Over 80% of Prior Auth Denials Get Overturned. Auto Approvals Should Have Prevented Most of Them.

Physicians and their staff complete an average of 39 prior authorization requests per week. They spend roughly 13 hours processing them.¹ When requests get denied, more than 80% of those denials are partially or fully overturned on appeal, meaning the care was appropriate all along.²

That is not a utilization management program working as intended. That is a system generating unnecessary friction, burning clinical resources, and producing decisions it cannot defend.

Auto approvals were supposed to fix this. Route the obvious cases through automatically. Free up clinical reviewers for complex decisions. Cut turnaround times. Reduce provider abrasion.

Most health plans tried some version of this approach. Few got the results they expected.

The Auto Approval Promise vs. the Auto Approval Reality

The math behind auto approvals is straightforward. If a significant share of prior authorization requests are routine, policy aligned, and destined for approval anyway, why route them through manual clinical review?

The problem is execution. In a recent KFF analysis of Medicare Advantage data, denial rates ranged from 4.2% at Elevance Health to 12.8% at UnitedHealth Group.² Those rates might seem low. But when over 80% of denied requests are overturned on appeal, the real story becomes clear: plans are denying care they will ultimately approve, just with extra steps, extra cost, and extra delay.

According to the CAQH Index, only 35% of medical prior authorizations are conducted fully electronically.³ Manual prior authorization transactions cost providers $10.97 each. Fully electronic ones cost $5.79, roughly half. For payers, the gap is even wider: $3.52 per manual transaction versus five cents for a fully electronic one.⁴

Auto approvals should be eating into these costs. For most plans, they are not.

Why Auto Approvals Keep Failing Inside Legacy Workflows

The failure mode looks the same everywhere. A health plan bolts an auto approval layer onto a prior authorization workflow that was designed for manual review. Every request enters the same intake funnel. Data arrives incomplete or inconsistently structured. Clinical documentation lands as bulk attachments, hundreds of pages of chart notes that no automation can parse reliably.

Under those conditions, auto approval rules get conservative fast. Exceptions multiply. Edge cases pile up. The system cannot distinguish between a straightforward imaging request that matches policy criteria and a complex surgical case requiring genuine clinical judgment. So it sends both to manual review, because it cannot trust its own inputs.

The result: auto approvals exist on paper but barely dent the queue. Reviewers still touch most cases. And the 40% of physician practices that now employ staff exclusively to handle prior authorization paperwork¹ see no relief.

Three specific blockers keep this pattern locked in place.

Intake is broken. Requests arrive via fax, portal, phone, and EDI, often missing required fields. When the system cannot confirm a request is complete, it cannot auto approve it. We wrote about this problem in detail in our piece on modernizing UM intake. The front end is where most prior authorization delay actually begins.

Policy logic is fragmented. Medical policies live in PDFs. Clinical criteria are configured in the UM platform. Benefit rules sit in the claims system. No single source of truth exists for “should this request be approved?” When three systems disagree, the default answer is always manual review.

Nobody owns the auto approval rate. UM owns clinical appropriateness. IT owns the platform. Compliance owns regulatory exposure. No single executive is accountable for the percentage of requests that bypass manual review, so nobody optimizes for it.

What Decision Ready Prior Auth Actually Looks Like

The health plans getting auto approvals right are not buying better automation. They are fixing the preconditions that make automation possible.

That means restructuring intake so requests arrive complete and policy aligned before any decision logic runs. It means centralizing medical policy so criteria are applied consistently, not interpreted differently by different reviewers on different shifts. And it means surfacing clinical evidence in context: extracting the three data points that matter for a routine imaging request, rather than dumping a 200 page chart into a reviewer’s queue.

This is the approach behind Mizzeto’s Smart Auth. Instead of asking “can this request be auto approved?” Smart Auth asks “is this request decision ready?” That distinction matters. A decision ready request has complete data, matches a known policy pathway, and meets explicit criteria thresholds, so the system approves it with confidence, not with crossed fingers.

CMS is pushing the entire industry in this direction. Under the Interoperability and Prior Authorization Final Rule (CMS 0057 F), impacted payers must respond to standard prior authorization requests within seven calendar days and expedited requests within 72 hours, effective January 1, 2026.⁵ Plans must also publicly report prior authorization metrics, including approval rates, denial rates, and average turnaround times, beginning March 31, 2026.⁵

Those timelines are not aspirational. They are regulatory. And plans that still route 65% of prior authorization through manual or partially manual channels³ will not meet them by hiring more reviewers. The only realistic path is systematic auto approval of decision ready requests, which means fixing intake, policy logic, and data quality first. We laid out the full compliance timeline in The Countdown to 72/7.

