In this episode of the Informonster Podcast, Charlie Harp talks about the notions of standardization and normalization, what they mean in the context of healthcare terminologies, and their relative pros and cons.
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In this episode of the Informonster Podcast, Charlie Harp talks about the notions of standardization and normalization, what they mean in the context of healthcare terminologies, and their relative pros and cons.
Contact Clinical Architecture
• Tweet us at @ClinicalArch
• Follow us on LinkedIn and Facebook
• Email us at informonster@clinicalarchitecture.com
Thanks for listening!
[inaudible] hi, I'm Charlie harp in this
Speaker 2:Is the infant monster podcast. Now today on the infant monster podcast, we're going to talk about the difference between normalization and standardization and how those terms are used in healthcare analytics and some of the confusion those terms create. So let's start by saying what it is not because if you're coming from a mathematics background or you're coming from a machine learning background, you're a data scientist. You run into your healthcare terminologist on the street, and they're talking about standardization versus normalization. The first thing that's going to happen is you're going to have a big cognitive dissonance between the two of you because in machine learning and mathematics, normalization, and standardization are part of something called feature scaling, where you're taking values and you are putting them into a common frame for analytic and comparison. And that's a valid definition for standardization, where you're looking at a value within the standard deviation and a normalization where you're normalizing data between zero and one. So you can do certain types of comparative activities. But when we're talking about standardization and normalization in the informatics world with terminology in mind, we mean something a little bit different. So let me, let me talk about that. So if you think about the ecosystem of a healthcare enterprise, you usually have N number of endpoints that are streaming terminology to a central location, or to you, you're receiving data from an number of endpoints. They're all using different code sets, or at least some set of different codes. That's across the collection of them. And you want to do something useful with that data. And to be able to do that, to be able to build rules and graphs and analytics and other things you need to do that based upon some normative reference terminology. So you have a standard set of terms or a normal set of terms that you have defined, and you were building rules of cancer. You're doing analytics against. And when all these different terms come in with all these different codes, you need to align those inbound concepts with the things you understand that based all your rules upon. And that ladies and gentlemen is what we mean when we talk about normalization and terminology and healthcare. So I've got an inbound code. It's in a local code system from the hospital. It's got an integer of 100 as its source code, and the word is banana. But when I land that data in my data warehouse, if I want to be able to do my banana analytic on that data, my code for banana is be 75. So that means when that code comes in, I have to assign it to be 75 for a truly to be recognized as a banana. And that's normalization normalization. When it comes to healthcare, terminology is typically done with something, we call a map, another thing that's commonly misunderstood because we also use the term mapping when we're talking about message and syntax. But in this case, we're taking that code of 100 that came in from that site. And we're saying when that comes in, assign it be 75, because it is in fact a banana. So normalization allows us to take the data coming from the outside world and normalize it go from the abnormal to the normal. And I don't mean the term abnormal pejoratively. I just mean that normal is what you expect and what you're used to. And these other codes are not that. So that's normalization of normalization is taking something from elsewhere and aligning it to the normative reference concepts that you expect and you're using then what is standardization? Well, that's an interesting question because it kind of depends upon whether you're talking about normalizing or whether you're talking about something else. And I'll explain what I mean in the context of normalizing standardizing is when you normalize something to a standard. So for example, if I'm taking codes, uh, medication codes that are coming from all over my enterprise, and I'm aligning them to a code that, you know, my pharmacist Stavros cultivated in the basement, and it's a fantastic code system of medications, but they're Stavos codes. I am normalizing the medication codes from across the enterprise to the codes created by Stavos. I am normalizing in my rules, know about stock versus code. And so everything works just the way it should, but it is not standard. According to the definition we're going to work with today. And I'll talk about scope in a minute. But typically when we talk about standardization, if I were to take those medication codes coming from across the enterprise, and I would normalize them to RX norm, which is a national standard that I have not only normalized those codes, but I have standardized the codes. You see what I did there? The reason why it's different is because a standard implies that it is shareable. It is some kind of universal. I have made it a standard, and therefore I can share it with anybody that has adopted that standard. And so this is where I'm going to diverge a little bit from some people that are hardcore standardization means this and that I would argue that standardization is in the eye of the beholder. So I would say that if I normalize to a national standard, then I have standardized my data at a national level. But if I'm part of a