Securing Patient Health by Understanding the Fraud and Risk Relationship

We human beings have been grappling with risk since antiquity. Like many things, the first recorded accounts of risk date back to ancient Greece. The ancient Greek poet, Heslod, lamented about the risks all farmers face in planting crops and working with nature to get the highest yield in the poem, “Works and Days”. Later, Thales of Miletus tried to hedge his risks of an olive harvest by offering an option to buy olive presses at a fixed rate. We deal with risk all the time. Lately at Qlarant, we’re focusing a lot of attention on the relationship between fraud and risk to help plans and agencies safeguard against fraud while managing their risk.

What Is Risk?

Simply put, risk is the chance of something bad happening. It could be anything bad. The chance of a car crash and the amount of damage it may cause, or it could be the chance of a bad provider billing for false diagnoses or services they didn’t render. We do our best to understand risk by measuring how much we’re exposed to if the risk happens, usually in money. In the case of car accidents, we use insurance to hedge our risks, and our premiums are determined by the chance of our car’s damage based upon our age, driving, record, and where we live. So how do we determine risks to healthcare plans? It takes multiple steps. But one way we use to uncover risks is by using indicators.

Indicators Trigger Events

If you know the lingo, you might hear someone mention they’re looking for a signal. A signal is a change or measurement. In our world, an indicator would look for a change in the billing for a specific diagnosis. Another signal could be spikes in billing during certain events like the popularity of certain weight loss drugs. RIViR indicators look for changes in these signals so we can investigate and understand what’s really happening in the health marketplace.

Behaviors Produce Signals

Indicators can point to shifts in behaviors. Incorrect and abusive uses of modifiers are one series of behaviors indicators look for. However, the most devious bad providers go beyond abusive behaviors. Non-compliant billing and systematically abusive coding require more than checking the boxes to make sure rules are followed.

Observations Bring It Together

Behavioral patterns extend beyond following the deeds of a single provider. Observations of the regional marketplace must be made to ensure outliers are truly outliers and prevent teams from chasing windmills. AI and machine learning can help with observing behavior, however key questions must be asked: what are the economic factors influencing care in this region? Who are the members and recipients requiring the most care and how are they getting it? Keen observational methods are needed to focus attention where it needs to be and ensure people are receiving care while keeping the bad providers away.

At Qlarant, we’re building tools to help our customers manage their fraud risk using technology and street smarts to observe behavioral shifts indicating bad behavior. We all know there is fraud, and bad actors continuously get more sophisticated in their schemes. We’re developing new and simple tools fraud busters can equip themselves with to unmask bad providers. We’re excited to share these, and there’s more to come.