The HL7 Da Vinci FHIR Implementation Guides (CRD, DTR, and PAS) provide the technical scaffolding for this shift, enabling real time coverage requirement discovery and electronic prior authorization submission.⁶ Plans that invest in FHIR based infrastructure now are not just meeting a compliance deadline. They are building the foundation for auto approvals that actually scale.

The Bottom Line

If your auto approval rate is stagnant, the problem is not your approval logic. It is everything upstream: incomplete intake, fragmented policy, and data your system cannot trust.

Start by measuring what percentage of prior authorization requests arrive decision ready. Complete, structured, and policy aligned before they hit clinical review. That number is your ceiling for sustainable auto approvals. Everything you do to raise it directly reduces manual review volume, improves turnaround performance, and positions your plan for CMS 0057 F compliance.

At Mizzeto, Smart Auth was designed to close exactly this gap. Not by approving more requests automatically, but by ensuring the right requests never need manual review in the first place.

If auto approvals exist in your organization but still feel fragile, that fragility is the signal. Let’s talk about it.

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Sources

¹ American Medical Association, “2024 AMA Prior Authorization Physician Survey,” 2024. ama-assn.org

² KFF, “Medicare Advantage Insurers Made Nearly 53 Million Prior Authorization Determinations in 2024,” January 2025. kff.org

³ CAQH, “Priority Topics: Prior Authorization,” 2024. caqh.org

⁴ 4sight Health, “The Costly Lever of Prior Authorization” (citing 2023 CAQH Index data), February 2024. 4sighthealth.com

⁵ Centers for Medicare & Medicaid Services, “CMS Interoperability and Prior Authorization Final Rule (CMS 0057 F),” January 17, 2024. cms.gov

⁶ CAQH CORE, “Navigating the CMS 0057 Final Rule: A Guide for Implementing Prior Authorization Requirements,” 2024. caqh.org

For years, prior authorization improvement efforts have centered on one metric: speed. Faster turnaround times. Shorter queues. Quicker determinations. When backlogs grow, the instinctive response is to push harder, add staff, tighten SLAs, accelerate intake, automate submission.

And yet, despite sustained investment, many health plans find themselves in a familiar place. Requests move faster into the system, but decisions do not come out any cleaner. Appeals rise. Clinical teams feel busier, not better supported. Regulatory scrutiny intensifies.  

The problem isn’t that health plans aren’t moving quickly enough. It’s that they’re optimizing for the wrong outcome.

The Question Leaders Should Be Asking

The critical question facing payer executives today is not how to make prior authorization faster. It is how to make authorization outcomes decision-ready.

In theory, prior authorization is a linear process. A request arrives. Medical necessity is assessed. A decision is rendered and communicated. In practice, speed at the front of the process often exposes fragility downstream. Requests arrive sooner, but incomplete. Data flows faster, but inconsistently. Clinical documentation is attached, but not usable.

What feels like progress—shorter intake cycles, higher submission volumes—often masks a deeper inefficiency: decisions still require the same amount of searching, clarifying, and rework. Sometimes more.

When speed becomes the primary goal, organizations optimize how fast work enters the system, not how effectively it can be resolved.

Why Faster Intake Often Slows Decisions

In our experience working with payer organizations, most delays in prior authorization do not occur because reviewers are slow. They occur because reviewers are forced to reconstruct meaning from poorly prepared inputs.

Requests arrive with missing or mis-keyed information. Clinical notes are uploaded as hundreds of unstructured pages. Policy criteria are technically met, but not clearly demonstrated. Nurses and physicians spend their time hunting for evidence rather than applying judgment.  

A routine imaging authorization, for example, may arrive with a 200-page chart attached—office notes, lab results, historical encounters spanning years. The information needed to approve the request may exist somewhere in the record, but reviewers must sift through dozens of irrelevant pages to find it. The delay isn’t clinical complexity. It’s the effort required to locate and validate the right signal inside too much noise. That friction compounds downstream, creating a clinical review bottleneck where highly trained staff spend their time searching for context instead of making decisions.

