Tag Archives: medical fraud

How AI Can Stop Workers’ Comp Fraud

Wondering how AI can help detect medical provider scams? Wonder no more.

Artificial intelligence (AI) is redefining work in nearly every industry thanks to the increase in accuracy, efficiency and cost-effectiveness that AI-based applications offer. One of the latest industries to benefit is insurance, where applications are now being deployed to help detect and reduce provider fraud through advanced predictive tools. Claims payers identify fraudulent providers early in the life of a claim and root out bad actors while saving organizations millions of dollars.

The Fraud Problem

Fraud involves deliberately presenting false information to extract a benefit. The most common examples of provider fraud include “phantom billing” (billing for services not rendered), submitting bills for more services than are possible in a provider’s day, providing services unrelated to the injury, using unlicensed or non-credentialed individuals to provide medical services, getting paid kickbacks in exchange for sending patients to third parties and referring patients to entities (such as laboratories or testing facilities) in which the provider has an ownership interest.

While most providers do not engage in fraud, those that do are extremely costly. According to the National Insurance Crime Bureau (NICB), workers’ compensation medical fraud costs approximately $30 billion per year in the U.S. alone.

Fraudulent provider behavior is hard to detect and prove, particularly in workers’ compensation data systems. Advanced data analytics based on AI, however, offers opportunities to overcome the inherent weaknesses in these systems while developing methods to identify and curb provider fraud. Let’s take a look.

Fragmentation of Payers

One of the biggest issues in provider fraud is that no one organization has more than 5% of workers’ compensation market share, so none can see the entire picture of a provider’s claims. This can cause a whole host of issues. For example, if one company has identified a fraudulent provider, other companies may not have this information and continue payments. In states where fraud information is publicly available, providers simply begin practicing in other states, avoiding the state that sanctioned them.

Using AI tools, however, organizations can tap into multipayer pools of aggregate information to spot fraudulent patterns quickly and reliably without compromising payer, employer and employee information. It also makes it easier to flag and curb behavior across a multipayer database.

See also: Untapped Potential of Artificial Intelligence

Inaccurate Provider Identification

The constantly changing complexity of provider identification is another major challenge. Data is often tied to names. Fraudulent providers know this system weakness and frequently change their organization names and addresses as well as other identifiers.

Using AI, data scientists can now reliably link multiple bills from the same provider using a National Provider Identifier (NPI) developed by the Centers for Medicare and Medicaid Services (CMS). Almost all providers have an NPI, and some have more than one. When supplemented with taxpayer identification (FEIN) numbers and license numbers, NPIs can reliably identify 95% of medical providers. As a result, machines can overcome the name game, detecting the long-term, multiyear activities of almost all providers and provider organizations.

Long Lag Times

The interval between when an instance of fraud occurs and when it is detected is often several years. For example, a provider may submit a bill on day one for services unrelated to the injury; the bill will be submitted for review 30 days later and will likely be paid in another 30 days. This practice will be repeated dozens of times by the same provider on the same patient over the course of months. If fraud is detected, the provider will have already been paid, and financial recovery is difficult.

To combat this problem, AI can detect the entire course of treatment on the same claim from the first through subsequent billings over multiple years. Software tracks the diagnoses and the number of procedure codes billed by the same provider on the same claim — per day, per month and per year. As a result, claims staff receive real- time alerts and can intervene when a fraudulent provider initiates treatment on a claim.

Complex Provider Supply Chains

The entire fraud supply chain often includes attorneys, medical providers, outpatient and inpatient facilities, interpreters, testing facilities, medical device suppliers, pharmacies, copy services and transportation services. Unless data sets capture all or most of these moving parts, the chance of detecting fraudulent patterns is very difficult.

With AI, it’s getting a lot easier. Data scientists can use aggregated data to track sequences of out-referral and in-referral, exposing links between fraudulent individuals and entities. Sophisticated techniques isolate consistent and repeatable patterns of relationships between multiple providers and third parties. Data scientists then can graphically display suspicious network clustering patterns inherent in fraud networks.

And these are just a few examples of how AI tools can greatly increase the detection of fraud.

