May 13, 2022: Today we walk through the impressive resume of Charles Boicey, Chief Innovation Officer at Clearsense. Beginning as a Paramedic, moving to Nursing and working at the LA county USC Medical Center in the 80s and 90s in the thick of a unit filled with gunshots, stabbings and other mayhem from man versus man. Moving on to Nurse Manager, Senior Clinical Project Manager. Completing Computer Science, Technology and Management degrees. Next was Clinical Informatics Officer then Informatics Solutions Architect at UCI followed by Co-Founder and Chief Innovation Officer for Social Health Insights. Charles is currently a Professor at Stony Brook and leader of Clearsense. He believes the doctor of the future will give no medication, but in the interest of patients give care of the human frame in diet and the cause and prevention of disease. Charles is dedicating his career to engaging people, not in healthcare, but in health.
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Today on This Week Health.
Make it simple. We don't necessarily do that well in healthcare. Let's take data governance, data literacy. We make like this religious thing, right. We don't need to do that. Make everything practical. As practical as possible. And that helps with that buy in from all the various stakeholders. If you come up with this religious dogma kind of approach it's very, very difficult to get buy-in. But if you help everyone understand what the benefit is to the organization, the value of the organization, and then downstream and whatnot, that gives you a little bit better ability to get to buy in.
thanks for joining us on This Week Health Keynote. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week Health, a channel dedicated to keeping health IT staff current and engaged. Special thanks to our Keynote show sponsors Sirius Healthcare, VMware, Transcarent, Press Ganey, Semperis and Veritas for choosing to invest in our mission to develop the next generation of health leaders.
Today, we are going to do one of my favorite shows, which is we're going to hang out with Charles Boicey and we're going to have a conversation. He's my go-to technology, officer data person, and whatnot. And we're going to talk all things. Healthcare, Charles, welcome back to the show.
Hey, Bill it's good to be with you.
we did a show last year, a conversation between a CIO and a CTO. It was pretty well received. The paper we're like, wow, that was interesting. You and Charles really went back and forth on some of those things. I'm probably going to do the same thing with you today, but here's what I'd like to do before we do that is you have one of the most interesting careers I can think of.
And I wanna, I want to sort of walk your resume. Like I would, for an interview, sort of do a resume walk. You actually started in the nurse Corps in the United States. Way back in the day. So you started off as a nurse way back in the day.
Yeah. Even I can even go further, further back than that if you want. I actually started out as a paremedic. But I, I got tired of getting beat up by patients and I got tired of the rain coming down on me and looking around in the emergency room in the hospital and whatnot, I go, Hey, Hey, there's a there's a roof over your head. And I don't think anybody's gonna take me out in this environment. And that's pretty much proved true.
Yeah. Well, you didn't move too far from that combat zone. When you moved into the into the work world, you moved into the LA county department of health services. what was that environment?
Well, I was pretty interesting bill, so that was all the LA county USC medical center. right in the thick of the things especially back in the late eighties, all through the nineties we had a 24 bed unit that was pretty much filled with gunshots, stabbings, and, and other mayhem from man versus man. And we kept it pretty busy everybody's on event.
Everybody was chemically, sedated and paralyzed. So they were, it was a pretty tough group to work with, but it was kinda interesting cause you think about the criticality of those patients and whatnot, and it led to quite a bit of innovation from a data perspective.
I actually saw this RS 2 30, 2 40. On the side of a monitor and what plug the cable into it, connect to the computer up to it. And what do you know? I now had a feed of physiological monitoring data. So that's kind of really how it started for.
I remember those the little anyway. and actually you weren't there for a short period of time. You were there for 13years.
Yeah, I was there for quite some time and I got to work with some really notable folks it's really interesting bill back then. I worked with Dr. William Shoemaker who really started the concept of critical care. And the society of critical care medicine and believe it or not, we were doing predictive models back in back in the late eighties, early nineties, but we didn't call it predictive models. We didn't call it AI machine learning. We called it
predictive models is as far back as I go, I don't know what you would call it.
We call it now
just, just call it what it is. So AI is basically math at its core, right? Yeah.
Yeah. And it'd be honest with you. What did we face from a problem perspective? Math, wasn't accepted then just like machine learning. It's kind of getting accepted of course, in healthcare and whatnot. But those that prescribe don't necessarily like to be prescribed to. So we really set it up for the the machines to tell clinicians what to do. And that was probably not the best way to get adoption and whatnot although very accurate and so forth. So we published our papers and whatnot.
