The Pivotal Podcast

Healthcare Technology Transitions with Joey

June 28, 2023 Ben Season 1 Episode 1
The Pivotal Podcast
Healthcare Technology Transitions with Joey
Show Notes Transcript Chapter Markers

Welcome to The Pivotal Podcast, where we explore the ever-changing landscape of healthcare technology. In this episode, titled "Healthcare Technology Transitions with Joey," we dive into Joey's career journey and the exciting world of healthcare technology. Joey, an Analytics Director at a leading provider of data and analytics technology and services to healthcare organizations, joins us to share his insights and experiences.

Join us for a professional, informative, and fun conversation as we uncover Joey's motivations for pursuing a career in healthcare technology and the pivotal moments that influenced his decision. We'll explore the various roles he has held in healthcare analytics, including domains such as inpatient, population health, and revenue cycle analysis, and discuss their importance in improving healthcare outcomes.



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 Welcome to the Pivotal Podcast where we unlock the future of healthcare technology and explore the journeys of those making waves in the industry. Join us as we explore the innovative minds, game-changing companies and inspiring journeys that are shaping the future of healthcare. Whether you're a tech enthusiast, a healthcare professional, or someone looking to make a career pivot, this is the podcast for you.

I'm Ben Marley, your guide through this adventure that will inspire and empower you to forge your own path in this ever-evolving field. All right, and today I am super excited to be joined by my friend Joey Trahan. Joey, thank you so much for joining me today. Hey, Ben. 

Good to talk to you. 

Yeah, absolutely man.

So let's just dive right in. Do me a favor, share a little bit about your background and how you got involved in the whole field of healthcare technology. Yeah. 

I studied mass communication actually as an undergrad and, you know, worked at a radio station, but I didn't really, I. I just didn't really feel it in terms of, of a career and I kind of started gravitating toward more, towards numbers.

And even, even when I worked at a college radio station, I was trying to think about how I could get these sound bites and then loop through 'em and put 'em together. And I kind of automate things, even as, as kind of a creative, you know, in a, even in a kind of creative setting. So I realized pretty early on that I, that I liked numbers, but I didn't really know why until I kind of stumbled around early in my career trying to figure out, you know, what am I good at and what do I like, and trying to find that combination.

So it took me a little while. To find that, but I always gravitated towards numbers and automation and kind of the data side of things and looking for patterns and data. You know, even if I was in a service job, like I would kind of gravitate towards that and I enjoyed that part of it. And then, and then I found out I could just do that for a living and people would pay me to just do that.

I said, wow. All right. So, and then, I wasn't planning on getting into the healthcare industry. But that's just, you know, kind of the job that was available. And I think that was fortuitous for me. Like, I don't, I didn't know it was in such, you know, data analytics and healthcare was in such high demand.

But I feel really fortunate to have gotten in that position and, you know, so now I can do the, the type of thing that I really like to do and. It's for ultimately for a really good purpose so I can feel good about, you know, what we're trying to do with it. 

Yeah, absolutely. And you know, just what you mentioned about kind of trying to stumble around and figure out what you're good at, what you enjoy doing, I feel like so many people can relate to that, right?

Very few people come outta school and know, okay, this is my career path and I'm going to be on this right from the get go. And. Never veer any direction, just go for it. Right? Like that's pr, maybe that happens not for me. Not for you apparently. And not for a lot of people I think. Right. Yeah. So I think a lot of people can relate to that.

So the fact that you have found something that you're good at, that you enjoy now, and that's really in demand. I mean, that's huge. That's amazing. And. And I love this, and we've mentioned this in a previous conversation, but I heard Andy Stanley say once your fully maximized strengths are gonna be significantly more impactful than your marginally improved weaknesses.

And I love that, and I found it to be really encouraging because I beat myself up in the past about not being great at certain things and have just come to realize, you know, God didn't have, he didn't, there wasn't an accident in the way I was wired, right? He wired me the way he wanted me for the impact that I'm here to have.

So, But, and not to excuse faults and to say, Hey, I don't need to work on those, or improve those things, but to really focus on, okay, there are things that I am good at and it's okay to focus on maximizing those. I think that's just, it was really freeing when I heard that and I had that realization.



