Just prior to being feted at the White House, Jonathan Hirsch, co-founder and president of Syapse, lays out the company’s aggressive plans to dominate precision medicine and bring “-omics” to routine health care.
Steve Krupa: Hello, welcome to the Breaking Health Podcast. I’m here with Jonathan Hirsch from Syapse, a precision medicine software company. Welcome, Jonathan.
Jonathan Hirsch: Thank you.
SK: So before we dig into this, I want to say that you’re probably like the fourth or fifth graduate school dropout I’ve had on the show, and I don’t understand how that happens. Whet’s going on with that? Is it just no fun to be in grad school anymore?
JH: well, they certainly don’t pay enough. But Stanford’s a fantastic place –
JH: And if you’re going to drop out as anywhere, Stanford’s a great place to drop out of.
SK: That’s right.
JH: Because the ecosystem in Palo Alto, as you know, is so incredibly friendly to entrepreneurs. Stanford does a fantastic job of making you feel welcome back on campus. Even after you’ve dropped out, there’s always a place to go back to grad school. It’s a fantastic environment for entrepreneurship out here.
SK: And there’s plenty of capital out there to fund great ideas. So you are, as I mentioned to you before we got started, the beginning of our exploration here into precision medicine. So if you don’t mind, I’m going to take advantage of the fact that you are a biologist by training and an entrepreneur of course, and just have you set the table a little bit for five minutes here for the listeners on talking about precision medicine. And I guess the first logical question that comes to my mind is give me your definition of precision medicine as it relates to your business and as it relates to what’s going on clinically today in the system.
JH: Yeah. So we think about precision medicine as actually a pretty broad concept. So a lot of people define precision medicine as using genetic data to guide the care of the patient. And that’s part of it, but that’s not the whole story to us. So when we think about precision medicine, it is really the precise, data-driven quantification of a patient’s disease state, and using that to determine the effective therapy. And that does include genomic and molecular data, because as we’re understanding right now, all diseases are underpinned by a combination of genomic and molecular information as well as behavioral data. But it also includes using the standard clinical metrics that we’re all used to tracking to reach a proper diagnosis of that disease. So it’s really just fusing together the legacy practice of observational medicine with the practice of precise disease biology quantification of measurement through genomic and molecular data, plus the phenotypic or the behavioral quantification, bringing those three things together so that you can properly diagnose a disease, and you can properly figure out the treatment. So oncology is a great example. What’s breast cancer? It turns out that breast cancer is 50 or more different subdiseases that all happen to manifest in the same anatomical location. And you need to figure out which of those subtypes you really have in order to figure out what the proper treatment is. And that’s just one of many, many, many examples. So that’s how we think about precision medicine. It’s data-driven quantification of a patients disease state, and using that to determine the right therapeutic options.
SK: And is it primarily today oncology? Is that really where all the action is?
JH: So there are 3 areas at a minimum, maybe even 4 areas, depending on how you break out the clinical services, where precision medicine is already a part of what we would consider standard routine care. So clearly, oncology is number one. Number two is an area that might surprise people, but it’s actually maternal fetal medicine. So that involves carrier screening of patients or of parents before they’re going to conceive. It involves a space called NIPT, non-invasive prenatal testing, which is really within the past 5 years just totally taken over how pregnancy is looked at. And then post pregnancy testing for complications. So it’s really, it’s a huge area. The third area is what people would call rare and undiagnosed diseases, where there are children or adults who have unknown disease origins, and you need to look at the underlying molecular and genetic data to figure out the cause. And then the fourth area is disease risk profiling. So the biggest is cancer risk, but there’s also risk for different severe cardiovascular disease, risk for diabetes, risk for various other areas. So those four clinical conditions are really standard precision medicine practice today. Now precision medicine is starting to move very aggressively into other areas, so areas like infectious diseases are becoming very popular, where you can figure out what the pathogen is that the patient has, and match that to the right antiviral or antibiotic a lot quicker. With antibiotic resistance, that’s becoming a huge area.