And what do you know, around 20, 20 14, 20 15, it became popular again. So it's good to see that we're working with it. But from a healthcare perspective, it had its origins a few decades ago.
So while you were there, those 13 years, you were staff nurse, nurse manager, assistant nurse manager, you worked the floor.
Yeah. So yeah, I worked in the environment and then I ended up directing the environment in later years and whatnot, absolutely. Wow.
So then you head on over to city of hope, great institution, right across town. But it looks like you, you changed your role a bit senior clinical project manager and clinical project manager. What was the change? What was that?
Sure. So a lot of it was about getting out of the line of fire literally and then taking what I learned from an adoption of technology, not necessarily the adoption of information systems, although we have them then, but really adoption of technology.
And at that time I also went back to Stevens Institute of technology and got the requisite, computer science, technology, management degrees, and really studied how best to implement technology, whether it be actual physical technology or information systems in healthcare based on how the air force did it how logistics that had financial and so forth.
And so at city of hope really helped them with the management of projects to ensure that there was adoption and sister and share success. So those were really good, interesting years. And then also On the research side of things really helped them with getting the requisite data in a proper form for for research purposes.this was three-year period,:
Sure. I think back then from a development side and from a product side and so forth and I learned this the product isn't what's in your head. It's what the clinical staff needs. What the organization needs. So. We did a lot of development back in that period of time, I'm not saying we, but we as an industry where we thought we knew better than what the folks actually required.
So then we would implement something and then it didn't necessarily work well within the workflow. It wasn't really features and functionality that were top of mind, if you will. It's what we kind of know, pushed on the folks. So I think we've done a lot better job. What I learned then is make sure that you have all the requirements in order that you'd brought in all the folks that are going to be participating so that they're participating from square one all the way to the delivery of whatever the product is.
So they they have they have ownership in it and we're finding that now bill with machine learning in AI, you need to bring everybody into. The perspective of data, what's the data that we're going to be using. How was it cleaned up? What are the features that are gonna be using the model?
What is the performance look like? And then ongoing optimization and so forth. So how can you expect. How could you expect clinical operations or even financial folks to rely on these machine learning products if they haven't been brought in from square one? It's, it's very, very difficult.
Yes, Charles, that's really interesting to me. I just got the, I do some coaching, various people throughout the industry, and somebody was just calling me about the conversation was about standing up their digital program within their health system. And as I was sort of describing it to them, It's like what a consultant would do is come in from the outside and say, look, here's what a framework looks like for digital.
And here's the different areas. And here's what you should focus in on. And there's a time and a place for that. But when somebody says, Hey, we're thinking of standing this up, it's interesting how my mind immediately shifts to all right. You need to get a coalition of. You need to get a coalition of people from your health system, into a room and get them to buy in on what it would look like, or just to have the conversation.
And, and so a lot of getting these projects off the ground is really identifying those people, bringing those people together, identifying the champion. Creating the frameworks for these things to flourish, for communication, to happen, for ideas, to be exchanged for buy-in to happen. People don't buy into things that they're just handed.
They generally don't. They only buy in if they've been able to speak into it. And that's as you were talking, that's one of the things that just struck me that I don't remember learning, but as soon as I got into healthcare, I learned it very quickly.
📍 📍 We'll get to our show in just a minute. As you've probably heard, we've launched a new show TownHall on our Community channel. This Week Health community. And it airs on Tuesdays and Thursdays. I'll be taking a back seat to some of these people who are on the front lines. TownHall is hosted by an array of talented healthcare leaders who are facing today's challenges head-on. We're going to hear from professionals and their networks on hot button issues, technical deep dives, and the tactical challenges that healthcare faces. We have some great hosts on this. We have Charles Boicey and Angelique Russell, Data Scientist, Craig richard v ille, Lee Milligan, Reid, Stephan, who are all CIOs. We have Jake Lancaster and Brett Oliver who are CMIOs and Matt Sickles, a Cybersecurity first responder. I'd love to have you listen to these episodes. You can subscribe on our Community channel. This Week Health Community, wherever you find and listen to podcasts. Now let's get to the show. 📍 📍
make it simple, we don't necessarily do that well in healthcare, we let's take data governance, data literacy. We make this like this religious thing, right. We don't need to do that. Make everything practical, as practical as possible. And that helps with that buy in from all the various stakeholders.
