In today’s healthcare industry, there is no greater and costlier problem than healthcare fraud. Not only does this type of fraud cost the industry tens of billions of dollars each year in losses according to the National Health Care Anti-Fraud Association or NHCAA, but it also causes misery to both health payers and patients.
Health payers suffer an increased overall cost of doing business due to higher customer insurance benefit costs. On the other hand, patients have to deal with higher out-of-pocket expenses and compromised health due to increased insurance premiums and unnecessary or unsafe medical procedures, respectively. In all scenarios, it’s a raw deal for everyone involved.
Thankfully, there are ways to address this issue, with one particular way gaining quite a bit of popularity and traction: big data mining and analysis through industry-specific enterprise-class AI solutions. But before we get into that, we first need to examine just what healthcare fraud actually is.
What is healthcare fraud? What constitutes as healthcare fraud, and who commits it?
The NHCAA defines healthcare fraud as such: “…an intentional deception or misrepresentation that the individual or entity makes knowing that the misrepresentation could result in some unauthorized benefit to the individual, or the entity or to some other party.”
Put in a simpler way, it means a deliberate attempt by an unscrupulous healthcare provider to charge a patient or health payer more than they actually owe, for the sole purpose of earning profit. This is, of course, a criminal activity.
Examples of healthcare fraud include—but are not limited to—the following:
- Charging for unnecessary tests, treatments, and procedures that were not, at the time, medically necessary
- Charging for tests, treatments, procedures or services that were never supplied in the first place
- Charging more than the actual cost of services, procedures, or supplies (e.g. charging for a more expensive model of wheelchair when a low-end model was actually provided)
As stated earlier, the NHCAA believes that tens of billions of dollars are lost due to healthcare fraud. The US Federal Bureau of Investigation (FBI) also estimates that 3 to 10 percent of healthcare billings are fraudulent. All that money lost, unfortunately, is a burden shouldered by not just healthcare payers but by patients as well—i.e. those who need healthcare the most.
What, then, is big data mining and analysis? How does it relate to the healthcare industry and how does it protect against healthcare fraud?
Big data mining and analysis means the usage of automated programs and solutions to gather and analyze the gigantic amount of paperwork generated in any industry—in this case, healthcare.
To put it more simply, it means storing paper trails and documentation involved in healthcare—be it medical records, transaction receipts, diagnostic test requests and results, insurance claims, and so on—in a digital format. Afterwards, sophisticated computer programs are used by data scientists and other professionals in order to sift through, organize, and analyze all that information.
This allows any requested records to be pulled up for viewing or reference, without having to go through the cost- and time-related inefficiencies of manual bookkeeping. It also lessens the manpower needed to manage the voluminous amount of data involved.
As for how such a method protects against healthcare fraud, it hinges mainly on the solution being used for big data management and analysis. This is where as enterprise-class AI solutions comes into play.
To date, there are many solutions available just for the necessary management and analysis of big data, and many organizations can certainly get by with just a standard, no-frills solution.
But to be able to protect against healthcare fraud, a health payer needs to use a solution that not only meets the specific needs of the healthcare industry but also employs best-in-class technologies and techniques in order to analyze that information.
This solution, ideally, should be able to automatically find anomalies, links, and clues in the voluminous amounts of data that it’s managing in order to find possible healthcare frauds and scams as they happen. Even better, the software should be able to predict acts of fraud even before they are committed.
For example, the solution could create risk profiles of suspicious healthcare providers and even patients with histories of fraud or attempted fraud, and automatically alert the health payer should these suspicious parties start trying to transact with them. Another is identifying suspicious links between multiple healthcare providers, where patients may be shuffled back and forth between them just to balloon billable costs and charges.
How is all this done? Through analytics delivered by artificial intelligence and machine learning. Without getting too technical, this means that the solution itself has its own automated capability to sift through voluminous amounts of data and find relationships and links within those data without being operated or managed actively by staff.
The software also has the ability to learn—the more it is exposed to a certain industry or organization’s data, the more adept it will be in finding new links and anomalies as the data itself is refreshed, modified, or amended.
Using these techniques, an enterprise-class AI solution will be able to not only detect fraudulent activities but also predict them ahead of time, enabling health payers to avoid being defrauded.
As such, we recommend health payers to look into purchasing or implementing these solutions as soon as possible. It is only through the implementation of such solutions can both health payers and patients be truly protected from the onerous burden of healthcare fraud.
