Or what Nick Bagley said:
The American Medical Association/Specialty Society Relative Value Scale Update Committee (RUC) has a process in place to regularly review Medicare physicians’ services’ work relative values (which reflect the time and intensity needed to perform a service). Its recommendations to [CMS], though, may not be accurate due to process and data-related weaknesses. First, the RUC’s process for developing relative value recommendations relies on the input of physicians who may have potential conflicts of interest with respect to the outcomes of CMS’s process. . . . . Second, GAO found weaknesses with the RUC’s survey data, including that some of the RUC’s survey data had low response rates, low total number of responses, and large ranges in responses, all of which may undermine the accuracy of the RUC’s recommendations. For example, while GAO found that the median number of responses to surveys for payment year 2015 was 52, the median response rate was only 2.2 percent, and 23 of the 231 surveys had under 30 respondents….
the RUC is a specialist-dominated committee that “donates” more than $8 million of its own services each year to Medicare, presumably out of the goodness of its heart.
The RUC’s job is to tell CMS how much time and effort it takes to provide medical services in the hopes of influencing how Medicare pays physicians. Since CMS has been starved of the resources necessary to independently review physician services, the agency has little choice but to rubber-stamp most of the RUC’s recommendations.
In recent years, Congress has taken modest steps to fix the problem. The Protecting Access to Medicare Act of 2014, for example, appropriates $2 million each year to enable CMS to collect information directly from physicians about the relative value of their services. But CMS doesn’t have a plan about how it will spend that money, and in any event $2 million won’t go far when it comes to reviewing thousands of physician services.
Good data is expensive. It is expensive to collect, it is expensive to curate, and it can be expensive to analyze properly. However good data is extraordinarily valuable if it allows us to avoid amazingly expensive, counterproductive and wasteful mistakes in the best case scenarios or self-interested rent capture. Cheaping out on data collection for a program that drives $70 billion dollars a year in payments should be criminal. Spending $70 million dollars a year to study how 1,000 times that amount of money is to be spent (or a dime per $100 spent) would have huge positive impacts.
But we can’t have that, as it would be government spending requiring taxes when there is a nice, respectable professional organization that is willing to tell us how to pay them.