If you come up with this religious dogma kind of approach and whatnot, it's very, very difficult to get buy-in. But if you help everyone understand what the benefit is to the organization, the value of the organization, as well as the value to the individual departments and then downstream and whatnot That gives you a little bit better ability to get to buy in.
So talk to me about data governance a little bit. We're going to get back to your resume here in a minute, but it's interesting when you talk about religion. Data governance was something that we had to stand up. When I came into the house system, it to say it was non-existent would be, would, would be overstating it but to say.
It was a defined practice within the system would not be understating it, I mean, it was not a defined practice. It was sort of ad hoc sort of happening. If you look at the maturity models that are out there. And one of the things that happened to us probably about a year into it is it became a religion very quickly.
and there was the head of the religion who made sure that people stuck to the dogma of of data science and it did become it became onerous within the organization. We had to, we need to step that back and redo it because we weren't getting the adoption. We want.
It's kind of funny. Cause we had this very top down this is how it's done and. And you would think you'd get more adoption, but we got less adoption. And so we had to step back, build build a different model that had a lot more inclusivity throughout the organization. What is data governance done right look like?
Sure. so governance is a horrible term, right? Nobody wants to be governed to a point, right? So you hear the term data literacy or. So probably a better way to describe what we're currently doing is from a data literacy perspective in what you know, and helping folks understand what's the value of having this type of a program.
Let's take providers, for instance provider information can be in hundred plus applications within it or healthcare organization, and it's usually wrong and 150. But by putting a certain literacy to that governance, to that in getting source of truth and whatnot, then we can make it correct through the a hundred plus applications, but you have to help all the way down from the user perspective on up, understand what the importance is in participating, because nobody wants to be in that room where we've got 50, 60 folks, and we're putting data visualizations on screen and somebody says, Hey that's wrong and they're right. It is wrong. So it's, it's really important going forward also from a pure calculation perspective, you want to know what the source systems were.
You want to know the whole lineage, right? So at the end of the day, everybody's in agreement, not only that. What data do we have available and where the hell is it? That's really important as well, and that's not well understood. And then you end up with data marts, data warehouses on top of data, warehouses and so forth.
And it just gets super fragmented and whatnot, and then pulling everything back. Nobody did this stuff. Unintentionally they did it out of need because there wasn't something in place, but unfortunately just like six. Your healthcare took it and kinda one, a little crazy with them.
yeah, the, systems I've seen that through data governance really well. They get buy-in through case studies. On their own data, on their own stuff. It's interesting though, they'll go in the first time and sort of talk to the executive team, or maybe even the board, and they'll say, this is what the data says.
And they're like, oh my gosh, that's crazy. If that data is right then that person's dead. And it's that kind of, we've all seen that example, right? Where, where some clinicians looking at it going, is that really what the data says? Yeah. That's what the data says. That person is. It's like they can't, they can't have that, that reading is impossible.
And so they just put the study of, Hey, here's what the data looks like. I'm just showing you what the data looks like. And it gives people a window into what actually exists behind there. And then they go, well, we're not going to be able to do anything with that. And then they step back and go, okay, let me tell you what we need to put in place in order for this to be effective.
But then they continue to come back to the board or the executive team and they go, okay, We're ready to show you what we've been able to do in this area with this data. And they, slowly begin to see the power of data in one area or two areas or three areas. And then they start to really expand. I think everybody wants to do. So we're going to get our data clean and then we're going to be able to do it all. I haven't seen that. I mean, I haven't seen that work that well.
Yeah, it's, it's an exercise. You gotta keep, you gotta keep doing it. I mean, you absolutely do. And then you have to put some nuances in there to make it work right. For whatever the department is. That's initially doing it the way it's worked outside of healthcare. It's usually been taken up by department and. Other department within the organization sees their success based on it. And then Hey how can I participate?
So then you become a clinical informatics officer. Is that like the precursor to, I mean, what, what is that a precursor to.
I figure CDO or your chief analytics officer. And this was for the the county of Riverside there the county hospital in Riverside I went in there to I spent 18 months putting them on a path, a pathway of digital transformation. this one actually built, this is going to crack you up. They were actually using CRT tubes. In that organization. So
they may still be it's still possible. No,
no, no. I'll tell you if you ask me, what did you do there that really makes you that the happiest I gave, and there's a cost in this, in the coding department for the coding of the post discharge. they have these like 13, 14 year CRTs. I brought in 20 inch flat screens and I boosted their productivity about 60% because they're able to put all their applications on two screens. That's the best. And I took a, I took what was a cable Tron from a network perspective. I brought them into the 21st century from a network perspective and then just from a physical infrastructure. So that was a real fun opportunity to take somebody, to take an organization and move them, move them forward.
