I am a runner. I don’t look like what many people visualize a runner to be. I am not fast, and I am not very likely to place in my age group, nor in anyone else’s for that matter. My spot is typically toward the back of the pack. And even though I’m not a fast runner, many a weekend morning, the girl who feigned illness to avoid running in gym in junior high now pays money to run several miles with a bunch of strangers.
Being the statistics nerd that I am, after crossing the finish line of a race, I eagerly check my phone for a text from the timing company, compare it to my running watch, and analyze my overall time and splits for this race. Once I get home, I assess my historical race data, and compare it to see how my performance at this event stacks up with previous endeavors for me.
After a recent race, while I was looking at my sports watch app and analyzing my data, I suddenly realized how similar running participation is to outcomes participation and analyzing outcomes data. And then, my statistics brain wondered what my data would look like on a FOTO Scorecard.The first question I asked myself was: am I in the top half? And I knew the response: Not likely, if I am at a 14 minute mile and many others are ten and under. Now, if there are a lot of other 14 minute milers in the group, my chances of being in the upper half certainly are going to increase. My comparison group does depend on who else has entered the race.
I remember hearing a statement at one of the first Outcomes Conferences I attended about comparisons: “Even in a group of really talented people, someone will be in the bottom half and someone will be in the top half. That is just how it works.” Imagine the gasps of pleased recognition and the heavy silence of sobering realization in a room full of PT’s and OT’s when we heard this. The speaker went on to point out that there are certainly a whole lot of people out there who are not collecting data, so we need to remember that their data is not part of the collection group. But it also reinforces that those who are doing the work and doing the collection, saw a need for collecting outcomes data well ahead of it becoming trendy or required, and thusly, are often the upper echelon of providers of care. But even within our peer group of excellent providers, someone will be in the top half and someone will be in the bottom half.
Since I do find myself in the bottom half of runners in most races, I am going to work to get better. To improve, I have worked with a coach, attended workshops and running schools, joined a running club, been fitted for proper footwear, studied hydration and nutrition, and rehabilitated through injuries and learned from my errors. I am not going to stop doing something that I enjoy just because others are faster. Just because I am not in the top half today is not any reason to stop monitoring my times, learning what I can, and getting out there several days a week and using the information I learn to improve myself. The more I work, I will gain consistency and the better my results will become.
So, consider this scenario applied with patient outcomes. If I am not in the percentile rank that I would like to be, would I stop collecting outcomes? Will I stop treating patients? Of course not. I would do everything I can to get better. Perhaps that includes finding a mentor, attending continuing education, shadowing a colleague, studying the literature and attending journal club meetings - similar to the things I did to become a better runner.
The clinics and clinicians who have determined that collecting outcomes is important are an elite group. They see the value in outcomes collection and analysis, and many more are joining them in toeing the starting line. By being motivated and eager to be successful, they have learned how to objectively use the data collected on every “run”, that is, every patient episode, and use it to become even better at what they love to do.