SK: I mean a lot has happened in a short period of time. I can tell you just from my personal experience, I’ve got three young – I’ve had two kids – sorry, three, I don’t know where that came from. I’ve got two kids, a three-year old and a one-year old, right? And the difference just between our experience in terms of the prenatal testing between those two children was substantial. I mean for my youngest, there was all sorts of available tests to sort of narrow down your probability of certain diseases at birth that were not offered just two years before. So the pace of innovation and the pace of these products getting to market has, I think, increased pretty substantially over the last couple of years. Is that something we should expect to continue over the next four or five years?
JH: It’s only going to accelerate as new testing technology comes onto the market that’s faster, cheaper, more accurate, and can be put – and this is the most important part – can be put into the hospital’s laboratory, so that it’s part of a standard routine test that’s done in the hospital’s clin lab or pathology lab. That’s unlocking the whole market. It’s pretty incredible, the rate of pace that you’re seeing there. The other thing that’s really interesting is that a lot of the times, physicians in healthcare get knocked for being slow adopters of new technology. But what you’re seeing here, I mean your personal experience bears this out. The past few years, the rate of adoption by the clinical community is just incredible for this new technology. So when the technology makes a difference on the care of the patient, the outcomes that are achieved, and the fundamental cost structure, people adopt these new technologies rapidly. So I think that’s really the best evidence of the clinical effectiveness of precision medicine is just the rate of adoption by the physician base of the technology.
SK: Yeah. So we’ve got, just to reiterate, you’ve got oncology, maternal fetal medicine, rare or un-diagnosable diseases, disease risk, which would be, you know, a risk for breast cancer, which we know about from popular culture is a very big issue. And then ultimately infectious disease. And I think what you might mean by that is that we have discovered, even in the hospital setting, either in our country, in other countries, certain infections that we have not – we have a lot of trouble breaking the code on. And just to expand on that one question, the idea that we can actually test, maybe break the code on that particular organism and somehow find something or some combination of drugs to treat that uniquely. Is that what we’re talking about when we think about using precision medicine for infectious diseases?
JH: Exactly. Every pathogen is – it’s an organism. It has DNA or RNA or some molecular signature, just like humans do, just like our cancer does. The similarities between infectious diseases and oncology as it relates to precision medicine are actually striking. So in both cases you have some sort of invading group of cells that are coming into the body and attacking it. Those cells have a different molecular and genomic signature from the rest of the body, and your goal is to kill those cells. A very similar process. So really what we’re talking about is if a patient is, let’s say, hospitalized and they acquire an infection in the hospital, and the infection looks to be more severe than ordinary, what you could do is you could do the standard culturing like is done today, where you culture for one condition after another. Meanwhile, the patient’s getting worse and worse. You’re throwing a bunch of antibiotics at the patients; you don’t know if they’re going to work or not; you’re promoting antibiotic resistance in the population by doing that. Or what you can do is you could do a rapid sequence of that pathogen. You precisely identify what the pathogen is up front in the course of a few hours, and you can map that pathogen to the appropriate treatment, whatever that treatment may be, antibiotic, antiviral, etc. So it’s really a much faster way to reach the appropriate diagnosis of the patient. And it has huge implications on the economic structure and the quality metrics for a hospital. You know, hospitals are being judged by hospital acquired infections. Hospitals sometimes won’t take a patient who is being referred to the hospital who has a severe infection because they don’t want their quality metric hit. And then of course, the more time the patient’s spending in the hospital, the more drugs they run through, the more supportive care they run through, the higher the overall cost structure is. And the interesting part about all of this is that that ties right into the whole shift towards risk based payment models and value based care. So how is a hospital in this new at risk environment going to take on those patients if the cost structure for treating the infectious disease patient is so high? But meanwhile, here comes precision medicine. You do a test up front, you figure out the right diagnosis, you figure out the right drug. You’re saving yourself a ton of downstream costs. Now, in a fee for service environment, maybe that’s less interesting. But in a fee for value environment, that becomes a game changer.