Yeah. I, I like, I like going into things that are really messed up, to be honest with you. Cause the only way to go is up and it is kind of fun that way you go from there and you go over to UCI. This is where we finally met informatics solutions architect. And you started doing some fun, some fun things at UCI. what are some of the things you did at UCI?reach out. And this is around:
If you look at Twitter, not much different than streaming HL, seven messages. And if you think about what Facebook is. Facebook is temporal. So every year everything you've done, it gets compartmentalized or put into a folder, whatever you want to call it. So no different than episodes of care.
So how can you take those technologies into, into healthcare and. Do better from a data perspective. And then Yahoo is another one that you know, is very, very instrumental in some really early technologies. So what it allowed me to do at UCI, which is really critical is bringing in everything, not just the clinical, financial, operational, but bring in the research data, bring in the images bring in genomics data and what.
Bringing the data external, so air quality, temperature, humidity we call it extras on data, everything from the house and and social media data. I mean, we had live feeds of all social media about what's being said about UCI. What's being tweeted from the hospital. all of that it's very, very interesting time to put all those technologies together and actually do something.
Can we still do that stuff today? Can we still see what people are tweeting about our house?
Oh, absolutely. Yeah. It's sentiment analysis and we all should be some of my favorite ones from healthcare organizations I wasn't affiliated with I've got all bill, I'll show you the next time I've got a library of sleeping clinicians that family members have taken pictures of. And then Hey, on that so-and-so facility, this is why nobody's taking care of my mom. Yeah, that's still goes on. Absolutely.
The number one thing that's tweeted about hospitals,
Oh, yeah. I've got a food library as well. Absolutely.
It's like, can you believe they're trying to nurse me back to help. And they gave me this food. I, although I think the food food's gotten better over the years.
And bill, it's not all negative. There are some great stuff that healthcare organizations don't realize that's being communicated by. Their patients and family members. So don't think it's negative. A lot of it is absolutely positive, but if something negative comes out, you want to get in front of it. If something positive you want to actually publish that fact. So there is a role for that still.
Well, this is the evolving role of marketing in a bunch of organizations. If something is negatively said about, let's say, Chick-fil-A, they get a response from. The internal rocketing team within minutes. Are we ever going to get to that spot within healthcare where somebody tweets something and we go, Hey, I'd love to reach out and talk to you about that bill that you don't understand Yeah.
They'll be watching you even more than that. Yes. That data's important. But we're now applying machine learning and AI to understand what patients are likely to turn by looking at our data, how well are we doing with referrals?
Are we doing really good with referrals? How well are we doing with scheduling? If we're just going to schedule people out a little bit too far, we'll get slide. It's no different than a restaurant reservation. I'm going to get on the phone then and find out if I can get myself scheduled somewhere else quicker.
If I've just been diagnosed with some type of condition and I'm all freaked out about it, and I'm being told that I can't be seen for another two weeks. I'm not going to be okay with that. I'm going to go get on the line. So. By having all this data at one place and it'd be in true.
We can do these exercises now and then do these reach outs and whatnot. And I think I've talked to you about it before. I'm finding patients in patient populations that are diabetic and hypertensive. That aren't being treated because we don't have the awareness that these patients are actually have those conditions and whatnot. So that's kind of what's cool about what we can do now versus a few years back.
The power of data right there. During this time at UCI. Got the entrepreneur bug again, it says co-founder chief innovation officer for social health insights. So they have, they gave you the, the runway to go ahead and start something up within that organization.
Yeah, it was, that was really, it was a department health and human services. Put a call out for somebody to build out an application that could monitor your social media in real time for disease outbreak. I think there was 300, 300 plus entrance and we actually. And that led to a whole series that application is actually used by CDC, world health and others.
it really led to lot of opportunity to further pursue that type of of activity and whatnot. We even got involved in some of the DOD and, and, and NAS and others. So that was, that was really entering.
It was really interesting to look back at the pandemic and to read some of the articles about what we can tell from Google searches. What we can tell from social media posts. Like we could take that information and look at the outbreak as it was going across the country in the different spikes across the country, that social media searches and that kind of stuff. It's a, it's a good predictor of.
