Naji Gehchan: Welcome to SpreadLove in Organizations, the healthcare leadership podcast where we explore leadership with purpose.
This episode is in partnership with MIT Club of Boston BioSummit. I am Naji your host, joined today by Dimitrios Skaltsas, CEO and co-founder of Intelligencia AI. Founded in 2017, Intelligencia AI leads the way in leveraging proprietary data, biomedical expertise and artificial intelligence with its patented technology to address significant challenges in the pharmaceutical industry. Intelligencia AI has been recognized in Inc. 5000 and as one of the Top 100 Healthcare Technology Companies. In 2021, the company completed its Series A funding and was named one of Forbes’s Top AI Companies to Watch. Before tapping into his entrepreneurial spirit and co-founding Intelligencia AI, Dimitrios spent over a decade in various leadership roles within the healthcare and life sciences sectors. Dimitrios holds an MBA from INSEAD and a law degree from University College London (UCL) and the University of Athens.
Dimitrios Skaltsas: Uh, great to be here. Thanks for having me. Well.
Naji Gehchan: Same here. I’d love first to hear more about your personal story from law to business, to AI in healthcare. What’s in between the lines of this inspiring journey?
Dimitrios Skaltsas: Oh, uh, for sure it has not been a linear journey, um, starting from where I am now. I’m, I’m an immigrant.
I have been in the states since 2013. Um, and I have been fortunate, like, you know, being at the right place at the right time to find fertile ground and grow my ideas and, uh, and create intelligentsia. Um. At the same time, you know, if I go back to my roots, I, you know, I often refer to ODI like Ulysses and you know, the guy who was away from home for 20 years and he was always trying to go back.
Um, yeah, thinking of. Ulysses and, and love. Um, you know, my journey, uh, has also been linking me back to Greece in some ways. So I founded the company in the US and, and most of our, uh, partners and customers are in, in the us. Uh, at the same time, much of the r and d. If you think of biologists and software engineers and data scientists and so on and so forth, they’re locate actually increase, and that’s my way of bringing something back and giving something back to my country.
Naji Gehchan: Um, this is great. I love how you’re framing it also from really as you said, your roots. And we discussed a little bit, uh, the linkage, uh, and going back. Um, so you, you did start studying increase before you, uh, immigrated and it was law. And then now it’s like one of the most, you know, hot topics of AI and in healthcare, which is another also like super intriguing, uh, space.
I imagine for you as you, uh, crack down AI in this space, how, how, how did you make these different pivots in your career?
Dimitrios Skaltsas: Yeah, it’s a bit crazy as you think of it, like going originally from, from load to actually pushing the boundaries of, uh, of AI within, uh, live sciences r and d, uh, identity, the commercial side of it.
Um, again, tying to, um, this podcast here. It is about love. Like, and, and I’ll tie it also to my roots. Uh, I grew up in a family where, uh, you know, I received a lot of love and I, I, I saw also love being expressed in some ways through, um. Healthcare and life science, like, um, one of my parents, uh, used to be a medical doctor so often I, you know, I could, uh, I could see how, you know, patients would, uh.
You know, who would send us wine and cheese and, and, and, and, and flowers and, and, and all those gesture of, uh, you know, saying thank you, um, for becoming healthier. And, um, and uh, also it was a family where, you know, I, I felt my parents were always giving both to me and my siblings, but also to the brother family, to friends around us and so on and so forth.
Um. And also families that, as most of the families were, uh, actually hit at times by, um, you know, medical, uh, malaise and we had people pass away. So that kind of, um, shaped my values system. Um. Now when I decided to what I want to study, I was thinking much more about, uh, you know, how have impact at the social or more broadly public level.
So that’s how I went into law. Not necessarily want to become a lawyer, but more want to have broader impact through how we live, how we regulate our lives, our societies, um. It was interesting, but I, I was also a bit, um, priceless, I would say. Um, and yeah, and then I, I, I made a thing at engineering, uh, which I never crashed from.
Um, electrical and computer, um, electrical engineer, computer science, um. And, and then, uh, you know, consulting somehow showed up. Like, I think I was in one of those people in their early twenties who at some point was not sure what to do with their lives. Uh, and, um, yeah, and the door of management consulting, um, opened up for me.
