Data Challenges in Healthcare: Why Health Plans Can't Afford Inaccurate Provider Information

Most health plans believe their provider directories are mostly accurate. Research tells a different story.

58% of members have encountered incorrect information in provider directories at least once, according to the 2025 Atlas PRIME Member Experience Monitor. Even more troubling: 80% of members who find these errors say it makes them trust their health plan less. For health plans already navigating CMS audits, member complaints, and No Surprises Act requirements, directory inaccuracy is not just a member experience problem. It is an operational crisis rooted in deeper healthcare data challenges.

What We Mean by Healthcare Data Challenges

Healthcare data challenges in the payer context refer to systemic problems with how provider information is collected, validated, standardized, and maintained across multiple systems and sources. For health plans, this means struggling with incomplete rosters from delegated credentialing groups, inconsistent formats from different provider organizations, and disconnected platforms that cannot share information.

Why this matters now: regulatory pressure from CMS and state agencies is intensifying. The No Surprises Act has raised the stakes for directory accuracy. Member expectations for digital-first experiences continue to climb. And operational costs from manual data cleanup drain resources that could be spent on strategic priorities.

The Hidden Cost of Healthcare Data Problems

Picture a regional health plan with 800,000 members. The network management team spends more than 120 hours monthly reconciling delegated provider rosters from 15 different provider groups. 

Each group sends data differently. One submits Excel files with 50% of NPIs missing. Another sends PDFs that require manual data entry. A third misses the submission deadline entirely, forcing staff to make phone calls.

Staff manually scrub, rekey, and reformat files for credentialing, claims, and directories. Despite this effort, members still call to complain. Providers listed as accepting new patients are not. Phone numbers are disconnected. Specialties are wrong. One member searches for an in-network cardiologist and calls five providers. Three are not accepting new patients despite what the directory says. One retired six months ago. One is listed under the wrong specialty entirely.

The member goes out of network, receives a surprise bill, and files a grievance. The health plan faces a CMS audit and cannot trace where the bad data originated. This scenario plays out thousands of times across health plans nationwide.

Research confirms the scope: According to the AMA, phone calls to a sample of 120 provider listings from 12 different health plans revealed that 33% contained inaccuracies, had nonworking phone numbers, or went unanswered.

These are not minor clerical errors. They create access barriers, compliance risk, and member trust erosion that directly threatens Star Ratings and retention.

Six Critical Data Challenges Facing Health Plans Today

1. When provider data arrives incomplete, inconsistent, or inaccurate

Delegated credentialing creates chaos because every provider group operates differently. Some submit complete rosters with current data, while others send partial files missing critical fields like updated phone numbers. When NPIs on submitted rosters do not match what appears in NPPES, health plans face a reconciliation nightmare that consumes staff time and delays directory updates.

Consider a mid-market health plan receiving data from multiple sources: one large physician group sends an Excel file with 50% of NPIs missing, another submits a PDF that cannot be parsed programmatically, and a third provides updates via phone calls because their practice management system cannot generate reports. Staff spend hours manually entering, validating, and correcting information, and by the time the data reaches the directory system, weeks have passed and provider circumstances have already changed.

The scale of this problem is massive: 80% of medical data is unstructured and disconnected from wider healthcare systems, according to research published in Healthcare Informatics Research. This unstructured data includes text documents, scanned forms, and information trapped in formats that cannot be automatically processed, making it nearly impossible for health plans to extract, validate, or integrate this information with their core systems.

2. No common language across payers, providers, and systems

NPIs do not match between sources, creating a cascade of confusion across platforms. One system might show Dr. Jane Smith with NPI 1234567890, while another displays Jane A. Smith, MD with the same NPI but a different address, and a third system lists Jane Smith under a group practice with yet another location. The credentialing platform shows one specialty, the claims system shows another, and the directory shows a third, leaving no one certain which information is correct.

Taxonomy codes conflict between submission sources, and addresses get formatted so differently that systems treat them as separate locations. When one source lists 123 Main St and another shows 123 Main Street Suite 200, matching algorithms fail and create duplicate records. 

Provider names vary wildly across systems: Dr. Jane Smith, Jane A. Smith MD, Smith Jane MD, and Smith J all refer to the same person, but the system does not recognize this. The result is the same provider appearing multiple times in the directory with conflicting information that confuses members and frustrates staff.

Without industry-wide standards for how provider groups submit data to health plans, each group uses whatever format their practice management system generates. Health plans receiving data from 50 or 100 different groups must handle 50 or 100 different formats, making manual reconciliation the norm and allowing errors to multiply across every handoff.

3. When your credentialing platform cannot talk to your directory system

Credentialing tools, claims engines, directories, and Salesforce all operate in silos, which means data updated in one system does not automatically flow to others. When the credentialing team approves a provider with an updated address, that information sits in the credentialing platform while the directory continues showing the old address for months because no automated sync exists between platforms.

Here is how this plays out in practice: a provider terminates their contract and the credentialing system gets updated, but claims processing continues because the claims system was never notified of the termination. The directory still lists them as in-network because the directory update process runs on a separate schedule from credentialing. Members try to schedule appointments based on the directory, the provider's office turns them away, and trust erodes while complaints pile up. Each disconnected system creates another opportunity for information to become outdated, and manual rekeying multiplies the problem.

Research shows that nearly half of U.S. hospitals share data with other organizations but do not receive data in return. This one-way data flow creates blind spots where critical updates never make it back to the originating system, leaving everyone working with incomplete or outdated information.

4. The compliance crisis hiding in plain sight

CMS Secret Shopper audits find that 52% of provider directory locations contained at least one inaccuracy in Medicare Advantage plans. No Surprises Act requirements demand accurate directories, and network adequacy standards are at risk when directories contain ghost networks of providers who are not actually available.

