An Innocent Mistake or Intentional Deceit—ICD-10 and Healthcare Fraud Detection

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In their recently released whitepaper—An Innocent Mistake or Intentional Deceit? —Jvion examines the impact that ICD-10 will have on healthcare fraud detection. They focus on the challenges that the new code set poses to current fraud algorithms, the impact that those challenges will have on providers and payors, and possible approaches to addressing potential impacts in a pre and post ICD-10 world.

With the introduction of ICD-10’s new coding language, fraud detection
algorithms effectively become a blank slate thereby blurring the line between genuine
mistakes and legitimate fraud.

In the recently published whitepaper An Innocent Mistake or Intentional Deceit? Jvion—the leading ICD-10 financial risk assessment provider—examines how ICD-10 will impact the way healthcare fraud is detected. According to Shantanu Nigam, co-author and Jvion CEO, “the core algorithms driving healthcare fraud detection are based on machine learning that identifies patterns related to the underlying code set. That is, they rely on programs that are continually refined based on new situations and circumstances, which ultimately increases performance and improves accuracy. With the introduction of ICD-10’s new coding language, those algorithms effectively become a blank slate thereby blurring the line between genuine coding mistakes and legitimate healthcare fraud.”

Payors and providers face significant challenges as old processes catch up with ICD-10. With the core algorithms used to identify fraud effectively handicapped for at least 18 months, payors will have a hard time discerning unintentional mistakes and justified outliers from actual fraudulent behavior. Conversely, providers will be at greater risk of fraud allegations and investigations, which will ultimately hurt their credibility in the industry. Either way, both sides stand to lose thousands if not millions of dollars because of the lag in machine learning and incipient algorithms.

Shantanu went on the say that, “addressing the gaps created by ICD-10 will require new approaches to identifying fraudulent behavior. Emerging technology that is less reliant on a claim’s underlying code set will help improve accuracy while we wait for core detection algorithms to ‘learn’ the new coding language of ICD-10. These new approaches leverage big data concepts and predictive analytics to augment current systems and lay the groundwork for more advanced kinds of algorithms that can analyze disparate data elements to more precisely identify fraud.”

To download a copy of Innocent Mistake or Intentional Deceit? click here or visit

About Jvion
Jvion is a healthcare compliance technology and services organization with a full suite of tools to enable the ICD-10 conversion. The company serves providers and payers in all phases of the ICD-10 conversion process with a simple value proposition—by using Jvion’s tools and solutions, organizations can do more to reduce cost, mitigate risk, and optimize reimbursements with fewer resources and in a shorter time line. Please visit Jvion’s website at for more information.


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Allison Alavi
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