Charles Boicey - MS, RN-BC joins us to discuss the state of Data Science in Healthcare and the path to becoming a clinical data scientist. We also go in depth into the process of data science and how to ensure you aren't spinning your wheels. Hope you enjoy.
Bill Russell: 00:11 Welcome to this week in health it where we discuss the news information and emerging thought with leaders from across the health care industry. My name is Bill Russell. Recovering healthcare CIO and creator of this week in health it, a set of podcasts and videos dedicated to training the next generation of health it leaders today, healthcare needs more data scientists. We take a look at the path to becoming a clinical data scientist. This podcast is brought to you by health lyrics, looking for someone to be your personal coach and healthcare technology. Let's talk visit health lyrics.com to schedule your free consultation. Our guest today is Charles Boicey, uh, Ms Rn, BC, lots of acronyms by his name, chief innovation officer for clearsense and assistant clinical professor at Stony Brook University. Uh, good afternoon Charles. Welcome to the show.
Charles Boicey: 00:55 Hey Bill, thanks for having me.
Bill Russell: 00:57 Well, I love having you on the show. We don't do it enough cause you're all over literally all over the world. The last time I tried to have you on the show, you were in India. And, uh, you know, next week you're going to be Cleveland. You're, you're literally all over the world. It's, it's Kinda hard to track you down. But we ran into each other at himss and we started talking about this topic of, of data scientists and clinical data scientists. And I got, I got kind of excited about having you on to talk to, uh, to talk to this specific subject because there's, there's so many doctors and nurses who are saying, hey, I see the need, I see the need to really turn data into insights. And into things that could be used to effectively drive down the cost of healthcare, improve access and better experience and better outcomes. And they get excited about it. And so I thought it would be great to just have you and I talk about what does that path look like for them to go from practicing physician or practicing clinician, uh, to, to the data science's role and, and that's what we're going to try to do today.
Charles Boicey: 02:00 Sure. So, um, let's kind of start off with, um, you know, something, you know, that, you know, probably people don't think about and, and they should, you know, really what is a, um, you know, what does the data scientist, you know, what does that all about? And I'm not going to get into a, you know, what degrees, you know, what certifications, I'm not going to get into any of that at the end. We'll talk about some programs that are out there that can help you get to that point. But, um, um, I really want to kind of go over, you know, initially, you know, what does a data scientist, you know, what are the three domains, if you will, that constitute, uh, you know, a data scientist, what do you need, et cetera. And um, and the three domains are a subject matter expertise, uh, computer science,
Bill Russell: 02:48 which they have the subject matter expertise and then domain science in our domain expertise in computer science. And then what's the third?
Charles Boicey: 02:55 The third one is, is math stats, mathematic skills, critical thinking skills.
Bill Russell: 03:02 Yeah. So we're, we're going to dive into those. But here's the thing, I know about the two of us. We get excited and we get going. So we needed sort of a framework to keep us from going off. The first thing I want to do is, um, so there's, there's three things, three movements we're going to do on this show. First is current state of data science. And what is data science? The second movement is going to be a typical data science project in healthcare. Uh, and what the process looks like. Cause I, I think some people think data science and they think that the report writers are the people who are doing the reports. And, and that's, uh, we'll call that like the beginner. But that isn't really data science. And we'll talk about what a pro, uh, a project looks like. And I think that will open people's eyes to what's possible. And then the third movement is going to be the path of becoming a, uh, a clinical data scientist. And we'll talk about the different programs and whatnot. Sounds good. So let's start with current state of data science and healthcare. So you're doing a lot of things. What is, I didn't even know where it, how to frame this question other than just say broad question, current state, what is it?
Charles Boicey: 04:05 Current state of data sciences, we're jumping into it and we're doing as much as we can, as fast as we can. Um, um, you know, basically who we've, we were jumped, we jumped on the horse and we're, we're, we're charging off the lively in all directions
Bill Russell: 04:20 so people recognize the need for it, but they don't know what direction to point it in yet. Is that, or are they finding ways to, to do it and they're sort of experimenting in a lot of different areas?
