{"id":4693,"date":"2016-10-08T12:41:16","date_gmt":"2016-10-08T16:41:16","guid":{"rendered":"http:\/\/ncjolt.org\/?p=4693"},"modified":"2020-06-04T20:52:59","modified_gmt":"2020-06-04T20:52:59","slug":"health-care-fraud-and-predictive-analytics","status":"publish","type":"post","link":"https:\/\/journals.law.unc.edu\/ncjolt\/blogs\/health-care-fraud-and-predictive-analytics\/","title":{"rendered":"Stomping Out Health Care Fraud Before it Happens: A Call for Using More Predictive Analytics"},"content":{"rendered":"

The Obama Administration made it a top priority to prevent Medicare and Medicaid fraud, and its efforts have yielded great results<\/a><\/span>. This past summer, the Administration announced that with sophisticated detection methods like \u201cbig data\u201d and predictive analytics, the Center for Medicare and Medicaid Services (CMS) was able to prevent $42 billion of improper payments<\/a><\/span> in fiscal years 2013 and 2014.
\nStill, all is not well. Many legislators are not content with CMS\u2019 efforts in combatting Medicare and Medicaid fraud, and believe that given the sophisticated fraud detection methods CMS has at its disposal, CMS is not doing enough to prevent health care fraud before<\/em> it happens. Recently, members of several House committees, including the Committee on Ways and Means, which has jurisdiction over the Medicare program, sent a letter<\/a><\/span> to CMS, citing concerns over CMS\u2019 continued \u201cheavy\u201d<\/a><\/span> reliance on a \u201cpay and chase\u201d model, under which CMS investigates claims only after<\/em> it has already paid the providers.
\nThe problems with a \u201cpay and chase\u201d model, of course, are not only inefficiency and the costs associated with recouping fraudulent payments, but also the threat that remains when delayed detection can result in criminals causing even more damage. As the House committee members stated in their letter,<\/p>\n

each dollar lost to fraud is a dollar that is not used to benefit patients<\/a><\/span>.<\/p><\/blockquote>\n

 
\nUnder the Affordable Care Act, CMS received additional funds for expanding its fraud prevention initiatives<\/a><\/span>. The Fraud Prevention System (FPS), CMS\u2019 \u201cadvanced analytics system,\u201d<\/a><\/span> was also implemented in 2011 to address the concerns with a \u201cpay and chase\u201d model with Medicare claims. Through the FPS, CMS is able to identify suspicious patterns by analyzing large volumes of billing information prior to paying the provider.<\/a><\/span> Predictive modeling, unlike data analytics which utilizes\u00a0 various post-payment analyses in addition to predictive modeling, attempts to apply various statistical and analytical techniques to historical data in order to predict future behavior and events<\/a><\/span>.\u00a0 It is estimated that the FPS analyzes 4.5 million Medicare pre-paid claims each day<\/a><\/span>.
\nAccording to an interview<\/a><\/span> with the Deputy Administrator for Program Integrity and Director of the Center for Program Integrity at CMS, while the push for \u201cbig data\u201d efforts has not come without challenges, it has led to more \u201cconfident decision making\u201d that has reduced both costs and risks. For instance, FPS was able to identify a Florida<\/a><\/span> home health agency that billed for services that were not rendered. CMS was able to place that agency on prepayment review and payment suspension, refer the agency to law enforcement, and revoke the agency\u2019s Medicare enrollment.
\nCritics woes have not fallen on deaf ears, however. CMS continues to show its commitment to prioritizing health fraud prevention through a more \u201cproactive\u201d way of dealing with fraudulent payments. Currently, CMS is working on designing a new system to develop \u201cnext-generation\u201d<\/a><\/span> predictive data analytics.\u00a0 The new design is expected to improve the FPS\u2019 utilization and efficiency. It will be interesting to see more figures showing the savings from investments in sophisticated and advanced predictive analytics tools.<\/p>\n","protected":false},"excerpt":{"rendered":"

The Obama Administration made it a top priority to prevent Medicare and Medicaid fraud, and its efforts have yielded great results. This past summer, the Administration announced that with sophisticated detection methods like \u201cbig data\u201d and predictive analytics, the Center for Medicare and Medicaid Services (CMS) was able to prevent $42 billion of improper payments …<\/a><\/p>\n","protected":false},"author":1,"featured_media":4694,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[51],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/posts\/4693"}],"collection":[{"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/comments?post=4693"}],"version-history":[{"count":1,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/posts\/4693\/revisions"}],"predecessor-version":[{"id":7163,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/posts\/4693\/revisions\/7163"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/media\/4694"}],"wp:attachment":[{"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/media?parent=4693"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/categories?post=4693"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.law.unc.edu\/ncjolt\/wp-json\/wp\/v2\/tags?post=4693"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}