Updated: Dec 3, 2020
In this episode of the CANA Connection Podcast our Host Rob Cranston speaks with long time friend and fellow analyst Rachel Stuve the Director of Data Science from Anthem Health Systems about: Smart Cities, A.I, and Data Science Analytics and how they could affect the future of the Healthcare industry. Along the way they may even talk about their favorite electric powered smart cars, so buckle up and enjoy the show.
Link to the podcast episode: CANA Connection Podcast - Smart Cities, Analytics, And The Future of Healthcare. A written Transcript of this Episode can be found below.
If you would like to learn more about Rachel Stuve, or follow her lively dissuasions on other podcasts you can find her contact information on Linked in at www.linkedin.com/in/rachel-stuve/
To find out more about CANA Advisors or to talk with our analytics professional head on over to our website at canaadvisors.com. While you are there consider signing up for our CANA Connection Newsletter or joining out community forums.
And as always remember; Analyze. Assess. and Execute.
Our Host: Rob Cranston (email@example.com)
Guests: Rachel Stuve (firstname.lastname@example.org)
Click below for a PDF written transcript of this podcast.
Rob Cranston 0:01
Welcome to the CANA Connection Podcast. I am here in Alpharetta, Georgia with a good friend and colleague of mine, Rachel (Stuve), today, I'm so excited about this conversation. It's a long time coming, we're gonna have a chance to talk through and discuss all sorts of cool topics in analytics, innovation, human side of analytics and scenarios that are related to operations. And I think we'll probably get into discussion about our favorite cars. Which, maybe, yeah, which, which is the Tesla versus all other electric car discussion. This is again, a great a great honor to have you here, Rachel, and you've been part of a lot of podcasts. And in fact, you (Rachel) were voted 2019 is top voice data science and analytics on LinkedIn, top voices pretty cool. So let's get at it and give us a background discussion on your career in analytics.
Rachel Stuve 0:50
Okay, My background, I didn't practice my elevator pitch. But I'll start so, I did go to school for analytics. It wasn't called analytics at the time, it was called advanced excel and information systems. And then I worked my career always been in data analytics for various capacities, law enforcement, so I actually did do it in jail and all the police cars on at the time it was in Michigan, so boats, police boats, around the Great Lakes. I've been in automotive, manufacturing, healthcare, and that's spanned from startup all the way to a fortune 50 company doing different analytics started out as a programmer, but now on his strategy, and digital transformation.
Rob Cranston 1:38
Yeah, so you're now been there for eight months or so? But Director of Data Science?
Rachel Stuve 1:42
Rob Cranston 1:44
Tell us about.
Rachel Stuve 1:45
So Anthem, big, huge health care insurance providers, almost everyone's familiar with the shield. They operate in 14 different states, very, very large organizations, and what they're really cutting to operationalize back end. Which people don't realize how complicated a healthcare claim. And there's hundreds and hundreds of data points on any healthcare. And so for even for human that can be really overwhelming to adjudicate a claim when you're looking at that many data points. So what we're really looking at is how we can leverage machine learning to predict claim claim action, how can we route through the system to find efficiencies, which not only save the organization money, but really save members from abrasion. So if you think of times that you've gone to a doctor, and you've had a problem with your claim question or something has to get redone, that can be really arduous process. So we're looking to really alleviate that.
Rob Cranston 2:48
So let's talk to these claims. I mean, with COVID. Now, and unfortunately, another uptick in full swing, bear this fall going into the winter is their application of different types of AI that's applied to just I guess, the uptake of different types of claims and claims and you think that could be applied to?
