The way we measure value is often flawed. I am not considering the squishy definition of value that provides an estimate of low- or high-value outcomes in healthcare. I am more or less referring to how we evaluate physician learning in continuing medical education (CME). I work with data often provided post-activity once all the front-end objectives and metrics become fire-walled and set in stone. Obviously not ideal but these datasets provide a systemic glimpse of persistent misconceptions in how we are evaluating learning outcomes.
Continuing medical education professionals assigned the task of working with outcomes data seek ways to describe significant patterns in informative and narrative formats. Numbers and units combine to yield accurate measurements. But what is the difference between accuracy and precision?
How many participants answered this question? How many answered the question correctly pre- and post-activity? These are all examples of accuracy. Basically, are my results aligned with the truth? It becomes problematic when we make leaps of faith. If we claim, accuracy is precision necessarily implied? Did participants clearly learn something they didn't know before participating in your learning intervention?
Sounds pretty straightforward but what if we aren't using the right tool or aren't using the tool properly?
When you measure, you must interpret the measurement against the standard established by the tool. In the process, you put a little bit of yourself into the measurement and, for this reason, the tool you use to measure has a big impact on the result you get. The existence of a measurement, not surprisingly, means that someone actually measured it. There is a natural limit to how well I can measure objects depending on how well I can ‘see’ it as well as how good of a tool I am using to see it.—Matt Anticole
What if your interface isn't built for precision? The excerpt above is from a short e-book about CME.
I don't work in CME often--just for a few clients. But as I navigate different stakeholder journeys in medical affairs, e-learning, health economics, medical journalism, and health policy I continue to share insights.
It is up to you to figure out what it means for your business model but the healthcare ecosytem is evolving and will include patients, treatment algortihms meant for the macropopulations--but what will that mean for the n of 1 patient? Its worth making a plan.
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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”
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