Machine learning and similar technologies have the potential to have impacts on many different areas. A recent study indicates that this could well include the health care fraud detection process.

The study was by researchers at Florida Atlantic University. In the study, the researchers used a data set involving Medicare claims to develop a process for mapping fraudulent claims activity and teaching computers to detect potential fraud.

The aim of this use of machine learning is to make the detection of health care fraud easier. One wonders what will happen with the future development of this use of machine learning and if this type of process will one day be a major part of Medicare fraud detection efforts.

What tools health care companies and investigators use in health care fraud detection is a very impactful issue. For one, it can impact what sorts of circumstances could lead to individuals facing federal health care fraud charges. This matters greatly, as being charged with such fraud can put a health care provider’s future, goals and career at risk.

Being convicted on federal health care charges can subject a health care provider to many serious consequences. This includes:

  • Harm to their professional reputation
  • Professional license loss
  • Significant fines
  • Prison time

So, whatever set of circumstances leads to a health care professional facing allegations of Medicare fraud or other health care fraud, it can be crucial for him or her to quickly get an accurate picture of his or her defense options. Speaking to an attorney skilled in federal criminal defense can be a critical step in this regard.