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My friend Charles Boicey joins to talk practical Data Science and Artificial Intelligence. We also discuss the push to a national unique patient identifier in healthcare. 

Transcript

This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.

 Welcome to this Week in Health It where we discuss the news, information and emerging thought with leaders from across the healthcare industry. It's Friday, February 23rd this week. Do we need a national, unique patient identifier, AI in the clinical setting, and how to get started with artificial intelligence project at your health system?

This podcast is brought to you by Health Lyrics, a leader in digital transformation in healthcare. This is episode number seven. My name is Bill Russell, recovering healthcare, c i o, writer and consultant with the previously mentioned health lyrics. Today I'm joined by a great friend of mine and a wicked smart data scientist, Charles Boise, wicked smart.

That's for my friends on the, uh, in the Boston area. Uh, Charles is Chief Innovation Officer for clear sense. A healthcare analytics organization specializing in bringing big data technologies to healthcare. Prior to Clearsense, Charles was the enterprise analytics architect for Stony Brook Medicine. In his role, he developed, uh, the analytics infrastructure to serve the clinical and operational and quality and research needs of the organization.

He was a founding member of the team that developed the Health and Human Services award-winning application, now trending to assist in early detection of disease outbreaks, utilizing social media feeds. Charles holds an MS in technology management from Stevens Institute of Technology and is the president of the American Nursing Informatics Association.

Good morning, Charles, and welcome to the show. Uh, good morning, bill. Good to be with you. Wow. You have so many bio bios out on the web. I hope that one was current. You, you do a lot of speaking and a lot of different things. Is that, is that one pretty current? It's pretty current with the, uh, exception. I, I'm now past president of the American Nursing Informatics Association, past president.

Alright. Yeah. Yeah. We'll, update And I, and I've, I've got, I've got one to add for you. Um, I'm the professor at Stony Brook Medicine. I, um, helped them develop out their, uh, applied to analytics, um, master's program. So I, I three classes and I teach three classes there. Wow. So, so can we start calling you professor?

Yeah, but you'd have to use it at like an assistant level professor , not quite the, the full-fledged. So yeah, assistant professor, uh, you know, the, uh, I, now we, we've worked together for a while. In fact, we were at competing health organizations and, uh, I was so impressed with your work I brought you into to talk to, uh, our team and then eventually to the leadership about what could be done with big data and big data analytics and, uh, And I've tried to hire you on several occasions.

You're the hardest person to hire. You just, you're so loyal to the people you're with, . Um, so I, I, you know, I've been stilted a couple times, but I, I still enjoy our friendship because you, uh, you really cause me to think about this, about these topics of machine learning, ai, and big data. So one of the things we do is we like to ask our hosts, uh, to give us an idea of what they're currently working on or, or what they're excited about.

So what, uh, what have you, what do you have going on these days? Sure. So there's a couple of things. Um, I'll kind of start out from, um, you know, the academic perspective or education. It applies yes, in the academic environment, but a lot of the, the, the work that I do, um, you know, with Clearsense is actually education.

Um, if you think about where, you know, healthcare is, you know, as far as analytics, machine learning, ai, many of us are still in. Some of us have progressed to, to data marts and so forth with some visualization, but very few have made the, um, leap into, um, you know, these big data technologies, you know, advanced analytics, um, AI and so forth.

So a lot of what I do and what I really am excited about doing, you know, is educating, uh, clients, uh, perspective clients. And then, as you know, for the eight or so, I've been evangelizing throughout.

On the, um, yeah, on the professional side, um, some of the things that we're doing with our clients that I'm, I'm really, really excited about are in areas of operation, um, operations as, as well, clinical practice on the operation side. Um, And we'll take a look at like, let's say patient access, if you will.

Um, we all have a problem with, um, you know, keeping, you know, the clinics, you know, full in that capacity and whatnot. So a lot of the work that we've done lately is developing out, uh, models that, you know, take into consideration, uh, patients, uh, future.

Uh, we take a look at traffic patterns. Uh, we take a look at a lot of different extraneous factors, put that all into, you know, a model and then, um, give our, give our clients the ability, you know, at the end of the day, these are the folks that are likely not to show up tomorrow, but not just that. Give them a list of those that are likely to be able to fill, fill in.