large hospital enterprise and I've created a PARP hospital or harp health systems standard, then I've also standardized to that. But I've standardized to a harp hospital standard, not a national standard, but when most people talk about standardization, you bump into a guy in the street and you know, you mentioned standardization, you'd say, Hey, I've standardized all my data. That person is likely to think that you normalize to a national standard. And that means that you can share your data with him as long as he understands that national standard, as long as he's from the same place as you, because if he's from Bratislava, they might have a different national standard. And then you are not standardized because then you require a global standard to do that. And that's why I kind of think standards are about scope, because it depends upon where you are. If you were to leave this planet and you were to get to, I don't know, the Horsehead Nebula, and you were to say, I standardized to the earth standard for medication terminology. They would say, well, that's all well and good, but when are you going to standardize to the galactic standard? Like the rest of us. So there's another way of thinking about standardization as well. And this is something that I've come across recently when talking to large organizations, sometimes when people talk about standardization versus normalization, they're not talking about how you normalize, whether you normalize something that's standard or non-standard, they actually mean something much different than that. And that is whether you're going to normalize, which is let the data that exists from all your end points, continue to be abnormal or, you know, local. And you're going to standardize that data when it lands in a central place for you to do aggregation. Versus when they say standardized, they're talking about it more as a function of data governance. And what they're saying is instead of letting all of my 50 hospitals all have their own codes for everything, and I will normalize them. So I can do things with them. When I get the data here, I am going to standardize all of those 50 hospitals. So they're all using the same codes as me. Now, standardization is a as a data governance function is a slightly different animal because really what you're doing is you're establishing a standard, whether you're adopting a national standard or creating an enterprise standard. And then you're basically mandating that all of your endpoints adopt that standard in their data dictionaries, thereby eliminating the need to normalize. So the assumption is, instead of everybody speaking their own language, we're all going to learn the same language. And that way I don't need to translate anything that comes to me. I'll just understand it. Then when I make decisions or do things, it's going to be great because we're all speaking the same language. Now, one of the things I want to say about that is it seems very attractive because normalization is a headache. Anybody that does semantic and to do analytics knows that the lifestyle choice, it's not a project you do once. And you're done. Cause terminologies are constantly evolving and things get added and it's a lot of work and you have to dedicate resources to it. And people don't like doing it. So it's a, it's kind of like mowing the lawn. The lawn keeps growing. You've got to get out there. You may not like it. Now. I happen to like mowing my lawn, but not everybody does, but you, you get out there. And somebody has to mow that lawn every week, or it gets out of control. Normalization is the same thing.
Speaker 1:So people
Speaker 2:Get lulled by this idea, by this utopia of standardization whereby they don't have to do that ever
Speaker 1:Again. But you can think of, we're
Speaker 2:Going to stick with the lawn mowing analogy. You can almost think of standardization as saying, well, I don't want them
Speaker 1:On my lawn. So I'm going to pave it over,
Speaker 2:Pave it over with concrete and I don't have to move anymore. And it's gonna be awesome. I think that, and it's not that I'm against standardization. I think that the problem with standardization is that most people do it because they don't like normalization. And also because people have committed egregious crimes against the data science out in the field where people don't practice good data governance. And so they create horrible terms that are meaningless or they create duplicate terms or they reuse codes with different meanings. And when that hits you in the aggregation, the analytics, it really hurts your data quality. And so it's a combination of, I don't want to do the normalization. The people at the sites are not terminologist. There are people who have normal hospital jobs and they're adding codes. Everything will just be better
Speaker 1:If we standardize now, I've, I've
Speaker 2:Seen people do this successfully or well, they implemented it, but in my experience, it hasn't been hugely successful. Now the exception that kind of proves the rule is when you think about drug terminologies. So as you might know, I was at FTB for 10 years and first DataBank, Metta span, multiple, those compendia are an example of a standard. There, there are third-party standard, but basically when you look at drug terminologies across a lot of organizations, they're all using first DataBank. They're all using Mehta span. Ideally they might be using NDC codes, but they're using something they're sending those in to whoever's using it. And usually they have RX norm codes on them and everything else as a result, medications, you know, they flow pretty well through our electronic ecosystem, but they're not perfect standardization didn't solve the problem. They still usually some normalization required in that process. Well, why is that? If standardization is in informatics utopia, then why do we still encounter issues? When we try to
Speaker 1:Standardize? The reason is for the same