Consider what members experience when they search for care: a member searches the directory for an in-network cardiologist and five providers appear. The first one retired eight months ago but remains listed, the second is not accepting new patients despite directory information saying otherwise, and the third is actually a family medicine physician listed under the wrong specialty. The fourth has a disconnected phone number because the practice relocated, and the fifth has correct information but a three-month appointment wait time. The member gives up, goes out of network, receives a surprise bill, and loses trust in the health plan.

This experience is not rare. Financial penalties from regulators combine with member trust erosion to create a compliance crisis that many health plans are only beginning to understand, especially as enforcement mechanisms strengthen and member expectations for accurate information continue to rise.

5. When you cannot trace where bad data originated

Without visibility into which provider group or source sent incorrect information, health plans cannot hold delegated entities accountable or maintain audit logs showing when data was updated, by whom, and from what source. When CMS audits arrive and ask how you verify that a provider is still accepting patients, there is often no documented answer because the verification process consists of manual spot checks without proper documentation or source tracking.

Here is a common scenario: a directory shows a provider accepting new patients at three different locations, but none of the locations are accurate. Staff begin investigating only to discover that the delegated credentialing group claims they submitted correct information six months ago, the provider's office says they updated their information through the CAQH portal last month, and the directory vendor insists they processed the most recent feed they received. No one can determine which submission was most recent, most accurate, or should take precedence because the systems do not maintain proper audit trails.

Lack of source attribution means recurring errors cannot be fixed at the root, so the same incorrect phone number keeps appearing in the directory every quarter despite correction attempts. The problem persists because multiple systems are feeding data, and the incorrect phone number keeps getting re-imported from a source that was never identified or corrected in the first place.

6. Technology built for yesterday's problems

Platforms designed for fee-for-service models cannot meet the demands of value-based care, particularly when it comes to handling real-time updates or managing variability from upstream data sources. These systems have limited integration capabilities because they were built before APIs and FHIR standards existed, which means onboarding a new provider group's data can take three weeks because the legacy system requires manual file transformation and dedicated IT team involvement.

The scaling problem becomes obvious as health plans grow: when the platform was designed, the health plan might have worked with 10 provider groups, but now there are 50. Processing time increases, staff workload grows, and error rates climb because the system was never architected to handle this volume. The platform cannot accommodate new data fields required by regulatory changes without expensive and slow custom modifications that create technical debt and future maintenance headaches.

The cost is staggering: healthcare organizations spend heavily trying to resolve data challenges. These costs accumulate from denied claims, rework, member services calls, audit preparation, and penalties that could have been avoided with modern systems designed for today's data complexity.

These six challenges are not isolated problems. They compound each other. Inaccurate data from one delegated group flows into siloed systems, creates directory errors, and leaves no audit trail when regulators come asking. Breaking this cycle requires understanding the root causes, not just treating symptoms.

Understanding Challenges Is the First Step

The scale is clear. 58% of members finding errors. 80% losing trust. Significant resources spent annually on data resolution. CMS audits, member complaints, and operational inefficiency are not separate problems. They are symptoms of systemic data challenges.

These challenges are systemic but solvable. Leading health plans are moving from manual processes to modern approaches focused on primary source verification, automation, and audit-ready governance. The path forward requires acknowledging these challenges exist and understanding how they interconnect.

If you have recognized these challenges in your own operations, especially around delegated provider data chaos, purpose-built provider data management solutions like PRIME® by Atlas Systems can eliminate the manual cleanup cycles and provide the audit trails regulators require. 

The first step is an honest assessment of where your organization stands today. Schedule a demo with our experts to get started.

Frequently Asked Questions

1. What are the biggest data quality issues in healthcare?

The biggest data quality issues include incomplete data with missing critical fields like NPIs or phone numbers, inconsistent data where the same information appears in different formats across systems, inaccurate data that is outdated or simply wrong, and unstructured data that cannot be easily processed. For health plans specifically, these issues create directory errors, claims processing problems, and compliance risks. 

2. How do healthcare data challenges affect health plans?

Healthcare data challenges create multiple problems for health plans. Operational costs escalate as teams spend 100+ hours monthly on manual data cleanup. Regulatory risk increases through CMS audits and potential penalties for directory inaccuracies. 

3. What regulations govern healthcare data accuracy for payers?

Health plans must comply with CMS provider directory requirements mandating quarterly validation of directory information. The No Surprises Act requires accurate in-network listings to protect members from surprise billing. Network adequacy standards at both federal and state levels depend on accurate provider data. HIPAA governs data security and privacy. All these regulations require audit trails showing verification efforts, and non-compliance can result in penalties, corrective action plans, and reputational damage.

4. Why is delegated provider data so challenging for health plans?

Each provider group submits data differently in terms of formats, timing, and quality. No standardization exists across the industry. Health plans lack leverage to enforce consistency because provider groups work with multiple payers and prioritize their own systems. Manual reconciliation becomes necessary but is time-consuming and error-prone. The result: downstream errors appear in directories, claims systems, and credentialing platforms. Provider groups may submit complete data to their own systems but partial data to health plans, creating gaps that are difficult to identify and resolve.

5. How common are provider directory errors?

Provider directory errors are extremely common. The 2025 Atlas PRIME Member Experience Monitor found that 58% of members have encountered errors at least once. Research indicates that 20% of provider directory listings contain inaccuracies. Common errors include providers not actually accepting new patients when the directory says they are, wrong or disconnected phone numbers, retired providers still listed as active, and specialty mismatches. Most concerning: 80% of members who find errors say it makes them trust their health plan less, directly impacting retention and satisfaction scores.