It is here folks! Machine learning and AI are going to be used in a proactive way to detect the fraud in the healthcare market. The investigators will be well down the trail of knowing who to audit before they even send the letter they will have a pretty strong idea of what they will find. Published in Forbes by David A. Teich article Machine Learning In Analytics To Limit Healthcare Fraud.
To most Americans, the phrase “Medicare Fraud” brings up images of individuals who cheat the system to collect healthcare when they don’t need it. While those cases do exist, that’s not what really concerns fraud analysts, insurance companies, and government agencies. The strong majority of fraud cases involve healthcare providers. The problem is that there are so many providers that current healthcare solutions aren’t advanced enough to identify fraud in the vast amount of information. One example is the problem of prescription billing abuse. That area is a great example of where machine learning (ML) can have a direct input on a real world problem.
While it is good to know that analytics can find fraud once it’s known what the analyst should search, there’s an obvious problem. Today, most occurrences of this type of fraud are only identified when a patient complains. Given the time that can pass before that happens and the information is sent to the right people, large losses can happen before the insurer finds out there is a problem. It is far better to be proactive, to identify the problem as soon as the data indicates it is there.
That is where machine learning can come in. Given the large data sets in today’s medical industry, ML can be trained to analyze refill patterns for individuals, pharmacies and regions. When ML is then included in the information infrastructure, exceptions can then be immediately flagged for human investigation. Further study can then determine is the flagged transactions are false positives, good prescriptions that fell outside the expected parameters, or real positives found early.
“Businesses can no longer afford to leave machine learning out of their fraud detection arsenal,” said Ashley Kramer, SVP Product Management at Alteryx. “Machine learning can rapidly detect anomalies in data that could indicate fraud, giving analysts the freedom to dig deeper into the data, which in turn, has the potential to significantly reduce financial loss for companies.” If only a few unusual events can indicate to an ML system that there might be fraud, humans can be notified by the system early in the fraud attempt, preventing the loses now being identified. Analysis of the large volume of existing data is important in stopping loss and starting the recovery process, but machine learning predictive analysis can shut off the loss much earlier, providing significant savings in the healthcare system.
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Special thanks to Forbes and David A. Teich for the article Machine Learning In Analytics To Limit Healthcare Fraud.