Speaker 2:Reason that mapping is never done. If you normalize most terminologies, most of the meaningful terminologies in healthcare are not static. They shift constantly things get added, things, change things, get tweaked. The problem with paving over your yard is nothing grows. And the same is true. When you look at a healthcare enterprise, if you mandate from the center, these are the terms you're going to use. The problem you have is the people at the edges are the ones that are actually in the trenches are actually the ones that are having to add things that they got an inventory needed to figure things out. Or the doctor is yelling at them. You need to add this. The problem is where is there a, some code system, some code sets you could standardize from a data governance perspective, if they're fairly static and they're not huge, then you know, things like gender, things like ethnicity, things like encounter type, a lot of those, those micro terminologies that we have, you should be able to standardize those and say, Hey, you're going to use these. The problem is when you start trying to standardize the bigger ticket items like procedures, charge codes, um, and I'm not just talking about clinical concepts. I'm talking about business and administrative concepts. When those things that people are interacting with in high volume that are highly dynamic, what ends up happening is if you mandate that those come from the center in a standard way that people at the edges will be stifled. And there's a, there's a decent chance that they will rebel. And I don't mean pitchforks and torches, they'll say, well, I couldn't find the code. So I'm not putting in a code. I'm just going to put it in the note. I'm going to hide it away where only a human can find it. The other thing that's going to happen is they're going to get angry. They're going to get angry because they need to add something. We need to say something. We need to do something. And now they're interacting with a process to get something added that is stopping them from doing what they need to do. Whereas if they could just go to their local person and say, add this to the drug dictionary in the system, they could do that and, and move fairly quickly. If now they have to go through some chain of command to get something approved, they're going to struggle. And what's going to happen is if you say you're going to standardize and you mandate it and you go out into the field and you say, this is what will happen. What I've seen occur more often than not is they try to do that. And then the system starts to fray. People start to get stifled. People start to find work arounds. Some people just outright rebel and say, I don't care. I'm going to add it. And then you've got this Frankenstein monster of a standard quote, unquote, a governance standard approach with normalization on the side, because you are trying to accommodate the people in the trenches that are ultimately the ones responsible for making sure the business is successful. I'm not really sure what to call this because I encounter this. People say standardization and some people go, yes, standardization is good because they think you're normalizing to a standard like snowmen or RX, norm or LOINC. But when you think about this idea of mandating terminology across your organization, I'm not saying you can't pull it off. You might be able to pull it off, but you have to have an excellent governance process. You have to have systems in place. So the turnaround time and the delivery of those enterprise standard concepts to where they're being consumed is almost zero latency. Which means that a lot of times, when we engage in this kind of terminology business, we elongate the process from a central data governance perspective and you really have to be agile or the edges will feel stifled and they'll start to work around and it'll all start to unravel. That's just my personal experience when it comes to this kind of thing. I don't know that it's a bad idea. I think, like I said earlier, there's a lot of terminologies where smaller terminologies, like, for example, if you could standardize locations and departments and specialties, things that you have firm control over, you could do that, that business entity type metadata is actually a really good thing. If you can pull it off, then you just got to find out how to deploy that and all the disparate systems that utilize that kind of data. And that's the other challenge with that kind of standardized governance is that, you know, when you're dealing with multiple EHR is the process for maintaining them and synchronizing them and feeding them is also a little challenging. Go into it with your eyes open, make sure you've got a very good process with a tight turnaround to get the data back out into the field and have a contingency plan, because I would almost guarantee that sooner or later you'll have to start making exceptions. It's just the nature of healthcare. I've seen a lot. And if you, if you've done this and you're successful, then I want to talk to you on this podcast because you know, something that, that other people who want to try this need to know and understand, and I'd like to learn how you pulled it off too. If you, if you try to do this kind of approach and it didn't work, I'd love to talk to you too. Let's face it. I'd love to talk to anybody on this podcast. So anyways, so that is my spiel on standardization and normalization. I really appreciate you tuning in. And, uh, we've started to get some more folks listening. We've got some exciting things coming down the pike in the future where it's not just me, but I would also say that if anybody out there has an idea for a podcast, if you'd like me to dig into something and give you clinical, architecture's take on it, I would be delighted to make that happen. So once again, this has been Charlie harp and this has also been
Speaker 1:The ProMaster podcast. Thank you. Stay well and stay safe.[inaudible].