What is Provider Data Accuracy and How to Improve it?

Accurate provider data is the foundation of a well-functioning healthcare system. It ensures that patients can find the right providers, health plans can process claims efficiently, and regulatory requirements are met. However, maintaining up-to-date provider information is a persistent challenge, leading to administrative inefficiencies and potential care gaps. In this blog, we explore provider data accuracy, its importance, key challenges, and strategies for improvement.

Atlas Systems also asked health plan members which specific pieces of directory information had been wrong – and the clear winner was: Whether a doctor or practice is accepting new patients, which was found to be inaccurate 50% of the time.

Other sources of incorrect information include:

28% called or sent an email to the wrong practitioner
26% discovered that the provider had retired or passed away
25% learned the health professional’s specialties were not as described
24% received an unexpectedly high medical bill

This blog explores the importance of provider data accuracy, common issues affecting data quality, and strategies to improve provider directory management.

What is Provider Data Accuracy?

Provider data accuracy refers to the reliability, completeness, and timeliness of healthcare provider information. This includes details such as names, addresses, specialties, contact information, affiliations, and credentialing status. Accurate provider data ensures seamless healthcare delivery, correct billing, and regulatory compliance.

Importance of Accurate Provider Data in Healthcare

Ensuring provide data accuracy benefits:

Patients: Access to correct provider directories prevents misdirected care and appointment delays
Healthcare providers: Eliminates claim denials and payment delays caused by incorrect credentials or outdated contract information
Payers: Reduces administrative costs and regulatory penalties due to non-compliance with provider directory accuracy mandates

The role of accurate provider data in revenue cycles

Provider data accuracy is foundational to a smooth revenue cycle. Inaccurate or outdated provider information can cause delays in claims processing, leading to increased denials and elongated payment timelines.

A claim submitted with incorrect provider data—whether it’s an outdated address, a missing National Provider Identifier (NPI), or an invalid licensure status—can result in rejections, resubmissions, and appeals, all of which can significantly disrupt the revenue flow.

For health systems, this means more time and resources spent on administrative tasks rather than patient care. Ensuring that provider data is consistently updated reduces these bottlenecks, allowing revenue cycles to function efficiently.

The faster claims can be processed and paid, the better the financial position of the organization, allowing for continued investment in quality care and infrastructure.

Enhancing patient access to care

Accurate provider data plays a critical role in ensuring that patients have timely access to care. Errors like an incorrect phone number, an invalid office location, or a provider no longer accepting new patients can lead to frustration and delayed treatment.

Patients rely on accurate information to make informed decisions about their care. Inaccurate data can erode trust in the health system and create unnecessary confusion, making it harder for patients to connect with the right healthcare professionals.

Additionally, payers and government agencies increasingly expect health systems to maintain current provider directories, particularly as telemedicine grows and patients seek care across wider geographical areas.

Freeing up time for doctors and administrators

The administrative burden in healthcare has been growing for years, and maintaining accurate provider data can significantly alleviate some of that pressure. When provider information is correct, doctors, administrators, and staff can spend less time resolving issues related to incorrect billing, scheduling, or claims processing, and more time focusing on patient care and other high-value tasks.

Health systems that invest in tools and processes to automate data updates reduce the risk of human error while also improving operational efficiency. For example, automating NPI validation or regularly syncing credentialing data can ensure that the system always has up-to-date information without requiring manual intervention. This not only improves accuracy but also creates a smoother workflow for all staff involved.

The Impact of Inaccurate Provider Data

A common consequence of inaccurate provider data is the surge of unexpected medical bills, often caused when patients unknowingly visit doctors listed as “in-network” but are actually out-of-network.

Recognizing this issue, the federal government’s 2022 No Surprises Act identified provider directories as a key contributor to these billing surprises and mandated quarterly updates to ensure data accuracy.

AnAtlas Member Experience Monitor survey shows that 62% of US consumers who have health insurance now or had it in the past have used online directories to search for providers.

Of these, more than half (55%) found inaccurate information about practitioners in the directories they searched. That translates to roughly one-third of all US consumers – or about 100 million people.

Inaccurate provider data can result in:

Regulatory non-compliance: State and federal regulations, such as the No Surprises Act, mandate accurate provider directories. Non-compliance results in hefty fines
Claim denials and payment delays: Incorrect provider information leads to rejected claims, increasing administrative burden and revenue losses
Patient frustration and poor access to care: Patients struggle to find in-network providers, leading to delayed or disrupted care
Increased operational costs: Organizations spend excessive time and resources correcting errors and responding to provider disputes

Factors Contributing to Provider Data Inaccuracy

Lack of communication

The timely flow of information from healthcare providers to health plans is a major challenge. Providers frequently change networks, retire, or relocate without promptly informing payers. With varying data submission requirements, providers struggle to keep multiple plans updated.

The No Surprises Act mandates quarterly updates, but both providers and payers face resource constraints, training gaps, and time limitations, making compliance difficult.

Too much information

Health plans handle vast amounts of provider data, with tens of thousands to over a million records at any time. The high volume of updates from providers, along with member feedback on incorrect information, adds complexity. Despite efforts to validate data frequently, keeping it consistently accurate remains an uphill task.

