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– So my daughter wants to say “hi.” I think she wants to be famous.
– No, father, I know you’re not famous.
– So she knows I’m not famous. Okay, good.
– That’s my oldest daughter.
One of the things I wanted to bring up was that analytics effectively is never done. I think we have this concept that analytics is born out of getting an answer. Maybe, it’s something like, “How is my practice doing?” or “How is my billing company performing?” or “How’s my billing department performing?” Or maybe, we’re trying to solve some problem, and we think, “Ah, I will get some answer” or “I will do some analysis to dig and solve a problem. There is a logical conclusion to this, and I’ll be done whether it takes a week or a month, or three months. That’s a relatively short period, and then I’ll be complete.” It is possible to analyze and get an answer and be finished, but it usually doesn’t work like that.
I think some analytics are ongoing, like performance monitoring. You want to track trends over time, whether that’s payment cycle time, charge entry cycle time. You will probably want to monitor these things not just forever because you want to see how they’re doing, but you want to see whether or not they’re improving. So you’re looking at a trend. That means that you will do it effectively continuously. Any practical project should include monitoring afterward to determine whether or not it was effective, whether this is the return on investment, or like, “Did the basic metric or KPI improve?”
Six Sigma ran out of favor from a consulting standpoint in general management, but I think that’s because it got overextended. It was basically for operational processes to monitor quality control. I think it was very sound, but it was unique energy, and we tried to apply it to everything under the sun. Many things don’t apply to Six Sigma, but the foundation itself is perfect, especially for solving problems and improving operational processes.
Let’s talk about control
The acronym that is used in Six Sigma is DMAIC. If you’re not familiar with all of this, we don’t want to spend the entire podcast talking about Six Sigma, but the last letter, C, in DMAIC stood for control. And control is about executing your analysis and making improvements. Invariably, you want to make sure that it continues to stay good whatever you have done. That means you have to measure it after the fact.
The critical part about control is capturing data on an ongoing basis afterward and measuring the improvement. That usually lasts a long time. It may even include iterative loops, where you’re improving something, and then you find something else. It may spiderweb in other areas. It may just take a long time of monitoring. It takes time to strengthen whatever that is because it may not be a one-time improvement. Also, it may be where it just goes off a cliff, and then suddenly it is at that new steady state.
It may be that it’s more of a gradual improvement, and it has to be continued to be worked upon to improve. Or it could be that you made some radical improvements and some change to the process. So, it’s better, and now you effectively need to monitor it on an ongoing basis to make sure it won’t slip. That process probably takes years or possibly even decades before it is ingrained into the company culture.
That’s my other daughter making herself heard.
Even then, you should still have quality control charts. Even if you’ve got less than Six Sigma DFx, which is quite extraordinary, you would still monitor and chart quality control and tracking.
The other thing that we run into is that one problem solved often leads to another problem and another problem and another problem, where you may say, “Okay, we solve one problem, we found this issue. We solved it, but that doesn’t solve 100%. It doesn’t get us 100% of the way there.” Perhaps they might say, “Maybe, that gets us 50% closer to the goal we’re trying to achieve.” Or maybe, you’re further up to 70-75%, but now you identify another problem.
Also, you’ve got to improve to get to 80 or 85%, and there’s another problem. You move through these problems, from one issue to the next crisis and then another. However, you’re still capturing data and quantifying the performance of the underlying process, that same process on an ongoing basis. It’s just that you’re identifying new causes and solving new problems and more
It’s difficult to manage changes
A more significant issue can be that things frequently break. That means the billing software could decide that they want to change the report they were putting out. We see this tons of times. It drives us crazy. I mean, they’re constantly fiddling with their reports and not documenting it, and not telling anybody. That means fields move. Further, fields disappear, and entire reports disappear. In addition, it drives people crazy.
They’re not good at documenting stuff. Nonetheless, even if they document it, that doesn’t mean they can proactively send a message out to whoever uses that report and say, “Hey! By the way, your message just changed. We’re screwing with you.”
There are other types of issues that come up on an ongoing basis as well, which is payers change the rules, the general market environment changes, all kinds of things happen, such that even if you’ve tackled a problem. You think it’s solved, that doesn’t mean that you can take your hands off the wheel and suddenly decide, “Okay, we don’t need to worry about this anymore.” You’ve still got to constantly capture the data. Why? Well, to ensure something else doesn’t change or disrupt all the significant progress that you made.