Yeah. I, I agree. And, you know, I wouldn't have even. Imagine that I would be interested in this kind of thing growing up. Like if you're talking about like statistics and computers and programs. Oh man, that's a snooze fest there. But, but really like, when I really get into it, into the guts of it, I, you know, I enjoy it and it's not for everybody.

Some people will think, oh man, that, that sounds terrible, but you know, some people might gravitate towards it like, You know, I'll always say like, thank God for urologists because like, you know, doctors are some of the smartest people in the world and you can get into a number of fields and a number of things even within the medical field.

And then some people wanna study the urinary tract and Alright, I'm super thankful for you. Go for it. Yeah. So anyway, just, yeah, it is a fortunate, I'm in a fortunate spot. 

Yeah, absolutely. And to be more specific, so you're an analytics director at a leading provider of data and analytics, technology and services to healthcare organizations.

Is that right? Yep. Okay. So what does that actually mean? Like what do you actually do on like a day-to-day basis? 

Yeah, so I, I don't come from a healthcare background as you mentioned before. So you know, I have to lean a lot on people who do come from that background. But when, you know, when a patient comes in and you as a clinician, you gotta click around, you know, you gotta make a bunch of clicks and enter in a bunch of data as you know when somebody's coming in.

But then, you know, all those clicks and all that stuff gets stored in the background somewhere, in some kind of structured way. So my job is to help. Pull that data out in an organized fashion and look for pattern and insights within that data. Work with a team to look for patterns and insights in the data and like opportunities to improve improve things within there.

So I do that I get my hands dirty a good bit with that. And then I lead a small team of analytic engineers who do that as well. Okay. 

And you guys are looking for trends in data to literally inform decision making in a healthcare setting or a number of healthcare settings. Yep. Okay, cool. But even when you got into healthcare, That's not where you initially started.

Right. So can you give me a little bit of an overview of the different roles that you've held? Like what was your first role as your first foray into this whole field of healthcare technology? 

In Into healthcare technology? Or even before that? Yeah. 

Well, so maybe even like one step before that it kind of led into it, and then where did you Yeah.

First get involved in this? Yeah. 

So like I said, I kind of, you know, tried to find my footing earlier on in my career. I, I, I was in sales at first at a TV station. I was really bad at it. Oh yeah. I was really, I wasn't very good at it. I was like, well, that's not my thing. So then I went to work for a bank for a little while and then a software company here in town, more on the services side of things.

But, you know, in all of that, I would. Default to, like, I just like kind of the, the spreadsheet side of things when I would get into spreadsheets and, and trying to hook those together and make data make sense in there. So that led me into, I was trying to help people buy and sell small businesses. At one point, just before working for Medical University of South Carolina.

And I gravitated towards the financial analysis side of that and kind of write, you know, like, and programming and, and doing, writing a loop to where I could kind of loop through and, and you know, do an analysis that way. And so that got me first into like kind of report writing and that got me into my job at the Medical University of South Carolina and where I was on the analytics.

Team there. I was an analytics developer and now the company I that I work for are not mentioning their name because they're a large publicly traded company. And I don't wanna, I don't know, whatever I say here is just my opinion. So totally seek approval to, to do a podcast. Sure. But now the company that I work for now, we do the same kind of thing I was doing at the Medical University of South Carolina, but for multiple hospitals.

So, Analyzing a lot of clinical, operational, some revenue data, some or data like, you know, length of stay, readmission, supply costs, utilization, those kind of things. 

Okay, cool. So how did you even learn to get into like that looping and the automation side of things? 

When I was doing the financial analysis for, for small businesses, I found myself doing the same things over and over.

I was like, well, if I could outsource this to somebody and tell them these are the steps that you need to do, just do it over and over. And then my brother showed me how in Excel you can press this little record button and it records, you know, your macros and vi. And then I was like, wait a minute. I don't have to try to outsource this.

Like I can just. I can just code it and then it'll go through and clone myself over and over. That's awesome. So, so 

wait, initially it was like record a macro and then you'd click the different things and like do some keystrokes and things, or was it just like all mouse clicking? Like you weren't actually like coding macros by typing them in like a programming way, right?

You were literally just like, Hey computer, this is what I'm doing now you do that? 

Yep, yep. I was just clicking and type in and then After you stop the recording of the macro, then you can go back and see what code it produced. And so you kind of remember the steps that you took and you could see, oh, that's what that was doing.

