Naji Gehchan: Hello, leaders of the world. Welcome to “Spread Love in Organizations”, a podcast for purpose-driven healthcare leaders, striving to make life better around the world by leading their teams with genuine care, servant leadership, and love.

I am Naji, your host for this special episode in partnership with MIT Sloan Healthcare and BioInnovations Conference, an event that brings the Healthcare Ecosystem Together. I am joined today by Nate Beyor, Managing Director & Partner at Boston Consulting Group. Nate leads Health Tech for BCG, based out of Southern California.  He is passionate about the interface between technology and biology, with a healthy appreciation for operational rigor. He has spent his career exploring different avenues at this intersection, from microfluidics, to biologics manufacturing, to stem cell therapy development, and now in digital health. At BCG, he has hands-on experience launching digital solutions remote monitoring, clintech, supply chain, and precision medicine.  Nate believes in the power of technology to change how we treat, how we heal, and how we live.

Nate – I’m honored to have you with me today!

Nate Beyor: Nice to be here. Nice to talk to you, and good to see you since we first connected at MIT.

Naji Gehchan: Yeah, good to see you again. Before we dig in, digital innovation, specifically data, uh, the topic of your panel, uh, at the the healthcare conference. I’m really eager to hear your personal story. What brought you to the focus on healthcare in a consulting world?

Nate Beyor: So, you know, you sort of led with the, the preamble there, which, uh, I’ve generally.

Always been intrigued and enthusiastic about places at the interface of different technologies. So for me, I, I came up more as like an engineering person. Uh, I studied applied physics in college and then very later on in my undergrad career, got into biology as well. But they were very distinct. There was like biology classes and then there were engineering classes and there were a few that were, where there was a nice overlap, but I would say it was still very early days there.

Um, And I got really into, uh, kind of exploring that interface and, and going into grad school, uh, in bioengineering. And I, I went to, uh, the joint program between uc, Berkeley, and U C S F. Uh, ended up at a lab at, at Berkeley, though. Started at one in U C S F. Uh, that was a nice way to explore that interface.

Uh, and, uh, how do you get into consulting from there? I, you know, I started doing some startup stuff along the way in grad school and realized that getting into, uh, top tier consulting, Was a way to get off the bench and I, frankly, I never thought of myself as particularly capable on the bench. Uh, but again, really worked well in interdisciplinary environments where I could bring that technical lens and more business environment.

Um, and yeah, ended up role in a role in consulting, you know, thinking it would be short term, but ended up liking it a lot and, uh, had been in and out on sort of consulting and startup side over my career. Well, thanks

Naji Gehchan: Nate for sharing this. Uh, if we go into, uh, digital innovation, uh, which was one of the topics you discussed in the panel, uh, how do you define that and what, uh, is included from your point of view in this large word of digital

Nate Beyor: innovation?

That is a large word. The the and, and so when I think about innovation, and I think my, my favorite joke about this are, you know, when you. Talk with companies that say we wanna be innovative. Show us someone where you’ve been innovative before. We wanna be innovative like them. And that’s sort of like, you know, by definition is not innovation anymore.

It’s, it’s repeating something that’s already been done. Um, so when I think about the word innovation, I think that it, it evolves doing new things in a, taking on risk. Uh, bringing together multiple disciplines is really critical here. Um, and that doesn’t necessarily mean that. I’m going to take a person that’s totally from the consumer world and drop into a healthcare setting and expect magic to happen.

Um, but it is about, uh, being multidisciplinary so that I can take someone that, you know, really understands what it means to have white glove hands-on services that consumers expect and apply that within a healthcare setting. But they might work alongside someone that understands the underlying incentive structures between, uh, clinicians and payers, uh, in order to.

To actually build something that’s transformational. Uh, the other piece that, that is key in innovation, and I I’d use the word intentionally in in the last statement, is build. You have to do not just talk about, uh, and so we work with our clients to build solutions. I’ve been. Part of many software solutions that we put in the market, and we tend to push our clients to get hands on as quickly as possible.

Uh, one of my favorite examples that’s been my dream project for years, I’m running it right now, is a, a slide free project. Literally zero PowerPoint slides. There’s only engineers on the team and they’re not allowed to make PowerPoint slides. They’re only allowed to, to create actual working solutions.