It's real-time bill that's. We were able to do help people understand what outbreaks were going on and what diseases were, what was happening in real time, because there's some latency in. So the hospital reports locally, the local report to the CDC, but family members are like on it right now. Oh my God. Like kids got meningitis. And then in the same area hospital a few miles away, same thing. And then another one. And what do you know? They were all in the same places. But that would've never been able to be well understood unless you're able to get that data in a real time perspective.
well, health system set up a social listening outpost for future pandemics. Do you think.
They should. They absolutely should. Is the tech there? Is the application there? Absolutely.
Yeah. Yeah, it is. I mean, when you, when you talk to sentiment analysis, you can get that with any number of tools at this point. Some are expensive, but you can get that with a bunch of different tools at this point. One of the things I want to point at at UCI, and then we'll get to. Is a save two and a half million utilizing open source and low cost commercial software. you decided to build out the data infrastructure at UCI with open source software. was that driven by money or was it driven by capability? I mean, what drove or
what brought that on? Yeah, again,
because a lot of health systems are just, Hey, Microsoft's got it. Boom. We're just going to head, we're going to keep on this path.
Sure. It had your skill sets, skillset. Within within the USI environment not just myself, but I had other colleagues that were highly skilled. The university of California, Irvine. I think we have, I can't remember how many Nobel laureates, if you will, but a lot of attack attack on the computer science side of things.
So. We had the right skillset there to do that. Not all health systems have that skillset. And it's really important. Bill back then technology was somewhat linear fashion. It's not linear and the need's not linear anymore. It's now an exponent. Situation where the technology is improving exponentially and the need for the technology is exponential.it now, you don't need it in:
This is really helping our mission. This is really Really beneficial to the organization. So, and then bill, I did what is now called app rationalization or archive? To fund myself. So I took all their systems archived and made them available and that savings ended up in, in my project. So a lot of what I do now in healthcare organizations to, to fund the work is to get them down that path.
Yeah. I've, I've heard that approach used several different ways, but you, you come in and you essentially say, look, you don't have to increase the it budget at all. Just let me keep my safe.
and apparat application rationalization ends up being a great way to find that savings pretty, pretty quickly. The challenge is archiving and building an effective interface to go back into that archive data. So I assume that's part of the work that you had.
Yeah. So it's not enough just to make it usable for him and clinical. It has to be used for perpetuity. And why is that important? Because from a machine learning AI perspective six months, a year, two years, if I can get 20 years my models are going to be better. And I'm going to be able to understand how things changed over time from a temporal perspective.
So archiving into PD. It's great for him maybe for clinical, but it's not so great for what we need to do going forward. I always talk about this bill and it's really important. We don't know all of the features that are going to provide value to certain models going forward. We thin we do, but we don't necessarily know. And we find more and more that the more features that we're able to throw into this and find value from those features, the better than model performance
So you go to Stony Brook medicine, enterprise analytics architect sounds an awful lot. Like your previous title. It looks like you did a bunch of the same things. You, you saved about 3 million utilizing open source, low cost commercialize software. On the analytics solution, what's something distinctive that you did to talk about how you interacted with that community within New York in terms of, of data and, research and sharing.
I think a couple of things one the district project had just kicked off. We had done that in California for seven years, and that was a a CMS. To better tear to the, the Medicare, Medicaid patients. And for Suffolk county, I think the most instrumental thing, and what I learned the most from is we we actually connected to over 400 systems different systems.
So the technologies that were required to pull data from all these different systems, a lot of we know, but a lot of them. Systems that were built in garages and whatnot. So it was kind of an interesting time from that perspective. The other thing that we did that is really essential from a research perspective is having all the data in one place.
So researchers could access it and not get into long conga lines from individual stewards. So there's nothing worse than coming up with a hypothesis and then waiting and have to wait six months for the data. If you're able to come up with a hypothesis. And then have access to the data, of course, in all the proper security rags and all that kind of good stuff.
And then go forward with your research. I think that was secondary to what we were able to do in that environment, making data available for research
define that term real quick, making data available. Was that through a set of analytics tools, they could, they could access it. They, I assume they're not downloading it.
No, it was it was actually put it, setting up an environment where a researcher could, we all know this as a virtual desktop environment, right. A VDI. So and this also helps from a regulatory perspective of not having to worry about does a researcher have a server underneath their desk or do I have a bunch of data on zip drives and things of that sort.
So basically they login. They had all the tools they needed, whether it was R Python, SPSS, jump, whatever that needed to be, as long as they had all the data that was required. And they could bring in collaborators into this environment. So from a data perspective, it was, I called them. The Roach hotel, the data went in, but it couldn't come out.