Data silos

Many providers and health systems store data in separate silos for credentialing, contracting, and scheduling. Health plans also have internal silos, causing delays and inconsistencies in provider directory updates. Fragmented data increases the risk of outdated or conflicting information.

Other contributing factors

Frequent provider changes: Relocations, retirements, and new affiliations make data maintenance difficult.
Multiple data sources: Uncoordinated inputs lead to duplication and inconsistencies.
Lack of standardized processes: Without uniform validation protocols, errors persist across systems.
Manual data entry errors: Outdated manual processes increase inaccuracies and inefficiencies.

Best practices for data collection and management

Automated data validation: AI and machine learning tools can cross-check provider information in real time.
Centralized provider data repository: A single source of truth eliminates discrepancies across departments.
Regular provider outreach: Periodic updates ensure data remains accurate and current.
Compliance audits: Routine quality checks help meet regulatory standards.

Strategies to Improve Provider Data Accuracy

Enhancing provider data accuracy requires a combination of technology, governance, and process improvements. Finding a balance is the first step toward greater patient satisfaction and a higher standard of member experience.

Direct communication between providers and payers can reduce the risk of low-quality data. A direct outreach model simplifies managing and maintaining data as much as possible.

Technology solutions like data analytics, artificial intelligence (AI), and machine learning (ML) help automate error-checking and provide insights into data handling that may further streamline the validation process. Third-party provider data management solutions can provide relief with proven methods for improving provider network management.

Monitor claims activity

The absence of claims activity over an extended period is a red flag that warrants further investigation. If it’s been more than 3 months since a claim was last received, this may be a red flag that warrants more attention. If this time span extends to 6 months or more, this may be a sign of inaccurate provider data. This is especially true if the health plan has not validated the accuracy of the data for more than 90 days.

Members are either not using the provider’s services or a provider is no longer practicing at the location in question. Both scenarios can be verified by contacting the provider’s administrative office to determine the status of that particular provider.

Confirm active provider service locations

All health plans are subject to access and availability standards that are intended to ensure members can see a provider without waiting too long. These standards are typically set by federal regulators for Medicare programs and by state regulators for Medicaid and commercial programs.

If a health plan lists more than 3-5 active service locations for a provider, this is typically a red flag as a provider can only be at one location at a time. Health plans should monitor their directories for this condition and investigate whether providers are routinely scheduling appointments at these locations.

Health plans should ideally differentiate between provider locations where appointments are taken, which should be listed in their directories, versus locations associated with a provider for claims processing which should not be included.

Look out for duplicate provider records

Duplicate provider service locations is another common error, typically caused due to inaccurate data shared by a provider organization or human error in connection with manual data entry. Another common reason for duplicate locations is when a provider has more than one office at the same address.

One way to address this issue is by using United States Postal System (USPS) address standardization. There are also data mining and analysis techniques, such as fuzzy logic or AI, that could be used to identify potentially duplicate address records so they can be corrected as soon as possible.

Conduct mock audits

It may be impractical to look at each of these problem areas individually, so a more comprehensive approach would be for health plans to periodically audit their provider networks using the same methodologies as regulators.

For health plans, a Centers for Medicare and Medicaid Services (CMS) program audit failure can trigger financial penalties, sanctions, and even enrollment freezes. The results of a mock audit can help insurers identify issues and actionable remediation tasks before a real CMS audit identifies them.

Steps to improve provider data accuracy

Establish data governance policies: Define clear roles, responsibilities, and standards for managing provider data
Use provider data management software: Automate updates, reduce errors, and ensure compliance
Integrate data across systems: Ensure interoperability between provider directories, claims systems, and credentialing databases
Monitor and audit regularly: Ongoing assessments help identify and rectify inaccuracies before they escalate

How Can Atlas Systems Ensure Provider Data Accuracy?

Provider data accuracy is a strategic priority in healthcare, impacting patient care, regulatory compliance, and operational efficiency. Investing in solutions like Atlas Systems’ provider data management tools ensures long-term success in maintaining accurate provider information.

PRIME by Atlas Systems offers a rigorous, proven approach to provider data validation and management. The PRIME goal is to help insurance plans deliver great member experiences and stay in compliance with mandates like the No Surprises Act.

Atlas PRIME has achieved best-in-class status with a provider data accuracy rating of 95% through client-audited quality assurance and up to 90% validation success. Other services often rely on aggregated data, using sources of unknown reliability. PRIME takes a different approach. We deliver data validation from the source, reaching out directly to contracted health systems, provider groups, and individual practices.

Our solutions are designed to streamline data accuracy efforts.:

Automate data validation and verification
Offer seamless integration with existing healthcare information systems
Reduce compliance risks with built-in regulatory tracking
Provide real-time provider updates to minimize inaccuracies

FAQs About Provider Data Accuracy

How can data governance frameworks support provider data accuracy initiatives?

A structured data governance framework establishes policies, accountability, and quality controls to ensure provider data remains accurate and compliant.

How does inaccurate provider data affect insurance claims and reimbursements?

Errors in provider credentials, network affiliations, or NPI numbers result in claim rejections, delayed reimbursements, and potential disputes between providers and payers.