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Posted by on Sep 19, 2017 in Uncategorized | 0 comments

Data Quality & Directory Accuracy

Data Quality & Directory Accuracy


Regulatory changes require new ways to look at provider data quality and the accuracy of directory data.

The Centers for Medicare & Medicaid Services (CMS) recently completed its first review of Medicare Advantage (MA) online provider directories. The review found that providers were not located at about 31% of the locations listed in directories.[1] Retired and deceased providers were also found in many directories.

CMS identified a “general lack of internal audit and testing of directory accuracy” as a contributing factor to directory inaccuracies. Issuers with high rates of errors were warned they could face fines or have enrollment frozen.

The accuracy issue isn’t limited to MA plans. The Office of the Inspector General (OIG) examined access to care in Medicaid Managed Care plans and found that 35 percent of the providers assessed “could not be found at the location listed by the plan.”[2] Iowa’s Medicaid program has also come under attack over directory concerns[3], as well as Marketplace and commercial plans, which have seen their share of directory complaints.[4]

what we did

Zelis is in a unique position to help improve directory accuracy. We collect data from hundreds of payers for thousands of networks on an ongoing basis. This allows us to curate a robust provider database containing information on nearly every dental and medical provider in the United States. We leverage this dataset in an iterative proprietary methodology that includes public data sources, and plausibility metrics to audit directory data and identify the most suspicious provider records to help ensure the accuracy of directory data.

why we did this

Our goal is to ensure our clients have visibility into how their provider data is being viewed in the market, not only by consumers, but regulatory entities as well. Data quality and accuracy are key to a member’s ability to access quality care in a timely manner. Our goal is to be an innovator in the provider network space and lead our clients into the future of healthcare. We give our clients the information and tools they need to adapt quickly to the changing healthcare landscape.

the results

providers that exceed a threshold distance

While some providers have locations over two-hundred miles apart, it is unlikely. These providers have likely moved and old locations are still being listed in provider data. By highlighting these providers for review, old locations can be updated.
In this test, we can see most networks have around 3-3.5 percent of providers with locations over 200 miles apart, with networks E and F as the exceptions.
It is important to note that network F is the largest included in this analysis, while network E is the smallest network.

over-stated addresses

In test 2, a similar story is told, but with the average network having roughly 5.5 percent of the total provider network flagged for having more than six locations.
Again, the largest network, F, has nearly double the percent of total network providers flagged on this test as the average network, while the smallest network, network E, has the fewest providers flagged.

over-stated specialties

For test 3, we see the largest disparity between networks for any test. Publishing multiple variations of the same specialty, non-primary practice specialties, and invalid specialties are the most common reasons why so many providers in Network F are flagged by this test.

NPI inactive providers

This test identifies providers whose NPIs are listed in the NPI inactive database (retired, deceased, etc.). These are easy targets for directory cleanup, and removing them can quickly result in more accurate directory data.
In this test, networks E and F are more in line with the other comparison networks.

exclusive provider-locations

In Test 5, we look at provider-location combinations that are exclusive to an issuer network.
Again, network F has the greatest percent of providers flagged for having locations exclusive to the network, while network E has the fewest flagged providers.


providers flagged as suspicious

An interesting trend worth noting, is the consistency in data health of the largest and smallest networks, networks F and E respectively. Both networks have the highest and lowest percentage of providers in nearly every test we performed.
Network E also only covered a regional geographic area, while network F was national, suggesting that targeted, regional networks may be easier to maintain than large national networks.

At Zelis Network Analytics we embrace the complexity of provider data. Our experience as the largest manager of provider network data in the industry gives us the expertise to deliver a reporting and monitoring service that effectively identifies the subset of your provider data in most need of review, saving you time and money.

For questions or to have your own customized analysis please contact


[1]HealthAffairs. Secret Shoppers Find Access to Providers And Network Accuracy Lacking For Those In Marketplace And Commercial Plans.

[2] Department of Health and Humana Services, Office of Inspector General. Access to Care: Provider Availability in Medicaid Managed Care. 2014.

[3] The Gazette. Opponents ask if Medicaid network is ready.

[4] HealthAffairs. Secret Shoppers Find Access to Providers And Network Accuracy Lacking For Those In Marketplace And Commercial Plans.


Analysis conducted by Patrick Innes

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