This is when I switched columns and things like that. So that's how I first got into it. Just trying to, trying to clone myself cuz I was just repeating the same things over 

and over. No, I've definitely, I love that, like figuring out how to do things, especially like the monotonous or repetitive things, how to do them more efficiently using tools that we already have.

I love that. So from there, did you then learn, okay, well I don't have to necessarily make all these clicks. I could either copy and paste this or learn how to even just type this code in? 

Yeah, yeah. I just figured out how to alter the code to. You know, make it do what I really wanted it to do and spit out the charts and graphs that I was really wanting it to spit out.

So that was my first, you know, kind of diving into it was, it was a little bit of a progression. It was like, good with Excel stuff, then I'm migrated over to pivot tables. Then I migrated over to like trying to loop through kind of pivot tables and pivot charts. And then that just got me into the world of.

You know, analytics and a little bit of programming. So 

how long have you actually been working in healthcare, technology, health analytics? 

Since 2011, so about 12 years. 

Okay. So do you, I know it's been a bit, but do you remember like sitting in that interview with M U S C coming from like the financial background or like the small business analytics kind of background?

How did you feel sitting there having this conversation? Like knowing you didn't have the healthcare background at all? Was that like pretty intimidating or how did that feel? 

Well I was actually interviewing for a different job. Oh. So the job I was interviewing for was, Somebody who helps more on the front end build, like the workflows.

So M U S C was migrating, I think from, I forget if it was Cerner, maybe to Epic. Okay. And so there are a lot of things that, like now when somebody comes in, a patient comes in and you click on things like making sure the right buttons are in the right place and stores down, you know, in the database the right way.

So it's more like the front end kind of building the workflows, which. Oh, we can get into it a little bit or, or maybe you did or go into with, with other, other podcasts. But that's like a job in itself, building those workflows to build those workflows. Yeah. But I was gonna turn down that job offer cuz it just, it, it just didn't sound, didn't sound as interesting.

And they're doing this big, big bang Go live for, for Epic. And I was like, man, I can kind of see this train coming. This is gonna be, This is gonna be really tough. And it was, it was, I thought, similar to a job that I just didn't enjoy as much in the past. So I was like, I think I'll pass on that. But then they called me and they're like, no, we don't wanna offer you that job.

We wanna offer you this one. So, well that's interesting. Let me hear more about that. And then that was like, you know, they saw in my interview that. I gravitated towards the data side of things and the analysis side of things, and I said, you know, we wanna offer you this report writing job, and it's not something I can really take credit for.

I think the Lord led me to a job that I didn't even interview for and an industry that was really, really a good one to get into. Healthcare is 

not going away. Right? Like people need healthcare. 

Yeah. Yeah. We'll always need healthcare. But anyway, just feel really for, so that, that's how I, that's how I was first hired into it.

And it's, it's been a really good journey ever since. Just constantly, you know, constantly learning. 

So when you say report writing, I think that what I'm picturing is probably really different from what it actually was, cuz report writing doesn't sound like automation. It sounds like I'm typing out like these long form like narrative or essay style things.

Like, so what do you, what does that actually look like? 

Yeah. I don't know why they call it report writing really. I mean, I guess you, you, I, I honestly don't really know why they call it report writing, but it's basically Report writing or business intelligence development. It's creating charts and graphs and things like that.

Hmm. Okay. So using data to create charts and graphs and like Correct. Like vi using certain tools to visualize that data. Is that right? Yep, yep. Okay. So did you come in already knowing how to use these tools or did you have to learn these tools? And what kind of tools were you using initially? 

Initially before I worked.

In U S C I was using just Excel. I was using pivot tables in Excel and Pivot charts which is a good, I, I think, a good place to start. And then from there it was, I got into Crystal Reports, which is kind of an older, older way of doing things now. Now it's more like Tableau and Power BI or, or you know, better visualization tools and more interactive.

But that's how I started off was just Excel and pivot tables and pivot charts and just getting used to understanding how to visualize data to get some insights out of it. 

So, yeah. What's the, the real value there of like pivot tables and pivot charts and then Power BI and Tableau? Like what does that actually enable you to do with data that you couldn't otherwise do with it?

Well, our brains are limited. And how much we can process. And so at some, at some point, your brain goes from trying to look at every, every single detail to trying to aggregate and summarize things in order to look for patterns. So it just helps your brain look for patterns. These visualizations tools was helped.