Uh, and you know that, that. I think for me is, is how we move the ball along a lot further. Uh, it get getting our hands dirty and, and building things.

Naji Gehchan: I love it. You talked about having a diverse group, uh, working together and then prototyping and building actually solutions rather than talking about it.

Uh, and this word digital that comes before innovation. Uh, how, how do you think of it? Right. In large organization, we can talk about digital from. Digital tech solutions that touches patients to just digitalizing whatever tools we were using commercially, for example, uh, into now whatever, you know, software we have.

How, how do you think of it when, in the broad length of healthcare and digital innovation?

Nate Beyor: Yeah, I mean, you sort of. You hit a good point of, uh, like the simple version is how do I go to screens versus paper, especially in healthcare where there’s just too much paper, but that doesn’t get you a lot of value, right?

Just going to a digital environment doesn’t necessarily change the way you work. Uh, for me, the litmus test is more along the lines of how did I remove steps to get something done? So if I have a 10 step clinical workflow, if it’s still 10 steps, but you know, the three paper steps are now three screen steps.

That doesn’t actually add any value. It just changes where I’m looking or you know, how I’m writing something down. I have to go from 10 steps to six steps to really get a fundamental change. And so, I think, and I think we’re getting there, and part of the enablement is, uh, due to better penetration of, uh, compute and storage, uh, and, you know, for the development of, of, you know, stronger analytics and algorithms.

We’re, we’re at this moment where we can see a lot of automation. On the horizon and, and happening already in some cases. So you can start to see places where the automation can take out steps in a process, whether they are in a direct clinical processes, processes in the background on the administrative side.

Uh, or I, I work often in pharma services. For instance, you might see more kind of B2B things on, uh, clinical development or supply chain or manufacturing, uh, where there’s a lot of opportunity for automation. So back to the critical piece, it is really, um, You know, digital is about changing the way you work through technology.

It’s not just adding in a technology component to something you already do. So

Naji Gehchan: you, you touched about algorithm and different solutions for us to improve the process. Actually, as you said, instead of just making it the screen, uh, be behind this, obviously there is data. So what is your take about data? Is it the new oil, as many would say?

And how do you think of data with, obviously in healthcare, we have a lot of this. Many times never talking to one another as, as the, you know, data rooms, et cetera. Uh, how do you think of this and what is the type of structure do you think we need to strive

Nate Beyor: for in healthcare? Uh, Well in, in health data, I think we’ve seen a lot of changes over the last few years.

Uh, even when I started at B C G coming on six years ago, the notion of integrating something within an E H R workflow was possible, but seemed so hard that. Nobody really wanted to deal with it. And a lot of solutions were actually stood up in kind of like a separate web environment that might force a clinician to go outside of their, of their E H R.

The, um, interoperability is much more real right now and know people understand the need to integrate within the workflow and within the systems that are in front of clinicians. The, um, That’s just in a, in a few years. And so, uh, I think what I’m excited about is from a data perspective, we are seeing, you know, a stronger infrastructure where these systems can talk to one another.

Um, even back then, five years ago it was harder to come by really scale data sets. But you have a lot of, you know, claims, data sets and, and, uh, associated solutions that are out there right now and easily accessible to drive analytics. I think we’ll continue to see more and more of that. Um, It’s not necessarily a consolidation at the infrastructure lever, uh, layer, but, but more access and more connectivity as that infrastructure layer that will go.

As I mentioned, we have claims we’ll get into clinical data. Uh, increasingly we’re having more remote solutions, whether that’s, you know, your Apple Watch kicking off data or, uh, eCOA systems as part of a clinical trial. Uh, those create other points of data creation and, and other types of data that we’ll have to, you know, connect to these systems.

Um, It will still be relatively fragmented there, you know, there’s still a lot of different players out there creating, creating solutions. There’s a need for, um, sort of a custom integration points, uh, through a p i or, or in other ways. Um, but I’m excited that we go from, you know, one dataset to the next.

