There are no outliers in healthcare…only real people
August 26, 2014 in Medical Technology
For emergency departments seeking to improve efficiency, it might seem natural to focus on tracking median statistics for patient care — such as Time to Provider and Length of Stay for Discharged and Admitted patients.
But in my experience, median numbers, important though they may be, tell only part of the story — and a slightly skewed one at that. Unless hospitals also gather statistics on the averages for each of these measures, they’re probably seeing a rosier picture of their operations.
More importantly, they run the risk of losing sight of the patients whose experience in the ED was far worse than the norm — that is, what some people call the “outliers.” It may seem logical to ignore or discount the statistics about the outliers. But each of these statistics is actually a person … a person whom the hospital may have failed to properly serve.
It’s no surprise that hospitals might tend to focus more on medians than averages. For one thing, CMS itself, the organization that determines reimbursement rates for specific hospitals, requires regular ED reporting in medians, not averages. In addition, many directors and managers, given the choice between two analyses of their ED’s performance, would prefer the numbers that look better; that’s just human nature.
Another reason is the all-too-common misunderstanding about the differences between median and average. In fact, many people use the terms almost interchangeably, and sometimes the numbers can indeed be very close. The problem is, others times, not so much.
Median vs. average: tracking the difference
If you already understand the distinction between median and average, please skip to the next section. If not, here’s a quick illustration.
Imagine you’re coaching a track team and you want to get a sense of how fast your six runners are, over all, in the mile event. You start with their best individual times in the event:
You could calculate the runners’ median time (that is, the point where half the team is faster and the other half slower) as 4:26 — a pretty respectable time. But the average time is a good bit slower: 5:03. Both calculations are based on accurate math, but one suggests that the team is significantly faster than the other. More to the point, as the coach, if you look only at the median time, you might be tempted to not worry as much about your two slowest runners. After all, they’re just outliers, right?
When I’m involved on a project for a FreemanWhite client, I’m generally focused on streamlining processes and reducing process variation. I need to find the data that falls outside the “normal” range, and then try to determine what caused the deviations. Perhaps a patient waited four hours to see his provider — or perhaps she was in the ED for a total of three days. My job is to find out what happened with each of these patients.
Of course, in some cases, there are legitimate reasons that make certain patients truly outliers, and that may justify omitting them from my analysis. But in other cases, I work to identify the causes of the sub-optimal results, so that our team can develop ways to prevent them from happening again.
For this reason, I typically calculate both the median and the averages for each key statistic. I also strive to gather a broader set of data: preferably at least a year’s worth, or even more if we’re projecting further out than a few years. The bigger the data set, the more likely I’ll find outliers — and these are the examples that often hold the key to process improvements that can benefit all patients. In my experience, a lot of firms in our industry don’t do as deep a data dive. Instead, they place more emphasis on interviews with staff and examining aggregate data.
Often our clients are surprised at the results of our analysis of statistical averages — and especially the “long tails” of data about patients whose experiences deviate greatly from the norm. Of course, many of an ED’s staff can’t forget these patients — such as the young man with a severe behavioral health issue who spent the weekend taking up an ED treatment bed while awaiting admission. But our analysis helps to not only quantify these instances, but also determine the underlying causes and recommend changes to address them.
My point is that before an ED starts making decisions about staffing changes, expanding or reconfiguring the footprint of the ED, it’s worth examining a broad dataset of healthcare performance. Only then can the department truly “take ownership” of every patient.
Just as the track coach in the example above can’t simply ignore his two slowest runners, an ED needs to figure out ways to bring the most serious causes of delays or inefficiencies into line. It’s not enough to just track the required data and focus on the most positive numbers. Rather, real improvement in efficiency and patient care comes from digging down to find the stories and details of the “outliers” … and then making the process improvements necessary to prevent them from happening again.