Charles Boicey: 04:31 Well, just like anything, you know, even bringing in data visualization and analytics as we did. Um, you know, there's some that are at a higher maturity level than others. And, um, what's really good is it's been recognized. Um, it's legitimize, uh, many organizations have legitimized, you know, data science and the application of, um, you know, data science project products. So that's, that's the good news. Um, the not so good news is it's still the wild wild west. There's still a lot of organizations that are hiring folks. Really without a problem to solve. I want to get into data science, I want to do predictive analytics. Let's hire a team. It's kind of like the religious movement of data governance a few years back and we all know it. We all know what that looked like.
Bill Russell: 05:21 Yeah. And you're, you're like smacking me around here cause I am one of those people who hired three data scientists. I put them in a, in a group and I said, all right, we're gonna. And they were like, oh, we know what to do. And I checked back on them six months later and they were writing reports and they were buried in places of the organization. And I realized, uh, you really do have to lead this. And so what does it look like? What does a uh, a leading health system look like that is applying data science today?
Charles Boicey: 05:51 Sure. So that's, that is the key word that you said was applied. They understand that there's a need to apply that, which is, you know, the products that are developed in a data science program. They actually have, um, and it's actually have a program in place. You remember when we started with a 30 day readmissions, you know, can you predict 30 day readmits um, the first question I asked when I was asked that, I said, yeah, absolutely, but do you have a program in place to address those patients that we discover that are likely to readmit? Because if you don't, we've got a problem. We've got an ethical problem. We've identified people that were not going to be able to take care of. And then secondary to that, we've just performed a statistical exercise. I'll predict them, we'll see if they readmit and we'll just keep some stats. So you really, really have to think this thing through. Uh, and you have to have a plan in place and you have to have, you know, objectives, you know, clear objectives. You know, why aren't you doing this? Not just because, um, you know, my health care organization, neighbor down the street. Now, how's the data scientist? I want one too.
Bill Russell: 07:03 Yeah, I mean you make a good point there. So, um, I was talking to a CIO and we were talking about in the consumer area how the chief digital officer, the CIO, the ops, the COO and the chief strategy officer needs to almost be tied at the hip because one begets another, begets another. I mean there are also, um, so closely linked, but in the data science world, the same thing's true in terms of operations. Everything you're going to be analyzing, it's going to kick off something, you're going to find something out. And it's like, all right, now what do we do? We know the people that are going to readmit, we got to put a program together.
Charles Boicey: 07:37 Yeah. So it covers operations and covers clinical practice. It covers finance. And if the organization happens to be tied to research, you have a research component of it as well. And you're absolutely right. They all have to be tied at the hip.
Bill Russell: 07:53 Where, so where does this typically report? I've, I've heard this now, I do a fair number of fair amount of consulting still. And, and we had this problem where I was the CIO. Uh, analytics was everywhere, right? And it should be everywhere. It should be close to where it can be applied. But from a recording standpoint, it literally was everywhere. And we, uh, we went through the organization and looked at everybody with the title of analysts, which is typically how they hide people within, uh, uh, budget. Uh, and we found a hundred and 180 of them or something like that, didn't report into it. They were just data analyst of some kind and they were all over the place. Um, you know, what does a, what does a good program look like? Where does this report into, uh, you know, what, what's the matrix, what's the dotted line? And then, and then how do they ensure quality throughout the organization?
Charles Boicey: 08:45 Sure. Um, so an organization that's mature that, um, you know, gets it, that, you know, is, is where they were, where they need to be. Um, I usually these teams, you know, reporting through, uh, the, the chief data officer, to be honest with you or that chief analytics officer. Uh, and these are, you know, if they're not at the end of maturity, you know, somewhere at mid point, um, they have their, their data needs taken care of. And what I mean by that, there is a central repository or platform if you will, that these types of activities can occur in, and you know, it's really interesting cause these types of organizations also have decentralized, you know, analytics and why not? That's great to have it at department level, but they're all working off the same data sources and whatnot because they've gotten to the point where it is absolutely centralized. And I say that because, data scientist. If they're spending 80% of their time data wrangling, you're not using them correctly, you're not using them wisely. They should spend their time. Um, actually, you know, applying data science to the data because the environment is such that they can do that. But usually the, uh, if there is not a chief data officer, this is something that the, as you know, you want to, the CIO needs to keep, uh, keep tabs on because it can, you know, go, go out of control pretty quick.
