Could CMS’ Fraud Prevention System be unfair?

The Centers for Medicare and Medicaid Services (CMS) uses the Fraud Prevention System (FPS) to detect improper Medicare payments by processing millions of fee-for-service claims every day. But the focus on monetary return might cause the system to focus on some fraudulent healthcare providers in lieu of others.

FPS analyzes data related to Medicare fee-for-service claims to detect improper claims and develop leads for fraud investigations. The large number of irregular claims shows the need for such a system: CMS estimates that 9.51% of its payments in the 2017 fiscal year were towards claims that violated Medicare policy, which translates to $36.21 billion in improper payments. Past years also had high improper payment rates: in the 2016 fiscal year, it was 11%, and in the 2015 fiscal year, it was 12.1%.

These losses are the reason why the Small Business Jobs Act of 2010 required the CMS to create the FPS, which uses predictive analytics to process all fee-for-service claims prior to payment and prevent the payment of improper claims.

The issue is that FPS has a huge impact, both in the operation of Medicare and the lives of millions of Americans.  According to Northrop Grumman, the defense contractor chosen to implement the FPS, the system includes 42 million Medicare beneficiary profiles and 2 million healthcare provider profiles as of 2013, with millions of claims processed daily. Even though the system was expensive to implement, costing around $51.7 million in its fourth implementation year (2015), the investment is yielding results. In 2015, it helped identify or prevent $654.8 million of improper payment, which means approximately a $11.5 return for every dollar invested, although the rate of return was not as high in previous years.

Although it is important to consider the return on investment when evaluating the FPS system, using that as the only measurement of success can lead to undesirable biases. A report to Congress mentioned the potential side effects of concentrating on return on investment: the program might focus on getting money back from “amateur” fraudsters rather than “professional” fraudsters who, for example, might offshore their illegitimate gains. An “amateur” fraudster might be a healthcare provider that treats real patients but also makes improper claims. It would be easier to get money back from this business rather than someone who offshored all the money. In the short run, this could mean using fewer resources and getting higher returns, which would look good in terms of return on investment. However, the professional fraudsters are also a problem, and it’s important to go after them as well. Is it fair to target “small” fraud instead of “big” fraud just because the money is easier to recover?

Furthermore, healthcare providers can be punished over errors even when most of their fee-for-service claims are legitimate. For example, CMS revoked the billing privileges of the company Arriva (which describes itself as “the nation’s largest supplier of home-delivered diabetic testing supplies”) based on 211 improper claims, which represents only 0.003% of its claims over the past five years. Even though this reduces healthcare fraud, the decision also affects Arriva’s ability to provide real medical service to its customers.

To investigate this issue, researchers and journalists could view CMS’s Return on Investment reports and the Government Accountability Office’s reports on the Fraud Prevention System.

Investigation into this algorithm can start by contacting the press contacts of the Centers of Medicare and Medicaid Services, as well as people involved in health care fraud cases, since the FPS generates a lot of leads for investigations. The FBI also has a website dedicated to health care fraud news.


Algorithmic decision-making systems (ADMs) now influence many facets of our lives. Whether it be in finance, employment, welfare management, romance, or dynamic pricing, these systems are practically ubiquitous throughout the public and private sectors.

Operating at scale ADMs can impact large swaths of people—for better or worse. Algorithmic accountability reporting has emerged as a response, an attempt to uncover the power wielded by ADMs and detail their biases, mistakes, or misuse. Algorithmic accountability entails understanding how and when people exercise power within and through an algorithmic system, and on whose behalf.

Algorithm Tips hopes to stimulate algorithmic accountability reporting and support a robust reporting beat on algorithms, in particular by making it easier to investigate algorithms used in government decision making.

To do this we curate a database of ADMs in the US federal government which are of potential interest for investigation (and hope to expand to local and international jurisdictions in the future). On our home page you can search the database for interesting algorithms using keywords relating to facets such as agency (e.g., Dept. of Justice) or topic (e.g., health, police, etc.). Next, on our resources page, you can learn how to submit public records requests about algorithms, or find news articles and research papers about the uses and risks of algorithms. We hope you can get some inspiration there. And finally, we actually dig into some of these leads ourselves and post write-ups to our blog. We hope that journalists and other stakeholders can build on these posts and develop even deeper investigations.

Algorithm Tips is a project of the Northwestern University Computational Journalism Lab. If you have any questions, comments, or concerns (or want to talk about how to help us expand the effort), get in touch: Thanks!