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Value Investing with Legends
Jim Flynn - Building a Healthcare Investment Platform Across Public, Private, and Venture
00:46:12
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- [00:02]
A
Welcome to a new edition of the Value Investing with Legends podcast. My name is Michael Mauboussin and I'm an adjunct professor at Columbia Business School and a faculty member at the Halbrand center for Graham and Dad Investing. I'm here with my co host, Tano Santos, the Robert Heilbron professor of Asset Management and Finance at Columbia Business School and the Faculty Director at the Heilbron Center. Hi Tano, how is all with you today?
- [00:25]
B
Everything is good. Just came back from London where I went to spread the gospel of investing for a week and it was a lot of fun. As always when we go to London we meet a lot of interesting investors but also see a lot of alarms. So a fun visit.
- [00:38]
A
Healthcare is a fascinating part of the economy and the market. Healthcare is about 18% of US GDP, touches all of our lives, and seems like an area that may undergo a great deal of change with the introduction of some of the new tools in artificial intelligence. It's also a sector that is both large in public and private markets and has substantial interaction with governments around the world. Our guest today is an ideal person to walk us through the opportunities and challenges that are out there. We're delighted to welcome Jim Flynn, Managing Partner of Deerfield Management. Deerfield is a healthcare investment firm that manages $16 billion in assets with a mission to achieve healthcare through investment, intelligence and philanthropy. Jim joined it in 2000 and is responsible for managing the firm and overseeing its investment activities. Under Jim's leadership, Deerfield has expanded into multiple areas within the healthcare system the firm has built. Deerfield Intelligence, expanded into venture and private structured financings, created Deerfield and Foundation, developed Deerfield Discovery and Development, and founded cure, a healthcare innovation campus in New York City. Before Deerfield, Jim was an analyst at Furman Sells, covering pharmaceutical medical device companies, served as vice president of corporate development at Alpharma, and began his career at Kidder Peabody. Jim earned his degree undergraduate in cellular and molecular biology and economics from the University of Michigan and a master's degree in biotechnology from Johns Hopkins. Jim, welcome to the podcast today. Great to be with you so much.
- [02:16]
C
Great to be here.
- [02:17]
A
Let's start at the beginning. Can you tell us a bit about your early life, your interest in biology and economics, which you studied as an undergrad, and how those two fields kind of led you to healthcare investing today?
- [02:27]
C
I think we all kind of just wend our way to a certain extent when we're talking about college and early years after college. My dad was a physicist and scientific thinking was always very prevalent in my household. My mom was a social worker. So human welfare was always a strong topic. And somehow I ended up exactly at the intersection of those two things. Scientific field that ultimately is about human welfare. So I don't think it was all accidental. But when I graduated from Michigan, I really just wanted a perch to view the world from. So I interviewed at consulting firms and investment banks and the Federal Reserve and all that kind of stuff. And I ended up meeting a guy who became my mentor at Kidder Peabody, who was a biotechnology and pharmaceutical analyst. And we hit it off. And so that's where I kind of started and ended, I guess.
- [03:20]
B
So Jim, do you remember when you were a young man when you were studying, being interested in investing, Were you already interested in a potential career there? Were you doing any trading stocks? What was your first memories of being interested in an investment career?
- [03:32]
C
I think it was less being interested in investment as again it was just interest in how things came together. So I was interested in the psychology of people in the market. I was interested in the analytics around business and the market. And the market is such an interesting combination of different components of humanity and our lives that I was generally interested in it. But it wasn't so much specifically investing itself that captured me.
- [04:06]
A
So Jim, Warren Buffett's got this famous line where he says being an investor made him a better business person and being a business person made him a better investor. You started your career as a healthcare analyst and then you spent some time inside a corporation working in corporate development and licensing. So can you speak a little bit about how that operational experience, along with that analytical experience affected or shaped the way you analyze, assess companies or businesses today?
