Suspecting analytics transformed—the rapid movement from clinical rules to machine learning

In the complex world of risk adjustment, health plans rely on suspect analytics to identify members with possibly undocumented conditions that could yield additional revenue. However, traditional suspecting approaches that rely solely on clinical rules may yield only the “low-hanging fruit” of hierarchical condition categories (HCCs). Rapid advancements in machine learning technology have produced a better approach to suspect analytics—one that gives health plans a more predictable revenue cycle and the potential for substantially higher return on investment.

 I hope you can join me for our on-demand webinar titled, “Data patterns and predictive magic: how to improve suspecting and chart valuations.” I’ll reveal pre- and post-analytics that underscore the power of a clinically driven machine learning approach to suspecting. By the end of the webinar, you’ll understand how:

  • A clinical approach to suspecting complements machine learning algorithms
  • Revenue and ROI can increase using predictive analytics
  • Clinical knowledge is critical in making these new models more effective

Don’t miss this opportunity to enhance your suspecting and chart valuation efforts.


Watch the on-demand webinar


David Costello
As chief analytics officer, David Costello works closely with product, consulting, data operations, and client service teams to ensure that Cotiviti's proprietary analytics help our clients meet their business objectives and remain relevant in the industry. Before joining Cotiviti, David was chief analytic officer at Press Ganey, where he was responsible for building a set of analytic products that allowed hospitals and provider groups to enhance the patient experience, solidify their reimbursements, and identify areas for process improvement and revenue enhancements. David also previously served as senior vice president of consumer segmentation and engagement strategies at Health Dialog. He holds a BA in business administration and sociology from Northern Michigan University and a MA/PhD in sociology from the University of Delaware. He also served in the United States Air Force.

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