If those folks, you know, indeed don't show, and so they're able to make those phone calls at night to, you know, get a definitive yes or no, I'm gonna be there. If it's a no, we slot somebody else in and we, we keep our clinics full. So that's pretty exciting. On the operational side. Um, on the clinical side, again, uh, You know, just working on the, you know, situational awareness type applications.

Uh, you know, loading up, uh, you know, the machine learning environment with, you know, all the physiology, um, all of the, you know, the, the laboratory data, physiology data, and really identifying patients that are likely to crash in the next, you know, 30.

We'll talk about, um, AI versus what I like to call int intelligence assist. So, so those are the kinds of, some of the things that, you know, that we're working on that are, uh, that are pretty exciting and it has to be practical and we'll, we'll get into that in a little bit too. Yeah. So we're, we're just, we're still scratching the surface.

I remember one of the first use cases you, uh, gave me was, uh, as a c I o I was sort of struggling with, uh, E M R implementation and our doctors were. Um, we're struggling to figure out how to, how to utilize the m r effectively, and you told me about how you used data science to collect all this big data to collect all this information.

Then you used machine learning and, and data science to determine which doctors were actually having. Difficulties utilizing the E M R, you were actually able to identify them by name so that you could, uh, really do targeted education and training of those doctors based on how, how they were getting lost in the system.

How many clicks, because we have all that data, right? Every click that happens in the E M R, were, we're tracking it. And you just said there's value in this data. We'll figure out how to use it. And I thought that was an interesting case. You still.

Yeah, that's the, the HIPAA lots and so forth. Um, and you know, an interesting, you know, an interesting side note on that was, um, and we did exactly what you described, but when you tap a somebody on the shoulder because you know where they are in any particular time, and then, you know, say, Hey, we noticed, you know, in the last couple hours you were doing these kinds of things and it looked like you were struggling a little bit.

You can, um, you can have that intervention and really kind of help them on the way and then track them going, going forward. Um, we had, um, we had somebody, uh, we had a, we had a clinician that was actually getting, um, 150, um, uh, alarm notices, alarms a day, um, which is quite extraordinary. And then once sitting down with,

So simple logs. There's tons of data in there. Yeah, there's, there absolutely is a cultural aspect of this. And we'll, we'll get to that in the, in the, uh, second segment when we, we get there. Let's get to the news. Uh, you and I, now, you and I can have really long conversations, so I'm gonna try to keep this to a half hour.

I, I doubt I'll Okay. But let's, let's see what we can do. So we'll take a look at the news. Here's what we do. Charles and I have each selected a story to discuss and I'm gonna kick us off. Uh, the story I picked is from the New England Journal of Medicine Catalyst. Has the time come for a unique patient identifier for the us.

So, uh, it's a 30 minute show, so I'm gonna, uh, not share all the credentials. But, uh, couple of, couple of names of note. Who, uh, are the authors of this, uh, Harpe sued David Bates, uh, John Halamka, C I o, Beth Israel, dig Deaconess. David Bates, c i o, Brigham Women's Hospital and Aziz, uh, Sheikh. Who's a professor in the University of Edinburgh.

Uh, here's a little synopsis of this story. It's time to revisit Congress's fears about the unique patient identifiers and institute a system that will enable more complete and accurate patient records. So, uh, a unique patient identifier was proposed as part of hipaa, but was shot down for privacy concerns.

The primary reason for their argument is that a national unique identifier leads to better care. So here's another quote. When accurate information is attached to the right patient, data access is timelier, inappropriate care reduced and health information exchange becomes easier within organizations as well as between.

So, and they also go on to talk about how some states have already, uh, implemented this, uh, the, uh, state of Nevada and Minnesota. And they say, you know, we can see how those go and scale 'em up. They close with this. So with billions of dollars having been spent on E H R implementations, the healthcare system must vigorously investigate more efficient ways to connect fragmented patient data.

An effort that is increasingly relevant to the, as the US, moves from fee for service to value based care. So, uh, So Charles, I'm, I'm gonna go on a little bit of a rant here because I, I think this, they, I think they accurately capture the perspective of a physician, but I, I'm not sure that's the right lens to be looking at this.