So they couldn't take the data out and do anything with it on their collaborators. But from a research perspective, from a research associate and from an expense, all they had to do was buy their RAs Chrome. That's all you needed was a keyboard in a screen. So they're able to do all of that work in a minimal cost environment. And then everything was safe. Everything was accessible. They did what they needed to do, and it was powered bill, if you're running pipe Python and you need to do XG boost on your laptop, not so well, but if you have a bank of GPU, is that can be dedicated to the work that you're doing. Absolutely made for A great research environment.
Pretty, pretty elegant solution. Then you while you were at Stony Brook, you start to become a part of the professor community molding the minds of the next generation of analytics professionals, teaching classes like healthcare, data visualization, big data technologies in healthcare applied healthcare analytics.
You just finished a class. I would say. Since we're, we're almost at the end of the semester. Did you, teach this semester?
Yeah, we're doing our group projects right now. So. We've changed the course of a little bit. So data visualization. Absolutely. And healthcare is absolutely essential. So these classes, bill are applied classes. very little theory, but all application and the emerging technologies is kind of what we just talked about, what I've been doing for the last 15 years or so, but on the advanced analytics, it's now a Python course. It's actually a introductory to data science and we're actually cheating. Clinicians on, I just finished last week at the American nursing association, I just finished a one day Python class.
So at the exit those nurse informaticist, how to general view a general practical ability to work within a Python environment, which is pretty cool, who better than clinicians to do this stuff. I'm a very, I'm an advocate of teaching physicians and. And nurses and other practitioners data science, they should be part of that team.
So, so yes, I still teach it. I'm still affiliated with other universities, but I'm very passionate about clinicians being involved in data science. They have 80% of what a data scientist is. The other 20% is statistics. They got that. And the other 20 is programming language and I can teach them.
Interesting. What's the placement. I assume the placement rate coming out of Stony Brook, this program around clinical health, informatics and whatnot. I would assume that placement rates.
The placement rates, extraordinary, everybody gets placed. And we do that as part of our, as part of our practice. So they're all placed in internships over the last few year, into the summer. And many of them get picked up in those internships. Those that don't get picked up pretty quick and we help them with putting their portfolio together, mock interviews.
Is this graduate level or is this undergraduate level?
No, this is graduate level, but they've already many of them were in the in the bachelor program and then they graduate to the master's program and they're absolutely ready to go. And there's a smattering of clinicians and the rest of your non-clinicians, but they've had six years in a healthcare environment.
Co-founder chief innovation officer for Clearsense, LLC. So you're back at it again, this is what you're currently doing. how did this one come about? Was this something where you're at one of the health systems and you say there's a great need here, or is this one of those where you went out and said, okay, we're going to, we're going to solve a problem that exists within healthcare.
Well bill for this story, you're absolutely involved in it. So I was at Stony Brook. I'm from Southern California. Didn't do well Didn't do well at all. So you actually offered me a position with your organization. But somehow in transit, these folks down here in Florida, kind of got ahold of me and convinced me to to start co-found, Clearsense with that.
And the reason in the thing for me and this bill, I want to reach as many people as possible with this technology. I really believe this technology is universal. And I think this technology will help, well, I call it in the digital disparity why do the. The more AF known organizations have the tech and why can't the safety nets and others have the deck. So I got a commitment to do that. Also got a commitment to make it fairly reasonable for folks to get into. And then I don't know something about the cost of living in Florida versus Southern California. So I came here, but bill, we definitely connected. You brought me into your organization and it worked out. So I thank you for that. It's just, we've kind of stumbled there for, for a little bit of time.
Well, it's yeah. It's, it's interesting. Cause if you remember, because I remember vividly you turning me down the company, we were going to stand up. When I was at St. Joe's, we had an incubator, we were standing up companies and we already had a name. It was called census. And I think we even had the URL. I think we had census.com was going to be the URL for the company. And to be honest with you, the business model was based on data. It was very similar to what was going on here. But back in the day, if you go back to 20 15, 20 14, 20 13, we were really struggling with the data.
How far have we come? Are we still seeing those same kind of gaps of the haves and the have nots with regard to their data expertise?
Yes. Yes we are. The other thing that we're seeing, what we're solving for is to make this automate. As much as possible. And what I mean by that is when you bring in a data source, let's say that coded and whatnot why does a human being have to validate that when you're looking at Phi, why does it have a human being have to go figure that out provider attribution, why does a human being have to figure that out?
