It’s been an exciting week to be interested in Medicare data. On April 9th, the American government opened up data from the Centers for Medicare and Medicaid Services (CMS) that show charges made to Medicare and payments received by over 880,000 entities. If you went to Bing on Monday, April 14, at about 12:30, chose to look at news stories, and typed Medicare money into the search box, here’s a sampling of what you got:
Medicare doctors: Who gets the big bucks & for what
The Medicare Data’s Pitfalls
Medicare Data Shines Light on Billions Paid to TX Doctors
Political Ties of Top Billers for Medicare
Bob Menendez Donor Tops Medicare Money List; Fla. Opthalmologist Raked In Nearly $21 Million
The top 10 Medicare billers explain why they charged $121M in one year
Who gets the most Medicare money in Nashville, and other fun facts from today’s CMS data release
44 Central Florida doctors get at least $1 million in Medicare money
A Look at Where Medicare Money Goes
As you’d expect, there’s a lot of focus on who gets the most money. Some articles draw attention to where the biggest numbers in the data are, other articles try to provide a bit of context so that we don’t assume that most oncologists are crooks. One thing you know for certain: if you look at a data set with over 880,000 observations, the biggest and smallest numbers are going to be wildly unusual.
Because the news media have taken care of highlighting the biggest values, I thought we should look at the data some different ways. What do we see if instead of looking at the biggest numbers, we look at the smallest? How much does the average really tell you about what you want to know?
Want to follow along? All of the data is available from CMS.gov.
The littlest numbers
Congratulations to one Doctor Shaw, who billed Medicare a total of $19 in 2012. That’s despite Dr. Shaw providing services to 15 Medicare beneficiaries! Dr. Shaw charged $1.00 for something recorded in the CMS data as “Assay glucose blood quant.” That $1.00 figure, by the way, is the average submitted charge for that procedure. Is Dr. Shaw more patriotic for being the anti-Dr. Qamar, who billed Medicare the most in the same data set? Who knows. But let’s take a moment to celebrate the man, the myth, the legend that is Dr. Shaw.
One of the headlines above notes that the top 10 Medicare billers charged Medicare about $121 million in 2012. Turns out that many of them are just the lucky individuals whose identities are used to identify an entire business, a fact that they’ve probably had to explain a lot since the database was released. Those explanations probably aren’t being asked of the bottom 10 Medicare billers, who accounted for combined charges of $339.14. That puts those 10 well below the average submitted charges of $286,608 for those who appear in the database.
To come out at that point is no mean feat, as you cannot see on the pie chart, because the slice for $339.14 is too small to see with the naked eye.
Who doesn’t get paid the big bucks, and for what
The first article from the Bing search results let us know that providers who report their type as hematology/oncology, radiation oncology, and ophthalmology tend to get paid the most. But there’s an opposite end to that scale, too. The 345 entities who got paid the least, on average, by Medicare in 2012 were billed as certified nurse midwives. The other two smallest, on average, are anesthesiologist assistants and mass immunization roster billers. Hopefully, this illustrates the folly of basing the value of a profession on what Medicare pays them. Midwives, as you would guess, work much more frequently with patients who are having babies—a group that I would surmise has little overlap with Medicare patients.
Other statistics are important, too
Maximums and averages are important summary statistics, but it’s important to remember that if you want to understand the data it’s foolish to focus on any one number too closely. According to the CNN story that was the first result on Bing, hematology/oncology, radiation oncology and ophthalmology were the specialties that collected the most Medicare dollars on average. But that list leaves out that the highest averages actually belong to provider types that CNN did not associate with a specialty. Here’s a look at the top 10 categories for receiving payments in the database:
Of course, just because those specialties have high averages doesn’t guarantee that we know very much about any individual. The amount that we don’t know is highlighted when we look at the standard deviations, which measure how inconsistent the payment amounts to providers are. I show all of the data in the first scatterplot that follows. In the second scatterplot, I omitted the categories with large averages that CNN did not count as specialties. In the third graph, I also omitted categories that had high standard deviations because of the range of services they supply: Independent Diagnostic Testing Facility, Unknown Specialty Physician Code, and All Other Suppliers.
Once we get down to the specialties, the relationship between the mean and the standard deviation is clear. The specialties with the highest averages tend to have the highest standard deviations. The higher the average for a specialty, the less we know about any individual provider.
It’s also interesting to see what happens if you look at medians, which are a better representation of what a typical provider received from Medicare in 2012 than the means are.
While ophthalmology is still a top category, the median value is $178,758, or roughly half of the average. Additionally, nephrology and cardiology now round out the top three specialties. Hematology/oncology, which had the highest average, falls out of the top 10.
Big numbers are exciting, and they tend to attract our attention. But focusing on a small part of the data rarely tells the whole story.
Of course, if you’re really into data analysis, you know that the first question to ask is not “What’s the maximum, but “Can I trust my data?” One of the first steps now that the CMS data is open is to find out how trustworthy the data are.