An alternative presentation of the ProPublica Surgeon Scorecard

ProPublica, an independent investigative journalism organisation, have published surgeon-level complications rates based on Medicare data. I have already highlighted problems with the reporting of the data: surgeons are described as having a “high adjusted rate of complications” if they fall in the red-zone, despite there being too little data to say whether this has happened by chance.

4
This surgeon should not be identified as having a “high adjusted rate of complications” as there are too few cases to estimate the complication rate accurately.

I say again, I fully support transparency and public access to healthcare. But the ProPublica reporting has been quite shocking. I’m not aware of them publishing the number of surgeons out of the 17000 that are statistically different to the average. This is a small handful.

ProPublica could have chosen a different approach. This is a funnel plot and I’ve written about them before.

A funnel plot is a summary of an estimate (such as complication rate) against a measure of the precision of that estimate. In the context of healthcare, a centre or individual outcome is often plotted against patient volume. A horizontal line parallel to the x-axis represents the outcome for the entire population and outcomes for individual surgeons are displayed as points around this. This allows a comparison of individuals with that of the population average, while accounting for the increasing certainty surrounding that outcome as the sample size increases. Limits can be determined, beyond which the chances of getting an individual outcome are low if that individual were really part of the whole population.

In other words, a surgeon above the line has a complication rate different to the average.

I’ve scraped the ProPublica data for gallbladder removal (laparoscopic cholecystectomy) from California, New York and Texas for surgeons highlighted in the red-zone. These are surgeons ProPublica says have high complication rates.

As can be seen from the funnel plot, these surgeons are no where near being outliers. There is insufficient information to say whether any of them are different to average. ProPublica decided to ignore the imprecision with which the complication rates are determined. For red-zone surgeons from these 3 states, none of them have complication rates different to average.

ProPublica_lap_chole_funnel
Black line, population average (4.4%), blue line 95% control limit, red line 99% control limit.

How likely is it that a surgeon with an average complication rate (4.4%) will appear in the red-zone just by chance (>5.2%)? The answer is, pretty likely given the small numbers of cases here: anything up to a 25% chance depending on the number of cases performed. Even at the top of the green-zone (low ACR, 3.9%), there is still around a 1 in 6 chance a surgeon will appear to have a high complication rate just by chance.

chance_of_being_in_redzoneProPublica have failed in their duty to explain these data in a way that can be understood. The surgeon score card should be revised. All “warning explanation points” should be removed for those other than the truly outlying cases.

Data

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Git

Link to repository.

Code

The problem with ProPublica’s surgeon scorecards

ProPublica is an organisation performing independent, non-profit investigative journalism in the public interest. Yesterday it published an analysis of surgeon-level complications rates based on Medicare data.

Publication of individual surgeons results is well established in the UK. Transparent, easily accessible healthcare data is essential and initiatives like this are welcomed.

It is important that data are presented in a way that can be clearly understood. Communicating risk is notoriously difficult. This is particularly difficult when it is necessary to describe the precision with which a risk has been estimated.

Unfortunately that is where ProPublica have got it all wrong.

There is an inherent difficulty faced when we dealing with individual surgeon data. In order to be sure that a surgeon has a complication rate higher than average, that surgeon needs to have performed a certain number of that particular procedure. If data are only available on a small number of cases, we can’t be certain whether the surgeon’s complication rate is truly high, or just appears to be high by chance.

If you tossed a coin 10 times and it came up with 7 heads, could you say whether the coin was fair or biased? With only 10 tosses we don’t know.

Similarly, if a surgeon performs 10 operations and has 1 complication, can we sure that their true complication rate is 10%, rather than 5% or 20%? With only 10 operations we don’t know.

The presentation of the ProPublica data is really concerning. Here’s why.

For a given hospital, data are presented for individual surgeons. Bands are provided which define “low”, “medium” and “high” adjusted complication rates. If the adjusted complication rate for an individual surgeon falls within the red-zone, they are described as having a “high adjusted rate of complications”.

1How confident can we be that a surgeon in the red-zone truly has a high complication rate? To get a handle on this, we need to turn to an off-putting statistical concept called a “confidence interval”. As it’s name implies, a confidence interval tells us what degree of confidence we can treat the estimated complication rate.

2If the surgeon has done many procedures, the confidence interval will be narrow. If we only have data on a few procedures, the confidence interval will be wide.

To be confident that a surgeon has a high complication rate, the 95% confidence interval needs to entirely lie in the red-zone.

A surgeon should be highlighted as having a high complication rate if and only if the confidence interval lies entirely in the red-zone.

Here is an example. This surgeon performs the procedure to remove the gallbladder (cholecystectomy). There are data on 20 procedures for this individual surgeon. The estimated complication rate is 4.7%. But the 95% confidence interval goes from the green-zone all the way to the red-zone. Due to the small number of procedures, all we can conclude is that this surgeon has either a low, medium, or high adjusted complication rate. Not very useful.