- [04:30]
C
As an investor, I can tell you that I felt like a pretender to the throne as an analyst. In the starting, you know, I was opining about Erk and GlaxoSmithKline and all of these things based on paper analysis and really had not been in the mealy myself. So I was very interested in understanding how business actually worked as opposed to kind of the ivory tower view. And I had the opportunity to work for one of the companies that I covered. And so I had the outside view and I had the perfect opportunity because they offered me vice president of corporate development in charge of strategic planning. And it turns out that, you know, as a 20 something year old who had never been within a business like that, you are utterly unqualified for that role, despite how much you might know about the industry. And I basically just read every single book I could find about corporate strategy, corporate development, and in trying to put it in practice, you realize that what sales wants to sell, manufacturing doesn't want to make, and what R and D wants to develop, sales doesn't want to sell. And they all have different functions and different interests. And you have to create a Venn diagram around those things. And there's this continual tension in real life between people of different phenotypes, persuasions and occupations trying to have all of the oars move in the same direction. And it was wonderfully enlightening about how hard it is to actually have a functional business. You know, how many things that seem really stupid from the outside are perfectly reasonable to happen from just a human perspective. So that was transformative in my ability to sort of rethink about what to ask about and what to think about when thinking about a company or an investment.
- [06:30]
A
You mentioned this sort of deep dive into strategy and development stuff. Did any of those books or ideas you find continue to be useful today from that formative period 30 years ago?
- [06:40]
C
I don't think so. I think they just sort of created different ways of organizing collective thought and trying to optimize the value output from that. So there are different charts and metrics and systems and ratings and processes, but I don't know, at the end of the day, it's economics and human behavior and the ability to have disciplined activities around those. That was my takeaway, really.
- [07:06]
B
But I find fascinating, something, I think you hinted at that in this business. Right. I love the way you put it, that what we want to do R and D on may not be exactly what the marketing department may want to sell, that there's kind of a misalignment there between kind of the social and private returns that you have to bridge as an investor. You have to understand this gap to see what is feasible, what is going to work in the market, and so on and so forth. Let me ask you a little bit. So you were talking about when you were with this company, Alpharma was the name of the company. And then in 2000 you joined Deerfield. It had been founded a few years back. What attracted you to make this jump and to go to Deerfield? What kind of firm you encounter and how is it different from the firm you're leading today?
- [07:52]
C
So I absolutely did not want to work at a hedge fund. So after Alipharma, I left because they merged and was temporarily lost in what I wanted to do. And I went back to the sell side and I worked at. Actually, the guy who hired me at Kidder Peabody had become the director of research at Furman sells. And so I called him up and I said, you know, I'm back. And he said, good. And so, you know, I went back to the role as an analyst, hopefully a better one at this point. And I did that for five years. And being an analyst is an interesting occupation because you basically get to make and publish your own product. It's your own thoughts. You publish it in the way that you think represents yourself and hopefully markets yourself. And then you go around the world and you try to drum up business around that. So, you know, in a way, it's kind of pure because you're marketing yourself. It's kind of fun. But at the end of the day, it's kind of a transient business as well. You don't look back and say, okay, here's the volume of my work. Here's the impact of my work. At my heart, if I have a religious philosophy, it's mostly aligned with Buddhism. And within Buddhism, there's something called the Eightfold path. And one of those Eightfold path is right occupation. And right occupation means, basically, look, if you're going to spend the most hours of anything you do on something, it should be aligned with your moral philosophy. Hedge fund was not. It was just as sort of hamster wheel as being an analyst, but even a faster hamster wheel. But the guy who founded Deerfield was someone I really liked and respected. I overlapped with him at Pitter Peabody, and then he went off and did healthcare for Juliet at Tiger. And then Deerfield was founded a little bit as a lark. So he was at the end of his career. A few other analysts in the space who had been there for 40 years were at the end of their career, and they said, hey, let's start a hedge fund. Let's do it for three years, then let's all retire. So they created Deerfield. Arnie kind of caught the bug, but the others retired. And so I was really replacing one of those that was retiring. And they said, this is the best thing ever. You got to come here. And I was kind of at a weak moment because I didn't want to do what I was doing. I didn't want to do that. But I liked the people, and I thought, okay, why not? So I ended up joining Deerfield as a analyst in 2000.
- [10:11]
A
One of things seems distinctive. I mean, a lot of most investment firms organize by sort of instrument like public equities or venture or growth equity or whatever it is. Deerfield seems to come at this a little bit differently, which is sort of like, what are the Healthcare issues or problems or opportunities and then sort of use whatever instruments make sense. First of all, is that an accurate assessment of the organizing principle? And if so, like, why is that the best way to approach this problem?