So, uh, you know, their argument is let's create a longitudinal patient record, uh, so that we have all the information at the point of care. Great. No one's gonna argue with that. I think you and I would both agree that, uh, a truly, uh, complete longitudinal patient record would improve care. But here's where my path sort of diverges with where they're coming from.

And I believe we should put the, the, the medical record in the hands of the patient, not the health system. Uh, if you really wanna change healthcare, we have to free the data. And putting the patient at the center of the equation instead of the health system or, or pharma or payers or, or the E H R providers or even researchers, is really gonna do that.

So when I throw that out, I typically get three. Uh, three kind of pushbacks. There's the, there's the argument against giving the patient data that, uh, Judy Faulkner was, uh, was caught espousing, but she's not the only one. There's plenty of physicians who have said that, and they essentially say, you know what?

The patients wouldn't know what to do with the data if we gave it to 'em. And, uh, you know, there's, there's just a certain level of arrogance that goes along with making that statement. Uh, I may say that to, to my five-year old. I don't have a five year, but I may say it to five. Uh, but never really to a grown adult.

Uh, another argument is, uh, that it will expose data to theft and, uh, you know, that sort of has a level of hypocrisy to it. 'cause in 2017, uh, I'm just gonna give you some stats real quick. So, in 2017, healthcare had 477 breaches and 5.7, 5.6 million records were lost. That followed a 2016 that saw 450 breaches and 27.3 million records lost.

And the article I actually pulled those from said, we're making progress because we went from 450 breaches, uh, in 2016 to 477. So we're slowing down the rate 27.3 million records lost. I, you know, it's, um, it, it's, it's crazy. Uh, you know, I have a stack of identity protection offers on my desk from various health providers.

And, and seriously, my credit card has never been stolen from Apple. Uh, we've seen models from really smart people, uh, who, who show that this is a viable off option, like the, uh, the blue button initiative from Pod Park. And I know I'm ranting here. I'll, I'll, I'll get to you in a second. And, and No, no, that really puts me over the edge on this is, you know, you just can't have it.

And I know that HIPAA says that we can get our medical record. In most cases, you know, we'll get it in paper or, or worse we'll get it as unstructured data. And, and, and the reason that's worse is they don't even do us the courtesy of putting it on, on paper and paying for the, the ink, uh, and the toner. Uh, they make us do it.

'cause the next healthcare provider we have to figure out a way to get it in whatever form they give it to us, uh, to them. You know what, and we've talked about this, it's not like we can't share discrete data elements. Uh, we've had the technology since the nineties and the health systems either choose to prioritize, not prioritize data sharing, or they don't have the appropriate skills, or they don't have, uh, the, the, the right incentives to get this done.

Uh, So, uh, you know what I'd rather see here? I, I know I'm ranting on what I don't like about it. What I'd rather see is, uh, sort of a change in our thinking of, of a, a patient-centric approach, which says, let's get, let's get the medical record in the hands of the consumer. So Epic and Cerner suspend your fees for developers and implementers and, and allow that data to flow out into the, uh, into devices that can actually be mobile with the consumer.

Because the consumer is the only conson at the point of care. I'd like to see us move from, uh, from HL seven to APIs. I'd like to see a, a new model where we have, um, maybe a. Health record or fitness or food or purchasing information so that people, data scientists like yourself can, can really, uh, do some things with it, but also that the, the consumer can benefit, right?

So the consumer, consumer can say, I wanna participate in this. In this study or, or quite frankly, they can sell the information. A lot of health systems do, uh, end up selling the information either directly or indirectly through, uh, through third parties. That's a, that's a long rant, but, um, and I know it's hard to follow rant, so let's change this up a little bit.

You're a data scientist and, uh, talk to me about how the, the patient identifier would make your life easier as a data scientist or. What would you be able to do if the federal government mandated an identifier that perhaps you can't do today? Well, couple things. First, I'm gonna um, agree with what you just said, even though I hate we didn't have any, we didn't have any pres on this.

You apples. Apple's gonna lead the way we, we know they're now working on for quite some time. Um, the, the data absolutely in the hands of the patient. Uh, we have, you know, technologies that allow for that. Um, you know, blockchain being, being one of them, uh, that'll let the, you know, patient decide. And then in terms of, you know, potential emergencies and and so forth, is there a need for a, a, you know, an identifier for data science?

