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PAYMENT INTEGRITY

Case study: Data mining for errors yields more accurate pharmacy payments

With the U.S. healthcare system spending more than $600 billion on prescription drugs annually, it’s critical for health plans to ensure proper payment for claims for pharmacy services and medications covered under the plan’s medical benefit.

But this can be challenging for payers. Identifying and recovering billing and payment errors in medical pharmacy takes time and administrative resources. Read our new case study to see how Cotiviti’s Data Mining pharmacy solution uncovered millions in inaccurate payments for two health plans using predictive analytics and specialist validation.

In this case study, you’ll learn:

  • How Cotiviti’s diversified team of experts monitor industry changes to build a unique content library
  • How a large Blue Plan identified >$10M in potential savings with data mining
  • How a community health plan identified a large overpayment trend in unclassified drugs

Read the case study

Tailor the data mining process to your needs through collaboration with auditing experts to identify and validate overpaid claims. Discover the value of data mining in payment integrity to help ensure thorough medical pharmacy claim review and increase payment accuracy for your organization.

To learn more about Cotiviti’s Payment Accuracy and data mining solutions, set up a conversation with our team. For a deep dive into how health plans can improve pharmacy payment integrity, watch our on-demand webinar,  Beyond prior authorization: Ensuring accuracy of specialty drug claims.

WRITTEN BY

Matthew Herbein
As the vice president of product and with over 12 years of experience in healthcare payment integrity, Matthew leads product strategy across Data Mining, Coordination of Benefits, and Subrogation. He focuses on helping health plans modernize payment accuracy, shift more value upstream, and reduce administrative cost. Matt partners closely with clients and internal stakeholders to guide investment decisions, align roadmaps to real use cases, and deliver measurable results without unnecessary complexity.

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