Accelerating intake without addressing these issues simply increases the volume of work that is not ready to be decided. Each incomplete request introduces pauses, clarifications, and handoffs. What should have been a single pass through the system becomes multiple touches across multiple teams.

From the outside, this looks like insufficient capacity. From the inside, it is capacity being quietly consumed by avoidable friction. Across the U.S. health care system, administrative burden tied to prior authorization contributes to multi-billion dollar annual costs, reflecting how inefficient processes absorb payer and provider resources long before clinical review begins.¹

This is where many modernization efforts stall. Automation accelerates submission and routing, but PA automation alone does not change the quality of what enters the system. Providers submit more requests because it is easier to do so. Intake teams process them faster. Clinical reviewers inherit the same defects at higher velocity. Speed amplifies whatever already exists—and when work is not decision-ready, it multiplies rework rather than reducing it

What High-Performing Plans Optimize Instead

Organizations that consistently control prior authorization performance focus less on turnaround time and more on decision quality at entry.

They ensure requests arrive complete and structured, reducing manual re-keying and downstream correction. Reflecting this shift, a significant proportion of health plans have already implemented electronic prior authorization systems, signalling both the complexity of modern workflows and the growing emphasis on reducing manual friction.² They normalize data so policy criteria can be evaluated consistently. They surface the specific clinical evidence needed for a decision, rather than forcing reviewers to search entire records. And they treat policy logic as a shared, governed asset—not something interpreted differently by each reviewer.

As a result, their systems move work through once. Appeals decrease because rationales are timely and clear. Clinical teams spend their time applying judgment instead of assembling context. Speed improves, but as a consequence of better design, not as the primary objective.

The shift is subtle but decisive. The goal is no longer faster authorization. It is fewer touches per authorization.

Why This Matters Now

Prior authorization sits at the intersection of cost control, access, and regulatory oversight. As CMS and other regulators increasingly expect decisions to be explainable, not just defensible—as reinforced by the CMS prior authorization rule—the cost of prioritizing speed over clarity rises. Under the CMS Interoperability and Prior Authorization final rule (CMS-0057-F), impacted payers must provide prior authorization decisions within 72 hours for urgent requests and seven calendar days for standard requests, and include specific reasons for denials to improve transparency and explainability of decisions.³ The rule shifts expectations away from throughput alone and toward consistency, traceability, and timely rationale.

Systems that rely on heroics and overtime may hit SLAs in the short term, but they accumulate risk. Systems designed for decision readiness scale more predictably and withstand scrutiny more effectively.

What executives experience as utilization management pressure is rarely a failure of effort. It is a signal that the system has been optimized for motion, not resolution.

At Mizzeto, we work with payer organizations to address this exact gap—connecting intake, clinical review, and policy logic so prior authorization decisions can be made efficiently, consistently, and explainably. This is the design philosophy behind Smart Auth, our prior authorization platform—ensuring requests arrive decision-ready, with structured intake, reduced rework, and clinical evidence surfaced in context rather than buried in charts.

Because in modern utilization management, sustained performance isn’t about pushing teams harder. It’s about removing the friction that never needed to be there in the first place.  

If your team is hitting SLAs but appeals keep climbing, let’s talk.

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SOURCES

1. Prior Authorization Statistics Statistics: Market Data Report 2026

2. https://worldmetrics.org/prior-authorization-statistics/  

3. https://content.govdelivery.com/accounts/USCMSMEDICAID/bulletins/3d5c65a

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Prior authorization backlogs are often described as volume problems. They show up as growing queues on operational dashboards, rising turnaround times, and escalating pressure on clinical teams. The explanation, almost reflexively, is that demand arrived faster than expected - too many requests, too little time.

But for most health plans, that explanation doesn’t hold up under scrutiny. Prior authorization backlogs are rarely caused by volume alone. They are caused by friction inside the authorization process itself. Friction that is well known, consistently repeated, and largely predictable.¹

The Question Leaders Should Be Asking

The real question isn’t why prior authorization volume increased. It’s why so many authorization requests cannot move cleanly from intake to decision. In theory, prior auth is straightforward: receive a request, assess medical necessity, render a decision, notify the provider. In practice, the work looks very different.

Requests arrive incomplete. Key fields are missing or entered incorrectly. Clinical documentation is attached as hundreds of unstructured pages. Nurses and physicians spend their time searching for the few sentences that actually matter. Decisions stall because they are clinically complex, but because the information required to make them is fragmented, inconsistent, or buried.