Rachel Stuve 3:11
Yeah, absolutely. That's a good question. So I'm also really active in the angel investment community. Yeah. And so really, what COVID has done has drastically increased the peace of digital adoption. So things like telehealth where you would have a video chat with your doctor. That's been around for many years, but people were very slow to adopt that. And COVID has just kind of propelled us a year ahead of digital adoption. So that is a trend that is very likely to stay. I see that a lot too in the startup community. There's a lot of startups that are looking at not just telehealth, but we're so if you have a watch can not only give you feedback, but also send that to your healthcare provider. And so there's been a lot of startups that have started or even that maybe we're not getting much attention that are now getting attention, because when we've all been forced to adopt this quickly, because of COVID. Be sure when the pandemic is over, more people will physically go to a doctor, but there's going to be a good population that are still
Rob Cranston 4:22
Going to use all of its advantages. Well, it's interesting, we have telehealth and the patient care needs right now within the teller kind of umbrella of services and offerings. I mean, from a from a standpoint of patient care, do you see that increasing, probably becoming more simplistic right, with access to public say, is that something that you guys at anthem, do you guys have a particular set of software that's services,
Rachel Stuve 4:50
So I can't I can't speak to them proprietary methods. But I am a big big proponent, and I've talked with other people about Healthcare isn't just what happens when you go and see your doctor, once a year or twice a year, go healthcare is really every day. And so that's where a lot of organizations in the healthcare space are looking at. How do we deliver health care? How do we make it into the wearables is very large segment, looking at wearable technology, another really huge advancement, where companies are looking specifically with machine learning, natural language processing, and NLP, where you have your phone, and you just talking to say, Hey, where's the closest COVID testing site, and your phone app is going to recognize that and give you a list based on your geolocation, where the closest COVID testing, and it's really that I think that kind of making it really intuitive is where a lot of healthcare organizations are going, and where they're really leveraging machine learning. Where you can even look up, hey, here's the symptoms that I have, what might that be? And, of course, you still need to see a healthcare provider, but it's going to look at that and guide you to that might be best for that type of condition.
Rob Cranston 6:19
Right. Yeah, that's great. Well said, I think it's interesting is that continues to evolve as a technology that, especially from kind of a geolocation of those other phones that have an app, it's uploaded under some states that are either symptomatic or asymptomatic, or they're willing to share that information. Yeah, absolutely.
Rachel Stuve 6:40
Just COVID, there's a lot. We've talked about this. There's a lot that can be done.
Especially with when you have wearable technology, your phone is always with you checking in and saying, Hey, how are you? I read a study recently, where the participants in the study had a phone app. And it just random points in the day they message through the app, and they had to go in and respond to what were they feeling? What were they doing in that exact time. And then what the researchers did was they can analyze that data to look at trends in depression, aggression, you know, loneliness. So there's a lot that can be done outside, of course, where we can really leverage analytics and machine learning, and behavioral in cardiac care. You know, I know wearables that can predict your vitals and say, Oh, my gosh, Here, have a heart attack. Oh, and rushed to the hospital. So there's COVID is talking about today. But there's a lot of a lot of different areas where machine learning can really advance healthcare. You know,
Rob Cranston 7:55
It's so this is critical just segues into question. And actually, one of the things that really, when we first met, there was, and this is advertised in a lot of podcasts. And I love this statement. And that is Rachel believes that data empowers humans, is what gives us the ability to solve problems and change the world with data, we can close gaps and move boundaries to become more interconnected with each other. I love that. Oh, that is so cool. I mean, that gives that analytics data over to many countries. Right, that application experiment. A little bit more about this empowering,
Rachel Stuve 8:31
Empowering data. Yeah. So one of the the challenges that I have in my career, and this has been across every role that I've had, is, there's oftentimes this fundamental belief that machine learning or analytics or AI is going to replace. And so you come into an organization and we want to do an analytics project or machine learning. And you immediately people put up walls and say, I don't want to be automated. Um, I, I can't tell you how many conversations I've had about machine learning that people reference the movie minority, because they think it's gonna know what I'm thinking before. It's gonna knock doors. Right? We see, we see that in hockey. There's so many movies like Minority Report by robot and all of that, where machines take over. But that's really, the thing that I tell people is, it's data analytics, and machine learning is never going to replace humans, it's actually going to free us up to do what we're better at doing, which is really complicated. And so by leveraging machine learning for some of the more mundane tasks like data, data, cleansing, data, pulling, looking at maybe more mundane patterns, that frees humans up to actually use their cognitive power to make decisions and look at a lot of complex different data points, eventually, with machine learning get there? You know, as a technologist, I want to say yes. But as a human, no, I really don't think that there will ever be a true place. So that's where I say, it really does empower us because it takes that load of our processing power so that we can process more complicated thoughts.
