Why Your Low-Quality OIG Exclusion Monitoring is Costing More than You Think


If you’re shopping for exclusion monitoring, you’ll want to be armed with the knowledge and questions you need to be a savvy buyer. If you’ve never directly interacted with exclusion monitoring before, you may not realize that the results you receive from the screening process can vary widely depending on the vendor’s technology. 

We love nerding out about what we do at ProviderTrust and how that makes us different. If data science isn’t particularly your thing, bear with us! You’ll feel like an expert by the end of this article. 

In this post, we’ll explain why the low-quality (and risky!) OIG exclusion matches that you’ll find from other vendors can end up costing your organization. 

Exclusion Monitoring 101

Exclusion monitoring is the process of identifying matches between client populations (employees, vendors, provider networks) and exclusion records from federal and state sources. Once it has been confirmed using SSN, Tax ID, or NPI, an exclusion match is born! This process is repeated up to daily since the exclusion source data changes often and irregularly.  

Learn more about exclusion monitoring sources and recommended frequency

Two of the things that sets apart ProviderTrust are our Monitoring Science and our Data Enrichment. Here’s what we mean by that: 

Our Monitoring Science, or the methodology by which we identify an exclusion, requires the use of a unique ID (SSN, Tax ID, or NPI) to verify matches. This is the highest standard of quality in the industry. 

Data Enrichment is the process of adding uniquely identifying data (like SSN and NPI) to public exclusion records. ProviderTrust has invested the time and energy to do this for every exclusion record on an ongoing basis.  

Most exclusion monitoring vendors only rely on publicly available data (such as name, address, specialty) to verify exclusions and match on data fields that are often unreliable. However, only using public data significantly increases the risk of missing exclusion matches while also producing more false positives due to incidental similarities of data. Our algorithms run through a verification checklist, considering all available and enriched data points. 

  • Name (including nicknames and previous names)
  • Address (past and present)
  • Birthdate 
  • Tax ID or SSN
  • NPI
  • Provider specialty

ProviderTrust requires the use of unique identifiers (NPI, SSN, or tax ID) to verify matches. 

You might think that such a high standard would mean we return fewer exclusions within a given population. But while the overall number of false positives will be lower, the number of true exclusion matches we return is significantly higher than our competitors. 

Ask your vendor: “Which data fields do you rely on to search for and verify an exclusion?”

False Positive Exclusion Matches

As we’ve written about before, the false positive exclusion match is a well-known goose chase for anyone who has dealt firsthand with a rudimentary exclusion monitoring service. A false positive occurs when the exclusion matching algorithm relies on non-unique fields (such as name, address, and specialty). For example, an excluded provider has the same name and specialty as a provider in your population. But without enriched data adding unique identifiers to the exclusion source, you won’t realize the excluded provider is the father of your provider, or that they simply have a similar name. 

If your team relies on exclusion monitoring technology that returns these low-quality matches, they’ll spend hours each month combing through them and tracking down additional information to verify or rule them out. 

Here’s the challenge: exclusion data is messy, inconsistent, and often sparse. This screenshot from the Alabama exclusion list provides almost no clues for confirming a match. So without a data enrichment strategy like ProviderTrust’s, there isn’t enough information about the excluded individual to say definitively that that provider is or is not the same as the one in your population. 

Our data enrichment eliminates false positives.

Only by enriching publicly available data can exclusion monitoring identify matches with 100% confidence.  

Ask your vendor: “What is your data enrichment strategy?” 

Potential Matches Waste Everyone’s Time

A false positive exclusion match may be presented as a potential or fuzzy match. This just means that the match hasn’t been fully verified and requires manual work to complete the verification. This work is tedious and time consuming (it often involves contacting state Medicaid agencies directly). In our opinion, this work is a waste of a perfectly good human. We like to say: let the robots do the robot work and let the humans do the work that is creative and engaging.

False positives and potential exclusion matches bog down your teams and introduce risk to your organization. 

Our monitoring science eliminates potential matches. 

Only by enriching publicly available data can exclusion monitoring identify matches with 100% confidence.  

Ask your vendor: “Do you ever return potential exclusion matches that our team needs to fully verify?” 

False Negatives: “No Exclusion Found”

False negatives are the exclusions  you don’t even know you’re missing—the unfelt pain of low-quality exclusion monitoring. False negatives occur when an excluded individual or entity effectively evades detection during exclusion screening, often through a combination of insufficient exclusion data, and shape-shifting. 

Shape-shifting frequently takes the form of name changes. If you don’t have a record of someone’s former name, you can’t possibly identify an exclusion attached to the former name—unless you match according to a more reliable and stable data point, SSN or NPI. 

Here’s a real example of an exclusion match that would never have been discovered without ProviderTrust’s data enrichment strategy:

Other vendors’ result: No Exclusion Found

When ProviderTrust screens against our enriched data set, we positively identify an exclusion by SSN and/or NPI. 

ProviderTrust: Exact-match exclusion found

Ask your vendor: “Can you identify an exclusion if the excluded provider has changed their name?”

Even though you can’t possibly be aware of them, false negatives cut to the core of why you’re conducting exclusion monitoring in the first place: they open your organization up to risk and jeopardize your compliance or program integrity. 

Our data enrichment eliminates false negatives. 

By starting with more robust enriched data about every OIG and state Medicaid exclusion, we cast a wider net to rule out an exclusion for every individual and entity within your population. With all those extra data points, we can identify exclusions you’d never otherwise find. 

The Keys to High-Quality Exclusion Monitoring

    • Data Enrichment
    • Exclusion matching based on unique identifiers (SSN or NPI)
    • Fully verified exact matches
    • Audit-ready documentation provided
    • Integrations to power all relevant workflows
    • E&O policy for constant peace of mind

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