- [10:36]
C
I would say yes, but there's sort of a 20 year gap in getting to there. So when I joined Deerfield, it was purely public equity. Purely purely public equity. Arnie asked me to run the firm in 2002, and in my mind it was absolutely not. I'm kind of here as a holding pattern. But then I thought, well, wait a minute. We have this incredibly rich intellectual dialogue about healthcare from these insanely experienced people across each of the components of healthcare. So medical technologies, healthcare services, biotechnology. I wonder if we could turn this firm into a company that legitimately advances healthcare. So how do you legitimately advance healthcare? Well, to me, there has to be a venture component. You have to be bringing new things to the table and bringing them to life that actually impact human health. And the other part was the not for profit side, because when you look at actually having a functional health care system, at least half of it is not for profit. And half of the motives you have to have in order to advance human health come from that side too. So to legitimately be about that, you could combine those. And guess what? That's right occupation for me. So let me see if other people were on board. So other people were on board. So I officially started managing Deerfield in 2005. That was the year we started the foundation, and that was the year that we started the march towards what you talked about. So first we did structured finance in public companies. It was the nearest adjacency to try to understand what additional skills we needed to start to structure bonds, royalties, other things like that. We then created a vehicle where we could do that same structured finance for private companies, which added another element. Then we acquired a company and then we started to take the step towards being able to start from the venture side. So we weren't naive about the fact that each of these links were tenuous and that the understanding of the science was the same, the understanding of the system was the same. But the application of it and the things that you needed to bring in order to be successful in each of those different components was different. So it took us about 20 years to fully make that march. You asked another question, like, is that the objective? Why should you have that objective? Well, in my view, you wake up in the morning and you say, okay, this stuff seems exciting. A year later you realize you're wrong. That's not the most exciting part. You wake up in one morning and you have 10% interest rates. You wake up the next morning, you have 0% interest rates. You wake up one morning, you're in a value environment. You wake up the next morning and you're at the top of a bubble. So should you really have a predefined definition of where in healthcare you're going to invest and what tools you're going to use to create the optimal return given the environment you're in? To me, what you want is you want to have a complete tool set and a complete knowledge set and every day wake up and say, hey, what's the right thing for me to do today? Not what did I think it was a year ago when all of the conditions were different. That's what we tried to build and the philosophy around it.
- [13:58]
B
If I can bring back what you said before, if you want to reach this multiplicity of objectives, which I understand in the healthcare industry because of its fair nature, right. It's are multifaceted. There are different stages. You may not know the stage at which a particular medical technology, a particular pharmaceutical company is at. You're going to need to have a lot of flexibility and in particular if you want to really bridge a little bit of that gap between social and private returns. So the kind of investment opportunity says something about what is the optimal vehicle to invest in that particular opportunity.
- [14:32]
C
The right vehicle also changes when you're already in it. So one company that you had suggested we might talk about was Melinta Malinta was a hospital anti infective company that was public and we did a structured finance in that we did senior secured debt plus warrants, which by the way, we love because there's a component that protects your basis and then there's a component that allows you to participate in sku. So whatever, we can bifurcate investments to try to protect what we have and also participate in the upside. And probabilistically, what upside tool you want to use. It could be a royalty, it could be a warrant. It depends what you think about the underlying conditions at the beginning. But Covid hit and what Covid basically meant is that anything that was inpatient hospital either went bankrupt or should go bankrupt and they did. So let's say we're just a credit company. Company that understands credit. All right? We lose all of our money or most of it, but instead what we did is we credit bid and we took over the company and we operated it for six years and we completely rebuilt the company. We Acquired new products, we re outfitted the management team, we gave it durability during COVID and ultimately, you know, sales went from 60 to 125 million. EBIT went from minus 30 million to plus 25 million and we sold it for over $300 million. So by having that full tool set, we went from being a credit investor loser to being a winner because we could also operationalize the company ourself. And there's many different variations of that same thing where you want to transition your expression protection and skew within an investment depending on how the company migrates or how the markets migrate. So to me it's a very, very powerful capability set.