Help your brain look for patterns in data to try to uncover, you know, to try to uncover stories, mm-hmm. Or root causes, you know, problems and root causes of those problems so that you can, you know, address 'em and, and hopefully do some intervention on the, on the front end of things and, and hopefully that'll make a positive difference on the back end of things.

Gotcha. Sure. So these tools literally take numbers and turn them into visual representations, right? Like charts and graphs so you can kind of see how things are changing over time or with different variables. 

Yep. Yeah. Two main categories of analysis, at least in, in my head, is data over time. And there's some, like, there's some basic things that you can do to, to help, you know.

Analyze data over time. And then there's data between and among categories. So like data over time you might analyze starting off with just a line chart that's going over time and you see if something's going up or down. But then you can get into a little bit more advanced things like statistical process control charts, which tell you, Hey, we're up two months in a row.

Is that something that I need to pay attention to, or is that just random? And then next month it's gonna go back down or whatever, so you know, you can apply additional. Statistical principles or analytics principles on top of just a line chart, and it'll help you not overreact and say, oh no, you know, these last two months, you know, we're up and so we need to do something different.

And you know, that's not necessarily the case. It might just, that might just be random. And if you do something, you'll be overreacting and prob maybe even moving it in the wrong direction. So there's, you know, there's like the basics of analytics and then you can, you know, you can layer on top of that a little bit more advanced analytics, and then you can get really advanced.

But honestly, I think there's, there's a lot of value that could be had in the healthcare industry even before you get to like the really advanced stuff. 

So does that. That has to do with like statistical significance kind of stuff that you're paying attention to, to figure out like, is this relevant? Is this real or is this noise?

Yeah. Yeah. There's, there's measures of statistical significance when you're looking at data over time and when you're looking at data, like between and among categories to say like, I see it's different, but. Is it really different? Is it only cuz you have a sample, you know, size of like 10? Or do you have like a pretty big sample size?

So yeah, that's, that difference is significant. Like it's not coincidence that these two things are different. Like there's something behind it, probably something behind it. Not always guarantee, you know, not guaranteed. 

Gotcha. So, When you were transitioning into like healthcare and health technology and data analytics what kind of challenges do you remember facing, like from that transition and what did you have to do to overcome those?

Well, I continued to face challenges. I think there is, there's certain in, in order to. Derive value out of healthcare data. And I imagine just not a, you know, just data in general. You need, you need to know several things, or you need people who are good at several things like, you know, process improvement.

You need to know somebody who knows, you know, who knows the clinical or the business or the whatever side of things. Somebody who knows the analytics side of things. And so a big challenge is like getting the right mix of people together to work on these projects, to, to know that, okay, this is really a thing that, you know, this is, this isn't just random, this is really a thing.

We've, we've kind of controlled for the things that we need to control for. And I think that people who are transitioning from direct patient care. One, they can learn just the, the analytics side of things, basically, like, I don't know what, you can basically learn that for free. The, the barrier to entry there I think is, is pretty darn low.

It's just time. And do you kind of gravitate towards that? Do you like spreadsheet and kind of things and pivot table kind of things and looking for patterns and data. So like, if you have some time to dedicate to like learning or being willing to learn some of the data and analytics side of things and you like that kind of thing you're gonna have an advantage over people like me.

Like I'll never go back and get a. A, a nursing degree or I'll never be a PA or whatever. That's just too big. I'd love to know that stuff. That to me, like right now, that's just too big of a barrier, you know, to go learn and then to actually be, you know, be in the workforce and, and apply that you're gonna.

If you've already been in that field and you're trying to get into the analytics side of things, you're already gonna know you're gonna be able to fill more of those roles. Like you can learn process improvement stuff, you can learn analytics and some coding. And you're gonna be able to fill that role also in a lot of cases of somebody who knows, like the clinical side of things, an operational side, and what things are really like.

In a hospital or in a, or in an outpatient clinic or whatever. And I'm just, you know, I'm gonna have to lean on other people for that. And which I think is good. And, and you know, somebody who has a previous healthcare background Will, will, will probably need to do that as well, but they won't have to ask them nearly as many questions and they'd be able to get to insights faster.

On their own or with limited involvement from other people. So anyway, I, I just think like people with that background who transitioned into analytics, they have something that a lot of us will never have. 

