Teaching Hospitals How to Swim with Robust Data Analytics

By | 2018-09-04T16:55:54+00:00 September 4th, 2018|

I have two kids, but never got around to actually signing them up for swim lessons. Instead, I taught them to swim myself. I’m a swimmer, but not a swim instructor. I’ve never taught anyone to swim, and I never bothered to learn any formal swim training pedagogy. It was simply opportunistic lessons when we were visiting a hotel or friend with a pool. I’d stand a few feet from the kids and encourage them to swim to me. Over time, I would increase the distance until they could doggie-paddle across the pool and back. Eventually they were safe in the pool and could be around water without fear. I taught my kids to swim, right?

Actually, no. I had not taught my kids to swim — I had merely taught them to not drown. As they got older and compared skills to their peers, it turns out they really did not know proper swimming fundamentals. Long story short, it took expensive and mildly embarrassing private lessons to really get them swimming. I would have saved money, and the kids would have saved pride, if I had given them actual swim lessons from the get-go.

I think of this story when I hear hospitals say “we already have business intelligence software as part of our EHR.” The default analytics package in most EHRs is equivalent to the swimming skills my kids had after my informal lessons. It’s a minimum set of survival skills, but not anything close to true mastery of the domain.

Purpose-built analytics software for hospitals has these three core advantages:

Interactive drill down: The ability to “unpack the numbers” and see the individual granular patient data that comprise the data. When a metric shows average boarding time in the ED, can you see the individual patients that actually did the boarding? Can you see the individual cases that make up a practice’s block utilization? Being able to drill down and validate the underlying data in a timely manner is critical to getting an organization aligned and accountable to an operational performance improvement initiative.

Configurability: The ability to see how different policies affect outcome. Can you change the threshold when a surgical case is considered late? When a patient is considered to be boarding? When a surgical case is considered out-of-block? Without the flexibility of taking into account hospital policy and lessons learned, metrics are rigid and will never be taken seriously.

Edge cases: Every perioperative analytics package reports on how surgical blockholders are using their blocks. But can it report on how non-blockholders are using non-blocks? Can it show any common attribute for ED boarders over 48 hours? Whether or not edge cases actually move the needle, they need special treatment in order to give leadership confidence they’re seeing the complete picture.

A “non-drowner” can splash around in a pool just fine. But a swimmer can be part of an organized team. Similarly, the default EHR analytics package is great for making some Powerpoint slides to present at monthly or quarterly exec meetings. But a robust data analytics platform enables leadership to organize a team around principled decisions to drive improvement. Taking surgical blocks away from underperforming surgeons or opening an inpatient flex unit due to forecast high census requires a level of confidence and data trust that is simply not possible without the ability to drill into data, configure the data, and explore edge cases—and of course, the ability to swim.

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