SK: Yeah. And we have done a couple of shows on fee for value, and I think we’re just – that’s sort of an economic problem, not as complex as precision medicine, but certainly the beginnings are in place of structuring the reimbursement with the delivery system on the basis of outcomes and success versus just a fundamental basic fee for service medicine. And that’s a good point, that we’re headed in that direction. And obviously, when we get to the bottom of this infectious disease issue, I mean it is, everybody knows someone in their family that has gone into a hospital and had to face that problem. It seems like it is the major concern when you get admitted to a hospital is you hope you don’t come out with an infection. So let’s dig in a little bit with the problem that your software is solving. So we know there’s a big industry out there for creating molecular diagnoses and diagnostic tools. We know there’s a big industry out there for sequencing genes and coming up with analytics around the genome, proteome and all the quote -omics that are becoming more and more available to us. But you’re solving a different problem with your software. So and I think it has something to do with the way professionals operate within this environment of precision medicine. So can you give me a sense for what the operating environment is today in terms if you were running a precision medicine program at a teaching hospital, or at any hospital, for that matter?
JH: Yeah. So the problem that science is solving is the problem of clinical delivery and clinical implementation of precision medicine. At the core, that’s what we do. So many of the people listening may not be familiar with how precision medicine processes operate today, so I’ll just give my one anecdote or my one day in the life. So let’s say you’re a cancer patient, and you are going in for a visit to a new, let’s say a new health system. Let’s say you’re a later stage patient, you’re stage 3 or stage 4. It’s likely first that you’ve been treated outside that healthcare organization before, so you need to get all of your records from those prior treatments sent over to that new institution. So now you have problem number one, which is the interoperability issue and being able to trade really complex cancer data across health systems. So that’s your first problem. Your second problem is then the patient or the physician is likely going to want to do a set of molecular and genomic tests. So if you’re a breast cancer patient, the physician is going to want to know your genetic status. So are you a BRCA1 or 2 carrier? Are you at increased risk for hereditary disease? Does your tumor have ER/PR/HER2 alterations, etc., etc.? So they’re going to want to know a set of basic facts about your genetic and molecular underpinning. They’re also going to take a tumor sample and do a molecular profile of that tumor, and they’ll send that to their internal lab or to their external lab. What they’re going to get back from all of these processes are literally a set of faxed documents. So –
JH: – you can do all of the wonderful genomic profiling in the world, you can do all of the analysis in the world, but at the end of the day, it’s a fax.
SK: Please don’t tell me that’s true.
JH: That’s what’s true. Oh, yeah. That’s what happens.
SK: That’s what I tell my friends and they don’t believe me. So thank you. We’re on record here. It’s true.
JH: It’s unfortunately true. That’s what interoperability still looks like for complex testing. And the physician gets that fax. And I don’t care if it’s an academic center or a community center; most of the physicians don’t know what to do with that information. And it’s not to blame them. I mean this is new. It’s a new testing paradigm, it’s new types of data. The physicians are not trained to work with that type of information. And this gets, you know, this gets to be a very acute problem as you move farther and farther away from the major research centers. So that’s your set of problems today. The physician gets this information; they don’t know how to act on it. And even if they knew how to act on it, the actual work flow process of implementing their actions is difficult. So let me just give you two examples. One example. Let’s say you want to go get a specialty drug for a patient based on that genomic profile. And a specialty drug would be, you know, the type of drugs that a Genentech would make or an Amgen or a company like that. Those are drugs that are in limited distribution, contracting relationships with specialty pharmacy, with lots of complex processes and procedures for getting approval to get that drug and getting reimbursement. That process is so incredibly complex that what usually happens is the physician has to do a bunch of manual work to get that drug requested. And then by the time they go through that process, it sometimes is too late to put the patient on the drug, even if they successfully navigate the process of getting the drug approved and paid for. So even if the physician knows how to act, the actual process of trying to get one of these drugs is so incredibly difficult. And the second example is clinical trials. So for many cancer patients, you really want to route the patient onto a clinical trial as they get later on into their treatment course. But knowing what trials are open, what trials the patient matches to is this huge, taxing combinatorial problem that really a computer should be solving. You might have 500 trials for that type of patient, and you may have 10,000 patients at your institution, and each trial has 50 to 100 eligibility criteria, each patient has tens of thousands of data points relevant to that eligibility criteria. So that map just doesn’t work. It’s not surprising that clinical trial enrollment rates are only about 3% for cancer patients when you have that sort of problem. And those are just two examples. We can go on and on and on with the work flow problems.
