John Nelson writes…
We now have access to the latest version of what in my opinion is clearly the best national source of data on hospitalist workload, compensation and other metrics: MGMA’s Physician Compensation and Production Survey: 2010 Report Based on 2009 Data. This September, SHM and MGMA will jointly release the State of Hospital Medicine: 2010 Report Based on 2009 Data.
In “The Hospitalist” I provided a brief commentary on the trend in compensation, including my view of what is a ‘benchmark.’ Here I want to have another go at what I see as the confusion around the term.
A benchmark is something that serves as a frame of reference or a data point “against which something can be measured or assessed” (from the dictionary). The numbers in the survey are exactly that; data that serve as a frame of reference for your practice. I think it is appropriate to use the term ‘benchmark’ when talking about survey data.
Too often “benchmark” is used in a way that implicitly suggests it is the right or optimal data point. Don’t make that mistake in thinking so about this survey data. There is no number in the very rich set of data that is the right or optimal number for any practice. So don’t make the mistake of looking only at the headline numbers like annual compensation for hospitalists nationally and decide that is what you should earn. I realize that people who already earn more than the survey shows aren’t like to suddenly lobby to have their pay decreased to the average or median, but those who earn less often do.
Here’s an example. The average or median weight of Americans could appropriately be called a benchmark. But almost no one would simply assume that is their own optimal weight. Instead, it is important to drill down into subsets of the data such as ones based on gender, height, body type, etc. And even such a refined number is still just a frame of reference (a benchmark) for what any individual should weigh. It isn’t necessarily the right or optimal weight for that person.
So you should pay a lot of attention to the survey data, and do a “deep dive” into all the ways the numbers can be subdivided based on a huge number of variables. I find the on-line survey tool to be really useful for slicing the data in all kinds of ways. But even after you’ve done this, you’ll still have to decide for yourself how much money a hospitalist should make. You’ll just be able to make such a determination in a much more informed way.
 This error is known as “salience bias,” which is a fascinating issue itself. This article about errors made by investors served as a really vivid illustration of the problem of salience bias for me. It is mentioned explicitly in the last paragraph of the article