Better connectivity, uh, ways to map identification or to de-identify. And then more power on the compute side is, is feeding this automation like we talked about before. So, you know, as,

Naji Gehchan: as you were talking through it, I, I get, but think about G D P R in Europe. I had to implement it as, as a chief marketing officer at that time, uh, in those countries.

Uh, what do you think about privacy? Privacy in the US is also kicking in, so I’d love your thoughts as all those interconnect and obviously we’re gonna be using those data to make better decisions for ourselves and probably for others. How do you think about privacy in all this

Nate Beyor: setting? You know, it’s a funny one cuz I, I remember building solutions and people’s people would have this reaction.

Like, oh, well they, I have to be HIPAA compliant when I build over in, uh, over in this way. And so that’s, that’s too burdensome. So I don’t wanna do that, do it. But I, I always felt like, you know, it’s a standard and a standard. It’s actually kind of nice cause it’s something specific you can build to. Uh, there are certainly, uh, sort of, uh, compliance, uh, associated, uh, aspects to it and regulatory ones that, that can be more burdensome and costly.

Uh, for instance, how you might have to set up Waldorf data environments or even distinct legal entities in some cases, depending on the type of organization you are. Uh, But in general, I like that there’s a standard, uh, and I think the standard is appropriate. So it, it’s a overall, it’s a good thing. Um, But you know, we don’t yet know the value of privacy at this point, which is, which is interesting.

I think there’s this general fear of people finding out about my health data, for instance. I don’t know what they’re gonna do with my health data when they find it. You know, my employer who pays my insurance already has all that data and can, you know, adjust premiums accordingly. So I, at the end of the day, I think we’re still operating very cautiously.

Um, But I do like the standards. Uh, I think they’re important. Uh, and, you know, we, we spend a lot of time worrying about this stuff, right? That engineering team I mentioned has been spending a lot of time thinking about, you know, how they operate compliantly, set up the right environments, uh, scrub the data when needed, uh, in order to meet those standards.

Naji Gehchan: So now that you talked about data standard and how to build, uh, to, to answer this compliant piece, you talked also about algorithm that will help us obviously make decisions either as healthcare, healthcare providers or as individuals to make better decisions for our health. I’d love your thoughts on.

Ai, which is obviously a hot topic these days, and chat G P T everyone’s talking about, and I remember during the conference there was this question about do you think AI will replace individuals and specific some healthcare professions?

Nate Beyor: What are your thoughts about this? I think inevitably AI is going to be the less risky option versus humans in some circumstances.

I think it’s. Naive to assume that humans are going to be making all the same decisions that they are currently making. And we are going to have to adapt to, uh, AI oriented solutions that are better at making those decisions. Now, are they generative? I don’t know that they might be a fixed algorithm in some cases.

Um, but certainly just very simply, we talked about data. There’s a lot of data getting produced. There’s more data than any one clinician can handle in the. 12 or 13 minutes that they see a patient. I’d rather have a computer looking at my data to make sense of it and at least triaging that and teeing up a range of insights that a clinician could then build upon versus completely relying on the human to to do that analysis in their head.

We’re already doing this to some extent, so the question is how far you draw the line. Um, that said, You do have to draw the line, right? If I think about it very simply in terms of like AI for radiology, um, you know, ultimately they, even in a triage environment, they’re going to have to highlight certain images or highlight certain areas of images that could be of interest for a clinician to make a diagnosis.

Depending on where they draw that line, which images to highlight or what areas we are making an assumption around sort of, uh, statistical tolerance, um, uh, around that AI system. Um, so there is some. Some critical design questions, uh, and that we have both from a systems level and from, uh, you know, a broader industry or society perspective in terms of, you know, how much tolerance we wanna allow for these AI system to have.

And, uh, even when they are, you know, feeding humans to make the final decisions. I love

Naji Gehchan: that. Certainly agree to it, and I think it’s, it gives also this power of a human connection at the end that only a human can do it, or a physician with a patient, but using the power of ai. To get us closer to a diagnosis somehow.

Nate Beyor: Although, did you see all this stuff last week about this, uh, this study that showed that the, the generative AI system, I, I don’t remember which one it was. If it was, I think it was GBT four, but it outscored the humans, uh, in terms of empathy. Um, Uh, in terms of the answers provided, I haven’t read the study in detail.