- [16:26]
B
Can I ask you a follow up on this, given that we're talking about it and the Melinta situation, which I read a little bit about, because I thought it was absolutely fascinating what you guys did there. I thought part of the problem in that space as well, at least according to my readings, and I don't know much about this space or as much as I should, is that part of it is that the. What is it that the antibiotics model was broken. There were a lot of companies that were experiencing a lot of problems. So you're facing not only whatever may be going on specifically with this company, but the larger challenges of this space. Do you select that company or were you monitoring that company because you thought this is an interesting space that is going through some structural challenges and we can benefit from understanding this well and select a potential winner in that group. How do you approach in the context of a larger antibiotics model is broken type of framework?
- [17:17]
C
The antibiotics market is a very tough market, basically because hospitals reserve their best antibiotics for the worst patients because they don't want antibiotic resistance to build. So you come along with a great product and they put it in reserve, which is a terrible business model. You want people to use as much of it as you humanly can. You know, that's a component of it that was broken. You know, we felt in the first instance that these would become big enough products that the company could become profitable and more valuable. But in owning it ourselves, we reconceived it not as a antibiotics company but as a hospital company. Which meant that we went in and we licensed and acquired additional products that effectively used the same skill sets that the company had, namely to be able to get on hospital formularies, to engage with hospital physicians, but had many more modalities to them than simply antibiotics. And some of them were still in the anti infected space. So for example, we licensed an antifungal product, but the mandate was broader and the potential was better.
- [18:28]
A
So, Jim, I'd love to shift gears just a little bit, but there's just a ton of excitement and hype and concern around artificial intelligence and drug discovery and healthcare services. Love to just hear from you, like, where you think the market may be underestimating the impact of AI, where the market may be overestimating the impact and maybe even how it sprinkles into Deerfield. Discovery and development or even cure from your point of view.
- [18:52]
C
Yeah, this is a huge topic, and there's a lot of different directions that a lot of different balls of yarn that we can untangle in this area. I would just start out and then maybe you guys can help push this in different directions that you find interesting. But I think it's going to have a massive impact on healthcare delivery overall. So just to give you a taste of that, if you feel like something is going wrong with you or a spouse or child says, oh, you know, this thing is happening to me, what do you do? You ask ChatGPT, and it gives you an okay answer. It gets you started on something. You know, keep that frame for a minute and then let's bounce over to this other frame and come back. So this other frame is most diseases are rare diseases. There's just thousands of them. Thousands and thousands of rare diseases. What is the chance that your primary care physician is going to correctly diagnose a rare disease? What's the chance that a specialist is going to diagnose? Why do we get second and third opinions? Now? When you think about a large language model, it can have everything we know about every single one of those diseases, and it can understand exactly how to narrow down on what you have and what you don't have. Okay, so keep that over there. Why is that a physician tool and not a patient tool? If you're going to look at ChatGPT anyway, why don't you look at the real version that just says, okay, have you taken a genetic test? No. All right, I'll go take a genetic test. What did it say? Here's what it said. Okay, have you had a blood test? Go take a blood test. Okay, why not put that in. The individual has access to much, much more than the best state of the art physician can give you today in most fields. So, you know, there's a glimpse of the future right there and how AI is eventually going to make the consumer the dominant force intellectually, practically, functionally. Because if you know it, your physician better know it and even if they really like the way that experience drove medicine in the old days, it's just not going to diagnose as well as a well trained algorithm. So fundamentally it is going to completely change healthcare and it's going to get rid of a lot of the waste, a lot of the confusion, a lot of the unnecessary anxiety that's related to between all of these complicated interfaces that we have right now. Drug discovery is a different world. AI has already had a pretty big impact. So you know, the Nobel Prize was won for the three dimensionalization of proteins. Proteins are the targets of most drugs and you know, there's software that's been developed since that maps the charges of proteins since they're multi charged molecules. And basically your drug needs to be an anti magnet for the right place on the protein so it sticks to it and it works. So all of that helps you design drugs. But you know, that's kind of at the earliest stage and AI has yet to really transform how we do clinical trials and things like that, which are the most expensive and time consuming parts of drug discovery today. You know, there are things that are coming.