Sure. Yeah. They've got that practical experiential knowledge, not just like theoretical or head knowledge.

Right. So what advice would you give to healthcare professionals who are considering making this transition into healthcare technology or analytics? So a couple things. What. Would you first focus on learning if you were in their shoes? Where do you think, because you mentioned you might, it's a pretty low barrier to entry and you could probably learn it, I think you said for free and online.

So where would you go to learn it and roughly how much time do you think it might take to learn what they would need to be ready to like apply for this, you know, next position, this next step in their career? 

I don't know of all of the, you know, I'll be interested to listen to more of your podcast to hear like, what are all these, you know, the options in healthcare technology.

The two main ones that I know of are people who build that front end, like I was talking about before, like build the front end workflows and make sure the right buttons show up in the right place for the doc, you know, for the docs or nurses to click and what that means and stuff like that. So, You know, if you don't gravitate towards like the spreadsheets and the data patterns and stuff like that, then maybe that's a good, you know, that could be a good route.

There's probably a, a number of other good routes, but if you like the data side of things then, you know, I think kind of a progression could be like. You gotta wrangle the data together and kind of clean it up and get it in the right format so you can start, you know, just by using Excel for that.

And then you start to get into like a relational side of things. So a little bit more advanced than Excel is v lookups. I'll get into like the, the detail. So, Behind it. But you know, that's a little bit of a way where you can have the da this data here and join out and go get some other data that's in a different tab or something like that.

So that's on like, just the, the data side of it. And then when you visualize that data, you can use you know, pivot tables and pivot charts within Excel and then. A little bit more advanced is like a Power BI or Tableau or something like that to, to help you visualize it. And oh, and then back on the data side of things.

So you go from like, you know, just excel to V lookups, maybe within Excel to learning sql, the structured query language. I think it's like w three schools.com or something like that, I think is, has some pretty, pretty decent sequel training. And lemme see, was there something else? 

Yeah, just what the reasonable timeframe expectation might be like to learn these sorts of things.

And then how much of it you really need to learn before you feel comfortable starting, like, hey, okay, I'm ready to, to go apply to whatever this new role might be. 

Yeah, well, I would caution against trying to learn too many things at once. Just because, you know, if you, if you're trying to learn something new and the instructions are in German, then first you gotta go learn German.

And to me that's just, you know, mind boggling to try to learn too, too many new things at once. So I think. If you have, especially if you have a healthcare background like learning Excel and pivot tables and pivot charts a little bit of sequel and maybe a little bit of power bi that would, you know, that would probably get you in, get you in the door in, in an analytics.

And it would probably be good just to confirm, hey, I actually do enjoy messing around with this type of stuff. Yeah. Right? Mm-hmm. Be a little exposure to it so you're not just like, I think I might like that. I don't know. But you could just kind of try it out on your own first and say like, you know what?

I think I could, you know, get it behind doing this and enjoy my day doing this. 

Yeah. Yeah. And you know it's best to start off with something that you already know and can kind of understand. So without, you know, getting into like really complex data models or something like that. Like just something pretty simple.

It could be healthcare related, it doesn't have to be healthcare related. But yeah, start off something with something simple and just start to try to look for patterns in the data and, and if that's the kind of thing that you like, then yeah, it might be a good, might be a good field for you. 

And based on another conversation I just had, if you're able to, you know, create one of these projects kind of on the side you know, you get to demonstrate, Hey, I am interested in this and I have learned something, and I can maybe write a report that you're talking about this report writing.

Right? And I can take a dataset that I was able to find online and then generate some charts or graphs to help. Visualize, what is this data actually trying to tell us? Mm-hmm. And then you can show, Hey, look at this. I've already, you know, had a little bit of exposure to this type of relevant experience.

And, you know, I was interested in learning it. And I think that ultimately what you're demonstrating there too is this willingness to learn this curiosity, right? Because. Healthcare technology. I mean, it's gonna be constantly evolving. So that willingness to learn and that like, that innate curiosity, that love of learning is gonna be something that you really can't be taught and something that's going to be valuable that will serve you for a long time.

Yeah, yeah. Totally agree. And a good place to start. Like if you're still in the healthcare industry, I'm sure there's data floating around you and like. A real world scenario of a problem that you're trying to solve. Like, you know, go in there and see if you can get the data, if somebody can send it to you or is part of your job already.