I’ve only seen the headlines and some lightweight analyses on it, so I can’t, I can’t claim to have a clear understanding of it. But, you know, there is, has been this notion that like, uh, one of my favorite posts was, was a friend say, say something on LinkedIn about, um, you know, they say the AI can’t hold your hand, but when was the last time your doctor held your hand?

You know? And, uh, and so, uh, Yes, humans are necessary and they add value, but realistically, where is that value and how do we double down on that and really sort of make that valuable and important and then automate what we can and use computational power, you know, where there is a lot of data availability, let’s use the computational power and take advantage of that and do the best most of it.

Yeah. Yeah.

Naji Gehchan: So when, when you look into the future, what are you most excited about in this technology? In HE healthcare?

Nate Beyor: Well, I’m excited about a lot of automation, um, making steps go away, not making me deal with chatbots instead of people along the way. That’s one. So like, you know, how can you automate, uh, scheduling and interaction points and, and, uh, delivery of information to patients and clinicians to, to make things more efficient?

Um, I am really excited about. You know, we talked about lightly on the, on the services side. I think there is a lot of headroom when it comes to clinical development. Uh, AI discovery is certainly one where there’s been a lot of investment to date. Um, now a lot of those companies are maturing such that you’re having compounds into the clinic.

And so we’re starting to see, you know, a lot of say AI generated molecules actually go through clinical development and we’ll see how that plays out. Similarly on the clinical development side, You have a lot of disparate systems in that IT chain, uh, that can be better connected. Um, and there’s a lot, uh, a lot of opportunity to drive towards more value, whether that’s through more focused trials around looking at an efficacy or safety signal or, you know, faster trials, uh, through more advanced protocols, uh, uh, being delivered, uh, in trial operations.

So I think at clin, clin dev side, ton of ’em for opportunity, uh, and that ultimately will lead to, um, you know, more and better therapies in the market.

Naji Gehchan: I wanna move now to a section where I’ll give you a word and I’d love your reaction to it. The first one is leadership.

Nate Beyor: Leadership. Uh, my reactions to it, so this is very open-ended, is it, uh, the, uh, You know, I’ll reflect on, uh, it’s, it’s funny, I think leadership, your first assumption is more of a top down leadership and, you know, guiding a group of people, I often found some of my best learnings were, you know, the, the people say managing upward, but it’s not necessarily about, you know, the political questions for it.

It’s, you know, how do you take ownership over. Something you’re guiding and create trust at cascading levels below and above you in an organization such that you can move it along as quickly as possible. Um, I think leadership really does is about an ownership mindset, having empathy, uh, upward and downward of the rungs in your organization and beyond, uh, in order to make sure that what you’re owning actually gets to success.

The second one is health equity. Uh, this is a very broad one. I’ve actually, this is the third time this has come up today in my conversations, um, that health equity is of high importance, uh, right now for all stakeholders. Um, I think one of the key things sticking out to me though is how closely it’s linked to data.

We can make a lot of calls around what needs to happen from, uh, for changes in health that could drive changes in health equity, whether that’s from an AR access perspective, whether it’s from a trials perspective. Um, But unless I have the visibility through data over where there are challenges and opportunities, uh, and I’m able to, to, you know, make changes by accessing the individuals that I, that, uh, are visible in that data, that doesn’t necessarily mean an identifiable way, but, uh, unless I can actually drive change, it’s not possible.

So I think there is a really close link between the technology systems, uh, and possible changes in health equity. So the

Naji Gehchan: third one was actually data.

Nate Beyor: Well, we talked about that a lot. I, I did. I want to double down on this point. We’re all excited about it. It’s really easy to forget how recent all this compute and storage is in the world, especially in healthcare.

I mean, all of. You know, US providers are not yet on the cloud yet. You know, I mean, it’s still, there’s still a lot of facts and so we talk about it like it’s all possible and it’s like right around the corner. There’s a lot of straight up work to do from an infrastructure perspective. Um, now, now to the cloud and compute, there’s a lot of opportunity.

To move things to, to cloud, um, and to leverage a lot of the computational power that’s coming online. And I’m excited about these gen AI tools because they are. They’re just creating such, uh, sort of enthusiasm in the market for people to get on there and start experimenting. And interestingly, some of the things that people are doing, they’re so excited about gen ai, but really it’s more traditionally advanced analytics.