- [22:14]
B
I appreciate the taxonomy between healthcare delivery and drug development. Let me go to the first one, healthcare delivery, which I agree with you. It can be absolutely transformative and I love that NLMN can be a tool for the patient rather than for the physician. But can I ask you a little bit, how much you guys think about issues of adoption, kind of the organizational constraints associated with adoption of these kind of tools? How is the American Medical association going to react to the introduction of AI? How are the doctors going to resist the potential competition that is going to come from an LLM that will allow the self referral of the patient directly to the specialist, bypassing the gp. How do you guys think about those issues? I understand the value of the technology is going to be phenomenal. I'm wondering about, for lack of a better word, the sociology of this, how this is going to be adopted by healthcare organizations and so on. How do you guys think about that?
- [23:12]
C
For me, it's kind of idiosyncratic and unknowable how this will ultimately shake out. But people conform to what they have to conform to. It simply will not stand that the patient knows more than the physician from a physician standpoint or from an institution standpoint. And when that becomes the case, things will change. I mean, you just simply cannot be a physician who has a worse opinion than your patient. So I'll just tell you from some of the institutions that we work with and you know, we have partnerships with medical institutions, research institutions. We just formed one with Hospital for Special Surgery, which has one of the richest data repositories in all of musculoskeletal disease. Which is part of the reason we're so excited, because the millions of patient records they have and the two and a half decades of follow up and the four million scans they have is going to allow us to create a lot for physicians and patients to be better educated about what best course of treatment is going to be. By the way, insurers also, and part of the vision is to create patient facing products, you know, if you're the first to do it. So if you have an HSS portal that allows a patient to understand what they're dealing with better and you know, that informs how they access the system and perhaps gives them preferential access to the system and it's from a global leader in that field like hss, then I think that can pave the way for a transformation in that space. And if a physician outside of HSS isn't up to speed, you know, that's kind of to HSS's benefit. So I think there are various ways that our institutions themselves can lead informational access and take the lead in this transformation. And I think there are some fields in which it's easier in some fields in which it's more complicated for the transition to occur. And it obviously has to occur in the easier ones first.
- [25:12]
B
So Michael and I were discussing precisely the point that you just finished your answer with, which is this issue of field selection. And we had picked up on this thing of this HSS program and your partnership. There is your selection of the fields that you're going into and the companies or the partnerships guided by the availability of data in a particular field that allows you to say, oh, I can see that I'm going to be able to train a model very, very well to really diagnose those conditions, the conditions in this space very well. Is that featuring the way you're thinking about your investments and partnerships, Jim?
- [25:49]
C
Absolutely, yes. But even more so, software in and of itself in most cases is hard to invest in. And the reason it's hard to invest in is you have no idea about obsolescence risk. You can't model it, you don't know it. Very difficult. Data, however, is a moat that can even be too deep for anyone to get over. You know, when we think about software investments, we really think about data investments. So do we know that we have data that is going to be really difficult for someone to replicate and that it can inform a software model of something valuable. And if the answer is yes and yes, then we get very, very excited about it because software clearly has the ability to have different kinds of output from, particularly if you can combine orthogonal data sets in an interesting way than it could in the past. And so we're very much on the lookout for, for data. We built the internal capability through our Deerfield intelligence group, which is, you know, more than 40 data and software people, to actually create the software around that.
- [26:59]
A
So Jim, I'd love to hear more about Deerfield intelligence, but also maybe we can start with Deerfield discovery and development. What led you to decide that you should get into that business? What insights are coming from that? Maybe tell us what it is in the first place.
