Published in HCCA Compliance Today
The contractor reform in health care brought a consolidation of Medicare contractors and new contractors, as exemplified by the Medicare Administrative Contractors (MACs), Medicaid Integrity Contractors (MICs), Medicare Recovery Audit Contractors (RACs), and Zone Program Integrity Contractors (ZPICs). These government contractors have different objectives, some are more fraud oriented (e.g., ZPIC), and others are focused on detecting payment errors (e.g., MACs, RACs). They conduct pre- and post-payment audits. However, no matter what their charge and CMS-assigned tasks are, these contractors have aggressively been monitoring and auditing claims that were paid to health care organizations by the federal and state health care programs. In their claims audits, the contractors typically assess whether there were inappropriate payments received by a health care organization and, if so, they determine the recovery amount.
Oftentimes the totality of cases (e.g., charts, claims, line item of claims, beneficiaries, or whatever the unit of observation may be), which may potentially be affected by a suspected billing error, cannot be reviewed. Time and cost constraints and benefit/cost considerations make a sample a much more viable alternative. If the sample is a statistically valid random sample (SVRS), such as a “probability sample” as set forth in the Centers for Medicare & Medicaid Services (CMS) Medicare Program Integrity Manual (PIM), then the contractor may draw conclusions from the sample to the universe (total number) of cases. Simply put, one can estimate the total overpayment in the total number of cases by projecting overpayments from a relatively small sample to the universe at large.
Similar considerations, which weigh the possibility of using the universe of cases affected by a potential payment error pattern versus a sample with appropriate projection, are increasingly also part of many providers’ internal auditing and monitoring strategies. So what does it take to develop a good estimate? Three aspects can be considered.
Correct interpretation of the projected estimate.
To begin with, it requires that the estimate is projected from a random sample that was based on the correct interpretation and application of the various medical documentation requirements and payer coverage rules. If the medical review, the application of coverage criteria, and case-by-case review findings can be challenged in an appeal or a quality assurance process, the overpayment estimate derived from the sample would not be tenable.
Statistically valid random sample
Another aspect of a good estimate is that it must be generated from a statistically valid random sample that was selected. If there is no statistically valid sample, then the validity of the projection of the total overpayment estimate is difficult to defend.
Confidence and precision
If each sampled case was reviewed correctly and the sample was a statistically valid random sample, acceptable confidence (i.e, degree of certainty that the sample correctly depicts the universe) and precision (i.e., range of accuracy) are the third piece needed for a quality estimate of the total overpayment in the universe.
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