No, but I even think, you know, kind of stepping back is, is there a need for regional, you know, health information exchange? There actually could be a national health information exchange that we could actually do in surrogate apply, uh, E M P I to to those patients based on a whole bunch of very, you know, characteristics that, you know, everybody on the understand how that's done.

You know, I think one, you're right, two, um, I'd like to see us get away from regional exchanges and then, um, regarding, uh, you know, the data science and, you know, profiling and whatnot. So, work that we're doing at the University of California Irvine with the, uh, Institute of, uh, future Health is exactly what you described.

We're building what we call. So that basically is your, um, is your profile. Um, and it's unique not because it's unique with a number attached to it because it's unique in all its characteristics that is you. Um, and that doesn't, your profile is much different than anybody else's profile. It's a combination of, of physiology, it's a combination of, um, uh, you know what?

Labs have been attributed to you. Your, your eating patterns, your, your exercise patterns. There's a whole bunch of ways that we can identify you as you without, you know, imposing a, um, you know, a national identifier to you. So from the data science aspect, um, yeah, absolutely sure it would make it easier.

But, um, you know, data scientists are supposed to work around issues like that. And so that's kind of, you know, how I would approach that. That's interesting. It sort of flies in the face of that. We're going distributed with blockchain over everyone. Almost agrees. You know, over the next five years we'll go to distributed ledgers and.

And this sort of flies in the face that says, Hey, let's keep it centralized. Let's, you know, let's create a, a, an index that we can utilize and whatnot. Um, no, this, this is consumerism, right? You know, consumerism will continue to, you know, eat into healthcare, I think for the better. Um, and, you know, we'll have to do it.

The, you know, the consumers, you know, consumer wants. Absolutely. All right, so let's, let's kick to the second story here. And, uh, this is your story. So, so take it away. Okay. Um, this is on the topic of AI and I think we'll be able to get a little bit controversial here, . Um, so I'm gonna bring that you haven't been on the last segment,

Well, let's even give a little bit more. So, um, this, um, this article is, um, by, um, Mike Millard, uh, January 30th in health Data and AI is disrupting clinical practice, so,

I'll kind of get into it, but, um, I'll go with a, a story really quick. So, back in the late eighties or early nineties at, um, at LA County, U S C, I'm, you know, I'm also, you know, a trauma nurse and whatnot. We did a lot of predictive models and so forth. I worked with a Dr. Williams Shoemaker who started the Society of Critical Care Medicine.

We actually built predictive models that, uh, for trauma. Depending on the therapy, what the outcome would be. Uh, we made a really big mistake back then. We call it prescriptive analytics. The clinicians went nuts. Uh, they didn't want a machine telling them what to do. Um, and this is really what this article is all about.

And this is, you know, this is almost, oh my gosh, this is almost 30 years later, right? So, um, so again, with AI we can build out, you know, beautiful models.

I would like to say can assist. I like to call it intelligent assist. To be honest with you, I don't like the idea of using this technology to tell somebody what to do. Um, I'd rather produce a cognitive trigger, and this is what is described in the, in the article. If, um, I can give you a heads up that something's going on that you may not have been aware, aware of.

But, um, so here's, here's a couple of quotes. Um, it has to do with skepticism, and that's what we encountered back then. You still, plenty of physicians and clinicians have skepticism, if not outright hostility. Um, You know, a couple other ones is really, it's interesting, you really can't force these issues.

You, if you come up with these great models and whatnot, you really can't cram down anybody's throat. You can't say surprise, you know's what the diagnosis is. Um, you know, somebody's gonna slam their fist down and say, Hey, um, I'm gonna go back to what I've been doing for the last 30 years. So, um, there's an adoption.

So how do you get, how do you get that adoption and needs be adoption the way you do it, you don't. Many folks out there have, you know, their models or their proprietary models and you know, this, that and the other thing. You've gotta show how you got to, you know, how you got to, where you got, what data elements you used, um, what weights you attributed to them, you know, what was a, a neural network that was employed, was it random force?

What did you go through all the way through the process to get to the point that, um, you're at now and how accurate is. How accurate is you? Do you have an R curve to show, um, you know, matrices and so what's the precision, what's the recall? You have beate data. And, um, you really can't make statements like, you know, uh, you know, this model will work everywhere.