Yeah, just just dive into it and, and try to tell, see if you can tell a data story. 

Mm, yeah. That's awesome. It's interesting to think like, Hey, there might already be a need in what you're doing right now. Mm-hmm. And then mentally trying to like juggle that with, oh, I know that our healthcare providers and professionals and like our nurses, they're pretty taxed right now too.

So it's, we're not sitting here thinking like, oh, you've got plenty of time on your hands. Like, just go do this extra thing. You know? But like, if you're able to find anything at all, or maybe if you've got like a day off where you're trying to gear up for this transition and yeah, it's gonna be investing a little more time.

But. It sounds like it would be worth it to try to explore, one, your interest, but two, your aptitude for it as well. Mm-hmm. And then of course, like not expecting that you're gonna have to figure all this stuff out on your own. Right? Like there are platforms like you mentioned with, it was at W three schools, mm-hmm.

And then also things like, it used to be linda.com, but apparently LinkedIn learning which apparently your library under the Charleston County Library. Offers access to this. So they've got a lot of different modules where you can learn quite a variety of things like related to data or even apparently related to like photography or just total different interest, right?

It's like Khan Academy for grownups, basically. But you can get that free access to it, maybe even through your own local library and then be able to explore some of these things. So let's, let's think about, you know, we're talking about this The value of this innate curiosity and how things are always kind of evolving.

What do you see or how do you see technology maybe specifically related to data analytics and visualization solutions? How do you see those or that revolutionizing the healthcare industry or where do you see things going right now? Is there anything you're particularly excited about that you'd like to talk about?

No, of course everybody likes to talk about ai. And I think, you know, in the healthcare industry there's a subset of people who are pushing for more. I. That it's augmented intelligence as opposed to artificial intelligence. Like it's not trying to replace doctors. I think, you know, the AI has a hard enough time with just reg, you know, every everyday regular life and trying to, you know, do AI functions in that.

And then like the complexity of the healthcare industry is just. Like, I don't think we're gonna be replacing replacing doctors anytime. Definitely no time soon, you know, I wouldn't think. But I think there are one, it can be used as augmented intelligence. Like it's not saying, here's definitely what you should do.

It's just like, Hey, here's something sort of interesting that you might wanna look at and use, you know, consider these other factors that it aren't even. Didn't even captured in the data or that we haven't considered. Like those are the, you know, factors to, to determine root cause and what is actually actionable based on your knowledge of, of what goes on in the health, you know, in the health system.

So, you know, augmented intelligence, you know, as opposed to, as opposed to artificial intelligence. But even before you even get to that level of. Putting some fancier data science on top of the data. I think there's a lot of insights and a lot of improvements that can happen from like lower levels of the analytics spectrum.

So, you know, you can, you know, you can start on, you know, with basic like counts and then percentages and averages and medians, and then you, you know, you get up to start looking at distributions of things and confidence intervals and you kind of start like progressing up and up. And then you get like way up here to like, you know, really complicated data science models and algorithms and things like that.

And, There's a, but there's a lot of value I think we can have in, in the lower part of the, the analytics spectrum. 

It sounds like some knowledge of statistics might be really helpful, even just getting started. So is that something that you think might be worth learning alongside, like learning Excel and pivot tables and those sorts of things?

Yeah, I think so. The best analysis comes from a combination of statistics and visualization, but some things you can recognize with your eyes that just one number can't really tell you, like it can't tell you that. You know, the shape of this curve that you're seeing or something like that, it can tell you like what the linear regression line is, but it, you know, or what the, it can tell you what the correlation is, but you, you don't necessarily know that.

It's like this really bimodal thing. So. But at the same time, you might have too many points and you're trying to look for correlation and it just looks a bunch of, like, a bunch of gobbly gok, and you're like, I don't know what that is. And it's like, oh, that would be helpful to have that correlation, you know, statistic in there.

So the, it is really like a combination of visualizing and having statistics, but I'd say start with visual visualization would be the the easier point to start with. 

Gotcha. So thinking to to maybe your time at M U S C, what was like the practical impact of the work that you were doing? We talk about statistics and data visualization, but like how does that actually translate into we're applying this to affect change in healthcare?

That was a good question. Especially at M U S C. In my career, I've started, I started out more like pretty far on the back end. So, you know, not stuffed in a closet somewhere. Actually, I, where I used to be with Our office was the old psych ward, and within that building they used to have the morgue also.