Rob Cranston 10:26
So that gets into that. And I can add, in our analytics Operations Group, we have Power BI team analytics. Well, that's the human side of that is the power of having data science ops research, subject matter experts and developers all in one bucket that can deliver this fusion of information. Absolutely. Right. And, you know, so but what gets can be tricky, is the translation of all of that into a client that has to understand English math. How do you really explain an analytics project? The back end of that to make sure that return on investment? So what are some of the techniques you use to make sure that your methods and community and community right, yeah,
Rachel Stuve 11:07
So I would say, if you sat and thought about the time that you spend doing is a basic data analysis? So how much time do you spend in Excel? How much time do you spend writing emails, answering questions, or sifting through financial statements? If I took all of that off your plate, and we could automate that and cleanse that you did not have to spend that time in Excel? What would you be able to do? And the answer is, I think when I asked that to people, they sit and think, well, Oh, my gosh, I really do spend a lot of time in Excel, or answering just basic emails, I had an executive that actually measured the time and it was hours every day. And he sat and said to me, you know, if I got those hours back, these are the projects that I could do that work for them. And that's what we use, then as the guidance for where our analytics programs targeted, was, if I don't have to sift through all of my inventory, and I just know that I have does all that time now that I can spend really deciding how to better use my than just, for example, so explaining to people in terms of how it's going to impact them. And what the actual deliverable is, is, is where I'm really stressed. I think, technical people have a tendency at times to also be over complicated, and they use really big words. And I've done that we all want it, because it makes us feel smarter. And you know, we can charge more for that. Um, but you really have to sit and look at it through someone else's eyes of how are they actually using it? And what is it going to see? You, for example, when I was working in law enforcement, one of the biggest things that we did that was I got the most feedback about the whole project was that we changed the visual on the laptop screen to accommodate police officers who. So when you think of a police officer that's outside, Sunny, it might be rainy, there could be chaos, they don't have time to sit and click around and read small print, or if it's funny, that changes how they can view. And it's just changed something like changing the way that your analytics and your reporting was displayed on the screen. Make that project adopted my officers, because they actually it was it was it was absolutely operational. Absolutely. Right. Yeah.
Rob Cranston 13:43
That's, that's a great example. Um, so last time I was here, we had a chance, or you invited me, which is fantastic to be part of. It was a smart cities discussion. It was just yeah, that for you. We're Atlanta, integrating different types of smart city concepts. And so my question to you, since then, analytics apply to smart cities.
Rachel Stuve 14:03
There are so many ways that analytics can apply to smart cities. So one of the biggest impacts is transportation. And so kind of somewhat, the biggest use case is traffic congestion. And looking at analytics of where were my traffic patterns, where are what times of the day. So you can mitigate congestion, but that also really leads to public safety. So if I can reduce congestion at the same time, I can look at safety. So where are my accident? What type of accidents Should I put around about an inch? I could have traffic lighting, you know, Did someone die? Was that a fatal accident, was it offender. You can look at all of those and start looking at infrastructure. From a citizen perspective. Think of You could tell, we have GPS that reroute. But think about if you could tell how fast the cars were coming if you're riding on a bike, and if you already had a warning that a car was coming, so you didn't cross the street and you stayed into the bike lane. So there's a lot that has to do with safety. There's a lot that has to do with that congestion, but even just moving.
So a lot of businesses really can be advantaged by smart cities from a perspective of targeted marketing. So if I know that my customers are closed, I can send them a message to say, hey, you're really close to the Starbucks, guess what it's happening our coffees. So there's a lot that you can do, from commerce perspective, from safety from congestion, a lot with even just think of penances. And moving those through our cities more effectively.
