Detecting healthcare fraud with graph visualization

In this blog post, we’ll look at how healthcare fraud visualization provides a valuable tool for detecting this increasingly expensive problem.

Despite a high profile zero-tolerance crackdown, financial losses due to healthcare fraud are estimated to be between 3-10% of total healthcare expenses. That could mean more than $300 billion a year.

Could graphs help to combat this? We’ll create healthcare fraud visualizations based on Medicare Program data to prove that they can.

A network visualization of practitioners and patients to help with healthcare fraud detection
Visualizing healthcare fraud detection

Medicare fraud – a billion dollar business

The national Medicare program covers 59 million Americans and accounts for 15% of the annual federal budget, costing $776.2bn in 2020.

With any program at this scale, the scope for fraudsters is vast. The number and complexity of transactions makes detecting fraud seem like finding a needle in a haystack. The problems are compounded by the desperate state of federal and state-level computer systems. Records are spread across multiple data silos, held by different agencies with no simple way to cross-reference.

But what if Medicare data were to be viewed as a graph?

As with all fraud detection, the key is to understand connections: between patients, procedures, practitioners and clinics.

Using this model, we can identify ‘normal’ patterns:

  • A patient will generally see a limited number of practitioners.
  • A practitioner will only perform certain kinds of procedure, based on their specialism.
  • A practitioner and patient will normally be limited to a geographic area.

We can therefore rapidly identify cases of probable healthcare fraud. Let’s take a look at some examples.

How fraudsters use stolen social security numbers

One of the most common, and costly, types of Medicare fraud is when practitioners bill for services they haven’t provided. Often this is done using social security numbers that have been stolen from seniors, either directly or using social engineering tactics.

In 2014, the FBI shut down one Medicare fraud ring operating in Florida using stolen social security and provider information to steal $28.3 million.

As millions of claims are submitted each month, Medicare authorities cannot check each bill with the patients concerned. In any case, many patients do not fully know or remember which services they received, and certainly don’t understand the obscure procedure codes used.

Rather than relying on tip-offs from members of the public, analysts could use our graph visualization technology to find potential cases of fraud.

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1. Practitioners billing multiple times for the same procedure

Here we have loaded a dataset of all claims made by practitioners on a certain day. We can see a standard pattern emerging:

Healthcare fraud detection - the initial dataset shows a pattern where typically 1 practitioner is connected to a single patient and clinic

Generally, each procedure is connected to one patient and one practitioner. The practitioner is also connected to a location or clinic. This standard pattern makes it easy to spot anomalies, like this very productive doctor who appears to have claimed for 5 of these procedures in a single day.

A network visualization showing a doctor who claimed for 5 procedures in a single day

2. Patients seeing two practitioners for the same ailment

This should also ring alarm bells, especially if the clinics are geographically spread from one another. Aside from seeking a second opinion, patients tend to stick with a single practitioner.

Healthcare fraud detection visualization showing multiple practitioners linked to the same patients

In the same data, we can find networks like these showing two patients receiving the same procedures from two or three practitioners. This could be another indicator of a fraudulent claim.

3. Two or more practitioners sharing many patients

If there is significant overlap in patients between two practitioners with no professional relationship, it may indicate two doctors exploiting the same stolen list:

Visualizing practitioners to reveal which are sharing patients

Taking data from a longer time period, we can find these two practitioners share dozens of different patients, despite being based at different clinics. For an analyst, this should be a signal to investigate further.

Find out more about healthcare fraud visualization

These are just a few simplified examples of how graph visualization can be used to detect possible cases of Medicare fraud, and how healthcare data visualization can reduce losses and improve provision.

Download our fraud detection white paper to see how one of our graph visualization toolkits, KeyLines, has been used to visualize fraud data. You can also request a free trial of our toolkits.

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