I, I joined McKinsey where I stayed for several years and. Um, yeah. Yeah. There I experiment again with public and social sector, along with many other industries. But ultimately I experiment also with healthcare. So when I joined, um, I told ’em I wanted to healthcare and public sector, and I, I tried both and at some point I work in life sciences and.
And, and I loved it. Uh, I loved the, the caliber of the people like, you know, very well educated, uh, very well spoken and dialectic in their approach. Um, I loved the, the, the concept of building, like, you know, bringing innovation to, to patients. Um, and. Yeah. At the same time, everything else that I was trying, uh, was leaving me a bit disillusioned, like, you know, with the speed of how things worked in, in other sectors and so on and so forth.
Um, so more and more I trained myself into life. Science was a hard path. You know, if you don’t have the life science academic background, uh, it’s a very. Deep, uh, shield climb, and I was worried about that. Um, but yeah, I guess I, I like the talent and it has helped me. I’m not, I’m not as deep as people like you in in world we two, but that has helped me by definition, create a broader kind of bird side view almost in the space.
Um, and, and that’s one of my strengths.
Naji Gehchan: Yeah. And, and here you are now, you are impacting, uh, you’re impacting healthcare, uh, through one of the most hype topic ai, but you’ve done it before it became a thing. So I, I would love here to learn more about what intelligent Gena AI actually does. Uh, and I, uh, I’ve been following your work.
Uh, so give us a pitch of what you do and then we can discuss a little bit more how you’re helping r and d in the biotech and pharma world.
Dimitrios Skaltsas: Yeah, so let me, let me actually. Lay the bridge to exactly what we do. And, and, and I also, um, built on the, on the love theme. I’ll keep building on it. So when I was at consulting at some point, um, McKinsey was experimenting with big data and ai.
That was back in 2015, 16, so relatively early days. And at that point I had realized for myself that one, I love technology. So I love building, I love creating, uh, I love innovation and doing things differently. Um, and that’s where I got an opportunity in, in New York to enter this new domain and build something specifically for r and d, both track discovery and development.
So that was my, my eventual preach to AI and, and data science. Um. And, and that has actually led to intelligentsia. Ai. Um, so one of the recurring themes of my work back in the day, and obviously these days, has been r and d productivity. So ever since I remember myself in the space, like since, you know, uh, two thousand nine thousand ten, um, RT productivity has been declining.
Um. And one of the, one of the key themes that you see, especially with larger companies, is that in a way it’s still r and d is a lot about portfolio management, portfolio strategy. You make bets, uh. On, on multiple tracks, on multiple programs at any given time. And you have to make decisions on which things to advance, what things to accelerate, uh, what programs sometimes to decelerate or, you know, trials that you discontinue and so on and so forth.
And, and people typically do an ENPV risk, astic, NPV analysis on this. Um. But my experience was consistently underwhelming in the sense that when it comes to risk, which is at the core, at the heart of drug discovery and drug development, like we all make bets and big bets, and that’s why those bets are rewarded through IP and and protected prices and so on and so forth.
But when you make these bets and you think about risk. There is not necessarily much science going into this. Uh, so I haven’t, I hadn’t seen very robust frameworks of how to define the risk. Um. Uh, I was working a lot in due diligences and m and a and portfolio strategy and commercial strategy as well.
And, and every time we’re going through the exercise, you know, we’re relying on historical benchmarks and obviously calling experts and, and creating something semi quantitative. But even at that times, often, you know, the, the people who say CRA here in the room actually would come in and apply their own, um.
Intuition, their own judgment and, and often almost throw out of the window, even this semi quant analysis. Um, so yeah, when I was exposed to more AI and other science applications, I thought that could be, uh, one of the holy trails, the space that can be addressed in a more appropriate way, um, through these emerging technologies and.
And that was the initial bet on intelligence. And that is the core of what we still do. We’re the first company to apply, uh, productized, uh, approach on addressing proba or assessing probability of success with machine learning, with predictive modeling, uh, we take a host of, um. Features or aspects into account?
Uh, we have a patent technology. Uh, we have accuracy that has been prospectively validated for several years. Um, obviously we’re not a hundred percent accurate and we’ll never be. Uh, but I do trust that we. Provide a very good, robust, consistent triangulation method, uh, for decision makers out there. And that’s why we are being trusted by, uh, a lot of the large pharma.
