Over the last year, generative AI has captivated the public imagination. Many of us have become newly acquainted with the concept of an approaching Singularity coined by John von Neumann or Nick Bostrom’s Paper Clip thought experiment. Fortunately, Microsoft’s office assistant, Clippy, has yet to dutifully transform our planet into
Prepare for the best – and the worst – when it comes to generative AI and health care fraud.
See how analytics fights back against ever-changing fraud trends.
Blockchain technology and payment integrity in health care Blockchain technology has arrived in the health care space, bringing anticipation of revolutionary change in operational efficiency, data management, security, fraud prevention, disease prevention, and perhaps even in payments. While these are not new goals, I am ever the optimist. As almost
Health care fraud is often depicted as the great, five-headed hydra in Greek mythology. When you cut off one head, two more grow back. But more to the point, health care fraud has been presented as one of the primary (if not the primary) causes of unnecessary healthcare spend. However, just because
Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits, health care, food stamps, tax collection,
The U.S. health care market has always had practices in place to try and manage, or at least limit, aberrant behavior, which includes activities that are often described as cost containment, payment integrity and affordability. In the past, many organizations have appeared satisfied with their efforts in this area
It has become clear after speaking with numerous health insurance carriers, both in the United States and beyond, as well as at conferences (such as NHCAA), that there is a mass movement towards the nirvana that is "predictive modeling." Now that our industry is realizing the importance of predictive modeling
In the United States, loss prevention trends in health care have seemed very loudly directed at health care fraud, and less so about waste and abuse. This may be for many reasons: if you’re a private carrier, fraud prevention allows for larger recoveries and greater avoidance of future lost revenues.
It has become more and more apparent, country by country and state by state, that many organizations do not have a clear grasp on what their exposure is to health care fraud. Whatever name it goes by – aberrant behavior, abuse, waste or fraud – and no matter how it is measured – in currency (dollars,
