Data Series Part 1: Data Data Data

There’s a famous quote that says, “In God we trust; all others bring data.” That sentiment has become ubiquitous across all industries and functions, and healthcare is no exception. In fact, one could argue that data is even more important in an industry like healthcare, where emotion—which can often cloud judgment—plays a significant role. It’s not to say that objective data is the only thing we should consider when making decisions, but it needs to play a leading role.

Before we get into what data to track and how to use it, it’s helpful to demonstrate why objective data is important. I’m sure YOU get it, but perhaps someone you work with thinks: “But I always trust my gut, and my gut is never wrong!” This mentality proves why data is not just important, but necessary.

The Importance of Objective Data

How is data more accurate than our guts? In short, because the world is complicated, and although our brains are powerful computers, we are not all knowing. So our brain uses shortcuts—behavioral scientists call these “heuristics”—to help us make decisions using the subset of data that our brains can most easily process.

For example, I’d bet you know at least one person who is afraid of flying, but who has no problem getting into a car. After all, every time there’s a plane crash it seems to dominate the news cycle for days. Who wouldn’t be afraid of flying?! Yet data shows that flying is, unequivocally, safer than driving. One study estimated that, for every 1 billion passenger miles traveled by car, 7.2 people die, compared with 0.07 deaths from air travel. The reason for this (relatively irrational) fear of flying is that, though rare, planes crashes DO make the news every time they happen, whereas fatal car accidents unfortunately occur so frequently that our gut doesn’t register those data points when determining our fears. This is an example of the“availability heuristic”, which is when we use the data points that come to mind quickly (perhaps because they are sensational, like plane crashes) and extrapolate off of those limited data points.

Other heuristics/shortcuts include:

My favorite example of a cognitive bias (aka, how our guts are not always to be trusted) is the better-than-average effect, which is where most people rate their abilities as “above average” even though it is statistically impossible for most people to have better-than-average abilities. In behavioral economics classes all around the country, professors ask their students on the first day of class to raise their hands if they think they will finish in the top half of the class grade-wise. Inevitably more than 50% of the hands go up, which would be statistically impossible.

Now that you are convinced of the importance of having objective data to use in your decision-making, we can figure out what to measure and what to do with it.


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