JH: So what we do as a company, then, is we’re really tackling the integration of data, the decision support at point of care, the clinical work flow, and probably most importantly then, the outcomes tracking and learning health system capability, so that you can continuously improve the care of your patients over time. So it’s those four things that we do as a company for our software. And there are many, many, many challenges associated with those four. So if you just take data integration, the first thing that we do, alone, we have to go in and extract data from electronic medical record systems. But that’s not the end of the story because really you’re talking about connecting to about 5 to 10 different systems for every hospital. You want to connect to pathology, radiology, their drug dosing systems, their pharmacy systems, and then all of the different labs that they’re using. So data integration is a challenge. Decision support is difficult as well. You need to work with the organizations to understand their clinical best practices. Clinical work flow again is a major area that we just talked about. And then of course quality improvement, outcomes tracking and learning health system. Most cancer care organizations are not very good at tracking their patient outcomes. So there are challenges in all four of those areas.
SK: So do your customers recognize these challenges specifically? Are they sitting their saying, this is unbelievable that I can’t sort of function effectively, given what’s at stake, right? I mean in the oncology market, somebody’s life is at stake, right? Or the discovery of a drug that’ll save people’s lives is at stake. How are your customers dealing with this? Are you going and saying to them, Hey, you have all of these problems and I can solve them? Or are they building their own solutions, if you will, to these problems until they meet you?
JH: You know, I think you see some academic medical centers who are trying to build their own solutions. And they recognize the problem; they acknowledge the problem, and they try to go about building their own in house solutions, which for the most part, doesn’t work very well because they confuse the problem. They think it’s an analytics problem. It’s not an analytics problem. It’s a software work flow problem, which are very different. And the academic centers don’t understand the difference between analytics and enterprise software. But the interesting thing that’s happened over the past three years is that three years ago, you would talk to a community medical center, and they really just – they would not – the wouldn’t see the problem. They weren’t feeling the pain at that point in time. And what’s happened over the past three years is that precision medicine, particularly in oncology, has become such an embedded part of cancer care, to the point where it’s now standard of care for many conditions in the later stage of the patient’s care journey. These community health systems have become so aware of the problem that they’re actually all looking for solutions. And unlike the academic centers who have a plethora of staff that they could throw a that problem and try to fill the gap through just throwing manpower at it. The community health systems don’t have that. So they’re really looking for these types of software solutions. There’s no greater driver of behavior change in this area from a health system purchasing standpoint than following patient volumes. So patients see the advertising, they see the ads from Cancer Treatment Centers of America promising precision medicine. They see the ads in their local markets from academic medical centers, talking about the genomic profiling that they’re doing on patients’ cancer. So patients see those ads, and they respond by voting with their feet. And they go to the medical center that is using, as they perceive it to be, the most advanced technology to find the cure for their cancer. Community health centers are not stupid. They see the patients moving, and they want to attract and retain patients, and they also want to provide the best care possible across the full continuum of the patient’s care journey. So they’re actually the fastest adopters of an enterprise, systematized approach to precision medicine.
SK: That’s interesting. It’s amazing how the competitive landscape can drive some of that, right? So they can come back and say – and plus, they’re probably under a little bit more cost pressure, I think, because they really do compete on that basis.