- [27:11]
C
So I think you guys might be interested in this because this gets back into thinking about advantaged investments, moats, investment models. I think it thinks a little bit non traditionally about the overlap of those things. So when we were making our stepwise move towards venture, we had the view that venture investing in biotech had been bad for decades. In looking at it, our view was that it was bad not because you didn't make a lot of money if you made a good investment, but that 90% of the time you were going to lose and you lost a lot when you lost. And so we thought about, well, how can we fail cheap? Because if we can fail cheap, this is the best business model ever because you make so much when you win. So in looking at that, the problem is that the venture model is to license in a compound, to lease a lap. All right, you're in for 15 million. Hire people. All right, you're in for another 15 million. Do studies. And by the way, none of those people will want to dissolve the company and they will always have another good idea. So for a minimum you're out 50 million, but probably you're out some number of hundreds of millions. So what we said is, well, could we turn that into a marginal cost business instead of a fixed cost business? We spent about $500 million and we built out the cure, which is a building in New York City where we have built our own labs and we have hired our own drug discovery team which is led by Frank Nestle. Frank was head of global research and development for Sanofi. So $7 billion budget, 125 drugs and we have a very senior drug discovery cast, but they work on everything new we do. So everything we do goes through CURE and this team and the funds are charged marginal cost. So when we're failing, we're failing for five or ten million dollars. And when we win, it can look something like new Valent, which is plus two and a half billion for the funds. So we've also built our own genetics team here, led by Matt Nelson. Matt was head of genetics at KlaxoSmithKline. He also wrote the first paper in Nature that showed if you had the right genetic association, your probability of success doubled. And as soon as we saw that, we said, okay, we have to have him and we have to build a team around him because we need to expand the idea of how to up our probability of success. And then we acquired an antibody company because antibodies are twice as likely to work as small molecules. So if we had two times the probability from genetics and we had two times the probability from modality, we're now four times the probability in operating on marginal cost. So that's the kind of thinking that went into building out cure the 3 DC, which is Deerfield discovery and development and some of the other associated teams that we have here.
- [30:08]
B
Dfili is one complex baby and I appreciate that very badly. So let me try to connect your answer you just gave to Michael with the public side of your portfolio, so to speak. So on the one hand I want to ask you on all these partnerships that you have with university and research centers, you know, it's an investment Dfield is making as well. How do you, do you worry about the knowledge being spilled over? Do you worry that somehow a lot of that research will be made public somehow and you won't be able to capture the returns associated with those 10, $15 million of marginal costs as you call it, that you're putting in all these different process and how does that help you? How does that knowledge help you? How does that inform the side of your public equity portfolio?
- [30:54]
C
Jim, look, we're one team, one dream. We actually have an 8:30am meeting every day with all 200 Deerfielders on. It doesn't matter what you do. Public, private, Deerfield intelligence, executive assist. You know, it's a team sport here. There's a lot of data out in the world that shows that innovation and idea momentum comes from lots of small interactions and people knowing where to self recruit and inform. And we have a lot of our ability to manage complexity that's driven from self navigation through corporate knowledge. Obviously we have disciplined also teams that are built around actually moving things to completion. But if you work on the public side, one of your biggest questions is when is this thing that this public company has going to be obsolete. What's coming behind it. And having a view into the private side clearly helps that. Likewise, you know, your private folks tend to be very good at digging in, looking at corporate governance, deal structure, navigating operational issues. But our public folks are completely current on everything any company that might buy them is doing so. All the big public pharmas, all the big public medtech companies, all the big public insurers, everything that's going on in Washington, all the current conferences. So if you're going to invest something on the private side, you better be aware of all of that as well. And so we see strong value on both sides. We have no issue restricting things here. There's any thing that we learn that is conflicting, we just restrict it and wait till we're not restricted. It's a small price to pay for everything else that it enables.
- [32:45]
A
So, Jim, a moment ago you mentioned New Valent. I wonder if we could take a step back and just you tell us the New Valent story a little bit. Is almost a case study of what you guys do at Deerfield. Manager. Love to hear it from the beginning. And it's a very large position portfolio. Now to where we are now.
- [32:57]
C
This is a great case study. This company came from an idea that a Harvard chemist had named Matt Sher to target tumors differently. So basically, like viruses, tumors mutate. And so you give someone a drug, it works, and then it doesn't work, your tumor is mutated. So his idea was to be able to attach to the proteins differently in order to mitigate that migration. We formed this pre ip, just kind of as an idea. And it was an idea that we liked. And we said, all right, we gotta prove that this works in the test tube. We allocated $2 million and we proved that it worked in a test tube. And then we said, all right, what are absolutely the best tumor types to go after? Or maybe we should go after antibiotic sort of comparables? And we investigated a bunch of stuff and we came up with some ideas which included lung cancers. And we said, all right, we have to prove that this works in animals. We gave the animals. And by the way, this was all a hundred percent funded by Deerfield. There were no syndicate, no nothing, just us. And we basically said, all right, let's give these animals two types of lung cancer with different mutations, one with Ross, one without, and let's see if we can create more durable products. But by the way, all of the drugs in this class seem to hit this very similar Target called trec. And TREC causes all these CNS side effects. So let's try to edit that out while we're at it. So we spent a lot of time trying to optimize the molecule and when we had done it, we demonstrated that it worked in animals and then it was really easy to syndicate at a much higher value. And so we did a syndicated round at about 400 million and then we took it public at 800 million. And we made it right through the window. When the biotech bubble was ending, it subsequently crashed. But Neuvela did great all the way through because its drugs have demonstrated that they're, I think, clearly best in class in both ROS1 mutant lung cancer and ALK mutant lung cancer. They're both filed with the FDA now. They both should be approved this year. And so it's a pretty exciting time. The company's worth close to $10 billion today on the back of those two trucks. And, you know, they've got a pipeline of other stuff behind it.
