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Big Brother's Using Big Data to Investigate Physicians

From Print Issue- Summer 2023
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Many of us have heard the term big data more and more in recent years, but what is big data? Big data refers to the technologies used to store, analyze, and manage large amounts of information. The use of big data provides the power to analyze and find patterns in large amounts of information far more quickly and accurately than before.

The Department of Health and Human Services (“HHS”) and its Office of the Inspector General (“OIG”) have begun to use big data to detect suspected fraud and abuse in federal healthcare programs. In 2015, OIG created the Office of the Chief Data Officer [1]. The Office of the Chief Data Officer works closely with the Centers for Medicare and Medicaid Services (“CMS”) to obtain vast amounts of healthcare data for analysis. Data analytics tools are applied to healthcare data, to lead OIG to physicians and other providers who might be engaged in fraudulent activity.

OIG is also using big data in predictive models. This allows OIG to identify trends and outliers, who might then become potential investigative targets. Once OIG identifies new trends and types of fraudulent schemes, its data analytics tools allow it to identify and target similar instances of fraud. OIG also uses data modeling to generate provider risk scores. Using various statistical methods, providers are rated as high, medium, or low risk. Providers flagged as high risk will be subject to more scrutiny from the OIG.


In October 2021, the Department of Justice (“DOJ”) announced that the use of data had led to the discovery of a fraudulent billing scheme. Four companies were charged with promoting improper billing involving P-Stim electro-acupuncture devices; the physicians who used the devices were charged with fraudulent billing. These P-Stim devices are not covered by Federal healthcare programs, but the companies selling the devices made false representations that P-Stim devices were reimbursable under billing codes meant for surgically implanted neuro-stimulators. These representations led to improper billing by physicians, resulting in thousands of dollars per billed P-Stim procedure. These improper billings were discovered by applying analytics to healthcare claims. DOJ offices across the country pursued and settled various False Claims Act (“FCA”) cases against P-Stim sellers and physicians, recovering millions [2].

According to Fall 2022 Semiannual Report to Congress, OIG expected nearly $4 billion in recoveries resulting from OIG audits and investigations occurring between October 1, 2021, and September 30, 2022. Approximately $1 billion of this amount relates to audit findings, and approximately $3 billion relates to investigative work [3]. These audit and investigatory findings were driven largely by the use of data analytics.


By way of example, the OIG used data analytics to identify 1,714 providers (out of approximately 742,000) whose billing for telehealth services during the first year of the COVID-19 pandemic posed a high fraud risk to Medicare. OIG concluded that these 1,714 providers had suspect billing on at least 1 of 7 measures that OIG felt might indicate fraud, waste, or abuse of telehealth services [4].


Per Christi A. Grimm, the HHS Inspector General, “HHS-OIG continues to provide essential, data-driven oversight and enforcement to drive positive change in HHS programs and for individuals they serve.” Since the focus on the use of data analytics, more non-qui tam False Claims Act cases have been filed than ever before. DOJ initiated 100 more FCA cases in 2020 than it did in 2019, resulting in the most non-qui-tam FCA cases filed in almost 30 years [5]. The 296 non-qui tam and 652 qui tam cases reported for 2022 reflect an increase over 2021’s new case numbers of 212 and 598, respectively [6].


So, what does this mean for physicians? While big data can be used to identify fraud and abuse, it can also be used by providers to catch and correct potential billing errors and avoid the scrutiny of the OIG.


Practices should consider incorporating data analytics into their audit and compliance programs, to minimize the risk of enforcement actions and investigations. Early identification and correction of potential improper billings can help practices avoid being caught up in costly investigations and prosecutions.





[4] Id.



About the Authors: 

Karina A. Shipman, JD, MBA, is an Associate Attorney in the Phoenix, Arizona at the law firm of Milligan Lawless, P.C. Her practice includes assisting physicians and other clients in a variety of healthcare law, litigation, and regulatory matters. Karina received her law degree and Master of Business Administration (MBA) from Willamette University in Oregon.

Robert J. Milligan, JD, is Co-Founder and Shareholder in the Phoenix, Arizona law firm of Milligan Lawless, P.C. He specializes in health care law. Bob limits his practice to the representation of individuals and companies in the health care and life sciences industry. In addition to his law degree, he also has an LL.M. in Biotechnology and Genomics. 

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