'cause they, they won't, they're very geo geospecific. What works in Southern California isn't gonna work as well in Sarasota, Florida. It's gonna need some tweaking because of the demographic nature and even some of the, the external factors. So I think, you know, Put a really nice, you know, package together.

You know, you know, saying, yes, you know, a AI is important, but we need to kind of go through it in a, you know, in an adoptive, you know, kind of way. And not just, um, you know, kind of throw it out there and, you know, where does it, where does it fit, invest, you know, uh, don't try to make a problem and solve it.

You know, try to solve problems that are, you know, already out there. Absolutely. The. This is now the job of, of the, uh, the, the leader, uh, either the INF informatics leader or the, uh, data science leader or, or chief information officer. It's cultural change. And, um, it's really interesting 'cause you know, you, you sort of mentioned, so, uh, let's, first of all, let's give.

So this really, it's an article from, uh, healthcare IT News. So it, it is a plug for the machine learning and AI sessions at hims, uh, in Vegas in a couple of weeks. Uh, March 5th. Uh, the, uh, project manager, they quote the, a bunch in this. It's Jeff Axt, it's project manager and system analyst. In the IT department of the hospital for special special care in, uh, new Britain, Connecticut.

And, uh, he does say, you know, if you go into a department and say, surprise, this is the diagnosis from a machine. You're just, I mean, you're just gonna, it's, it is a visceral reaction, but I think that's also why someone like yourself has been successful. You know, you have that, that clinical background and, and, and being in the er.

And, and really understanding how, uh, it, it, how these things, uh, play out and how technology is adopted. We, we almost need more, uh, clinicians to get into this space so that they can, they can help people to make those transitions of, of saying, you know what, Hey, I understand that the, the AI model isn't perfect.

Neither is a human. A human isn't perfect either, right? And so if the two can figure out how to help each other, and as you say, you know, those, those cognitive, uh, triggers that, that help. Both become better that the clinicians are training the AI to be better. And the AI is, is helping the clinician who's, you know, busy running from patient to patient to see something that maybe they didn't see and, and that, that transition is gonna be interesting.

Uh, so anything else you wanna say about this article? We're gonna jump right back into AI in the next segment, but anything else you wanna say about this article? No, that that, that's fine. And we'll continue, we'll continue and I'd like to, you know, kind of bring in, um, you know, my students' take on it as well in the next segment.

Oh, that'll be interesting. Uh, alright, so, uh, second segment. We typically talk about leadership or tech talk and, uh, clearly we're gonna jump into AI. And, um, so, uh, you know, give us a couple more use cases, uh, around AI and healthcare. What do you see? Well, actually. Uh, are you doing work outside of the US with AI at this point?

Yeah, so, um, we're working in the, in the UK with, um, it's in the, in the mental health arena. So, um, in, in the UK the number one cause of death for males under 50 is suicide. And, uh, we're taking a little bit different approach and the, the concept is to identify those at risk. Uh, but you know, we're never gonna be able to, um, and I don't believe we'll be able to, um, actually determine where lightning is gonna strike.

We're gonna take a, you know, we'll let you know where the thunderstorms are. And then those that might be affected by those thunderstorms and, you know, you can, you know, take the necessary action. So the idea is to really understand, um, some of the factors that are, uh, involved in, in, um, in, you know, somebody, you know, making a choice like that.

So, um, this is where, you know, big data and, um, And, you know, the data science comes into play because, uh, we gotta bring in, you know, social media for that. We gotta bring in the, you know, the temperature patterns we gotta bring in past, you know, suicide patterns. We've gotta, uh, with consent, bring in the, you know, the various patients, you know, social, social feeds and whatnot.

So you think about bringing all that in and then letting the folks that are following, you know, those. These are the folks that are, you know, you know, susceptible at any particular point in time. And that really changes, you know, as the days go by. So enough information so that, you know, they can, you know, reach out and, you know, make sure folks are okay and whatnot.

Um, that is a, a, a little bit different approach. That's fascinating. Uh, and actually pretty relevant given the, uh, the school shooting that. And I actually wrote this down, you know, lightning and thunderstorms, we're probably not gonna be able to predict that this student at this time is gonna go into this school and do this, this action.