JH: Well, that’s the – yeah, that’s the other side. They’re under cost pressure because I heard a wonderful statistic. So we’ve been working with Intermountain Healthcare for over 3 years now. And I heard a wonderful statistic from Brent James, who runs their –
JH: – analytics and basically cost effectiveness group, you know, legendary figure. So what his economic model shows is that health systems who are at risk for 25% of their patients or more, those health systems actually focus on cost containment measures. Those are measures that are more likely to increase their profitability as a health system. Whereas health systems that are under 25% of patients at risk are actually focusing more on revenue enhancing technology. So no matter which side of that divide you’re on, if you’re a community health system, you’re looking at precision medicine saying either it’s going to help me save cost because it’s going to reduce wasteful care, or it’s going to help me increase revenue for [recruiting?] more patients and bring in more clinical trials revenue, and helping me get reimbursement for off-label specialty drugs, etc., etc. So no matter which side of that divide you’re on, precision medicine offers something for you. And I think the community health systems are really seeing evidence of that in their local markets.
SK: So technically, you are an enabler of scaled precision medicine, it sounds like. Sort of like with this system, you’re going to enable oncologists to do more clinical work as opposed to administrative work around trials and drugs and interpretation of test results, etc., and the general work flow of bringing a patient through the analysis and the treatment. Your product is designed to create a software environment in which that work can take place. Is that a good description?
JH: That’s a great description. You know, if you want a Syapse sales job, we’re hiring.
SK: Yeah, I was a salesman when I was in my 20s. I may have forgotten the rest of how to do it, but you know, I’m in. I like it.
JH: I think the other key part, too, is you really want to empower the physician to being the expert quarterback of the patient’s care. And what’s happening now is the physicians are being hit by this new type of testing that they don’t have a 20-year track record interpreting and utilizing in clinical care. And you really want to help that physician understand the clinical implication of this test result and help them connect that into the delivery and provisioning of care for the patient. So you’re really freeing up the patient – or freeing up the physician to practice at their credential level, rather than doing a bunch of busywork and library functions to figure out what a test result means. So it’s all about scaling precision medicine across the community. And it really is a mission for our company. If you think about where care is delivered in the United States, it’s not, by and large, at the academic medical centers. It is out in the community. I think it’s 90% of patients receive their cancer care in the community. So for us, it’s wonderful, and we work with Stanford, we work with UCSF, we do amazing work with them, they do amazing work outside of us, obviously. And that’s great. That pushes the envelope. But what about the person in St. George, Utah? What about the person in Olympia, Washington? They need that level of care, too. So it’s a mission for our company to democratize access to precision medicine, to make sure that all patients, regardless of their location, regardless of their income can have access to this technology.
SK: Awesome, awesome. So I know you just raised a little venture money, right? Congratulations to you for that.
JH: Thank you.
SK: That’s always a fun process. And I guess it’s Ascension and Safeguard are your big investors. Are there anybody I’m missing in that sort of list?
JH: Well, that’s Social Capital. They believed in us before anyone else. They led our A round.
SK: Oh, terrific. I love those guys. The guys that come in first are the guys with the guts, right?
JH: Yeah. Well, so we met Chamath. So Glenn, my cofounder had worked with Chamath before Chamath was at Facebook. So back in I think this is 2004 they had worked together for a few months. And we had approached Chamath through a mutual friend after reading about some of the stuff that he was doing in healthcare, or desired to do in healthcare. And this is back in – I think this was summer of 2012. So this is before that whole wave that we were just talking about, the whole wave of precision medicine really being clinically adopted.
SK: That’s right.