- [35:23]
B
You know, one of the things that I surprised to see this when we're getting to know DFE a little bit better, is that your second largest position is actually in senior care. I've been trying for a long time to develop an interesting case on senior care for my students because they're fascinating market where the economics are really important and the data that the annual reports give is quite a bit. And I wanted to understand, I mean, it's incredibly rich set of fields that you guys are present in all throughout the value chain associated with healthcare provision. How do you end up with this position? How do you come about it? What do you think the challenges are in that space? You think it's going to be very competitive? How are we going to solve some of the problems not just in the United States, but in many other places when, as the population gets older. How are you guys thinking about that?
- [36:08]
C
Overall, some PCs are more sophisticated than others. This is pretty easy one in some ways. So Covid happened and a nursing home was a terrible place to be. So 10% of people within nursing homes died. And the nursing home business is kind of like the hotel business. No occupancy, you lose a lot of money, full occupancy, you make a ton of money. So occupancy went down to 60% or something like that. And all of these companies, which are highly levered, were losing cuts in life. It was a very, very, very precarious place to be. And coming out of COVID even when you realize that the environment was not as precarious for people who were going into residential care in some form. Nurse pricing was up to about a million dollars on your incremental nurse because of the scarcity of nurses. And so the business model was horrible, like really, truly terrible. However, as we probably all are aware, there is a demographic wave and the average age of getting into a nursing home is 80. And the number of people turning 80 is a million a year. And it doesn't take much to do the math unless nursing homes are. Supply is expanding. So because of the marginal cost and the unprofitability, and by the way, the price of building going up, and by the way, the price of interest rates going down, the supply of nursing homes was actually trickling downward. And so you just sat there with a piece of paper and you said, okay, everyone hates this, they're losing money. But how does the math not work on this industry over 5 years? How does it not work? And so we did a lot of work on how much pricing nursing homes were going to be able to push through in the interim. You know, we came back satisfied that it was meaningful. And, you know, we watched carefully the price of the marginal nurse as we got farther from the pandemic that came back down to normal. But interest rates stayed higher and building costs stayed higher. So actually the number of nursing homes even today is flat to down from when we entered Covid. And if you look at companies like Brookdale, which were at 64% occupancy, they're now up at 84. And what happens is when you start hitting the 80s and 90s is you start raising your price a lot. Because when you have no occupancy, you need to discount. When you have lots of occupancy, you can afford to charge a premium. And so your average mix actually improves as your volume increases. These companies are in extraordinarily good place today.
- [38:46]
B
It's funny that you speaking like a true value investor during the pandemic, you will be amused to hear with the students, we're looking at industries that were incredibly problematic because of those very tragic circumstances. And we should not make light of it. So we look at tourism, we look at senior care. And that's how I got interested in this space. Because I had exactly the same reaction that you guys seem to have. You know, that there were awful places to be in that particular moment. But that of course, course, the long term need for these type of services was not going to go away because of this.
- [39:20]
C
Well, you'll be glad to hear that our investments Private or public, are long term cash flow based probabilistic models. Because without that, you can't really look at the interface between different types of securities and the probabilistic trade offs that you're making on the risk and return. So everything we do here, even long term drug discovery, is really in a value context.
- [39:45]
A
Jim, this is a perfect setup for what I'd like to ask you next. Drug pricing, reimbursement, FDA regulation, policy uncertainty, these are all part of healthcare investing you guys have to deal with. How do you consider those political and regulatory risks when you're thinking about probabilities and scenarios for outcomes?
