It can be valuable to trend some data over successive surveys. For example, you may want to know the trend in the average hospitalist’s wRVU productivity over the past two surveys. You should look at the questions asked in both surveys to make sure there hasn’t been a change that could influence the responses. In the case of wRVUs you will need to understand how the January 2007 change in wRVUs values for many services provided by hospitalists was handled by the survey.
Pay attention to how data are lumped or split. Some data are appropriate for analysis of hospitalist groups, other data for individual hospitalists. If half of hospitalist groups use a shift-based schedule, that doesn’t mean half of individual hospitalists work such a schedule. Shift-based schedules are more common in larger groups, so even if half the groups in the country schedule by shifts, there may be 80% of individual hospitalists who use this schedule.
Salary incentives illustrate another way responses are lumped or split. As of the last survey (reported in 2006), most hospitalists had a variable component to their compensation—most often based on productivity or quality. There are relatively few ways hospitalists are paid on productivity (basing it on wRVUs is most common). But there are myriad quality incentives, based on things like Centers for Medicare and Medicaid Services core measures, and patient and referring physician satisfaction. Depending on how you aggregated these different categories in the 2006 survey, you might reach different conclusions about whether more hospitalists have productivity-based incentives or quality-based incentives (productivity was more common in the 2006 survey).
Drill down to respondent populations that most closely match your group. There is a real temptation to overemphasize the “headline” numbers in the survey like the average total salary for a hospitalist. Yet in many cases, it may be more useful to drill down to a population that matches your group. You might be most interested in compensation for hospital-employed hospitalists in non-teaching hospitals in the South (thereby excluding academicians and pediatric hospitalists from your comparison group). Just make sure to look at the resultant sample size (the “n”) reported for that subset of the data to make sure it is large enough to be meaningful.
Remember that the survey is not telling you what is right for your group. The survey simply describes a number of metrics relevant to hospitalist practice. It is not SHM’s position on the right compensation or productivity for a particular practice. It is the best source of national data regarding hospitalists. (See my column “Comp Close-Up” for a comparison between the SHM and Medical Group Management Association surveys [July 2007, p.73]). Things like the two other hospitalist practices in your town probably will have a lot more to do with influencing your group’s productivity and compensation metrics than any national data set.
While it’s tempting to reduce things to a single number (e.g., how much is the average hospitalist paid?) this is falling prey to salience bias. Try to grasp the stories behind the numbers by understanding the survey methods and looking at responses for different subsets of the survey population. And realize that the right or optimal compensation and productivity for a group might be quite different from the survey means and medians. TH
Dr. Nelson has been a practicing hospitalist since 1988 and is co-founder and past president of SHM. He is a principal in Nelson/Flores Associates, a national hospitalist practice management consulting firm. He is also part of the faculty for SHM’s “Best Practices in Managing a Hospital Medicine Program” course. This column represents his views and is not intended to reflect an official position of SHM.