But, but the whole idea of thunderstorm, there's enough activity going on that you might want to take shelter or you might want to look into something. So data sciences isn't at the point of, of saying, you know, it's this person at this time, but it is at the point of saying, Hey, there's. There, there's a storm forming over here.

We, we might wanna, might want to get in front of that. Uh, is there a difference in the UK versus the US in terms of adoption? I mean, are they more, uh, prone in the uk or are we more prone in the US to be, uh, adopting AI to, uh, type models? I, I think it's pretty much the same. It, it's, it meets with the initial skepticism, which is important, um, because it keeps us on our toes.

Uh, so it's, it's, it's, it's, it's not, you know, tell me, show me, you know, you know, prove it to me. Gimme, and, uh, so real quick, gimme three, AI, healthcare, uh, models in healthcare that, that, that you've seen that are, that are effective. So you're, you gave us some mental health. Give us, give us a couple more real quick.

Sure. I think, um, uh, we did mental health, uh, the patient deterioration that I talked about earlier, patients that are likely to, you know, crash, uh, patients that are, uh, likely to enter a sepsis pathway. So treatment can be, you know, be, you know, earlier. Outside of that, you're looking at, you know, deep learning, you know, machine learning, uh, finding those, those patients are to be.

Um, uh, and we're doing this for, um, that are likely to be, uh, have. If you think about all the different data points that you can bring in and do those triangulations and whatnot, you know, identifying, I think even from a data science, not necessarily an ai, but a data science. You know, finding those, those patients within our, um, populations that are, uh, pre, pre-diabetic or that are hypertensive yet undiagnosed, um, the other UK.

Have AFib that are not being treated, they're at risk for stroke. Um, so there's a lot we can do with the data that we have, you know, you know, initially. And I think that's how, you know, data science makes its initial wins. Um, and yeah. Yeah. So I mean, so you have, you have Watson and, and we've seen some, uh, you know, some crash and burn type scenarios with, with Watson.

Um, Specifically n b Anderson was a, was a crash and burn kind of thing. And when we talked about it, when I've talked about it with others, they said, you know, it was the quality of the data. We couldn't get the data to the point of actually, uh, being able to do the things we wanted to do. Is that 'cause uh, there is a data quality problem or is that because we're not choosing the right use cases?

Sure. So, I b m does some wonderful things in ai. Watson in particular was, uh, was a system developed to answer questions. Yeah, like Jeff, I'll, you'll it as simple as that.

If the answer's within the, um, the confines of, of Watson, if it's not there, then there's no response. Um, got it. Yeah. There's a lot more, there's a lot more. Again, this is where, um, I, I really think that it's really important that, um, we think about how these technologies can assist us assisted intelligence.

So what, what's, uh, give us an idea of some of the, some of the good data sources that you're utilizing. I assume, you know, bedside data is pretty consistent, right? It's, it's sending you the, uh, I mean that data is very, uh, consistent and, and you're consuming probably as much of that bedside data as you, you possibly can.

Are there other sources of, of, uh, high quality data that you're utilizing? Yeah, sure. So, um, anything off of physiological monitor? Um, ventilator smart pumps. So you think of, you know, anesthesia machines, they all are accurate, pretty accurate, and so they're accurate, like you stated. Um, you know, every now and then you'll get a weird signal.

Somebody will turn a stop cock and you'll get a C V P of, you know, 13 that jumps up to three 10. Or an arterial line is, is occluded and you know, that jumps up. Those can be taken care of within, within a system. Um, you know, laboratory data coming in, pathology, uh, all of the, you know, various source system, you know, you know, very clean.

Um, especially, you know, if it's been ontologically right-sized. But we can make, um, we can, we can fix that. Um, It, it, it really is. You know, what, what data do you need to, to do whatever it is you're gonna do with it? Um, if you try to chase a hundred percent, you're gonna be in trouble. And within the realm of data science, we can make, um, we can make, uh, We can help out if things aren't, you know, totally perfect.

There's things that we, we can do. Um, there's methods to account for missing data or data that's, you know, outlier or out of range or not expected. Yeah. And, and at the risk of going a little bit over on this episode, um, yeah. How, how does a, how does a health system get started with their ai? Sure, sure. So, um, A commitment to do the education process to really understand, um, what it is you wanna do before you jump into it.

