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.