Backlogs form not at the moment of clinical judgment, but long before that judgment can even begin.

Where Prior Authorization Actually Breaks Down

Most prior authorization backlogs are built upstream, during intake. Provider offices submit requests with missing clinical details, outdated codes, or attachments that don’t align to policy requirements.² Internal coordinators re-key information from faxes, portals, or PDFs, introducing small errors that force rework later. Many prior authorization delays stem from manual processes and technology gaps, leading to inefficiency and error-prone workflows.³ Each defect is minor on its own, but together they create a steady drag on throughput.

Downstream, clinical reviewers inherit this friction. Nurses sift through large medical records to reconstruct timelines.⁴ Physicians pause decisions while clarifications are requested. Requests bounce between teams. Appeals increase, not always because the decision was wrong, but because the rationale was delayed or unclear. The backlog grows quietly, one stalled case at a time.

Why This Feels Like “Unexpected Volume”

From a distance, all of this looks like a surge. Executives see more cases aging past SLA. Leaders see staff working harder without visible progress. The conclusion is that volume must be overwhelming capacity. In reality, capacity is being consumed by rework.

Every incomplete intake, every mis-keyed field, every unclear policy reference turns a single request into multiple touches. What should have been a linear process becomes a loop. The backlog isn’t driven by how many requests arrived, it’s driven by how many times each request must be handled before it can be resolved. That multiplier effect is predictable. And yet, it’s rarely modeled.

Why Automation Alone Doesn’t Fix Prior Auth Backlogs

Automation is often applied at the intake layer, with the promise of speed. And it does make submission faster. Providers submit more requests. Intake teams process them more quickly. But if the underlying issues remain - missing information, poor data normalization, unstructured records, automation simply accelerates the arrival of flawed work.

Clinical teams feel this immediately. More cases arrive faster, but with the same defects. Reviewers spend less time waiting and more time searching, clarifying, and escalating.⁵

This is why many health plans modernize prior auth technology and still experience worsening backlogs. Automation has increased flow, but not decision readiness.

What High-Performing Plans Do Differently

Plans that control prior authorization backlogs focus less on speed and more on decision quality at intake.

They invest in ensuring requests arrive complete, structured, and aligned to policy requirements. They reduce manual keying wherever possible. They use technology to surface the right clinical evidence, rather than flooding reviewers with entire charts. And they treat policy interpretation as something that must scale consistently across reviewers, not as tribal knowledge.

Most importantly, they measure where requests stall and why. Backlogs are treated as signals: indicators of where information breaks down, where policy is unclear, or where rework is being introduced.

As a result, their queues are smaller and not because demand disappeared, but because requests move through the system once, instead of three or four times.

The Preventable Nature of Prior Authorization Backlogs

When prior authorization backlogs are framed as staffing or volume problems, they persist. When they are understood as information and workflow problems, they become solvable.

Prior auth backlogs don’t originate in clinical decision-making. They originate in how information enters the system and how much effort it takes to make that information usable.

What executives experience as UM backlogs are almost always prior authorization system outcomes. They reflect whether a health plan has designed prior authorization to support clean, defensible decisions at scale.

At Mizzeto, we work with payer organizations to address this exact gap. Connecting intake, clinical review, and policy logic so prior authorization decisions can be made efficiently, consistently, and explainably. Through Smart Auth, we help plans ensure requests arrive decision-ready: structured intake, reduced manual rework, and clinical evidence surfaced in context rather than buried in charts. Because in modern utilization management, sustained performance isn’t about pushing teams harder. It’s about removing the friction that never needed to be there in the first place.

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SOURCES

1. https://www.ama-assn.org/practice-management/prior-authorization/prior-authorization-delays-care-and-increases-health-care
2. ‍https://www.aha.org/system/files/media/file/2023/10/aha-urges-cms-to-finalize-the-improving-prior-authorization-processes-proposed-rule-letter-10-27-2023.pdf ‍
3. https://www.atlantisrcm.com/knowledge/single/prior-authorization-delays-the-new-billing-bottleneck-in-the-u-s ‍
4. https://www.aha.org/system/files/media/file/2022/10/Addressing-Commercial-Health-Plan-Challenges-to-Ensure-Fair-Coverage-for-Patients-and-Providers.pdf ‍
5. https://blog.nalashaahealth.com/prior-authorization-automation-for-healthplans

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If you're a VP of Configuration, CIO, or COO at a mid-size health plan, you've likely heard the horror stories. A health plan system migration that was supposed to modernize operations instead creates months of claims backlogs. Provider networks revolt over payment delays. Members flood call centers with complaints. The project that promised transformation becomes a fight for survival.

