilization management and should be treated as such.
The technology is ready. OCR and AI can accurately digitize most intake documents. APIs can perform real-time queries to enrollment and provider databases. Provider directories are becoming increasingly standardized and accessible. The obstacle isn’t technical — it’s organizational focus.
As health plans invest in broader digital transformation — from automated decision engines to generative AI support tools — they must not neglect the foundation. A truly intelligent UM process begins with intelligent intake.
That means ensuring the request is clean from the start: the right member, the right benefits, the right provider. Once that’s established, every subsequent step — clinical review, approval, communication — can proceed faster, with greater confidence.
Plans that prioritize this foundational step today will be better positioned to reduce administrative costs, meet regulatory mandates, and deliver faster, safer care. In an environment where delays can have real clinical consequences, getting the first step right has never been more important.
It’s time to modernize eligibility and validation checks — and unlock the full potential of utilization management.
Jan 30, 2024 • 6 min read
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.
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.
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.
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.
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.
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.
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.
Health plan leaders should:
By embracing these strategies, health plans will not only comply with CMS mandates but also position themselves as industry leaders in utilization management.
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.
Jan 30, 2024 • 6 min read
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.
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.
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.
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.
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.
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.cbo.gov/publication/61469?utm_source=chatgpt.com
Jan 30, 2024 • 6 min read
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.
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.
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.
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.
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.
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.
Sources Cited
2 SPEEDING UP CLAIMS PROCESSING
3 Top Benefits of OCR in Insurance
4 Extracting Medical Information from Paper COVID-19 Assessment Forms
Jan 30, 2024 • 6 min read
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