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

A New Year's Resolution for WC Medical Management

by Karen Wolfe

2015—a whole new year. Will it be back to the old routine or to a new resolve for Workers’ Compensation medical management?

Same old…

Managed care programs in Workers’ Compensation have been in place for over twenty-five years. Among them are provider discount networks, bill review, medical case management, utilization review, and peer review. They all continue to operate pretty much as they always have, yet medical costs continue to increase.

Lackluster results

With adherence to traditional managed care methods, the medical portion of claim costs continues to increase. Not all can be attributed to inflation or the medical system in the US. Logic says change is needed in medical management methods. We need to implement new and different initiatives to actually make a difference.

Keep the baby

This is not to say the old methods can or should be discarded. To discard them would be to eviscerate the medical management system, definitely a risky and costly move. Instead, traditional programs should be enhanced and supercharged using new methods. They need to be improved, accompanied by honest evaluation of their contributed value.

Supercharge medical management

Happily, new options are available for tackling the old issues of claim complexity and cost. Appropriately applied technology methods can put managed care programs ahead of trouble and make it easier to resolve problems at less cost.

While technology has advanced exponentially in the past twenty-five years, few of its advantages have made their way to Workers’ Compensation. However, with minimal effort and cost, the gains can be amazingly substantial. Some enhancements to current processes include the following.

Value-driven provider networks

Poorly performing medical providers are guaranteed to increase costs and generate substandard outcomes, a fact proven repeatedly through industry research. Yet, little attention is paid to selecting quality medical providers. Nevertheless, providers can be vetted by analyzing the data. The evidence of performance is there.

Providers associated with poor outcomes, high cost, and even fraud can be identified by scrutinizing the data. The next step is obvious—when they are found, don’t use them. Avoid directing injured workers to the costly providers even when a discount is promised.

In states where directing care is not possible, the list of suggested providers should not include the bad ones. Additionally, the best practice list should be updated continually to insure continued performance.

Save 20-30%!

By choosing best-in-class medical providers based on the analytics means 20-30% or more savings in overall claim costs measured in reduced medical costs, indemnity costs, and overall claim costs. The numbers are impossible to ignore.

Electronic data monitoring

Integrating and monitoring claims data can also produce equally powerful results. Integrating the data means combining claims data from the multiple silos that typically house it. Monitoring means electronically monitoring the data using a specialized rules-based system and alerting the appropriate person when certain conditions occur.

Electronic data monitoring carries the advantage of early intervention into troublesome or threatening claims. It also adds structure to existing adjustor and medical case management processes.

Structure delivers efficiency and efficiency generates cost savings both administratively and in medical cost control. Moreover, structure allows definitive and accountable analysis of savings.

Proof of value

Service organizations, particularly medical case managers, have long had the challenge of measuring and reporting their value. Too often individuals, not the organization, determine when to respond and what to do.

However, when the response is consistent for similar conditions, measures of value are possible. Continuous data monitoring allows a tally of the number of times the same or similar conditions occur in claims and calculates savings benefits of their documented responses. That leads to choosing the most effective responses to specific conditions, thereby continuously improving the process.

A New Year’s Resolution

Rather than returning this January to the old routines, resolve to update and upgrade medical management using the new and more powerful applications available.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

 

A Trail of Abuse--Spotting Medical Fraud in Workers' Compensation

by Karen Wolfe

While there is considerable talk about fraud in Workers’ Compensation, the chatter usually refers to claimant or employer fraud. Unfortunately, fraud and abuse also occurs in medical management.

Medical fraud

Poorly performing medical doctors are 100% predictive of 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!

When a doctor knowingly over-treats, costs increase and outcomes are compromised. Disciplining such providers by not paying them is one solution, but a more proactive approach to managing them is to avoid them altogether. Identify the bad doctors and carve them out of networks. Avoiding corrupt and inept medical doctors prevents the needless spiral of high costs and adverse outcomes resulting from misbehavior.

Identify the perpetrators

Efforts to solve the problem should focus on identifying the perpetrators by means of a well-designed analytic strategy. Most agree with this philosophy, yet few medical networks in Workers’ Compensation have seriously addressed the issue. The data, when analyzed appropriately, will point out medical doctors who perform badly.

Trail of abuse

Fraudulent medical doctors and other providers leave 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.

Among the outliers found in the data are exploited frequency and duration of medical treatment. Fraudulent providers have higher treatment frequency and duration than their counterparts. Other outliers found in the data involve use of 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.

Subtle abuse

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. But systems can miss subtle combinations of diagnoses and procedures and allow payment. If a doctor consistently overbills, what is the motive?

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.

Another method used by disreputable providers is to obtain multiple NPI numbers (National Provider Identifier) from CMS (Centers for Medicare and Medicaid Services). Once again, the data is obfuscated and deliberately made misleading.

Referral “rings”

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

Litigation association

Medical abuse also occurs in other ways. 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 it could be an indicator they are part of a strategy to encourage litigation and certain attorney involvement. Kickbacks are not in the data, but the question is raised.

Calling a spade…

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

Happy trails

Happily, the good 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.

Outcome-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 the Workers’ Compensation context over time. Subtleties of questionable performance can be teased out of the data. The most powerful approach is to monitor the data in real time so that interventions effectively thwart the perpetrators.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org