Like we’re working now with more than 10 of the top 20 parts, pharma, uh, and also with the host of Meat Cup, Liz. Less often with smaller biotech. Uh, both because of our business model, which is mostly software as a service. So it’s, you know, carrying access to a product that, that tends better to, you know, larger portfolios.
Um,
but also, you know, if you’re a biotech, you kind of live and die. Hmm. By your, based on your decisions of development track, right, exactly. You have to maximize it times for these, uh, one or two drugs, uh, more than decide on how to calibrate your portfolio. Yeah,
Naji Gehchan: yeah. So in your, in your work and your help, obviously, like this is an ultimate important, uh, problem to solve for, you know, probability of technical su uh, success of the trial decision making between different prioritization.
So your work is a cross portfolio, or, or how do you optimize actually the PTS because. There is the, uh, probability of success there is. I’m sure you’re faced with several questions on efficiency of the trial’s, productivity, the length of enrollment, and then like all the execution of the trial. Like I’d love to understand what is the main focus you’re doing, uh, through your patented technology, uh, and what are you thinking for next steps of improvement, uh, of drug development?
Dimitrios Skaltsas: Yeah. So we took, uh, a more high level, more strategic approach where we say, okay, let’s try in this to define probability of technical and regulatory. Well, let’s, uh, let’s train a model where the objective function is with this program. Actually receive FDA approval down the line, be. Nine years from now or be, you know, nine months from now, depending on which phase, uh, the program is.
Um, and then we gathered a lot of historical data and we create, we engineered features and, and we train models that could apply pattern recognition. Stronger partner organism, what with humans apply with more data than what we typically humans have. And, and, and differentiate problems between more likely to succeed, less likely to succeed, right?
And apply a specific probability. So that’s how we started. And then over time what we did is we have been, uh, become stronger also in the phase transition. Probably this, which is more of the. Technical success and also providing visibility into the underlying drivers that will help people also be more, um, prescriptive if you.
Interact with like, how can I improve my probability of success at the asset level by changing things that I can change? Not your question. Uh, we are, how are we planning to expand the one is more sensitivity, more what ifs, scenario planning, like invest more into the prescriptive part of the equation.
Like how can you. Prove probability of success at the asset level and at the program level. Um, the other thing is more broadly, if you think about portfolio strategy, um, one expansion has been ongoing in terms of therapeutic carriers, like we started oncology, then we move to immunology, inflammation, CNS, now metabolics, cardiovascular, renal.
But we still haven’t covered everything out there like. For instance, we’re going after Infectious Disease Index and so on and so forth. The other one is, um, geographic expansion. We started with the US FDA, we cover a lot of, uh, EMA. We are venturing out China, which has become increasingly relevant for innovation.
Um. The other thing is functionality wise, you know, we realized at some point we, we were forced to create a very clean, high quality summary level clinical development data set. Which, if you ask me, I think it, it, it’s likely to be the best, uh, exists right now in the space. ’cause we’re trying to. Feed the algorithms in a very proper way to solve these, uh, specific problems.
Right on this crown, our sponsors, school, our partners, our customers have all. Quite often been pushing us on developing more things, more functionalities. So now for instance, we have been developing, uh, something around target product profiles. Uh, we have been developing some functionalities on, you know, more broadly, track development insights and, and executive level landscapes.
Um. We have been, um, exploring more broadly NPV analysis. So we’re not the experts ’cause we don’t bring all the elements there, but that’s, that’s how we expect. Typically we get feedback from, uh, or gently push from, uh, one of our, um, customers. We test it with others and if there is need. If there’s market for that and if we think we can be best in class in what we do, then we venture into that.
Naji Gehchan: Well, one of the things, Dimitrius, as you were giving, why you started the company, uh, I, I wanna understand also and get your view as you have built now the company is, you said like some folks. We’ll throw out even the semi quant analysis and go with their gut and, and we hear it, right? Like I’m in drug development for a while now.
Like we all say, like there’s an art to it, there is science to it. There is different aspects that we look, but there is this human decision making that several of us actually enjoy and like, so I’m interested during this journey. There’s, I’m sure a piece of change management that you help the companies.
Get to as you bring a more data driven approach that is based o out of the models. Do you have any examples? ’cause I’ve seen several times, as you said, like all the data would show something, but then someone still believes that our intuition is stronger than data. Like how, how do you manage the art of data with the scientific and medical intuition?