JH: It was before all of that. It was still, by and large, just a research experiment, frankly, at that time. We always believed that precision medicine would take off as part of clinical care. We met Chamath. He believed in it too, and he came in first and it’s been a great ride since. What we didn’t know at the time was that – this is now published; she wrote a big blog post about it – but his family basically, I think it’s 75% of the males in his family have had, I think, type 2 diabetes, and his father passed away last year; many other family members have had very severe manifestations. And he was really motivated to figure out why. And some of the work that we’ve since done in conjunction with Mike Snyder’s group at Stanford, where Chamath is involved as well, tracking through a multi-omic approach the development of pre-diabetes and then type 2 diabetes actually in Mike Snyder himself has really proven out that precision medicine can offer a lot of insights into the development of some of these severe conditions. So that’s been very interesting.
JH: But in terms of the broader venture round that we just raised, which was our series C, it was important to us to have a few things. One was we really wanted market validation that health systems are adopting precision medicine and that Syapse is the solution for those health systems. So when we were looking around at who our partner would be for this round, and we were approached by Ascension, it was really just a great match.
JH: And a number of their limited partners are actually customers, including one of our longest standing health systems customers, which is Intermountain. So it was a great match.
SK: So I’m going to ask you just a few questions that I would imagine you were answering while you were raising that round, just to sort of get a feel for the business. And I guess the first question that I would say is you’ve helped the problem. I think you’ve defined the value proposition as being double sided, right, revenue enhancement for certain types of hospitals, but for all hospitals definitely the ability to improve efficacy and efficiency in the precision medicine process. How big do you think this market is for your product, though? Have you scaled it and said, OK, the market is X dollar size?
JH: As Donald Trump would say, it’s HUGE.
SK: Oh, man, you had to ruin my Podcast, didn’t you? No, I’m just kidding.
JH: That’s not political commentary. So I’ll just say it’s really tough to size the precision medicine market. And the reason why is it keeps growing and changing constantly. So two years ago would you consider the precision medicine market in oncology alone just to be the patients who are being tested? Or would you say precision medicine is all stage 4 patients? Or how would you slice and dice that two years ago? I think it was pretty much unknown. Well, today you could look at precision medicine in oncology alone being a significant chunk of the administrative dollars spent on cancer care. And if you look at the cancer market in the US alone, the cancer market is $100 billion, about something – or 120 billion, excuse me, can’t forget that extra 20. You know, a good chunk of that is taken up by drugs and radiation and chemo, you know, direct expense. And then some portion is through hospitalization. It basically leaves you with about 20 to 25 billion of administrative expenditures. And to us, we look at precision medicine and oncology, and we say, well, that administrative cost, a good chunk of that could be taken over by precision medicine, whether it’s through direct purchases by health systems, cost savings that could be achieved. Slicing and dicing the market, though, is very, very difficult –
JH: – in that. So that’s one way we look at it. Just what portion of the clinical delivery markets that we target are the administrative expenditures that we can impact, what component of it are the drug expenditures that we can inform from change, because a lot of what we’re doing is shifting the spend from things like radiation and chemo to specialty drugs. So what is the upside potential look like there? So there’s a broad expanse as to what the precision medicine market is. The other dynamic that’s very interesting is health system consolidation. That really plays into it because what I mentioned, as I mentioned before, one of the market dynamics is that 80%, if we’re just talking oncology, 80%, 90% of the patients are treated in the community. But the trend that you’re seeing that’s moving very rapidly is the consolidation of community oncology practices. So the 3 to 5-person practice independent practitioner is a thing of the past. Those independent practices are just not viable in today’s healthcare market. So they are increasingly being purchased by larger health systems or are signing affiliation agreements where they effectively become part of that larger health system. So you see that trend playing out in the Bay Area, where Sutter Health and CPMC are consolidating oncology practices. We see playing out in the Pacific Northwest, where a customer, Providence, is affiliating practices at a rapid rate. Of course Intermountain has been doing that for a while. And even the academic medical centers are getting in the game. So UCSF just launched UCSF Health and their affiliate network, and Stanford launched the same thing as well. So that all impacts the market too because what you have is fewer and fewer organizations that represent more and more of the physician user base. That’s a great trend for us, given that our customer is really the health system or the large network of community physicians.