Bill Russell: 10:14 Yeah. That's, uh, that's one of the conclusions we came to is that the, uh, it, it function was going to be that data wrangling. It was going to be the interoperability pulling all the data together and providing the tools and uh, helping to establish the dictionary a, you know, what did the, what is the terminology mean? Um, because that's the other big problem you end up with is people generating insights and it's based off of data that may or may not have the definition they thought it had.
Charles Boicey: 10:41 Yeah, sure. So, so the ideal state for a data science team is absolutely to have that governance in place, to have that catalog in place, to have things such as length of stay, um, defined for the entire organization. And that can be multiple hospital entities. So you're, you're absolutely correct.
Bill Russell: 10:58 So let's talk about a typical data science project. Um, so I mean, you, you used to readmissions, I don't know if you want to use another one.
Charles Boicey: 11:07 Yeah, I'll use it. I'll use another one that's, um, um, that I think is, you know, is an important one. Let's look at, uh, we'll use two of them we can talk about too, because some of the elements are pretty similar. Let's look at, let's look at it in a, in a couple of levels. So let's say, let's say somebody came to me and said, hey, Charles, um, we'd like you to develop a predictive model for predicting patients that are likely to become hypertensive or let's say likely to become type two diabetics. That's, you know, that's, that's reasonable. Um, but I would push back and say, hey, let's do that, but let's start here first. Let's find those patients within our population that are undiagnosed diabetics or undiagnosed hypertensive pateints. You all have them. So that would be step one for me. Step two would be, there'd be the predictive model because, you know, I, as a clinician, I want to help as many people as I can, you know, quicker.
Charles Boicey: 12:06 And that's one of the benefits of having a clinician data scientist because they got the domain, the, I totally understand the domain and they would come to, you know, something similar to that. So, um, so, so what do we do? What does that look like? Um, you know, really first step is what I call business understanding, if you will, really what are the defined objectives, uh, what are the data sources that we need and let's do some research. Um, I'm not going to be the first one to build a model for, for those two, those two items. I'm sure a lot of work has been done prior to me, so let's find out the work that's been done. Um, let's do the research and then let's start, um, you know, bringing in the data that's required for that. Um, so let's bring in the data, explore the data, get the data quality, um, you know, work all the kinks out there. And then, um, at that point we've got what we want to do. We've got the data that we need to do it in. And now we do a what's called a feature selection.
Bill Russell: 13:11 All right, so in the business understanding, so yeah, uh, you're going to define the objectives. You're gonna, you're gonna identify the data sources and then you're going to do some research. Where would you go for the research? I mean, do you go to,
Charles Boicey: 13:22 sure. This is a pub med for our purposes being in healthcare, this is peer reviewed, peer reviewed journals and so forth. Um, it's, it, it's really important. Um, uh, you know, Google search in this case, usually it doesn't cut it, although you might, you know, you might find a few things that are valid, but again, you know, pub med, you some of the peer reviewed journals and whatnot, just to kind of get a flavor of what's gone on before you and be honest with you, it actually kickstarts and gets you thinking. Um, you know, a lot of data science is really about, you know, you know, having that self talk or if you will, you know, that cognitive exercise of, you know, what we're really trying to do and whatnot. Um, and having, that's why a clinician is doing this, um, have the, the discipline and understanding of certain features that are likely, I'm going to put some likely emphasize that, um, that will be of benefit in a model like this. But there's also other data elements out there that we need to look at. Um, you know, such as social determinants, um, exposome data, um, other, other data elements within the, you know, the, the healthcare data set if you will, that, you know, play a role in that. So, um, we're now doing the modeling. We're doing feature selection and I mean I'm oversimplifying this bill cause you know, this is,
Bill Russell: 14:46 well you almost have to, I mean cause at this point you've, you've done your research, but you have to get the data. Now in a, in a, in a mature organization, that data is going to be, you know, just available to you either in a, in an EDW or uh, or accessible through a, a big data platform or set of Apis. Um, but generally there's an awful lot of work that goes on there of wrangling that data. And cleaning it up, I would assume. Yes.
Charles Boicey: 15:13 So in that case, um, again, remember I told you, you don't your data science or data science guy or gal to be, um, you know, doing data wrangling. Um, so that's where, you know, a data engineer or a data wrangler would come into play. Um, data scientist says, Hey, this is what I need. Data Wrangler goes up and you know, wrangles it up if you will. Um, and delivers it to the data scientists so that they, you know, have a, you know, have a Dataset that they can start working with.