Rob Cranston 16:04
Do you think that I mean, those are city planners and you know, those the strategists within cities are, are really what they're building the resilience of strategy for the city that applies to what you just brilliant concepts of being smart and efficient, especially in transportation. But I mean, are they really bought into they have? And is the community in the pockets of kind of broken? Question of like, are there? So part of smart cities worries others? Like, oh,
Rachel Stuve 16:34
Yeah, so that's kind of what we talked about is, city planners are in a really difficult position. Because there are a lot of advantages to smart cities, from safety, to transportation, to be more efficient, but typically, residents aren't going to sit and say, Yes, I would like my taxes increased, so that I could feel safer when I ride my bike. It's kind of when you look at even the automotive industry kind of went through the same thing. where, you know, there were extra cost to the vehicle for seatbelts and airbags and anti lock brakes, and all these things that are safety. But when they were first rolled out, people didn't want to pay for that. Because what do I need an air? Right, I'd rather have some really fancy wheels. But over time, the automakers had that challenge of adding that safety and that cost in and trying to still balance what consumers would pay for. So from and with the automotive industry, a lot of these really kind of driven by regulation, adoption. So that's where I think city planners have been tough. But that's where I think us in the infrastructure and data industry, that's our job to be kind of evangelists, to people to say, here's the benefits. First, why we might want to one of the really great case for smart cities is public safety, right? Not only can I get my officers more quickly to an incident, but maybe I can actually preemptively place officers where I think there might be
Rob Cranston 18:15
And predictive, a little bit of predictive.
Rachel Stuve 18:17
Bit of predictive. And that's kind of another story, because there's ethics around that kind of conversation. But do you find what you're looking for? But I think that's where there is a lot that could be an advantage to a smart city from residents. Absolutely. From commerce, absolutely. From logistics. But when we think about safety, efficiency, there's a lot of good cases. We'll get there. It just takes more conversations.
Rob Cranston 18:47
Yeah. And that's what we're gonna do next time. You're talking about the automotive industry. So would you tell everybody what the name of your car is?
Rachel Stuve 18:55
I'm a huge Tesla fan. We have we have sparred quite a bit. I did, did shed a tear when you bought that Audi.
Rob Cranston 19:01
Yeah. And for our audience out there. Rachel does have a Tesla, has a redTesla hat on. But it's a beautiful, beautiful car. And it just, I think, in general, getting back to the discussion of, you know, efficiencies and that smart technology. Most of these companies, although it has a bet ter battery, I mean, then a Tesla... (laughter)
Rachel Stuve 19:23
For the audience that can't see I'm very upset by that comment. (laughter)
Rob Cranston 19:27
Well, I love it. You are an incredible mind. Huge leader within the analytics community and just the continued growth of where analytics is gonna go. I appreciate you being on this podcast I look forward to more.
Rachel Stuve 19:41
Likewise. Yeah, I know CANA is doing some really phenomenal things with not just analytics that can be leveraged today, but I really am impressed with what you're doing to set people up for the future. Like my Thank you for having me for this conversation. All be it a Tesla is better than Audi by a...
Rob Cranston 20:05
That'll be what we'll go into. That'll be our different
Rachel Stuve 20:08
Different conversation. Yeah,
Rob Cranston 20:09
Yeah, we just have those two (cars) and a podcast. Right. Yeah. The facts.
Rachel Stuve 20:13
Yeah. I don't know if you wanted to be an analyst by nature...
Rob Cranston 20:20
800, I mean 600 lbs heaver. I got a lot more space.
Rachel Stuve 20:23
A lot of data.
Rob Cranston 20:25
That'd be great. Awesome. For your audience. Is there anything to share?
Rachel Stuve 20:29
Yeah, for my audience, I recommend people reach out on LinkedIn is the best platform where I've got some other podcasts published and messages. That's really probably the best way to get a hold of me for good conversation, questions. Always, that's the best way that I can.
Rob Cranston 20:49
It's so responsive. Holy cow. I see it firsthand.