SK: So if it’s a big market, it really tells us that you’re going to get competition for what you’re doing, right?
SK: Give me a sense for the competitive dynamic out there, how people are coming in to compete with you, and where you feel you’re differentiating yourselves relative to that competitive dynamics.
JH: The complexity of the problem and the complexity of the solution that needs to be put in place means that it’s very difficult for another startup to come in at this stage of the game. I’m sure we’re going to see someone coming in at some point, but right now there aren’t very many other startups, if any, that are really coming in as competition. Mostly the startups coming into this space are focused on what I would call the smaller point problems, so they’re focused on the analytics that the lab needs. They’re focused on building clinical content databases that they could sell. They’re not focused on kind of those big problems around data integration and decision support and work flow, etc.
SK: So –
JH: And that’s kind of one –
SK: Just a quick question. A company like Flatiron Health, which has gotten lot of attention in my neck of the woods here in New York, would they be a customer or a competitor to you?
JH: So they’re actually – they’re neither. They would actually be a partner because at the end of the day, Flatiron is an EMR company that has analytics on top of it, largely focused towards operational metrics as well as analytics and data services for biopharma. And we don’t do any of that. We’re really focused on precision medicine, decision support, clinical work flows, etc. So just like any EMR company with an analytics and data business is a potential partner for us, whether it’s Allscripts is in that game, Cerner is in that game, to less of an extent Epic, but Varian and Elekta are also there. Those are all potential partners. I’ll use that with a small p right now, small p partners in that any of those organizations may be interesting to work with just in an integration level, and certainly we have integrations with your popular EMRs out there. And then there might be big P partnership, meaning more of an alliance, where there are joint opportunities to work with those organizations in a more meaningful way. So I wouldn’t call any of those organizations competitors by any stretch. We’re actually in different businesses and quite complementary businesses. The organizations that are really kind of coming in to be competitors are the larger healthcare related software companies that have a presence in this industry. So the one example that everyone will know of is Oracle. Oracle has been trying to make a go of it in precision medicine since 2010. And it’s – we regard it as market validation that such a big company with such a huge source of revenue thinks that precision medicine is so important that they’re putting a large effort into it from a marketing and sales standpoint. From our side, we welcome the competition. We make sure that our product is 3 to 5 years ahead of any competitor, including them. And the thing that we think is so critically important is connecting the data to clinical delivery and clinical care. And that’s really the big thing that every health system wants, and it’s the big thing that our product delivers on, and it’s what none of our competitors, whether it’s our friends in Redwood Shores or others have been able to crack. So that’s why we consistently beat out anyone else for business. It’s connecting the data to the provision of clinical care.
SK: Well, very good. Let me say I really appreciate your time. I learned a lot from this discussion. I think we’ve touched on the product and the problem, but we haven’t really touched on sort of my favorite closing question for founders. I want to understand how you started your company and what you wanted to make sure the company was like for the people that work there. Can you talk about that a little bit?
JH: That’s a great question. That’s my favorite question.
SK: Well, you know, it’s sort of like you get to make your own rules if it’s your company, right? I mean it’s your place.
JH: Yeah. No, and for the people who know me, they know I could never work for a big company. It’s not my personality. So the driver behind starting the company was a few fold. So my personal experience is I grew up as a neuroscientist. So I actually started learning how to pipette in a lab when I was 7 years old. I did my first animal study when I was 12, which is of dubious legality.
JH: But we won’t talk about that.
SK: That’s right.
