Identifying healthcare fraud, waste and abuse is a highly evolved practice that is best done with a marriage of artificial intelligence (AI) and human intelligence (HI) capabilities. As the losses attributed to fraud continues to grow, unfortunately, we all share the responsibility of paying for it. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are in the tens of billions of dollars each year.1 The payment integrity review process of analyzing a healthcare claim can be strengthened by implementing a hybrid approach of both HI and AI. However, it's important to understand the benefits and limitations of each to avoid pitfalls that can arise. On July 20, 2022, the Department of Justice announced criminal charges against 36 defendants in 13 federal districts across the United States for more than $1.2 billion in alleged fraudulent telemedicine, cardiovascular and cancer genetic testing, and durable medical equipment (DME) schemes.

Cybersecurity is a growing area of risk in healthcare, and organizations are grappling with the vulnerabilities and the ways patient data can be used against patients and organizations. From identity theft to healthcare fraud, waste and abuse, cybercriminals breached 642 accounts of 500 or more patient profiles in 2020. That's a rate of more than 1.76 per day, reports HIPAA Journal, adding up to 29 million healthcare records breached last year. Security breaches cost healthcare companies $6 trillion dollars by the end of 2020. According to Health IT Security, three security data breaches in 2020 alone affected almost 2,000,000 records, opening opportunities for identity theft and online fraud.

The precise figure is unknowable because only 10% of such fraud is ever detected. Of course, health care organizations work tirelessly to thwart FWA. Pharmacy benefit managers (PBMs), which manage prescription drug benefits on behalf of health insurers by negotiating with drug manufacturers and pharmacies, are particularly adept at detecting FWA in drug dispensing, but typically lack visibility on the medical benefit side. "I could tell you all the drugs a patient received from a hospital, a retail pharmacy or a mail order pharmacy. But I couldn't show you the diagnosis attached to that claim," explains Jo-Ellen Abou Nader, Vice President of FWA and Supply Chain Optimization at Prime Therapeutics (Prime).

The precise figure is unknowable because only 3 to 10% of this fraud is ever detected. With more data in health care than ever before, what is the opportunity to use artificial intelligence (AI) and other advanced analytics techniques to improve fraud detection? That was the topic of a recent webinar featuring Prime Therapeutics, the pharmacy benefit manager that Fast Company named among the world's most innovative companies for 2020 for their use of SAS Detection and Investigation for Health Care to fight fraud, waste and abuse. SAS Medical Director Steve Kearney, PharmD, hosted the webinar. Prime Therapeutics integrates pharmacy and medical claims into an advanced analytic engine to identify cases for their investigators.

Artificial intelligence applied by health insurers could help save millions of dirhams a year in fraud and abuse within the UAE healthcare system, analysts claim. Swiss software company Netcetera has completed a successful trial of its latest technology within a major health insurer, spotting almost 37,000 suspicious claims made by more than 4,000 doctors between 2016-17. Those bogus claims totalled Dh21 million and would usually have gone unnoticed by insurers, who would end up paying out for unnecessary treatments given by hospitals and clinics. The system developed by Netcetera uses an AI model that runs an algorithm to detect a pattern of behaviour. The AI model built into an insurers claim system will recognise a usual pattern of prescriptions and raise a red flag when a doctor delivers an abnormal course of treatment.

Machine learning and artificial intelligence (AI) works best on large volumes of data. One would think that with all its complexity and its mountainous volumes of data, the medical industry would be the perfect place for AI to be a disruptive force. The problem isn't that AI isn't acquiring enough data to be a disruptive force. The problem is that it's not acquiring the right data to solve some of the most egregious cost increases in the history of health care industry. In 2014, the U.S. Government Accountability Office reported 77.4 billion in improper payments of Medicare and Medicaid collectively.