If we use as a basis the total number of active physicians practicing in each region(*) and compare this to the activities of the Recovery Auditors, then an uneven pattern emerges.
One would assume that the audit patterns would be roughly similar, but they are not.
(Source: Barraclough analysis.)
It appears that HDI audits the least number of claims per physician, but in exchange recovers the greatest amount of claw backs.
NOTE: (*) Source: The Henry J. Kaiser Family Foundation
It appears that if you were located in the Western Region of the United States, your chances of having a claim paid is more than twice as high as if you are in the North East, which has the lowest return rate for claims that were not paid but should have been.
Here is the data according to the Recovery Auditor:
There are four Regions of Recovery Auditors in FY 2012:
In other words, for each $1 dollar spent, $5.05 dollars were returned to the taxpayer.
Is this high or low? The $6 dollar amount seems standard in many settings, so the cost of collection appears to be in line.
It would be interesting to see what this ratio is for the Recovery Audit Contractors (RACs).
The Recovery Audit Contractors (RACs) have been moving towards big data analysis of claims from health care providers. According to the CMS report “Recovery Auditing in Medicare and Medicaid for Fiscal year 2012“, more than 67% of all audits are done through “semi-automated” reviews.
Health Care Providers have been unsuccessful in getting judicial review of the complex algorithms used in these automated reviews. For example, it is impossible to determine how fair is the targeting process. When auditors are asked to provide this information, health care providers are told that this information is a “trade secret“, and in any case is not reviewable by the Administrative Law Judge (ALJ).
How successful are automated reviews? The data shows that 91% of the claw-backs from health care providers were the result of so-called “complex reviews”, not dependent upon the semi-automatic review process.
In writing about semi-automated reviews, CMS states:
“The first part is the identification of a billing aberrancy through an automated review using claims data. This aberrancy has a high index of suspicion to be an improper payment. The second part includes a Notification Letter that is sent to the provider explaining the potential billing error that was identified. The letter also indicates that the provider has 45 days to submit documentation to support the original billing.”
Statistical extrapolation in Medicare and Medicaid audits can be problematical. They are not always done correctly, and it actually has been our experience that they frequently are not done correctly at all.
Steven E. Skwara identifies three challenges that can be made against statistical sampling in health care fraud cases:
- Reproducibility. If the results can not be reproduced, then there is reasonable argument that the results are not scientific. Documentation is crucial.
- Sample Size. Larger sample, more accurate results. There is a great deal of leeway given by Medicare courts.
- Variability. This is not often looked for, but a high degree of variability in the data may signal problems. Ask your statistician.
Sometimes a Medicare audit can lead a health care provider into bankruptcy. This happens when the provider is unable to sustain the financial pressure from:
- Cost of litigation
- Withholding of reimbursements
- Possible down-grading of equity shares if the provider is a public corporation
The San Diego Hospice closed reportedly due to Medicare audits: “the organization discontinued patient and family care services . . . and has sold its Hillcrest campus through a court-approved auction process”. It filed for bankruptcy in the Southern District of California. (Case 13-01179-CL11)
See also Peter R. Roest, “Recovery of Medicare and Medicaid Overpayments in Bankruptcy“, Annals of Health Law 10(1), 2001.
Approximately $77,000,000,000 was paid out to physicians and other health care professionals in 2012.
To examine more details, visit the physician dataset here.