JH: And I’d been working in the nascent field of molecular medicine for a while. I just didn’t know it. So a lot of the work that I did early on was related to Alzheimer’s disease and trying to figure out the molecular signatures for a diagnostic for Alzheimer’s. And that was way back in the day at this point. And what I saw when I got out to grad school at Stanford was that I had had a bunch of work experience in the lab in small startup companies, in biopharma, and I looked at all of these organizations and saw a few trends. The first was that molecular medicine, or what’s now called precision medicine was becoming really part of clinical care. And the second trend that was happening at the time – this is 2007, 8 and 9 – was that physicians in health systems were being prompting to adopt electronic medical records that really didn’t provide the sort of data driven functionality that we had come to expect in our personal lives through using Google and Facebook and all these other tools. Even in our business lives, people in other industries had access to tools like Salesforce.com and had access to data analytics tools for tracking information and work flows. And I was kind of looking at these 2 trends, saying, you know, these things are incompatible. At some point, a physician is really going to need to understand and digest information on the patient’s underlying molecular condition. And these tools coming out, these medical records systems are not going to be the systems to do that. And it was really kind of painfully struck home to me when I was in a class at Stanford, and the former VP of Research and Product Development at a company called Genomic Health, which was one of the pioneers in this space in cancer molecular testing –
JH: – she was giving a series of talks in that class, really talking about the poor state of information management and delivery to physicians, and how it impeded the growth and utilization of molecular testing. And everything just kind of clicked for me right then and there. And that’s when I decided OK, gonna start a company, gonna drop out, gonna make a go of it. And it happened. And of course we’re still in touch, myself and this person from Genomic Health. She’s been a pivotal figure in the company as have many other people from the early days. And I teamed up with a few people that I had met during the same time. One was an advisor to a class that I was in at Stanford, and he became one of the cofounders. And then the other actually happens to be his next door neighbor, and he became our CTO, because only in Palo Alto can someone like Enterprise Software operating experience and someone who’s the world’s expert in semantic knowledge representation and reasoning software in the biomedical domain – only in Palo Alto are those two people next door neighbors, and know each other and [start a?] company together.
JH: So we all teamed up and started it. And then really, the culture of the company and the people we hire, you know, we’re not one of these startups that has the pink water coolers and the exposed brick office and all of the things that give many startups a bad name, frankly. Everyone we hire is mission driven by improving the care of cancer patients and patients with other critical diseases. Everyone is here because of that mission. And the thing that’s very interesting is when you have a mission like that, you can recruit some really amazing people that otherwise would be untouchable. So we have people where they have family histories of cancer. We have people where their children have a rare disease that’s genetic in nature. And we just have so many amazing people at this company that are driven by this mission and are here not because we’re some hot, cool startup with tons of venture money. They’re here because we’re actually making a difference in patient care, and we can actually go into Intermountain and see the improvement in patients’ lives, the extension of life, the improvement of quality of life. My favorite story, which – I mean this just says it all to me. So we’re working with one very large health system that I won’t name because I don’t want to give away this person’s identity. But the chair of the board of directors of that health system came up to me at a conference and said, “I just can’t thank you enough for what you’re doing, especially in bringing together data across medical centers so that we can all learn from each other.” And I said, “That’s great. What’s going on?” And he said, “My son has a rare type of a sarcoma that has a strange and unique signature and molecular aberrations. And we took him to all the major cancer centers. We took him to MD Anderson, to Memorial, etc.,” and none of those organizations could tell us all of the patients that they’ve seen that match to this guy’s son’s cancer subtype and what the treatments were that those patients got, and which patients had better outcomes than others. And he said, “In six months, our community health system has surpassed all these academic medical centers, pulled together this information, and now I have confidence that if my son recurs, that he’s going to be able to get the best care right in this community medical center because of you guys.” And it’s stories like that that you know, they say it all to us and it’s why we do what we do.
SK: Awesome. I’m going to end it there. I want to congratulate you on your company, on your fund raise, on your success and stories like that. And I want to just say that I love the fact that we’re recruiting the best and the brightest into healthcare at this time in history. I think we’ve got a lot to do, and it sounds like your company will be a big part of that. Thank you, Jonathan Hirsch, CEO of Syapse. Great talking to you.
JH: Thank you so much.