What Healthcare Organizations and Liverpool FC Have In Common
Full disclosure: I have almost zero knowledge when it comes to British football clubs
I am so glad that I became a writer and avid reader of Medium, because every day I get a perfectly curated email digest right in my inbox. Recently this article was at the end of my digest, and while it had no mention of anything remotely having to do with the American healthcare industry, I found myself thinking pretty much every paragraph “Yup. Uh huh. THIS.” So, can I just say bravo to whoever wrote Medium’s algorithms, else I would not have had the pleasure of reading such an interesting piece!
Without further ado, here are the ways that “The Reds” (yes, I had to google “nickname for Liverpool FC”) are strikingly (or as the Brits would say, gobsmackingly) similar to a typical US healthcare organization:
The “scorn” experienced by data analysts and data scientists
The club was run by a series of managers who had little interest in [Liverpool FC’s director of research Ian Graham]’s suggestions…The reaction, almost uniformly, was scorn. “ ‘Laptop guys,’ ‘Don’t know the game’ — you’d hear that until just a few months ago”
Needing to use low-tech, brute force techniques due to lack of resources
Graham, who earned a doctorate in theoretical physics at Cambridge, built his own database to track the progress of more than 100,000 players from around the world.
…But in a field where data analytics is rather new, then simple stats may be all you need to get the job done
Graham and his team could report that a club’s strong-footed left winger sends booming crosses over the defense toward the goal. But the data indicates that the less impressive crosses coming from the right wing, often accurately placed, result in goals far more frequently. That sounds rudimentary. In soccer, it is practically a revolution.
And this:
One afternoon last winter, he pulled up some charts on his laptop and projected them on a screen. The charts contained statistics such as total goals, goals scored per minute and chances created, along with expected goals. I was surprised to see Graham working with such statistics, which he had described to me as simplistic. But he was making a point. “Sometimes you don’t have to look much further than that,” he said.
Hard to get Good Data
Soccer is the sum of thousands of individual actions, but the only ones Graham’s model can evaluate are the passes, shots and ball movements that are downloaded from the official play-by-play. “There are still fundamental limitations in the data we have,” Graham says.
And last but not least, this extremely specific similarity
Recently for a healthcare client which already had a nursing protocol in place called “No One Walks Alone,” I created a predictive model to identify patients who were at the highest risk of suffering a fall during their hospital stay.
Now, take a look at The Reds’ official team crest:
US Healthcare can take a lesson from UK Football
Just as Liverpool had a great season thanks in part to the work of Graham and his data science team, so can the healthcare industry ultimately have better outcomes for patients if clinicians and healthcare managers can work together with the organization’s data analytics team. If leadership could provide the necessary sponsorship for improvements in data infrastructure and were more appreciative of what the “laptop guys and gals” have to offer, then healthcare data analysts and scientists would be better equipped to come up with the useful insights that could help doctors and nurses improve care for their patients. That’s healthcare #goals!