- [40:03]
C
I'm very long term about it. The nice thing is that we've had proving grounds all around the world. So Japan thinks about it one way, the UK thinks about it one way, Italy thinks about it another way. But guess what, if someone comes up with something transformative, people pay for it. Where the arguments are is with therapies, where there are trade offs, this works, but it only works for a month, or this works, but you feel horrible. There are people have different value systems around what to pay or if to pay. But when you look economically at something like Alzheimer's, where we're assumed to have 8 million people costing the system $200,000 a year each, you could have a $100 billion drug and save the system money and create all kinds of economic value on top of that from the functionality of those individuals that previously would have required just care. There's always a good economic argument for things that are truly valuable. And yes, each system struggles with how to pay for it changes its mind about where the edges are around the things that you should and shouldn't pay for. Tries to squeeze money out of the system, but it's a wobbly line that wobbles around reasonable because we want what drug discovery does for us. One of the interesting things for me is think about how amazing the US system is right now. So right now a generic will get 98% of volume within a month of when a patent goes off. So just imagine for a second that every drug for every disease could be invented for tomorrow. Society paid what it needed to pay for the 20 year patent, which is the maximum. And then every disease had a cure for almost free forever after that. That would be unbelievable. So really what you want is you want as much innovation as fast as you can possibly do, bite the bullet and then society's in an amazing position for the rest of time. And the problem is that it kind of happens in these dribs and drabs. And we see this big expensive drug and we're not prepared for the next big expensive drug. And so each one is kind of this point in time argument about cost benefit. But when you really look over the long term, this is an amazing system for ultimately getting an incredibly cost effective approach to treating almost all serious disease.
- [42:36]
A
So, Jim, one of the things we try to do is prepare our students for successful careers in investment management. I'd love to hear from you, you know, what skills you think will make a healthcare Investor more useful 10 years from now than they are today. I guess another way to say it is what skills would you look for in young people that you might want to bring into Deerfield?
- [42:55]
C
You have to make good probabilistic assessments, but that means you have to be able to understand what goes into building up that probabilistic assessment. We have people here who started everywhere. Science is just a language. It's like learning Spanish. Anyone can learn to speak science, anyone can learn to speak economics, anyone can learn to speak nursing home. But you have to study the language and you have to really become fluent in it, which requires a lot of discipline and parallel learning. And some people are really good at parallel learning and some people understand the framework that that parallel learning needs to go into. You have to be very good at hierarchical assessment of what to learn next, because you have to be learning the thing that matters the most and that changes. And so it's a field where perception and calculation walk step and step.
- [43:56]
B
That's a wonderful way to enter into a last segment. We always ask the same two questions at the end of our podcast, so I'll start with mine. Jim, so what worries you about the future of healthcare market, society, investing, and what excites you most? What keeps Jim flim awake at night?
- [44:14]
C
So I've been doing this 38 years, right? You have to come to peace with a lot of different things to be in the field. I live and die every day through the companies and products and services that we're trying to advance. And sometimes I feel like it's such a tragedy when some of them are going to fail. And you die a thousand deaths when you realize the impact that something can make and just so many things that get in the way of progress. So for me, when I worry and stay awake, it's really kind of tactical. It's not sort of the big picture or, you know, markets are doing this or that they're going to. But sometimes there's bad luck, sometimes there's good luck. And you just live and die the day to day of that.
- [44:59]
A
And Jim, what are you reading or listening to these days and are there books or a particular book that you would recommend to our listeners?
- [45:07]
C
Yeah, I'm very hierarchical. So when I needed to understand large language models better, it was just every second I spent was trying to make sure I understood how they calculated things, how you did them differently, what they could apply to how they're moving. I tend to be doing really boring sounding deep dives into different areas like that. There's no book recommendation. There's no it's just very contextual for me.
- [45:39]
A
Well, this is a fabulous conversation. Jim Flynn, thank you for joining the Value Investing with Legends podcast. And to all of you, thank you very much for tuning in and we will see you in our next episode.
- [45:50]
C
Thank you for listening listening to this episode of the Value Investing with Legends podcast. To subscribe to the show or learn more about the Halbron center for Graham and Dodd Investing at Columbia Business school, please visit grahamandodd.com Thank.
- [46:12]
B
You.