To begin, I believe that the recent audit of Northwestern reflects the generally poor processes and statistical modeling used by Centers for Medicare & Medicaid Services (CMS) auditors when scrutinizing a healthcare provider.
In particular, there seems to be this general idea that there is some degree of homogeneity within the broad range of diagnostic and therapeutic treatments (as well as the delivery of medical supplies, drugs, and other services and procedures) routinely performed on patients. For example, durable medical equipment (DME)-type claims should not ever be subject to extrapolation because there is such a large degree of variance surrounding nearly every aspect of how hardware, drugs, and supplies are prescribed for any given patient. It’s a bit absurd to include supplies that are required for cervical traction with codes that denote procedures such as neuromuscular stimulation. Even stratification can’t fix these types of problems, yet I see it happen quite often. We’re talking about apples and oranges.
Because the purpose of this type of audit is to extrapolate findings to a larger universe of data, we work within the realm of inferential statistics, which is different from descriptive statistics. In inferential statistics, when we attempt to “infer” the results of a sample to a larger universe, we have to be very careful how the sample is selected and very certain as to how our calculations are made. Because we are going outside of the sample itself, it is critically important that the sample is representative of the applicable universe – or in this case, the sampling frame. In pretty much every case I have worked on, government auditors worry about one thing and one thing only: is the samplerandom? This is great if all you are doing is trying to describe certain characteristics of the sample, such as some point estimate or tendency towards some location of data, but it is simply not enough when the goal is to infer the results of the findings in the sample to some data set that is larger than the sample. Just because you stratify a sample doesn’t mean that it was stratified properly.
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