It’s not necessarily, you know, true gen AI at this point, but whatever it is, uh, To, to get people motivated. I, I’m loving that there’s just a lot more traction right now to move things to cloud, get more experimental, and think about data as core to anything that we do, uh, in the sector of health tech. Can I, can

Naji Gehchan: I just double down on, uh, mixing both health equity?

You talked also about diversity in clinical trials, uh mm-hmm. And data. So there’s obviously data that we have, uh, available. Unfortunately, we know how diversity in clinical trials or the data that we have in healthcare, uh, that is many times. Inequitable unequal. We don’t have data for underrepresented group, et cetera.

Uh, and then obviously though these, this is the data that is feeding in systems for us to be able to make decisions. Uh, but also those data are helping us see where we have issues for us to be able to address them as we are building current clinical trials. So I, I want to have your views about.

Diversity, equity, uh, in, in healthcare using this data are things we don’t have. And we need to make sure that it’s a conscious, uh, bias that we’re looking at,

Nate Beyor: uh, and how to address that. I mean, there’s a lot of fragmentation, right? The, the, if you just think about, you know, one system to the next, and the way that they organize their data, Let alone how accessible it might be, you might introduce bias, um, purely in the friction it takes to actually connect to systems and for no other reason, right?

Um, and so,

You know, on the plus side, it’s a very hot area. Companies are investing in this. Whether or not they have any direct r o i, they recognize this as a necessity right now. Um, which is great. Um, it is a, uh, The connections to the underlying data infrastructure are huge. Um, and I think that, you know, with you, similarly on the data infrastructure side, people that wanna invest in that often struggle to prove their I r I.

So to have, you know, this kind of marriage between the need for broader data infrastructure, the imperative on health equity, I think there, there’s a way for, for these. Initiatives that seem distinct to actually push together. Um, and you’re also having a lot of conversation around sort of, especially on the clinical development side, on access, uh, to patients internationally too.

Um, which, which can support, you know, uh, goals for, for each of those threads too. So the

Naji Gehchan: last word is, is spread love and organizations.

Nate Beyor: I’ll come back. I mentioned the word empathy associated with leadership, but I think, you know, in healthcare that’s, uh, obviously a critical one. We, we think about, you know, how you are, um, empathetic to patients and for certainly patient centricity is huge. I’d like to be possibly a bit more cynical on that and think about, you know, empathy associated with, with all stakeholders.

Uh, and, you know, think about clinicians. Um, we often think about their workflow. Um, we think about the burdens, uh, placed in front of them. Uh, and we talked about the potential to automate a way the clinical work that they do. But right now data shows that half their day is spent entering information into their.

It systems anyways, so I’m sure they’d love to automate that away. You know, I’d love to consider sort of spread love and, and you know, broadly empathy, uh, around, uh, what they’re doing and, and to change the workflow. Because in my mind, unless we think about all stakeholders involved and, uh, you know, how their worlds need to change and that needs to go, like I said, well beyond just a patient-centric view as to consider all stakeholders, to incentivize them to make their lives better.

We’re not gonna see the clinic clinical clinical impact that we wanna see. I love

Naji Gehchan: that. And you find a word of wisdom named, uh, for healthcare leaders around the world.

Nate Beyor: As I said earlier, build first. Just get out there hands dirty. You’re gonna learn more by trying to do it than you will by, uh, you know, spending a year debating what to do.

So I’m, I’m just a big proponent of, you know, getting the technical team involved early and, and trying things. But always staying compliant. That’s a key aspect to all that. For

Naji Gehchan: sure. Thank you so much, Nate, for being with me today. I appreciate our discussion.

Nate Beyor: Yes, fantastic. Thanks for the opportunity.

Naji Gehchan: Thank you all for listening to Spreadlove in Organizations podcast! More episodes summarizing the MIT Sloan Healthcare and BioInnovations Conference are available on spreadloveio.com or on your preferred streaming app.

Naji Gehchan: Thank you all for listening to SpreadLove in Organizations podcast. Drop us a review on your preferred podcast platform

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