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I’m going to do another one of my rants because I’m in one of those moods. It seems like when I’m in a bad mood; I do a rant. Also, I don’t know if I’m in a bad mood. I feel like, “What the hell are people doing?” Some of this stuff drives me crazy. Does anybody else get frustrated by this? You can drop us a message. Some of you contact me. It’s great. I appreciate it when you guys do that. I love to hear about the things you want to hear about. If there’s something you would like us to dive into or look at or evaluate, please feel free to drop us a message. We’re available. In addition, you can find me through LinkedIn. Further, you can find me through our website. My email address is smcsweeney@apachehealth.net.

Webinar or sales presentation?

There is a webinar from the MGMA. The whole purpose of the webinar, in fact, the webinar’s title, is about benchmarking. Benchmarking, of course, for me, means data and comparative data for some benefits, although not everybody has that perspective. This webinar, unfortunately, turns out to be sort of like a sales presentation for many people.

However, one of the people, not the first person to start talking, is a consultant in the revenue cycle management business. In general, I have a hard time with many people in our industry for a whole series of reasons. You’ll hear some of those. They talk about revenue cycles like whack-a-mole. This person doesn’t suggest that it should not be whack-a-mole, that we should have an organized way of going after things, and we should have data to prioritize things, solve problems, take preventative measures, and be predictive prescriptive rather than just descriptive. 

Out come the charts

Most organizations aren’t even descriptive, so it should not be whack-a-mole. And if we resign ourselves to whack-a-mole forever, then that’s poor management. We shouldn’t think that way.

Then, she starts throwing up some data. Wow! That was interesting how I said that – “throwing up some data.” Yeah, it was kind of like vomiting data on people. She throws up a chart that says, “Average Components of AR.” She says, “This is the first metric to focus on figuring out. It’s outstanding AR.” First of all, I don’t think outstanding AR is the first metric we should focus on analyzing. Ultimately, organizations care about revenue and profits, not AR. Yes, how much it’s tied up in AR is a component of how much revenue you make, but it is not the only driver, and it’s certainly not the most important.

What does this say about the business?

It is then stated that the average components of AR are a good representation of the business. I don’t know what business we’re talking about, but certainly, it’s not a good representation of anything that I can think of on the subject. She goes on to say, “As we look at this chart, it starts high, it dips down in April (this is 2020), and then it slowly goes back up during the course of the rest of the year.” She says, “It starts great early in the year, then it goes down. The worst is April, and then it rebounds later.” 

First of all, no, it doesn’t. Yes, the graph goes down, an AR goes down, but we don’t know anything about the business. All we know is that AR went down. If we don’t have any other metrics other than AR, it looks like the industry got better in April because AR dropped. And if you don’t have anything to tell you that revenue went down and you only see that accounts receivable went down, you might suggest, “Hey, we collected better, and we got better in the course of April.”

Use the right metrics

I’m not too fond of misleading metrics. I mean, having things that not only are clear but explain what you’re trying to answer and what question you’re trying to answer in this. Here, she’s trying to say how the business overall is performing. AR is not only not a good indication of that, but if your AR goes down, that suggests that things are better by all countable metrics unless you have some other data to indicate that’s not the case. 

Maybe, AR days or something like that would be a better indicator, or why don’t we look at revenue or even profits? But if we’re in the revenue cycle management, we can’t control profits, but at least we can control revenue. So no, this is not a good representation at all.

There’s confusion on the part of this consultant to make the difference between accounts receivable and revenue. Because this whole webinar is supposed to be about benchmarking, she then talks about “How do you compare?” and says, “Can you look at your data and compare it to this?” No, you can’t because there’s not enough here to compare. I mean, this doesn’t tell us anything. And it’s undoubtedly not detailed and specific that’ll allow us to reach our business. So no, we can’t. You haven’t given us anything of any value whatsoever.

Where’s the value?

Then, she talks about some other metrics like the industry trend of AR and bad debt. Then, she says, “Well, nine days of bad debt. Does this offer an opportunity for improvement?” First of all, everything has an opportunity for improvement. I don’t think I’ve ever seen anybody who is perfect. That doesn’t exist. There’s always some opportunity for advancement.

Then, she goes on to say, “You probably need to continue to look at this harder.” God, I hate it when people say things like that. What does that mean? That’s this vague, garbage consultant speak of not actionable anything. It’s vague. There’s nothing you can do about that. Just look at her. Are we going to stare at the screen for a while longer? Okay, I’m looking at it. I’m still looking at it. It hasn’t gotten better while I’m looking at it. 

So it doesn’t seem like looking at it is going to solve anything. So why are we doing this? It drives me nuts. Do we want eyeballs? It reminds me of the 90s of the Internet bubble where we paid for eyeballs. You know if you were there. I mean, this is a wrong approach.

She puts up another slide. This is “Average AR Metrics.” But then, putting up the slide, she says, “Here are just some numbers.” That kind of sums up this presentation perfectly – “Here are just some numbers.” Well, why? Why are you just throwing up numbers for no intent, for no purpose? How about giving us a goal or a reason?

On the other hand, give us an answer to a question that we want to have answered? What are we doing with this? It can’t just be, “Here are some numbers.” God, there’s a sea of numbers out there. What are we going to do with it?

What’s the point?

These are average numbers for this particular client. Then, she goes on to say, “Well, off the top of my head, I’m thinking an average blah-blah-blah.” And I’m like, “Wait! What? Off the top of your head, you’re thinking of an average? Nobody should think of an average. We don’t think of averages. We calculate averages.” We have data that are supporting. Please give us an average! We don’t think of an average. It’s utterly antithetical to anything we should be doing every day. This should not be part of a benchmarking presentation. This person should be fired or thrown out or not allowed to be involved in this process. This is so bad.

She goes on to say 51 average (she’s talking about days), average AR without bad debt. So 51 days could be a hair high. How thick is your hair? Is it like a micron? Is it like a millimeter? How thick is your hair? Is it a day? I wonder, is hair a day thick? Or is it three days thick? 

Here’s where it gets confusing

What the hell does that mean? A hair is higher. For her, it’s a hair higher than she would like it to be. Well, why would it be just a hair? Why not select a hole to pay? It should be a lot less. What if we’re going to pick a liking? By the way, what does liking have to do with benchmarking and then process improvement? Who wants to like it? I don’t even know what to say. This is so nonsensical. I don’t even have words to describe how moronic and stupid this is.

If this person hears this presentation, I apologize. You’re a moron. You should quit your job and retire or do something else. I don’t know what, but you should get out of this industry. You certainly shouldn’t be involved in consulting and anything to do with data whatsoever. Sorry, not sorry.

Solve something

The problem is, I think many people feel like they need to have their lips moving. If you say to me, “Hey, you’re just throwing shade at everybody else. You’re not doing anything in this particular podcast. You’re not solving anything,” you’re right. This isn’t a here’s-the-answer podcast. But check all of our other podcasts. There’s plenty where we say things like, “This is how you calculate something” or “This is how you solve this problem.” 

But I’m not just going to sit up here and vomit garbage data on top of you in a talk, make vague statements about what people should do, or say like, “Oh, I’d like it if it was a hair better.” What the hell does that mean? What’s hair? We’d like it to be a lot better, but how much better and based on what data and what are you going to do about it to solve that problem?

To summarize

You should use data and analytical processes and solve problems, not make garbage vague statements like, “Oh, we should stare at something for a while longer” or “Something’s a hair higher.” Use data! It’s available. We can help, or you can find somebody else to help.