Dimitrios Skaltsas: I’ll, um, I’ll address this in two ways. I’ll go in two directions. The first one is that when we started, you know, one of our values is humility, like being grounded, but still, when we started having a powerful tool in your hands, like ai, um. Sometimes we felt, oh, we we’re getting it right. Like no, uh, we’re getting more right than everyone else.
And sometimes there is also a validating this, but there has been a mindset shift, like the more you work with the people on the ground, the more you realize you ought to be a decision support tool. Like your, my role, our technology’s role is provide insights. For people like you to make better decisions, like, um, apply your own intuition or run the dialogue internally in, in a more data driven way, so.
With that in mind, um, the way we quite often work with our users is even large companies often don’t necessarily have a specific, um. Uh, process around p you will see that the ownership, uh, is, is quite different from one company to the other. You’ll see that workflows, uh, they may not even exist or when they exist, they, they run a different way.
Uh, what I have seen work. Increasingly well our process where it’s a mix of one, they, they’re cross al like it’s. There, there are multiple people coming together to, to actually inform those decisions, even if ultimately it’s one person making those decisions. But there are multiple people involved in the process, often owned by like the recommendation owned by the program managers, but not necessarily.
Um, and then it’s typically a mix of historical benchmarks. And I’ve seen again, companies that use not none or rely on papers. White papers or external papers, commands that use three or four historical benchmarks. You see all the, all the spectrum there. But typically there is some baselining that can be done with historical benchmark.
This base line is informed then triangulated by what we do, um, more. Data achievement, more smart approach. And then there’s a dialogue where either it can be done around some heuristics. Some companies have heuristic or create heuristics where they say, you know, we’ll use those heuristics to apply, uh, or to, yeah, to apply our own infusion and, and, um, change the pt, the PTS or PTs, up or down.
Um. Or sometimes, you know, we help inform those heuristics because we have our own drivers who have the explainability so that people can go inside the system and say, oh, you know, it’s, uh, 25% and it’s driven by this, this, and that. Actually, we do not believe that, you know, these drivers should have that much influence because we know our track better and we think that, yeah, the MA has not been, um.
Validated so far, but, uh, we know a bit better because we have some more data. So this should not be a minus two, it should be a plus three. Right? Uh, so that’s, that’s how it works. And then still people, yeah, people actually will apply. Sometimes still people will, uh, have the tendency to throw this out of the window, but listen less.
I, I see the. See the industry is more and more hungry on, it has always been hungry on data, but it’s more and more hungry on, you know, systematic approaches. And the more we get validated on this specific niche, for instance, the more the, there’s word of mouth. It’s like, okay, you know, we can do it is a bit more systematic.
But always people will ultimate rely on their own intuition. You’ll never get away from that. And, uh, I’m not sure that. We should, like ultimately, you know, there are decision makers and, and people who have honed their skill, uh, in a way that our machines, I think we’re still far away from.
Naji Gehchan: And I know how you framed it, right?
Like providing insights to make better decisions, like keeping, uh, there, there is still a place for intuition and art in all those, uh, models. Like, because it’s, uh, being smarter in the way we do our decisions rather than anything else. And, and I know you’re, we’re, we’re talking about art. I know you are.
You love art, you paint, and I would love to, uh, you know, like open, uh, my last questions about. Painting the Greek countryside, this is how you spend most of your time. How, how do these passions influence your leadership and your, your personal decision making?
Dimitrios Skaltsas: Yeah. Uh, I’ve never thought about how they influence my leadership.
Uh uh, yeah. If I start with painting, I love painting. I love drawing. Uh, like these days I will, uh. You know, when I do paint or show half the time, I will be painting. People I know, uh, and often write because, because, you know, you, you focus on someone. If I start focusing on painting you, then it feels like you start to know the other person better.
Like you, you start connecting emotionally with them and, and trying of unpeeling their own personality, their own characters, their own emotion. So I love that part. And there are half the times I’m just throwing. Colors on, on big canvases, and it’s more of the art that I would do when I was seven or eight years old, like abstract art, uh, which is very creative because it’s open-ended.
Uh, the one makes me focus a lot. The other one, um, it’s very open-ended and maybe that, that that’s the linkage to leadership. Like I think.
