How to Spot Medical Fraud in Workers' Compensation

by Karen Wolfe

The chatter about fraud in Workers’ Compensation usually centers around employee or employer fraud. However, fraud and abuse in medical treatment and management is evermore prevalent. Finding the perpetrators is key. 

Incompetence is not fraud 

Poorly performing medical doctors are 100% predictive of high costs and poor claim outcomes. They are associated with adverse events during treatment resulting in poor outcomes. Post-operative infection and medical complications can signify a doctor’s poor performance when it occurs with some regularity. Additionally, lost time and high indemnity costs can indicate the doctor is unaware of the unique needs of Workers’ Comp. However, incompetency does not necessarily mean the doctor is fraudulent or abusive. 

Medical fraud 

When a doctor knowingly over-treats, costs increase and outcomes are compromised. The best approach to managing these doctors is to avoid them altogether. Identify the low-value doctors and carve them out of networks. However, deliberate fraud ups the ante.

Avoiding inept medical doctors prevents the needless spiral of high costs along with injured workers’ inconvenience, financial drain, and pain. However, medical fraud and abuse takes provider performance to another level altogether.

Poorly performing treating physicians are out there and they can be found by analyzing the data. But when they are also dishonest or corrupt, the damage can be exponential. These perpetrators can also be found in the data, but different sleuthing is required. 

Anti-fraud analytic strategy 
Efforts to find the perpetrators requires a well-designed analytic strategy. Most would agree with this logic, yet few medical networks in Workers’ Compensation have undertaken the challenge. The data, when analyzed appropriately, will point to medical doctors who are abusing the system. 

Trail of abuse 
Fraudulent medical doctors and other providers leave a trail of abuse in the data. Integrated bill review data, claims payer data, and pharmacy data, including history will paint a clear picture of undesirable practices. Outliers underscore themselves.

Among the outliers found in the data are exploited frequency and duration of medical treatment. Fraudulent providers have significantly higher treatment frequency and duration than their counterparts for the same medical conditions. Naturally, such inflation increases cost.

Other outliers found in the data involve use of the most costly treatment procedures as first and short term treatment choices. The timing of treatment can produce suspicion of corruption. More aggressive treatments like surgery are selected early after injury rather than less aggressive, more conservative approaches. 

Subtle abuse 
More subtle forms of medical fraud involve manipulating the way bills are submitted. Standard computerized systems can be fooled with tactics such as overbilling. Bill review systems will automatically adjust the bills downward, but consistent, excessive over-charging can be an indicator of fraud. 

Misleading identifying codes 
Similarly, codes used to describe procedures can deliberately mislead. Choosing NOS (Not Otherwise Specified) diagnostic codes makes analysis difficult. Likewise, electing a CPT code such as 99199, which is “unlisted special service, procedure, or report”, allows almost any charge to slip through without review.

Another tactic is to bill under multiple tax identifiers and from different locations. Computer systems will automatically treat these as different providers, thereby creating duplicates in the system. Performance analysis of multiples of the same provider can be misleading and their abuse completely missed. Results of analytics are skewed, as well. Provider records must be cleansed, merged, and then re-evaluated to arrive at more accurate performance scores. 

Multiple NPI’s 
Still another method used by disreputable providers is obtaining more than one NPI number (National Provider Identifier) from CMS (Centers for Medicare and Medicaid Services). Once again, the data is obfuscated and performance analysis is misleading. Combining all the data related to an individual provider for analysis is made difficult because perpetrators deliberately misrepresent themselves. 

Referral “rings” 
The data can also be analyzed to discover patterns of referral among less-principled providers and attorneys. Referral clusters in claims should be monitored. Kickbacks will not be found in the data, but questions should be raised about repeated associations. Referral clusters almost always result in litigation, claim complexity, and high cost. 

Calling a spade
Many medical providers who skirt ethical practices would be shocked to be called fraudulent. Yet, they are. Changing the name does not whitewash the behavior. 

Happy trails 
Happily, value doctors are also easy to find in the data. Their performance can be measured by multiple indicators in the data as they float to the surface with the best in class. When analyzed over time and across many claims, they consistently rise to the top. 

Quality-based networks 
Selecting the right doctors and other providers for networks is a complex but important task. Data from many claims where individual providers and groups are involved must be analyzed to distinguish how physicians perform in Workers’ Compensation over time. Subtleties of questionable performance can be teased out of the data.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation, analytics-powered medical management company. MedMetrics analyzes and scores medical provider performance and offers online apps that link analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org