These cautionary tales aren't outliers. According to research from McKinsey and the University of Oxford, large-scale IT projects run an average of 45 percent over budget and 7 percent over time, while delivering 56 percent less value than predicted (McKinsey, 2012). In healthcare specifically, Gartner research indicates that 83 percent of data migration projects either fail outright or don't meet their planned budgets and schedules (Gartner, 2023). For health plans managing complex claims systems like QNXT or Facets, these statistics should be a wake-up call.

The Real Cost of Getting It Wrong

When a health plan system migration fails, the consequences ripple across every corner of your organization. Claims processing grinds to a halt, creating backlogs that can take months to clear. Providers lose confidence when payments are delayed or adjudicated incorrectly, straining relationships you've spent years building. Members experience frustration when their claims are denied in error or their benefits information is inaccessible.

Perhaps most critically, regulatory compliance can be compromised during a troubled migration. With the CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F) requiring impacted payers to implement non-technical provisions by January 1, 2026, and API requirements by January 1, 2027, the margin for error has never been thinner (CMS, 2024). A botched migration can put your organization at risk of failing to meet these mandates, potentially exposing you to penalties and damaging your reputation with state regulators.

McKinsey's research reveals an even more sobering reality: 17 percent of large IT projects become "black swans"—catastrophic failures with budget overruns exceeding 200 percent that can threaten the very existence of the organization (McKinsey, 2012). For a regional Medicaid MCO or Medicare Advantage plan operating on thin margins, a project of this magnitude going wrong isn't just an inconvenience. It's an existential threat.

What Success Actually Looks Like in a Health Plan System Migration

Too many health plans define migration success narrowly as reaching go-live. But true success extends far beyond flipping the switch. A successful health plan system migration delivers operational stability from day one. Claims auto-adjudication rates remain high. Provider payment cycles stay consistent. Member services teams can access accurate information to resolve inquiries.

Configuration accuracy is equally essential. Your benefit plans, provider contracts, and business rules must translate precisely from the legacy system to the new platform. Even minor configuration errors can cascade into major payment inaccuracies, triggering provider disputes and regulatory scrutiny. According to KLAS Research, network and provider contracts are among the biggest challenges to manage in any claims processing platform, and misconfigurations during migration are a primary source of post-go-live problems (KLAS, 2020).

Staff adoption matters just as much as technical execution. The most elegantly designed system delivers no value if your configuration analysts, claims examiners, and customer service representatives can't use it effectively. Success means your teams feel confident, not overwhelmed, when they log in on day one. Finally, regulatory compliance must be maintained throughout the transition. Whether it's HIPAA data security, state-specific Medicaid requirements, or the looming CMS interoperability mandates, your compliance posture can never take a back seat to project timelines.

Key Phases of a Successful Migration

The foundation of any successful migration is a thorough discovery and assessment phase. This isn't a cursory inventory of your current system—it's a deep dive into how your organization actually operates. Which benefit configurations are standard, and which represent years of accumulated customizations? What undocumented workarounds has your team developed? Where does institutional knowledge live that might not survive the transition? Rushing through discovery virtually guarantees costly surprises later.

Parallel testing is where theory meets reality. Running both systems simultaneously on real-world claim scenarios exposes discrepancies before they become production problems. This phase requires patience and rigor. A regional health plan that recently migrated from a legacy platform discovered during parallel testing that their provider fee schedule translations had subtle rounding errors. Catching this before go-live prevented what would have been thousands of incorrect payments and the administrative nightmare of recoupment.

Data validation cannot be an afterthought. Member eligibility records, provider demographics, historical claims data, and prior authorization information must transfer accurately and completely. HIMSS Analytics research indicates that 78 percent of healthcare organizations have either completed or are in the process of migrating data to new systems, and data compatibility issues remain a top challenge (HIMSS, 2023). Establishing clear validation protocols and acceptance criteria before migration begins gives your team objective measures of success.

