Workers' Compensation Medical Provider Fraud A Sum of Subtle Maneuvers

by Karen Wolfe

 
That medical providers might commit fraud is disconcerting. Exemplary performance in life and work is generally expected. While doctors’ performance is evaluated and scored regarding treatment processes and outcomes, actual fraud is rarely considered. We assume all providers in an approved network are honest, dedicated medical professionals, but that is not always true. Some use subtle maneuvers to disguise their excess profitability.
 
Some providers, primarily doctors, deliberately mislead employers and payers by obfuscating the data. They game the system with subtle billing maneuvers and other evasive tactics that are not easily recognized.

Gaming the system is easy
Workers’ Compensation data is known to be of poor quality, especially provider demographic records that are overwhelmingly inaccurate and incomplete. Duplicate records abound thereby creating the opportunity to further complicate identities, affiliations, and actions. Untangling data deception is challenging and time-consuming at best.

Fight medical provider fraud
The best way to fight medical provider fraud in Workers’ Compensation is to prevent it. If the data were of good quality, the opportunity for fraud would be nearly eliminated. Complete, accurate, and non-duplicative provider record data would prevent medical fraud because analyzing quality data is easy. Analyzing bad data is complicated and elusive. Requiring medical providers, possibly on pain of non-payment, to submit bills with accurate and consistent demographic data is the first critical step. Maintaining that accuracy in the payer system is equally imperative. But that is not happening.

Cut to the chase
An immediate way to fight medical provider fraud, after preventing fraud by guaranteeing pristine medical provider record data, is to engage the assistance of experienced Workers’ Compensation medical data analytics professionals. Based on knowledgeable analysis, subtle data maneuvers are uncovered. The doctors that should be avoided are identified as well as the reasons for avoiding them. Remove, avoid, or transform fraudulent doctors in the network to save money, improve outcomes, and elevate the entire network.

Of course, analyzing medical providers on a broader scale is even better. Performance based on medical costs, treatment patterns, prescription practices, return to work, medical and disability outcomes along with accurate billing practices presents a more comprehensive portrait. Fraudulent providers find multiple ways to cloak their activities for greater profit.

Transform fraudulent doctors
Publish the documented fraudulent behavior to dissuade the perpetrators of further attempts to muddy the data water. The Hawthorne Effect[1] applies, whereby the behavior of subjects improves based on the knowledge they are being observed. Publish documented data analytics to inform providers they are being observed and evaluated. Public embarrassment is a strong deterrent. Distributing graphic portrayals of the perpetrators’ behavior will inform and transform even the most recalcitrant.

More dramatic outcomes are not infrequent when fraudulent providers are “outed” through analytics. Litigation is sometimes a successful result because analytics documents and proves the errant behavior. Fundamentally, medical provider fraud is costly, but it can be addressed and eliminated.

Karen Wolfe, BSN, MA, MBA is the founder and President of MedMetrics®, LLC, a Workers’ Compensation analytics-Informed medical management and technical services company. MedMetrics analyzes the data and offers online insights that link analytics to operations, thereby making them actionable and measurable. MedMetrics also uncovers medical fraud. Contact: karenwolfe@medmetrics.org

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[1] The Hawthorne Effect is the alteration of behavior by the subjects of a study due to their awareness of being observed. www.dictionary.com

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