8Here are some other examples.

Adjusted complication rate: 1.5% on 339 procedures. Surgeon has low or medium complication rate. They are unlikely to have a high complication rate.

5Adjusted complication rate: 4.0% on 30 procedures. Surgeon has low or medium or high complication rate. Note due to the low numbers of cases, the analysis correctly suggests an estimated complication rate, despite the fact this surgeon has not had any complications for the 30 procedures.
3Adjusted complication rate: 5.4% on 21 procedures. ProPublica conclusion: surgeon has high adjusted complication rate. Actual conclusion: surgeon has low, medium or high complication rate.
4Adjusted complication rate: 6.6% on 22 procedures. ProPublica conclusion: surgeon has high adjusted complication rate. Actual conclusion: surgeon has medium or high complication rate, but is unlikely to have a low complication rate.
6Adjusted complication rate: 7.6% on 86 procedures. ProPublica conclusion: surgeon has high adjusted complication rate. Actual conclusion: surgeon has high complication rate. This is one of the few examples in the dataset, where the analysis suggest this surgeon does have a high likelihood of having a high complication rate.

7In the UK, only this last example would to highlighted as concerning. That is because we have no idea whether surgeons who happen to fall into the red-zone are truly different to average.

The analysis above does not deal with issues others have highlighted: that this is Medicare data only, that important data may be missing , that the adjustment for patient case mix may be inadequate, and that the complications rates seem different to what would be expected.

ProPublica have not moderated the language used in reporting these data. My view is that the data are being misrepresented.

ProPublica should highlight cases like the last mentioned above. For all the others, all that can be concluded is that there are too few cases to be able to make a judgement on whether the surgeon’s complication rate is different to average.

RStudio and GitHub

Version control has become essential for me keeping track of projects, as well as collaborating. It allows backup of scripts and easy collaboration on complex projects. RStudio works really well with Git, an open source open source distributed version control system, and GitHub, a web-based Git repository hosting service. I was always forget how to set up a repository, so here’s a reminder.

This example is done on RStudio Server, but the same procedure can be used for RStudio desktop. Git or similar needs to be installed first, which is straight forward to do.

Setup Git on RStudio and Associate with GitHub

In RStudio, Tools -> Version Control, select Git.

In RStudio, Tools -> Global Options, select Git//SVN tab. Ensure the path to the Git executable is correct. This is particularly important in Windows where it may not default correctly (e.g. C:/Program Files (x86)/Git/bin/git.exe).
1Now hit, Create RSA Key …

2_rsaClose this window.

Click, View public key, and copy the displayed public key.

4_rsaIf you haven’t already, create a GitHub account. Open your account settings and click the SSH keys tab. Click Add SSH key. Paste in the public key you have copied from RStudio.

6_add_keyTell Git who you are. Remember Git is a piece of software running on your own computer. This is distinct to GitHub, which is the repository website. In RStudio, click Tools -> Shell … . Enter:

git config --global user.email "mail@ewenharrison.com"
git config --global user.name "ewenharrison"

Use your GitHub username.

10_who_are_you

Create New project AND git

In RStudio, click New project as normal. Click New Directory.

7_new_project

Name the project and check Create a git repository.

8_new_project_with_git

Now in RStudio, create a new script which you will add to your repository.

9_test_scriptAfter saving your new script (test.R), it should appear in the Git tab on the Environment / history panel.

11_initial_commitClick the file you wish to add, and the status should turn to a green ‘A’. Now click Commit and enter an identifying message in Commit message.

12_inital_commit2You have now committed the current version of this file to your repository on your computer/server. In the future you may wish to create branches to organise your work and help when collaborating.

Now you want to push the contents of this commit to GitHub, so it is also backed-up off site and available to collaborators. In GitHub, create a New repository, called here test.

5_create_git In RStudio, again click Tools -> Shell … . Enter:

git remote add origin https://github.com/ewenharrison/test.git
git config remote.origin.url git@github.com:ewenharrison/test.git
git pull -u origin master
git push -u origin master

13_push_pullYou have now pushed your commit to GitHub, and should be able to see your files in your GitHub account. The Pull Push buttons in RStudio will now also work. Remember, after each Commit, you have to Push to GitHub, this doesn’t happen automatically.

Clone an existing GitHub project to new RStudio project

In RStudio, click New project as normal. Click Version Control.

7_new_projectIn Clone Git Repository, enter the GitHub repository URL as per below. Change the project directory name if necessary.

14_new_version_controlIn RStudio, again click Tools -> Shell … . Enter:

git config remote.origin.url git@github.com:ewenharrison/test.git

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