All these different techniques that we use that we're primarily handed by data engineers, Why aren't we processing that from an RPA perspective. So we've taken and what I built what I've always done is gone to look at the industries that were ahead of healthcare and bringing that technology. And whether it be logistics finance retail. So what have they been doing? That's sped their processes up what, what did Amazon do? From the I want to buy a book. The book is delivered in cut off cut out all those middle processes and whatnot.
So we're doing the same thing with data. And that really is my mission to get it from a raw state to a curated state, as quickly as possible with as little human intervention as possible, other than the QC checks and whatnot.
All right. So I save this till the end. We were going to do a newsday episode. We ended up doing a a keynote episode. But I still want to talk to you about some of the things that are going on in the world. You started in the clinic as a nurse there's clinician burnout was what we were saying before, but now we have this story in the news of a nurse committing suicide while working at Kaiser, actually committing suicide on their shift. We have employees walking out unions striking and that kind of stuff because of conditions. how are we going to get through this? For the last two years we've been pushing the nurses at a, probably an alarming pace in a very difficult situation. What's it going to take to get to the other side where we return to a, whatever a normal workload looks like and really try to put the right parameters in place around mental health and sustainability of the nurses in their environment.
Sure. And bill, I'm going to, I'm going to speak for our colleagues with a disclaimer that I have not been at the bedside for quite some time, though, on a, a daily basis I do work with those that are providing that care. I think that's, that's really important. So the last couple of years absolutely have been extremely extremely tough. Especially on nursing and so forth. And what can we do doing forward? I've got colleagues at, at Vanderbilt that have put together programs to minimize the amount of interaction let's say within the EMR.
Did we take a minimalist approach? No, we didn't. We did not take what is actually required from a documentation perspective, regardless of the discipline to ensure proper and adequate care. We looked at the EMR as a way to throw everything, but the kitchen sink into her. And then if you look at it because we can what's actually being used what's being used with that data.
And is it worth, even bothering with, and what we're finding out the answer is no. If you look at the datasets, look at how many, excuse me, orders. Looking at me, order sets have been created and you go back and you look at the utilization of those orders. That's it's minimal. So you know, what the heck, what are we doing?
So what does that look like? From a tech perspective, are we there yet to actually listen to every patient interaction? Follow every clinician and whatnot. So that can be documented. There's a lot of folks that are trying to do that. Not necessarily been successful.
I think it goes back to the basics, bill understanding what the workflow is, what the alert workload is, and then minimizing whatever we can from a process perspective to ensure we have the proper patient outcomes. And ensure that adequate staffing and all of that there's a, there's a legislation. I mean California has a staffing ratio. There's many that do, but it's just something that. We need to think about when we're implementing technologies cause sort of the technologists, right. Or the information system, what can we do to minimize the impact on the workload?
Do we really need to bring this system in, do you really need a document on this? Is that really required? Again, my approach has always been minimalist bill.
It's interesting to hear you talk about the minimalist approach. It's also interesting to watch what Dale Sanders is posting out on social media in terms of the measures it's like, do we need these measures? Let's heart let's step back and start to strip away. Some of these things that the federal government is asking for that we have to document that quite frankly, it goes into a black hole and it's not using.
Yeah, bill, even, even from our own organizations there's nothing more maddening than seeing that we've met a certain quality measure for the last 10 years at 98%. Let's get rid of those and let's get something in there that we may not be thinking about. That's less than whatever it might be. Let's focus on what we need to focus on. And then from a quality measure perspective, we can do those quality measures in real time. Why aren't we doing them retrospectively? Why are we having these meetings where we're looking at something that happened three months ago, we shouldn't be building that. From the adherent and now people should be monitoring that in real time, we can monitor this stuff in real time so that when that patient leaves they're at 98% or whatever it is, and we're not doing this retrospective and stuff, there's no need to do. Yeah, you got it.
Very short. Very short bursts on two topics, one health it tech investment has gone way down in the first quarter. I don't know if that's a new trend that we are going to see. And then the second is we're seeing some significant hits here and you and I were talking earlier, and you mentioned two of them.
We talked about Teladoc on the, news station. Last week. And we talked about the fact that they had to take a write down based on their acquisition of Lavango and just the actual value. It had to be written down. There was a, anyway that it's just a, an accounting principle of you can't show the value of, of it.