At least in my case, but it applies more broadly, uh, leadership of a function of a company, of a team, of anything is, is it’s both about having the ability to, you know, take a few steps back and, you know, uh, look at the broader picture in a more relaxed way. Ideally, uh, and, and do some idea generation, um, and, and also at the same time be able to connect with the people, understand the people you are working with, um, inspire, get inspired.
That’s, that goes both ways, uh, and, and go also sometimes into the details, like the things. Um, and I do less and less of the latter, which is relaxing to me, like avoiding sometimes going the details. But building a company quite often, you, you, you still a little bit a plumber, uh. I remember like we’re now in the eighth year, we’re about a hundred people strong and definitely we have a very strong, nice, mature, in many ways, leadership team, but sometimes still things will, you know.
Fall and or be orphan like, you know, something new will come up and no one is responsible. And you have to, you have to be the ultimate protector to come in. Like the person will go in, solve it and make sure next time, you know, there’s a better way for this to be done. Um. Yeah,
Naji Gehchan: I love it. Uh, what is the first word that comes to mind when you hear the word, uh, leadership?
Actually,
Dimitrios Skaltsas: uh, what I often tell to, to my team members is. Like, for instance, we had a person who, who got promoted now, and, and I recall in our discussion, the first thing I told him is, you know, this is about being servant leader. This means that you have more responsibilities over our team members now and external world, uh, than before.
It doesn’t mean that you ask more people from more things from them. It means that. People will expect more from you and I’ll expect more from you. Um, I think it’s, it ties back to responsibility in my mind. Leadership is, uh. Being able to take responsibility, enjoy it ideally. So I think people who thrive in leaders’ roles are people who enjoy responsibility in building, but also even when you don’t enjoy, and there are times where, you know, responsibility can’t fall quite heavy on our shoulders.
Um, you know, you, you’ll bear with it. You’ll make sacrifices. Um. It’s, yeah, that’s, that’s what I think about leaders taking responsibility, conserving others.
Naji Gehchan: What about, um, the first thing comes to mind when you hear clinical trials,
Dimitrios Skaltsas: uh. Uh, expensive and a, and a blessing because often people outside our space, outside our industry, uh, don’t realize why.
You know, we have to run these very well regulated, uh, again, long and expensive, uh, process. Um, I think it’s a blessing that, you know, our societies have come up with this process where we ultimately protect, uh, the patients, uh, what translates, or, you know, what applies well and preclinical. Settings quite often doesn’t translate as expected into human biology.
So I think it’s really good that we have this gated approach. Um, I do think there are many opportunities to get faster, better, stronger, um, and, and we see it happening. Like I think COVID was. It was very interesting in helping us see that there can be different part times of, you know, accelerating innovation.
Um, uh, yeah. I, I think it will be, and I’m glad that it will be eventually, um, really, really long process until we get there. And because it means, uh, you know, pe it still stays well regulated and people don’t take. Too many experiments. I think we ought to experiment and learn and apply technology. That’s what we do.
Um, uh, you know, you have to test things first and, and then things,
Naji Gehchan: well, talking about technology, the third word is ai. Poof.
Dimitrios Skaltsas: You know, it’s in our name, so we cannot escape it. And it’s in our technology. I mean, that’s. In many ways that’s, that’s what we stand for. Um, okay, let me see. It’s an opportunity, it’s a huge opportunity for humanity, for life sciences, for all of us.
Even the person who live. Um, it can, it can help generate a lot of efficiencies, a lot of productivity, uh, unlock our minds and how we do things. It’s risk is, um, it can be a major risk again to. Humanity. I think if, you know, we, um, we don’t apply some appropriate frameworks and ethics and, uh, and discipline into how we do ai.
Um, and that’s long term. I think also in the medium short term, it’s risky in the sense that, uh, you know, we get more and more. Familiarize with those technologies and if we don’t, if we’re not careful enough, they can mislead us like special generative ai. It still hallucinates. I know. You know, it gets better and better and better.
Specialist CP five that was just released. I play a bit with it. It does hallucinate less than the past, but it does hallucinate and, and I see myself where I use a lot of same AI in my, my daily life. Sometimes I forget to apply limits. I, I get too comfortable with it, uh, until I have a reality awakening or that whoa, uh.
Yeah, I think it’s, overall, I think it’s, it’s a blessing. Like most technologies, it can be very, uh, it can be very risky if not applied, of course, in the proper way.