Bill Russell: 15:43 So they'll say, I spend a lot of time in business understanding, hopefully very little and the data ingestion and acquisition and then they spend a, I assume the data scientist spends a fair amount of time in the modeling and, and that's, that's where they're earning their keep really, I would think.
Charles Boicey: 15:58 Yeah, that's their training. That's their, that's their discipline. That's, that's, that's their gig. That's what they do.
Bill Russell: 16:04 And do you start, I mean this is one of the things that somebody had said to me is do you start with the, the hypothesis or do you start with the data and the data sort of tells a story.
Charles Boicey: 16:14 I am the operatory approach, which is let the data speak for itself. Um, others have the other approach. Neither one is wrong, but one should consider, uh, you know, the other, let's say a diet, the diabetic model, I'd be foolish if I left out a one cs right now, it would make absolutely zero sense to me. Um, or BMI, a BMI calculation. Um, so there are some features that we know that are going to play a role, but let's look at the, the rest of the data and see what comes up with it. And tools such as, you know, linear aggression and so forth will, you know, help us understand that the benefit that we have in healthcare is we have diabetic type two patients. We have a type, you know, we have hypertensive patients. Um, and if we get into predictive models are patients that are likely to crash.
Charles Boicey: 17:06 We have all of that. So, um, you know, understanding those patients, those data sets, those features and whatnot, or if an absolute benefit, um, for the, for the design and then considering, you know, features outside social determinants, I get a big boost personally in, um, inaccuracy by using certain social determinants of health within these predictive models, especially for, um, you know, patients likely to become diabetic and hypertensive and so forth. So you've got to kind of take it from, you know, all, all aspects. Um, and then you've got to have the discipline, hey, if I come up with a model that's, you know, 0.5, well that's a flip of a coin that's really not going to, you know, do me any good. This is where data science in the training and the rigor around it really comes into play. You have to be disciplined to say, okay, this model isn't working.
Charles Boicey: 18:01 It doesn't have the accuracy that I'm looking for. I got to go on to something else. Right. Um, and I'll experiment. I'll do what I need to do, maybe doing an ensemble model. But at the end of the day, if my accuracy is not such that it's going to work, then I've got to, I've got call it at that point, um, I'm not going to go Ben features and you know, do manipulations and so forth so that I get an area under the curve. That's, you know, in the high nineties it is what it is. Maybe in some models, you know, 0.75 with will will be acceptable and others may be 0.8, .9 will be acceptable, you know, and that's where the communication comes in. Bill, everybody's involved in these projects, um, asked to be there for all of this. Um, this, none of this can be black box. They have to see the datasets, we have to see the feature selection.
Charles Boicey: 18:56 You have to see the math, we have to see the output and whatnot. That's the only way you're going to get buy in as well. All right, so just a little tangent just because you said that, uh, we've been talking a lot about AI recently on the show and we've talked to different people about, about Ai. And one of the things is, you know, you have these vendors who come in and say, well, we have these algorithms and they're the sort of behind the curtain. Um, and it sounds like what you're saying is there's no such thing as behind the curtain when you're talking about clinical efficacy within clinical environment. So you have to, they almost have to reveal some of that, some of the algorithms, how the math is done and how they come up with their, uh, their findings. Otherwise, there's going to be really hard for us to, to just implement some of these things within the clinical environment.
Charles Boicey: 19:39 Yeah, absolutely. And you know, I'm going to, I'm going to tell you that, um, you know, a certain model in, uh, you know, in Irvine, California may not be applicable in Sarasota, Florida and a healthcare organization that the predominance of patients is 65 and above. Um, these models have to fit the fit the population. And that's kind of where we go. You know, awry, kind of in healthcare where, you know, a lot of our best practices and models and so forth. We're built on a population that may or may not still exist or not exist in the, in the, in the, you know, the Geo location that we're going to try to apply it. And this is not a one and done, you know, as we go through the process, yeah we have to operationalize it, but there's a lot more than operationalizing, uh, we've got to continue to test and validate. You can't just dump the back blackbox off at the door and you know, walk away from it. You got gotta be really monitoring that over time. Not only is there as an accurate but for crying out loud, did it actually affect any change? Was anybody paying attention to it? So, um, these products not in a workflow, you know, there's going to be some issues there, especially on the adoption side.
