The No Surprises Act is going into effect in January 2022. Will there be audits and penalties? No one knows yet, but the law doesn’t preclude it. Also, the statute does specify audits around plan payments, which is avoidable with the right strategy.[1]

While we wait for the regulation details to materialize, it is important to understand your baseline today to get ahead of what’s to come. We do know that payers need to verify provider information, and without a verification, they must be removed from directories. This is going to create a data problem even outside of directories.

Compliance is not the only reason why health plans should prioritize provider data accuracy. Most organizations use data to make decisions, and provider data is essential to operations. Providers move, stop accepting patients and leave the program, creating a constant data churn. Payers must protect their data like their bottom line, because choosing to protect it is a financial decision. Patient dissatisfaction and penalties are at risk if provider data accuracy is not a priority.

Inaccurate Provider Data – A Problem for Everyone

As a company, we help organizations assess the accuracy of their data, and we see many types of inaccuracies.

Provider address has one of the highest rates of inaccuracy that we identify in payer directories across the board. It is one of the hardest attributes to track because there is no authoritative source, and providers are constantly moving or changing their affiliations. We also see an average of over 12% inaccuracy of provider locations in our evaluations.[2] This is a critical component to address when verifying your directory.

Though patient satisfaction is important to maintain HEDIS scores, our data indicates that 14.56% of provider records have incorrect phone numbers. Patients are likely unhappy when they call a provider from your directory only to find it’s a wrong number.2

Just over 40% of records are duplicates.2 This is a data governance concern. Duplicate records cause duplicate work. When discovered, they must be evaluated. Then, how do you determine which one you are going to load into your provider directory? You might have to do provider outreach – which phone number are you going to call? Inaccuracy costs.

Who wants to go to a healthcare provider that is sanctioned? Our data found that 12.66% of provider records were of providers that have been sanctioned.2 Among many other downstream impacts, there are claims payment repercussions if a claim is generated from a sanctioned provider. Rejected claims can impact patient satisfaction.

Processes Need to Be Easily Repeatable

With the No Surprises Act, health plans will be expected to update their provider data quarterly. Your business will run more smoothly with accurate provider data. Keeping accurate data takes effort. It’s critical to develop processes that can be easily repeated. The continual process may seem daunting, but you don’t have to do it alone.

The first step is to assess your data quality. Determine where the problems are. If you have deceased or inactive providers, you’ll want to remove them before you do outreach. For inaccurate data, some solutions will auto-replace from a referential database. For the most cost effective, efficient outreach effort, it’s best to fix most data problems before you do outreach.

Determine who needs to be contacted to verify information. The next step is to do the resource-taxing outreach. To make this process easier, outreach efforts can include a mix of phone calls and provider portal updates. Provider outreach isn’t something you have to do; you can outsource it to a trusted partner.

Why You Should Keep Provider Data Accuracy as a Top Priority

If not for patient satisfaction or better data governance, do it for the bottom lines: cost savings and better health outcomes. Remove obstacles from providing healthcare to help people enjoy better health.

Learn more about improving your organization’s provider data quality and staying compliant with this new regulation.


[1] H.R.3630 — 116th Congress

[2] All statistics cited in this article are based on LexisNexis® Risk Solutions internal data analysis, 2019