Naji Gehchan: Yeah. Goes back as, as you said, like any technology to the ethics. Integrity and how you use it. Uh, and if you’re using it for good, but it’s, uh, I love how you started it.
It, it is an opportunity for humanity, uh, as several of the advances in life sciences too, uh, that, that are blessing. But if well used the last word is, uh, spread love and organizations.
Dimitrios Skaltsas: It, it is what inspired me to, to come to the podcast. Um.
I think it’s about caring, which again, is one of our values. Um, it’s how you we care about,
about the people and about the organization. And I would say it. It’s different by the context, by the by,
by the level of maturity or by the states are Wes. The organization needs, uh, love for me was very different when we’re. A couple of people in the organization. I was in love with the idea. I was in love with the concept. Uh, and everything was frozen, by the way ’cause we didn’t have too much responsibilities back then.
And then it was different when we were 10 or 15 people. Then I had a very strong instinct of, um. Being in a crew, like almost in a wolf pack. Uh, and I, and I felt this, this love over, over, not necessarily what we do, but over the team. I’m like, you know, we, we need to succeed. We need to protect each other.
We need to take care of each other and grow together. And it’s different now that we a hundred people where, um,
it’s.
Speaker 3: You
Dimitrios Skaltsas: know, it’s a lot of caring for the people, obviously the people that we started with, but also more of the newcomers, but in a different way. ’cause I don’t even know them. Some of them personally, like I know them by never having to. But it’s more about making sure, you know, they have the appropriate career paths and they can grow and they can get opportunities, and they can have a quality of life that fits them, so on and so forth.
Uh, but in a more impersonal level. And it’s also care obviously about the, the customers and what we deliver. So we’re getting closer and closer by the years with our actual users because. You, you know, our work is such that when I think of impact, I’m like, you know, there is this broad impact in the space where we can make this help make the space more product more efficient, so ultimately get better drugs patients faster.
But in our daily reality, we interact with our. Users and much of the energy, much of the reward, the emotional reward we get is by, you know, how we help our users. Uh. Have a better life, like be more productive, their work, be smarter in their work, have better outcomes, and so on and so forth. Um, and caring about the organization as well, like, uh, shareholders.
Uh, I would compete. It’s, it’s a more multifaceted love, I would say, but practical, I think love. Always to be practical, like expressed in, uh, through our contact, through our actions, uh, and, uh, typically translation to care.
Naji Gehchan: Uh, that’s, that’s a great definition, uh, about caring leadership and, uh, love in organizations.
Any final word of wisdom, uh, Demetrius for healthcare leaders around the world.
Dimitrios Skaltsas: I was, I was listening to some other podcast, um, you had, and I was always inspired by that part. Uh, I thought, before I call, the one word that came in my mind is prevention, which is not what we do. It’s very different. But I’m like, if I think about healthcare and life sciences.
I think the world would be a better place if we collectively exercise, invest more in prevention, in how we avoid having to take certain drugs, um, get, go to the hospital, see our, um, see physician or ZP, so on so forth. Like if. It goes back again to where I started. Again, my roots like more of the social public sector, but like if you can incentivize people, society, uh, and, and educate.
People in society and create the right mechanism. Systems, we’re in process where, you know, we can eat better, we can sleep better, we can uh, we can go to our GP earlier and, and make exams and test ourselves and, and yeah, avoid getting, uh, sick, um, to the extent that’s possible, right? It’s in many cases will not be possible, but yeah, eventually.
Naji Gehchan: Well, I feel there’s a new venture that might be building up, uh, for prevention. I’m, I’m all in for it with you. Well, thank you so much again. Thank you so much for being with me today. It was, uh, a great chat and, uh, again, a huge, uh, kudos to all that you’re building and you’re doing to help, um, all of the pharma and biotech world, uh, bring innovations to patients faster.
There is certainly a lot to be done still in the healthcare world. So thank you for much for all that you do,
Dimitrios Skaltsas: Azi. Thank you. Uh, pleasure connect with you and thanks for, you know, creating these, uh. Podcast and, and ground for, for this type of discussion.
Naji Gehchan: Thank you. Thanks for listening to the show. More episodes.
Naji Gehchan: Thanks for listening to the show! For more episodes, make sure to subscribe to Spreadloveio.com or wherever you listen to your podcasts. Let’s inspire change together and make a positive impact in healthcare, one story at a time.
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