Bill Russell: 20:54 So when it, when it goes into operationalize what you're operationalizing it, that data science is, is still a pretty, pretty heavily involved in terms of testing validation in keeping the, uh, making sure that, uh, that the model is working out the way that, that everyone expects it to work out. I mean, their job is to Sorta, uh, act as the scorekeeper and say, hey, you know, the model is still holding, uh, the results are good and those kinds of things. I mean, am I seeing that right or am I saying that right?
Charles Boicey: 21:26 Absolutely. So let's take the, let's take the diabetes or the, you know, hypertension, you know, did those patients that were identified in fact, you know, you know, become hypertensive or even, you know, looking at patient deterioration type models, you know, you know, every month looking at the rapid response team responses, you know, looking at the code blues and so forth. And actually going back to the, you know, the Algorithm, why not were they scored? Did anybody take action on it? Did we miss anything, you know, are false positives or false negatives? Yeah. This is not, this is something that you really have to, uh, you know, pay a good bit of attention to, um, you know, ongoing. Um, you know, especially in the, you know, the first stages of deployment and whatnot. But you've got to go back and you know, you've got to go back and look at this stuff
Bill Russell: 22:16 now. So there's a data scientist handle like two or three of these kinds of projects at the same time or are they typically focused in on one?
Charles Boicey: 22:24 Sure. Um, so my personal is I like to do one. Um, there are folks that can do two or more at a given time. That's okay. When you're, I'm kind of doing the data wrangling and whatnot, but when you're really have to be concentrated and focus. Uh, you know, for me, um, I really have to be into that one until I get to the, you know, the conclusion of I've got a model that I feel is, is fit and ready for deployment
Bill Russell: 22:53 and you can justify that cost. And I've had this conversation with executives. You can just find that cost because you know, some of the outcomes dramatically improve outcomes or dramatically improve efficiencies to the point where you're, you're freeing up millions of dollars and you're keeping people healthy. And I mean, there's, there's significant benefits from, from these projects.
Charles Boicey: 23:16 Generally there's significant benefit from the project. There's not single benefits from hiring individuals because if you don't have that complete program in place, then I don't care how many accurate models you build. Um, if they're not deployed properly, you know, if they're not, you know, you get you, I won't reiterate it, but you get the picture
Bill Russell: 23:35 and you get the picture. I mean, that the system, uh, the system has to have the ability to, uh, to act on the data once they see the data. All right, so let's, let's take a look at, uh, let's take a look at becoming a clinical data scientist. We had a, we've had a couple of shows. Now we're where people have talked about the fact, um, you know, I mean, Lee Milligan's been on the show and he's, you know, he's a physician who became a CMIO, who's now become a CIO who now oversees their, their data practice. And we're seeing a lot of clinicians move in this direction. Um, you know, what does that look like? What does it look like for a clinician? Um, do they generally, I mean, for Lee, the Path was, you know, essentially, uh, a somebody who really understood the data and understood the Emr and then they said, hey, why don't you become the CMIO and help us to implement this? He implemented it and then he became, hey, why don't we, we'll put you over analytics cause you seem to get analytics. And then he just kept learning and growing into the role. Do you see that, I mean, is that generally what has happened and do you see that transforming into a more disciplined path where people will actually get degrees and move into it?
Charles Boicey: 24:47 Sure. So a couple of things. One, um, that was an organic progression. That's my story as well. Really. Um, you know, it happened organically. Um, um, when, when I talked to clinicians now, the first thing I asked them, I asked him a question, are you running from something or you're running to something. Because if you're running from something, this probably isn't going to work for you. But if you're running to it because you really want to embrace it, then you know, let's keep talking. So, you know, kind of the pathway, the, the domain knowledge is there. Um, you can be a physician assistant, you can be a, uh, a nurse, nurse practitioner, a pharmacist, dietician, physician, clinician. You have that domain knowledge. You know, how a hospital works, you know, in the ambulatory environment works. You absolutely have that. That's gold that is, you know, that will get you a majority of the way there.
