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Fraud Protection: AI Solutions in Healthcare

February 12, 2019

As if the US healthcare system weren’t complex enough these days, fraud is also on the rise. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to healthcare fraud are in the tens of billions of dollars each year. This may account for up to ten percent of US healthcare spending, says HealthLeaders Media. That’s why it is crucial that health care providers take steps to prevent fraud and malpractice. In an earlier post, we discussed how artificial intelligence (AI) can and will help healthcare institutions provide value-based care. In this post we will tackle the question of fraud protection by leveraging AI technology.

The cost of fraud

Fraud and counterfeits have a critical impact not only on healthcare companies’ costs of operation of but also on their liability and brand image. These extra financial and quality-of-life costs detract time and attention from more critical aspects of healthcare. This is even more so when it is related to products and services; it could have a negative impact on the physical health of patients.
Common examples of fraud and abuse in healthcare include the following:

  • Treatment is paid for, but not provided
  • Falsified claims
  • Multiple claims by different providers for the same patient/treatment
  • Stolen patient identities are used for reimbursing medical services never provided
  • Multiple treatments and diagnostics requested but not required

How can machine learning help?

The classical way of dealing with these behaviors requires highly trained people with acute audit senses. They need to “catch” patterns in distributed, incomplete data. Predictive Analytics and Big Data systems can help prevent fraud. These technologies permit us to identify inaccurate claims, unwarranted procedures, occluded duplications, and so on. Using machine learning, we can begin to leverage these professionals’ expertise and scale up. Analyzing historical claims, for instance, especially when some of them prove fraudulent, can teach us. We learn what a normal claim looks like, when the abnormal claims happen, what conditions they have in common, and plausibly how early we can tell the likelihood of a fraudulent claim.
Furthermore, we can assign claims and providers a “trustworthiness score” to avoid costly manual checks. We can also flag low-scoring claims or providers, along with anomalies and suspicious billing patterns, for detailed revision. Day to day, field agents are constantly making repeated decisions with limited information. Machine learning strikes gold precisely in those two areas. It leaves “long tail” decisions to the algorithm, and includes previously impossible-to-process sources like social media, news articles, and so on.
Some possible initiatives for machine learning in healthcare—to prevent fraud and reduce costs—are:

  • Indicating whether a given claim is a fake
  • Predicting the total cost of following through with a fake claim
  • Finding common factors missing in fraud cases, in order to devise new policy
  • Predictive filtering of the claim’s validity upon filing to avoid wasting time on suspicious ones
  • Including an interactive helper when filing a claim, to minimize misfiling
  • Revealing many large-scale trends that are otherwise invisible to local teams, because on the individual level the signals are soft or undetectable

An example of what AI can do

Initiatives involving AI, Big Data analytics, and smart packaging can be a game changer for healthcare fraud protection. In terms of consumer items, smart-packaging is a great option. It allows field agents to check the authenticity of products by scanning certain details and markings on the package. In this way they can ensure the authenticity of the products, and minimize the chances of product tampering. Also this technology allows for tracking their origin. In turn, this helps with compliance, product recall, information for end users, and quality management.

The digital journey ahead

As far as Artificial Intelligence technology and healthcare are concerned, there are still many areas left to explore. Leveraging AI and Big Data, healthcare providers can take fraud protection to a whole new level. And, done successfully, this ensures reduction of fraud losses, and also improves the brand image and overall user experience.

This post is the third in a healthcare series we are working on. Look here for the second post.

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The Healthcare & Life Sciences Studio aims to reinvent the life sciences industry ecosystem through tangible technology-driven solutions. Globant aims to bridge the gap to help life sciences and healthcare organizations to achieve their mission of delivering innovation and services faster and more efficiently to enhance patient value and improve outcomes.