That's what you paid over and above. Then you take a hit on your financial. So Teladoc took a significant hit. Their stock's down 30, some odd percent health catalysts from when it went public, I think is how. Of what it was when it first went public. are we seeing a slow down, are we seeing companies be valued for something other than their future potential and being more valued on what they're actually delivering in terms of revenue and value to the, to the market.
I think what we're seeing is the VCs and the private equity over the last couple of years have brought on people to their organizations that really know healthcare IT and what we saw from a Covid perspective anything that happened to do with telemedicine or even close to it, of course everybody went crazy and it spiked and whatnot.
And what The PE folks and the VC folks are looking at right now is what is it going to look like for your organization if you're a startup or you've been around for a couple, three years, what is that going to look like in a year from now and then potentially, and in two years from now, and is. Is it best for me to invest or do I just hang onto it and see how some of these do, there was an extraordinary amount of investment over the last couple of three years. And like you said, just in the last few months, there's been a little bit of a pullback, there's still investment, but they're they're making very, very well informed decisions on, who they think is gonna make it for.
Yeah. And you know what? I don't want to paint a picture that I believe there's a significant pullback. I think there is going to be a pullback. I think there's a lot more discernment as you talked about, they're bringing in people who really understand healthcare and now we're, instead of hearing these stories that we sort of scratch our head and go you and I will hear some of these startups and they'll say their story and we'll walk away from the booth and go, yeah, that's not gonna be.
I mean, like we just know intuitively that's not going to make it. Well, the VCs, the private equity have those kinds of, that kind of expertise available now. And they're, they're not just throwing money just to be in the $3 trillion space, if you will. Last one Truvada.
So Truvada has my data in it. It's a consortium of about 15 to 20 health systems, large health systems that are bringing all their data together for the good of mankind. Right for sharing and whatnot. The thing I wanted to ask you about it. If I were on their board, if I were in one of the 15 investors, because it is a separate company, a separate entity, they're hiring a bunch of Microsoft people so that the leader is Terry Myerson.
He's from Microsoft, they're chief data officers from Microsoft. And we see this happen from time to time where people come in from outside of healthcare and they go, oh, we got this it's, we've got this. We, we know how to handle data and that kind of stuff. What are the gotchas? That a team from outside of healthcare might run into. If they're not familiar with maybe some of the things that are specific to healthcare.
Sure. And I understand the affinity for Microsoft. Healthcare is predominantly Microsoft. So totally understand that. One of the things that I think is, is going to be really important for them.
And I talk about the the ethical and responsible use of data. Yeah. these are very large datasets with many, many members of the US population. And from a research perspective, during the research there are going to be. There are going to be things that are found out from patients that know, I talked about it earlier that were not known.
So the ability to tokenize this information and leave tokens and each individual organization, so that if there is something that is discovered by researchers, they can get that back to that provider. They can get that back. So to the patient, I think that's absolutely essential. And then understanding it from a rare disease perspective, how to properly handle rare diseases so that those patients don't end up being identified in time.
I think that for me as a researcher, I think this is a great opportunity from research from pharma and from others, but just truly healthcare organizations to understand that their patients are consenting to this for purposes of, of the greater good.
And I, I do agree with the concept of the greater good but understanding that You know that, that the data's clean once they get it, or they have the proper mechanisms in place to ensure that the data is clean, that the patients are who they should be. All the the fraud stuff has been eliminated.
The de dupes have all been taken care of. Patients are properly attributed. All of that is in place to ensure that the quality is there. And again, that this data is being used in a ethical and responsible manner.
So one last question while I have you here. What directionally, what's the future of healthcare? What's what's the future from the seat that you're sitting in right now?
And bill, no one can say it better than Thomas Edison started in the mid twenties and I'm going to quote him directly. But this is really where my focus is now from a career perspective going forward, this is really what I'm concentrated on. And it's a quote that I've had with me since my time at the bedside, but it's accurate and we'll be able to do it. I believe sooner than, than later. And his quote is the following. The doctor of the future will give no medication, but will interest his patients and the care of the human frame, diet and the cause and prevention of disease. He said that almost a hundred years ago, bill, and I think we've never had the technology to actually do that, but we do now. And that's really what I'm going to work on for the rest of my career.
And that's engaging people, not in healthcare, but in health. I mean, it's engaging, they're engaging me in a, in educating me, engaging me, giving me the tools I need to be healthy.
Absolutely. Right now we don't deliver and we haven't delivered healthcare. We deliver sick care. People come to us when they're sick, period.
Yeah. That's really interesting. Appreciate it as always Charles. Great to talk to you.
All right. Thanks for, well.
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