Oh my goodness. 

A real positive vibe. When you walk in, you're hearing like, did you hear that was, no. I don't know what you're talking about. What, what are you talking about? What'd you hear? 

Hear anything. Exactly. Yeah. Don't, you wouldn't found me there too late at night. But anyway, so, so we're just, you know, back away from everybody and, you know, kind of doing our nerdy thing in the background and, and not knowing, you know, trusting that there's a good process and a good reason.

And it was being used for good things and I believe it was. But now as I progressed through my career, I'm getting closer and closer to the front end side of things and, you know, kind of taking the, you know, something. From the front to the back end. But now I'm, I'm getting more involved in projects where you actually like get down, help people get down to the root cause and intervene, and then measure like, is that, is that intervention working or not?

And that's where I feel like people who have a healthcare background, like direct patient care background, are gonna be able to get to. Those root causes and like practical interventions a lot faster than I would be able to, like, it won't be 11 years in until you're being able to do that. Like you're gonna be able to do that really quickly.

Hmm. Yeah. Just because you, you're like, oh, well I, I know that I've seen. In person, you know, what the results are of these different initiatives that we're taking or these protocols we're following. Right? So now that I can see the data backing things up instead of like an in an individual patient experience or case by case you're seeing more like in aggregate.

Okay. I can see how these different what did you call 'em? They were the, the things that they're following. Compliance, I think, to different procedures and protocol and things, right? Like how that's actually shaping outcomes on a larger scale. So it's not just, again, case by case. But overall people tend to respond a certain way when we follow these different procedures and protocols.

Yeah, that's one way is to be able to know what processes influence what outcomes. So, And go look for variants within those processes. That's definitely one way. Another, another thing that you'll be able to do faster than just, you know, data people alone is to be able to say, you know, somebody on the data side of things who hasn't had exposure to a certain area might say, Hey, look, look at this.

There's this big variance between these different departments or something like that. But you know, with these outcomes and you, with your healthcare background, if I don't have anybody to ask, I'm just like, well, I don't know if that's a thing or not. But with you, with your background, you may be able to say, well, yeah, I, I would expect this type of patient to be different from this type of patient because of their diagnosis or their diagnosis group, or because of this and that.

And I need, I'll need to consult with the sme. Well, I need to consult with SMEs less and less now. But I still need to consult with SMEs on a lot of things. But you'll be able to know how do I get to, you know, how do I get to kind of apples to apples before I start looking around for different insights?

And you'll also know what are practical potential interventions. You know, somebody without your background will either need, you know, somebody on the on the bat phone to call. And see like, what, what happens operationally? What are some potential interventions? And you might be like, well that doesn't, that would never fly, that, that doesn't make sense.

So not even going down that route, but you might be able to you know, come up with better, better theories to test and better potential interventions that like would actually work in a hospital setting or an outpatient setting or 

whatever. Sure. And when you reference SMEs, you're talking about subject matter experts?

Matter experts. Yeah.  Okay, cool. So I can imagine how access to subject matter experts or different people with varying levels of experience would be really, really beneficial, especially transitioning into like an analytics role. Right. Especially if, and correct me if I'm wrong, but a number of roles like this are done remotely.

Is that right? Yep. So feeling like, Hey, you're operating remotely. You've, you're in a brand new role. You've got a lot to learn, but you're not on your own in this. There are people on your team that you can go to. You're able to collaborate still and have that that support in your learning. Is that, did you find that to be the case?

Yeah, 

especially as I've progressed and, and, and now I'm involved with more interdisciplinary teams.

Yeah, so well the, the thing that stands out the most to me in my head, and I, I just thought of this, is people who are transitioning from direct patient care, you can add value to an analytics team very early on without much technical knowledge, I think. 

Hmm, interesting. 

How do you mean? Because you can, you know, help serve in that, that subject matter expert role or kind of a liaison between the data side of things and the subject matter experts to know, okay, here's where you would expect to see some variance.

And that's okay. So we need to, you know, control for these things. And then here's where you can look for like additional opportunities. To improve things and here's how, you know, this would affect this, which affects this downstream. Like just understanding all of that is really important to how, you know, even it'd be nice before you build any data solutions to understand that.

So I, I feel like somebody like that could bring that before you even write any kind of code. 