Staff training deserves far more attention than most migration plans allocate. Your configuration analysts need hands-on practice with the new system's logic, not just theoretical walkthroughs. Your claims examiners need to understand how familiar processes translate to new workflows. Change management isn't a soft skill—it's a critical success factor. A phased rollout approach reduces risk by allowing you to identify and address issues at manageable scale. Finally, post-go-live stabilization requires dedicated resources and realistic expectations. Even well-executed migrations require weeks of close monitoring and rapid issue resolution.

Common Pitfalls to Avoid

The most dangerous pitfall is underestimating configuration complexity. Health plan configurations are living systems shaped by years of regulatory changes, contract negotiations, and operational refinements. What appears straightforward in documentation often conceals intricate dependencies. Plans that approach migration as a simple lift-and-shift inevitably discover—usually too late—that their new system doesn't behave as expected.

Insufficient user acceptance testing is equally perilous. Under pressure to meet deadlines, organizations often truncate UAT cycles or limit testing to sunny-day scenarios. But edge cases and exception handling are where migrations most frequently fail. The claim that adjudicates perfectly in testing may error when it encounters an unusual modifier combination or a retroactive eligibility change. Comprehensive UAT requires time, realistic test data, and involvement from the staff who will actually use the system.

Inadequate change management rounds out the most common failure modes. Technical excellence means nothing if your organization isn't prepared to adopt new ways of working. Resistance from staff who feel blindsided or unsupported can undermine even the best implementations. The Standish Group's CHAOS Report consistently identifies lack of executive support and user involvement as primary drivers of project failure (Standish Group, 2020).

The Role of Experienced Partners

Health plan system migrations are not the time for on-the-job learning. The complexity of claims configurations, the stakes of regulatory compliance, and the operational risks involved demand expertise that comes from hands-on experience across multiple implementations. Partners who have configured QNXT, Facets, or other major platforms bring pattern recognition that internal teams simply cannot develop from a single migration.

Specialized consultants can identify configuration pitfalls before they become problems, validate data migration completeness, and provide the supplemental staffing that allows your core team to maintain operational continuity during the transition. They bring objectivity to project planning, helping executives set realistic timelines and budgets based on actual experience rather than optimistic projections. For mid-size health plans without dedicated implementation teams, external expertise isn't a luxury—it's often the difference between success and costly failure.

Modernization as Competitive Advantage

The health plans that navigate system migrations successfully don't just survive—they emerge stronger. Modern core administration platforms enable the operational agility that today's healthcare environment demands. They position organizations to meet CMS interoperability requirements not as a compliance burden but as an opportunity to improve member and provider experiences. They create the foundation for AI-powered automation, real-time analytics, and the kind of operational efficiency that translates directly to competitive advantage.

The question isn't whether your health plan will eventually need to modernize its systems. The question is whether you'll do it on your terms, with careful planning and expert support, or be forced into a reactive scramble when legacy platforms can no longer keep pace with regulatory and market demands.

Partner with Mizzeto for Your System Migration

At Mizzeto Healthcare Technology Consulting, we specialize in helping mid-size health plans navigate the complexities of system migrations. Our consultants bring deep, hands-on experience with QNXT, Facets, and other leading claims platforms. We understand the configuration intricacies that can derail a migration, the regulatory requirements that can't be compromised, and the operational realities of keeping a health plan running while transforming its technology foundation.

Whether you're planning a migration to meet CMS 2026 mandates, evaluating new core administration platforms, or recovering from a troubled implementation, Mizzeto can help. We offer migration readiness assessments, configuration validation, staff augmentation, and the specialized expertise that turns high-risk projects into successful transformations.

Contact Mizzeto today for a free migration readiness assessment. Let's discuss how we can help your health plan modernize with confidence.

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References

CMS. (2024). CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F). Centers for Medicare & Medicaid Services. https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-and-prior-authorization-final-rule-cms-0057-f

Gartner. (2023). Data Migration Project Failure Statistics. Referenced in Barcelona Health Hub analysis.

HIMSS Analytics. (2023). Healthcare Data Migration Survey Report.

KLAS Research. (2020). Payer Core Administration Platforms: New Decisions and New Life. https://klasresearch.com/report/payer-core-administration-platforms-2020

McKinsey & Company. (2012). Delivering Large-Scale IT Projects on Time, on Budget, and on Value. McKinsey Digital.

Standish Group. (2020). CHAOS Report: Beyond Infinity. The Standish Group International.

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