See also: Impact of COVID-19 on Workers’ Comp

Defining the Future of Claims

AI differs from more traditional research approaches because it can generate its own rules to detect fraud and look across large data sets nearly instantly. Via machine learning, databases are continually refreshed, becoming smarter and more effective all the time. By incorporating AI-based solutions, insurance payers can defeat fraud at a systemic level and realize significant financial benefits in return.

As first published in The CLM.

Urgency of Rising Medicare Fraud

Ho-hum: The FBI arrested 46 doctors and nurses…largest Medicare fraud bust ever.

That is from a headline in a recent CNN story. Seems the thieving doctors and nurses got away with $712 million before getting busted.

Per the story, “In total, 243 people were arrested in 17 cities for allegedly billing Medicare for $712 million worth of patient care that was never given or unnecessary.”

Note the word “unnecessary.” If there are doctors and nurses doing this to Medicare patients, they are defrauding self-insured benefit plan patients, too.

This has been getting worse and worse every year for 20 or so years. I say “ho-hum” at the beginning of this post because almost no one in the private sector takes stopping this kind of thing seriously. There is a lot of talk and little action.

I urge readers to start taking steps to stop this mess.

Easy Way to Spot Workers’ Comp Fraud

While there is considerable talk about fraud in workers’ compensation, the discussion usually refers to fraud by claimants or employers. Unfortunately, fraud and abuse also occurs in medical management.

Poorly performing medical doctors produce high costs and poor claim outcomes. When they are also corrupt, the damage can be exponential. We know poorly performing and corrupt doctors are out there.

More importantly, we also know how to find them!

Disciplining providers by not paying them when they knowingly overtreat is one solution, but even better is avoiding them altogether. Identify the bad doctors and carve them out of networks.  Most agree with this philosophy, yet few medical networks in workers’ compensation have seriously addressed the issue.

Efforts to solve the problem should focus on identifying the perpetrators by means of a well-designed analytic strategy. The data, when analyzed appropriately, will point out medical doctors who perform badly.

There is a trail of abuse in the data. Bill review data, claims payer data, and pharmacy data, when integrated at the claim level including both historic and concurrent data, present a clear picture of undesirable practices. Outliers float to the surface.

Fraudulent providers treat more frequently and longer than their counterparts. They also use the most costly treatment procedures, selected as first option. The timing of treatment can produce evidence of corruption, such as when more aggressive treatments like surgery are selected early in the claim process.

Some of the more subtle forms of medical fraud involve manipulating the way bills are submitted. Corrupt practices attempt to trick standard computerized systems. They consistently overbill, knowing the bill review system will automatically adjust the bills downward. Systems can miss subtle combinations of diagnoses and procedures and allow payment.

Likewise, some practitioners bill under multiple tax identifiers and from different locations. Unless these behaviors are being monitored, computer systems simply create different records for different tax ID’s and locations, making the records appear as different doctors. When attempting to evaluate performance, the results are skewed. Provider records must be merged and then re-evaluated to arrive at more realistic performance scores.

Disreputable providers may obtain multiple NPI numbers (National Provider Identifier) from CMS (Centers for Medicare and Medicaid Services). Once again, the data is deliberately made misleading.

The data can also be analyzed to discover patterns of referral among less principled providers and attorneys. Referral patterns can be monitored.

The data can be scrutinized to find doctors who are consistently associated with litigated cases. That may mean they are less effective medical managers or could indicate that they are part of a strategy to encourage litigation and certain attorney involvement. Kickbacks are obviously not shown in the data, but the question is raised.

Many doctors who skirt ethical practices would be shocked to be called fraudulent. Yet that is exactly what they are. Changing the name does not whitewash the behavior.

Happily, the good doctors are also easy to find in the data. Their performance can be measured by multiple indicators, and, analyzed over time and across many claims, they consistently rise to the top.

Selecting the right doctors and other providers for networks is a complex but important task, and subtleties of questionable performance can be teased out of the data.

The most important approach: Monitor the data in real time so you can intervene and thwart those trying to commit fraud.