Charles Boicey: 25:46 Um, a lot of the tools we use are, are developed, are based on having some programming knowledge. So you've got python, you've got are, there's a few others out there. You have to be able to, you know, um, amp those skills up and on the mass side, we're all pretty well versed in that. If you think about, um, you know, clinicians education pretty good with math, pretty good on the statistic side, yes, there'll be a need to brush up on that. But I would say that, um, uh, the computer science side is really the area that we need the most focused on. There's some pretty good programs, Bill, that you can audit within the Coursera environment. Uh, and that's where I would start. And the reason I say that to folks is before you start, you know, spending, uh, you know, tuition dollars and so forth, um, take a look at, you know, what you're getting involved in.
Charles Boicey: 26:38 Um, and so you can make a really good choice, you know, based on your interest in and so forth. This isn't going to be something that you can, you know, jump in. It's not going to be easy. It's going to take some discipline and rigor. Um, but you know, the transition is absolutely there and I'm not going to get into the, I'm not going to get into the MS versus phd argument. Okay. Um, cause that can be a whole nother show. And you know, I may not do, uh, the benefit of both. Um, I will say that those that have gone through a doctoral program have a pretty good sense of rigor and that is beneficial in this kind of environment. Uh, the last thing you want are models that were created to get to a certain goal as opposed to it is what it is. You know what I mean?
Bill Russell: 27:29 It's interesting to me that you say R and python cause uh, you know, for people who feel like, you know, a programming language has changed very rapidly and I'm gonna have to learn something new. And three to five years, the R and python have been the sort of bedrock for this for well as long as I can remember.
Charles Boicey: 27:46 Yeah. In the last, in the last year and a half or so. Um, python is actually eclipsed r and for the, uh, for the clinician that's pretty, pretty cool because python is more of a language, natural language if you will. Um, where r as a little bit more programming language to it, there's a little bit more, you know, syntax that is, that's learned. Um, again, you know, I'm at Stony Brook University and I flipped my, I flipped my data science program to center around a python. Now I do teach a little bit r, but it's primarily python, German and the students had been, I've had better success within the python environment
Bill Russell: 28:27 and my son's at one of the big four firms and they, uh, they required them to, uh, to learn python because that's, I mean it's, it's now becoming one of those things. If you're going through a computer science program at a college or university, I would assume you're going to be picking up python as a language as opposed to when you and I would have gone through it. It was, uh, you know, it was c and it was basic and some other crazy things. Um, so, but there's data science programs springing up all over the place and you talk a little bit about those and then, and then you, and a clear sense we're involved in one, I think that was pretty interesting. It gives us an idea of what those programs look like.
Charles Boicey: 29:08 Sure. So you've got a complete master's program. Uh, you've got a phd route as well. Uh, many clinicians, you know, you know, I have to be us or ms or you know, phd level. So, you know, pick your entry point. There's also some really good certificate programs. I would stay with organizations that are academic medical centers. Uh, I'm not going to name names, but you know, there, there are several and they are the ones that are really, you want one that's focused in health care. Uh, you really don't want it, one that's outside of healthcare because it doesn't really make any sense. You got to do a hell of a lot of, you know, adapting the curriculum to, to, to meet her needs. I would definitely do that Coursera thing for audit purposes, and then make the decision whether you want to, um, you know, do the certificate program, um, in, you know, from that, you know, ms or Phd, uh, you know, health care is employing data scientists and there's two things that are happening.
Charles Boicey: 30:11 One, organizations are hiring folks not knowing what a data scientist really needs to do or what one really is. And then six months later they figured out that they find out that they didn't hire what they thought they hired. So really all that front work and putting the program in places is, you know, really, really essential. Um, and then the other is hiring data scientists from domains outside of healthcare. And I have you just have to work with them and you know, put a data wrangler or you know, an informaticist alongside of them to work side by side because they can go off in weird tangents if you will, because of not having the domain knowledge. So let's talk
Bill Russell: 30:56 by that. So I'm going to build that out. I got to build the program out within my, uh, uh, system. Am I better off taking some clinicians and putting them next to, taking some clinicians and giving them the skills they need to start to play with the data? Or Am I better off bringing in data scientists and, and matching them with clinician? I mean, what,
Charles Boicey: 31:19 what have I seen that's been the ideal? Uh, you've got some really good clinician data wranglers. You've got some nurse informaticist on, on your team. Many of us have, uh, marrying them up with a data scientist from outside of health care. We'll do two things. That knowledge transfer of domain, will absolutely occur. What's, I don't know, maybe more important that spark of working with a data scientist and seeing what can be done. You know, with the data will absolutely occur and that'll propel, you know, that clinician to, you know, further along their training and studies and whatnot. That's what I've experienced and that's what I've seen that's worked, you know, worked really well.
