CMS Goes Predictive: Why Improving Intelligence Improves Outcomes - DoctorsManagement CMS Goes Predictive: Why Improving Intelligence Improves Outcomes - DoctorsManagement

CMS Goes Predictive: Why Improving Intelligence Improves Outcomes

By Frank Cohen, Director of Analytics and Business Intelligence

As originally published by Racmonitor.com

Beginning in July 2011, the Centers for Medicare & Medicaid Services (CMS) entered the world of predictive analytics. According to a press release, effective June 30, 2011, a total of 100 percent of all Medicare fee-for-service claims were (and now are) passed through a complex set of predictive algorithms prior to being paid.

And while I am not privy to the exact algorithms that they use, predictive analytics follows some pretty, well, predictable processes – and if the system works the way it was designed, then it represents a paradigm shift in CMS’s efforts to identify fraud and abuse. In general, a predictive algorithm is designed to analyze the variables within a unit (in this case, a claim) and guess (or predict) the likelihood that that unit meets some criteria inherent within the algorithm. For the purposes of fraud and abuse, the algorithm is designed to predict the likelihood that any given claim meets the CMS’s criteria for fraud or abuse. In my work on predictive modeling, it is common for the algorithm to “score” the value, in essence establishing a sort of prioritization with regard to the prediction.

For example, the algorithm may score a given claim as not very likely to meet the criteria (maybe a probability of 25 percent), having an even probability of meeting the criteria (maybe a probability of 5 percent) or very likely to represent fraudulent or abusive activity (maybe a probability of 85 percent). Imagine that there are billions of claims going through the system every year and CMS, like any organization, has some finite limitations with regard to its available resources to manage the process. Their auditors and auditor contractors have access to this data, and since it would be unlikely that they could even review the majority of them, they establish a threshold above which they are motivated to pursue a claim. If, for example, a claim is kicked out with some high score, the agency will pull a more comprehensive set of claims for a given time period associated to the NPI or TIN number for that claim. Now, the auditor has the ability to not only look at a given claim, but to test for any trends that might indicate a broader problem of overpayment. So two things are occurring here. First, claims suspected to constitute fraud and/or abuse are not being paid, and a deeper dive occurs into the providers or practices from where these claims originated.

Read the full article on Racmonitor.com