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How NLP and AI Can Improve Risk Adjustment

How NLP and AI Can Improve Risk Adjustment

In this edition of Gibson Talks, Trey Wheless, a risk adjustment and quality reporting executive at Gibson Consultants, and Gabe Stein, Executive Vice President of GeBBS Healthcare Solutions, sat down to discuss the benefits to payors and providers of adopting technology that impacts risk adjustment. Gabe shared his perspective as an executive for a leading technology-enabled provider of revenue cycle management solutions.

Q1. How is technology changing the way that payers and providers are looking at risk adjustment?

For the past few years, payers have been shifting risk to the providers. As we see more providers getting into risk-bearing arrangements, that shift brings the responsibility of closing care gaps to the providers. The question then becomes, how do providers and payers work together to ensure that the clinical documentation is accurate and justifies the proper risk score? There are several ways of doing this. Payors and providers can retrieve and review charts independently or use their vendors for this. In some instances, payers give providers software tools to help them identify charts that warrant a closer look.

From our perspective, the significant trend we’re seeing in the market right now is how providers can address this at the point of care or pre-bill. This means integrating a risk adjustment tool within the EMR so that providers can view the patient’s past diagnoses, allowing them to understand what needs to be addressed to close HCC’s and HEDIS gaps. The provider can open up the patient’s medical record in the EMR, in addition to the tool’s suggestions, and then can confirm or deny the conditions.

The question that’s left is, while you have all this technology and data, who will actually do the work? Here again, are multiple options. After the provider confirms or denies the information in the record, that information goes to a software tool, and then either the providers’ coders, or the vendor’s coders, audit that information. The provider can be queried in real-time, and they can change the documentation if they feel appropriate.

Now there is a better HCC capture at the point of care. This method is also reducing costs because you no longer have to retrieve records or do a retrospective review, and maybe you even minimize the number of in-home assessments performed.

What makes this type of tool so powerful is whenever you close out the EMR record, the encounter immediately goes through AI software which identifies any issues. A coder then looks at it through a “documentation improvement” lens and, when complete, it queries the provider to review proposed changes. This gives you a more complete record.

That’s the most impactful change compared to retrospective reviews; for example, a year and a half later after an encounter, it’s a much harder conversation.

At the end of the day, this technology is going to drive accuracy, improve RAF scores, and reduce costs.

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