Healthcare and the World of Squishy Metrics

David Deas
June 15, 2023

Almost everyone has heard the famous Peter Drucker quote, “what gets measured gets managed”. It shows up in a lot of presentations around data in healthcare and is a staple on the conference circuit. The healthcare system, in particular, has codified this into their DNA. It is called other things: “Evidence Based Care”, “Core Measures”, “Balanced Scorecard”, etc., but at the basic level it is a way of managing what you can measure. But there are a couple of issues with that original Drucker quote: First, he never said it and second, it is a truncated version. The actual quote was from a man named Simon Caulkin who was summarizing a 1956 paper by V. F. Ridgeway. The full quote is:

“What gets measured gets managed — even when it’s pointless to measure and manage it, and even if it harms the purpose of the organization to do so”.

That full quote is not only startling different, but it also rings more true in the real world than the one we hear all the time. Now, I’m not saying we shouldn’t measure things; I spent many of my years in healthcare working on core measures and scorecards. But it is obvious that not everything that matters can be measured and not everything we can measure matters. The fun part about healthcare comes when these things cross paths, a data point we can measure that at the core is made up of a lot of data points that we can’t measure at all. I refer to these as squishy metrics.

Squishy Metrics seem solid on the outside but as soon as you start digging in to try and fix things the walls of hard data collapse quickly. A few weeks ago, we posted a poll about these metrics and here are the results:

All of these are complex metrics, but Readmission edged out a victory. Readmission is difficult; we know down to the hundredth of a percentage point what our rate is with exact clarity as to which patients readmitted. But when you ask what we need to do to improve this metric it starts to get squishy.

There are any number of reasons why a patient might readmit. The guiding assumption is that we get a readmission because we did not provide proper care on their initial visit, but how likely is that really? Maybe we didn’t explain the discharge properly, maybe they didn’t get the home care they were supposed to get, maybe they didn’t have a pharmacy close to their house and couldn’t get the prescribed medications filled or couldn’t afford to get them filled. What if the patient doesn’t read well and gave up on trying to understand the mound of discharge instructions we sent home with them? What if they can’t drive and thought their daughter would be able to take them to their follow-up appointment, but she had a work emergency that day and couldn’t make it? Then we have to account for the vagaries of the human body where something completely unpredictable could have gone wrong. With new medicines come new side effects. Genetic differences affect how certain medicines work, how they are processed by the body, and their effectiveness.

As you can imagine, the conversation around fixing the readmission rates of a hospital quickly becomes a confusing and difficult one. It devolves into anecdotes about particular patients or educated guesses based on a few scraps of available data. Most of the time you leave a conversation like this unsatisfied. You went into that room dedicated to fixing this problem and left feeling overwhelmed by the complexity. This doesn’t mean we stop trying, but we have to at least recognize that there are data points we aren’t capturing that have a big impact on some of these squishy metrics.

The key lies in not only recognizing the squishiness, but also in open discussion around what data points are missing, and how those could be captured. This conversation is hampered by a lack of integration options, the exact problem we are trying to solve at Red Rover. Giving you access to all of your EMR data while also enabling you to easily connect and share data back and forth with appropriate 3rd party apps can bridge a lot of these gaps and allow us to work toward a healthcare system where “what needs to be managed gets measured” instead of the other way around.

Share any thoughts you have around squishy metrics you deal with in your organization in the comments below.

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