Oh yeah, just knowing, hey, these kinds of tools would be really useful. Like in my time directly caring for patients, I always wish we'd had access to something that did X, Y, Z. So if we could develop that sort of solution, that would really, really be useful for, for nurses or other healthcare, you know, practitioners or providers.

Yeah. Or as you're looking through the data. Okay, this is not really an insight because of this or that, or you need a filter for this first before you start to, you know, really look for insights. 

Yeah. Let me ask you something a little different. Cause I know a lot of our conversation has been focused on like, say, nurses wanting to transition into healthcare analytics or healthcare technology.

Have you had the experience on any of the teams that you've been on working with other people kind of like yourself who didn't have a direct patient care background? Maybe even like Like a teacher background. Cause I've also been talking with a number of teachers, just, you know, being the time of year that it is when they come to the point where, Hey, I've just decided, you know, not to renew my contract for next year.

It's summertime. I'm looking to make a transition. And you know this, if there's that love of analytics, love of numbers, that could potentially be a fit. Have you found that to be the case in any of 

the teams you've worked on? Most people don't have. A healthcare background who do these data analytics jobs.

So they could come from any background as long as you have an interest in the analytics side of things. If you don't have a healthcare background, then you'll need to be stronger on the analytics side of things and. I would just encourage that person to try to use it in real world scenarios. Start with the same things, Excel, pivot table, pivot charts, and then move into Power BI and look for patterns and tell data stories with like with real data and real data stories that you, you, that you need.

From your job, like ask if you don't have something, like ask your supervisor, whoever, whoever supervisors are, I'm sure they're asked for, for data and analysis and things like that. Like they just have to do that. So they'd probably be like, Hey, yeah, please help me with this. So I, I would, I would think you need to get, you know, more reps in and be stronger in that, but it's totally doable, you know, that's, How I got into it.

Yeah, absolutely. And I think somebody else mentioned to me that it's basically like you probably need a bachelor's degree and then like a, that curiosity and that interest in numbers and spreadsheets and analytics. 

Yeah. Yeah. I went and got my master's in, now I, I mean, I got an mba, like just kind of a general, a general masters of business and now I, I mean, I don't think I'd go.

I don't think I'd go back. I, I know I'm interested in data and analytics and I just learn everything for free. 

That's awesome. That's good. So it's not necessary to go get an MBA or a Master's or something. You can often, yeah. You kind of explore, like you mentioned before, you can learn a lot of these things for free online.

Yeah. And from the teacher perspective I don't know if, if any of your other podcast guests will mention it, but. There are trainers for the healthcare technology. So for example, epic is a big, you know, a big e h r electronic health record, and they have people who they just, they just train people on that.

So, you know, that's a, that's a possibility. 

It's like a corporate trainer or like a teacher mm-hmm. In a healthcare setting, basically. Yeah. Mm-hmm. That's awesome. Well, Joey, I really, really appreciate you joining me today and sharing your insights and experiences. Is there anything else you wanted to talk about before we wrap up?

I think that was it. Yeah. Awesome. Well, I've really enjoyed this conversation and I know that anyone listening to has really benefited from your experience, your insight your encouragement and like the practical advice that you've offered for best next steps. So thank you for that. That's huge. For anybody listening, I would love to encourage you to subscribe to the Pivotal podcast and stay tuned for future episodes.

Le feel free to leave a review, provide feedback, or even, you know, if you found value today, share this podcast with others who might be interested in pivoting careers or just be interested in the, the field of healthcare technology in general. But Joey, it's been a real pleasure. I really, really appreciate you joining me today.

I always enjoy when we get to chat. 

Likewise. Thanks, Ben. 

Yeah, absolutely, man. So we hope you've enjoyed today's dive into the field of healthcare technology. I'll leave you with this thought. The fusion of healthcare technology holds endless opportunities, so stay inspired, stay connected, and meet us here next time on the Pivotal podcast.


Introduction
Joey's Background and His Journey to Health Technology
Embracing Personal Strengths and Finding Fulfillment
Navigating the Transition into Healthcare Technology and Data Visualization
Mastering Data Analytics Challenges and Solutions
The Power of Augmented Intelligence and Practical Impact in Healthcare Analytics
Leveraging Subject Matter Expertise in Healthcare Analytics
Transitioning into Healthcare Analytics: Breaking Barriers and Embracing Diversity
Conclusion