Bill Russell: 32:02 You know, I, I mean there's a lot of exciting things in health care. I love the cloud because it brings new levels of efficiency. I consumer trends are going to, um, you know, really help take the friction out of health care. And I think that's exciting. Iot is gonna allow us to reach beyond our walls, but data clearly has the ability to transform healthcare if applied correctly in the right places. Uh, it's one of those, it's one of the things that it's going to affect outcomes, cost, quality, access, experience. It can, it can impact all, uh, all aspects. And in order to do that effectively, uh, an organization, I, I just, I feel like a, and this is the message that we're really trying to get out there. An organization has to say, you know, we have to be, you know, we can be a B in infrastructure.
Bill Russell: 32:52 We can be a B and some other things, but we have to be an a in our use of data. And, uh, and, and, and what you're seeing out there is a health systems are going to start to differentiate themselves on the ability to use data. And you're starting to see some of those articles where, uh, where, you know, the headline, will read, you know, they, uh, impacted this, uh, outcome or the impact of this. Um, these financial metrics are, or, uh, and, and you could see, uh, you could see some distance starting to generate are to separate these. And I think that the use of data and much more so than some of these other skills is really going to be to be the driver. I mean, that's my experience. And I assume that your experience as well.
Charles Boicey: 33:37 Yeah. And it, you know, for my operation financial perspective, it's survivability and sustainability. You know, two letters; MA. You're going to become an acquisition if you're not, if you don't have this skill set and you know, we can't fly operationally, financially by the seat of our pants anymore, it's not working out well and you're subject to your subject to be an acquired. Um, and on the clinical side of this application of data science, you know, I look at it from a, um, an intelligent assistant, you know, perspective, Hey, look it, we're not telling anybody what to do. We're really assisting you, right? These are the patients or patients that we found your population that are likely undiagnosed hypertensive patients and, or, um, you know, diabetic patients, hey, let's take a look at them. Um, um, these are the ones that are likely to, these are the patients that are likely to deteriorate. These are patients that are likely to intercepts this pathway. Um, again, using this technology to, to help, not to beat somebody up over the head or tell him what to do or to, um, you know, say you have to do this therapy that therapy really, but just kind of be in their eyes and ears out there for them and machines do a great job with that. So,
Bill Russell: 34:57 oh, Charles, always great to have you on the show. A, is there a, an easy way for people to follow you on social media?
Charles Boicey: 35:03 Sure. Um, you know, the, the Twitter handle is @N2informaticsRN, and that, that pretty much covers it.
Bill Russell: 35:12 You know, one of the other things I wanted to, to touch on it, but, uh, we, we do have a bunch of college, uh, professors and people within academic institutions following the show. Um, you know, those who are interested in developing this kind of program or are, are those the kind of people that you generally have conversations with on what they would do to develop a program at their college or university?
Charles Boicey: 35:33 Yeah, and I've helped, I've helped several do it. Um, you know, Meharry Medical College, uh, just briefly, um, we now have a program in place where they're medical students when they, when they graduate this July, they'll graduate with a certificate in data science. Uh, the next couple of classes, ms and then three years out we're looking at a phd program. So, uh, basically physician data scientist and I have helped, uh, you know, Georgetown, I've helped other organizations with their curriculum. I'd be more than happy to help an organization with that. And again, my stance on this is of an applied nature, a little bit of theory, but a lot of application, which is, you know, kind of my thing. I'd rather apply it then talk about what we could do. You know what I mean?
Bill Russell: 36:24 Yup. And My, my two people to talk to whenever that comes up as you and Dale Sanders, cause I think you two, are very focused on, on making stuff happen. So
Charles Boicey: 36:33 he's the guy. Absolutely.
Bill Russell: 36:35 Absolutely. So I appreciate you coming on the show. The show is a production of this week in health it for more great content. Check out the website at thisweekinhealthit.com or the youtube channel at thisweekinhealthit.com/video thanks for listening. That's all for now.