I see healthcare organizations that hire data science teams that really don't know what a data scientist is. And I'm gonna give you my definition. I say I participate in data science. I do not call myself a data scientist, and I'll tell you why.

Prepares you for the rigor that's required for data science. It's absolutely essential because you can get sloppy, you can get lazy, you can, you know, jump off the, you know, the wrong track and you can really lead an organization, you know, down the wrong path. Um, um, folks like myself are very assistive to data scientists.

Data scientists on my team are our folks.

Before you go out there and hire the team, uh, make sure it is, you understand what you're hiring, what you want them for, and then maybe bring in some folks to help you, um, you know, put that, that team together. Because if you don't know what data science is really all about or what a data scientist is, um, you could be six months down the road and you go, uh, oh.

Now you've, you know, lost six months. So, Spend as much time as you can on, on education. Yeah. And, and I would say I, I made, uh, I made some mistakes there. I, I brought in some data scientists and they were immediately gobbled up in the, uh, and, and pushed back down into the, the day-to-day analysts and yeah.

Um, it was, it was an awful waste. It took me about six months to, to extract them out of some of those projects and, and, A little higher level, uh, things. So, uh, and, and, and, and Bill, from an interview perspective, just one little tidbit, if you're, if you're, um, if you're interviewing a data scientist and you don't understand what the heck they're saying, then you probably are interviewing a data scientist.

Yeah, that's probably true. You know, they, they, uh, I, the, the distinction I found was, um, they don't answer questions. They tell you which questions you should be asking. That's correct. It, they, they look at the data and the data informs them and, and, and it, it's amazing the number of tools they have, uh, good ones have in their, in their bag.

Uh, so time to close the show. Favorite social media posts of the week? I'll, I'll start it off. Uh, this is from William, uh, Walders. Um, and, uh, it's just a. It has a gentleman sitting across from a receptionist and the receptionist saying to him, you cannot list your iPhone as your primary care physician. So Charles, to you, what's your favorite social media post?

Sure. My social media posts, uh, today, uh, comes from a colleague of mine, Brian, Brian Nordi. Brian Norris, um, colleague we've worked together for for years. Um, his Twitter handle is geek nurse and he put out, um, this week fellow nurses. We need to elevate our seat at the machine learning AI tables. Bring that clinical and digital acronym to bear.

I think we need a nursing coalition of nursing data scientists, folks. Driving the digital aid forward with a shout out to myself and, uh, Judy Murphy. Awesome. So, um, that's I. Oh, I'll definitely be there as well as all my students will be there. And they, and I, the last kinda shouting part, they cited on mostly millennials.

They cited on the side of AI or, you know, the, the, the systems telling people what to do versus the systems assisting people. I just wanted to get that out to, yeah, I, I, I trust the machine more than I trust the person. Well, we could do that. I, I would love to, uh, I'm gonna be at HIMSS as well, and, uh, I would love to, uh, catch up with you and your students.

That would be, uh, be a lot of fun. So, uh, that's all for now. Uh, you can follow Charles at, uh, N two Informatics RN on Twitter and me at the patient cio. And don't forget to follow the show, uh, on Twitter as well this week in h i t. And check out our new website. And, uh, Charles, where are you calling? You're calling in from, uh, Jacksonville.

Yeah, calling from Jacksonville, Florida. Clear sense at, um, clear sense.com. All right. So I will, uh, one of the things we do on the, uh, on the website you'll notice is, uh, the image is usually a skyline of where the, uh, guest is calling in from. So we will have a, uh, skyline of Jacksonville. Jacksonville has a skyline, I assume.

Yeah. Pick Jacks Beach, , or maybe just a beach shot. That's, that's probably a good way to go. If you like the show, please take a few seconds, give us a review on iTunes and Google Play. Uh, that's all for now. Please come back every Friday. Uh, where we will do this again with, uh, another great thought leader next week.

Dr. Uh, David Benzema is gonna be here two weeks. Uh, David Baker, c i o for Pacific Dental. I think that'll be an interesting conversation 'cause that's also the week of hand. So, uh,

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