How payment integrity tools can help payers fight fraud, waste and abuse

The United States spends an average of $3.8 trillion a year on health care. Some estimate that fraud, waste and abuse (FWA) costs the country at least $114 billion a year, which is more than 3% of overall healthcare spending.

So what can payers and providers do to reduce the massive amount of overspending? To begin, let’s define FWA.

Definition of fraud, waste and abuse

Fraud: An intentional deception or misrepresentation intended to defraud a health care benefit program for an unauthorized benefit or payment. It involves knowingly, willfully and intentionally making false statements or misrepresenting material facts.

Examples include deliberately charging for a service that was never performed, charging for a service with a higher reimbursement than the service provided, or altering claim forms or electronic medical records.

Waste: Misuse or overuse of resources, services or practices that result in unnecessary costs. For example, providing services that are not medically necessary.

Abuse: Supplier practices that are inconsistent with sound tax, business, or medical practices, resulting in erroneous or unnecessary costs; reimbursement for services that are not medically necessary; or services that do not meet recognized professional standards of health care. Abuse is similar to fraud, except that it is not necessary to prove that the abusive acts were committed knowingly, willfully and intentionally.

For example, billing for an uncovered service, misusing codes on a claim, or improperly allocating costs on a cost report.

Understand the complexity of the health system

It is important to understand the different types of FWAs and their differences before you can deal with massive overspending in healthcare. This is not a new trend, but as the healthcare system has become increasingly complex over recent history, we also tend to see instances of wastage on the rise.

Increases or changes in regulation, such as a new administration, change in policies or a new CMS directive, introduce complexity into the system. Lack of understanding between suppliers often leads to incorrect billing.

For example, the use of telemedicine exploded during the Covid-19 pandemic, with providers seeing 50 to 175 times more patients via telehealth than before. As a result, CMS has introduced several policy waivers and extensions to accommodate the increase in telehealth services.

Although telehealth offers many benefits to patients, it introduces operational complexities for providers and raises legitimate concerns about fraud, waste and abuse for payers.

On the other hand, we also see an increase in cases of fraud, which is a more intentional act, mainly committed by finding loopholes in the directive to obtain a higher refund. The Office of Inspector General says the United States has lost more than $6 billion from a single case of telemedicine fraud. The rise of telemedicine amid the Covid-19 pandemic has heightened the risk of FWA.

Turn to technology to fight fraud, waste and abuse

When it comes to incorrect billing or misrepresentation of services rendered, it is the responsibility of payers to identify these instances of FWA – or risk of overpayment. The practice of identifying instances of FWA to increase payment accuracy and reduce overall spending waste is often referred to as payment integrity. Accurate, end-to-end payment integrity has never been more critical to health plan operational costs. Technology can help.

Natural Language Processing (NLP) capabilities combined with Machine Learning (ML) methodology can be applied to train systems to work like a human auditor to identify overpayments and billing inaccuracies.

When reports come in from vendors, auditors review parts of the request (such as medical records) and capture all vendor notes on the backend. From there, the machine starts learning the trends in the reports and creating these new ML algorithms, which help identify outliers or errors in future claims.

Therefore, by deploying NLP and ML techniques, organizations can reduce the manual effort that was previously required and specify which claims are paid incorrectly or identify suspected cases of FWA.

Now is the time for payers to double down on payment integrity solutions to avoid errors that lead to inflated or unnecessary spend.

Photo: Feodora Chiosea, Getty Images

About Matthew R. Dailey

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