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MECE (/mi:s/). No, I didn’t mispronounce that. That’s not /maɪs/. There’s a coordinate data-wrangling concept called MECE (Mutually Exclusive and Collectively Exhaustive). It’s an acronym. Mutually exclusive means, effectively, there’s no overlap. And collectively exhaustive means you’ve effectively covered all of the bases. You didn’t miss anything.
Consider this illustration
An easy example to demonstrate this is a die, meaning like one of two dice. One die can only roll one number at a time, and that’s mutually exclusive. So one is mutually exclusive from 6. You can’t roll 1 and 6 at the same time with one die. The range of possibilities is 1 to 6. So if you list 1, 2, 3, 4, 5, 6, that is collectively exhaustive. If you list 1, 2, 3, then that’s not exhaustive.
Occasionally, but not too often, we see an example of where this comes into the data wrangling for revenue cycle management. If you want to bucket claims and AR days, most people think 0 to 30, 30 to 60, 60 to 90. Is that a problem? People often say 0 to 30, 31 to 60 because you don’t want to overlap, where something is on the 30th day, and it falls into two different buckets.
Think about whether it’s collectively exhaustive
There’s a good example where you need to make sure that it is 0 to 30, 31 to 60, and so on. With some systems, you may get into something interesting if it doesn’t round or something like that. What about if it’s 30.5 days? It would not be collectively exhaustive anymore. That will fall out of the bucket. Pretty unlikely, but again, these are just the kind of things you want to think about, which is, conceptually, “Do we have all of our bases covered? Is everything included?” and “Are they not in multiple buckets? At the same time?”
Dates are continuous, not discrete. It gets a little bit messy, even with things like time zones. We’ve seen problems where there are servers in one time zone, and somebody else is in a different time zone. Something happens after midnight in that time zone (it’s GMT or something like that, or it’s in India or whatever it is), and yet, it’s a different day somewhere else: in the United States or India.
Don’t forget about overlap
Then suddenly, you have these weird situations where there’s a cut-off, and it may cut off early or late, or you have some overlap where things get included that shouldn’t or get missed that should not. That happens pretty frequently with some offshore stuff as well, trying to set a cut-off.
Another example you can run into is putting payer categories or carrier categories like commercial and government. We recently got some data back from a client. We said, “Okay, bucket these payers,” and they came back and said, “Commercial and government.” Is that MECE? Well, they didn’t include something like workers’ comp or some other categories, so it’s not collectively exhaustive.
Is it mutually exclusive?
Even something like commercial and government may not be mutually exclusive. If you have a UHC Medicare plan, is that commercial because UHC is a commercial entity, or is it government? Most people have a strong opinion one way or the other, but that doesn’t mean everybody will have the same idea.
What if it’s a Managed Care or Medicare Advantage plan? Is that commercial or government? Again, it’s not so much “Do we all agree on the definition?” For instance, “Are you paying attention to whether something falls in more than one category? Would you have a problem where it’s essentially being double-counted or being left out completely and not being counted at all?”
Categories are crucial
You need to make sure that you’ve categorized everything effectively so that there are no problems. How do you do that? Some might argue that this is definitively this type of plan, or this, or whatever else it is in your data. It doesn’t even really matter.
I think the more significant point is that there can be problems if you don’t have MECE. You may miss things unintentionally or exclude items unintentionally. Or things get double-counted, and therefore the numbers don’t tie out between reports, and people are confused. Or even you may have artificially inflated problems that make you look bad or make things look better than they are.
Is collaboration straightforward?
Additionally, you may have a hard time communicating. For example, it’s an HL7 interface or reports or even just verbally with other people in your organization or across entities. So, if you don’t have the same agreement on what those buckets are and how you’re labeling, data wrangling, and more.
Having documentation for this information is extraordinarily important because other people in the organization may have a different definition than you. You may be right. It doesn’t even matter who’s right, but you may be right. However, somebody else may be doing it differently and incorrectly and, therefore, have a problem. Or they may not even know where to look in the report. They’re looking in the wrong place.
They’re like, “Oh, I’m going to go look in the commercial. I don’t see anything there. Therefore, there is none.” They looked in the wrong place. They didn’t realize they should be looking somewhere else because that’s not how we categorize in this organization. Well, that’s not how I did it at place X.
MECE isn’t a panacea to cure-all. Some challenges get a little bit too complex to sort of drop into in a podcast. But thinking in this context can have many benefits in data wrangling. It will avoid many problems, help you solve many issues in analytics, and therefore improve cash flow.
The last thing I’ll leave you with is, “Is the acronym MECE pronounced /mi:s/ or /mi:sɪ/?” There’s some debate on it. Most of the consulting world says /mi:sɪ/. I don’t. A part of that is because I didn’t grow up in McKinsey or BCG. I have colleagues that didn’t. Some of them say /mi:sɪ/.
Yet, the lady, who came up with the concept and gave the acronym to the idea, pronounces it /mi:s/. So I’m going to go with /mi:s/ and stick with /mi:s/, MECE (Mutually Exclusive and Collectively Exhaustive). Check it out and see if it has any application to you and your reporting and analytics and help your organization to be more efficient in this arena.