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Ian Maurer — Chief Technology Officer at GenomOncology and co-lead of the Cancer Informatics for Cancer Centers’ AI working group.
With over two decades of software experience, Ian has led GenomOncology — a Cleveland-based software company — in their pursuit to provide the healthcare community with data-driven insights to improve cancer care and strengthen precision oncology programs by transforming valuable, but unusable data, into actionable oncology treatment options and strategic insights.
Founded back in 2012 and backed by Cleveland-local investors like JumpStart, NorthCoast Ventures, and Zapis Capital, GenomOncology was early to understand the implications of the convergence of genomics and artificial intelligence as it applies to oncology and Ian has been pivotal in architecting and creating their precision oncology platform to this end.
This was an incredibly insightful and informative discussion — Ian lays out the state of precision oncology and cancer care, how the field has progressed over time, the founding and evolution of GenomOncology to meliorate this space, solving hard problems, building in Cleveland, the ever-changing frontiers of AI and genomics, exponential technology, and a whole lot more!
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LINKS:
Ian Maurer's Personal Website: https://imaurer.com/
Connect with Ian Maurer on LinkedIn: https://www.linkedin.com/in/ianmaurer/
Follow Ian Maurer on Twitter/X: https://x.com/imaurer
GenomOncology: https://www.genomoncology.com/
Follow GenomOncology on Twitter/x: https://x.com/GenomOncology
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--AI-Generated Transcript--
Ian Maurer (GenomOncology) [00:00:00]:
At the end of the day, all of health care is gonna be molecular. I want genome oncology to be a core part of that, at least from a knowledge perspective, if not from a bioinformatics and analysis perspective. I think we we have the software and the the people and the expertise to help people do that. And then the goal is, how do we make it so the provider? Right? That's the the industry lingo for the oncologist or the treating physician. How do we reduce the administrative burden, the technological burden, etcetera, so that they can spend their time thinking about the patient and bringing empathy and reasoning and their judgment to helping that patient as quickly and painlessly and effectively as possible. That's the goal. I have lost lots of folks to cancer in my life that I love. I want to help be part of that solution to help.
Jeffrey Stern [00:00:55]:
Let's discover what people are building in the greater Cleveland community. We are telling the stories of Northeast Ohio's entrepreneurs, builders, and those supporting them. Welcome to the lay of the Land podcast, where we are exploring what people are building in Cleveland and throughout Northeast Ohio. I am your host, Jeffrey Stern, and today, I had the real pleasure of speaking with Ian Maurer, the chief technology officer at GenomOncology and co lead of the cancer informatics for Cancer Center's AI working group. With over 2 decades of software experience, Ian has led GenomOncology, a Cleveland based software company, in their pursuit to provide the health care community with data driven insights to improve cancer care and strengthen precision oncology programs by transforming valuable but unusable data into actionable oncology treatment options and strategic insights. Founded back in 2012 and backed by Cleveland local investors like Jumpstart, North Coast Ventures, Zapis Capital, GenomOncology was early to understand the implications of the convergence of genomics and artificial intelligence as it applies to oncology. And Ian has been pivotal in architecting and creating their precision oncology platform to this end. This was an incredibly insightful and informative discussion.
Jeffrey Stern [00:02:19]:
Ian lays out the state of precision oncology and cancer care today, and how the field has progressed over time. The founding and evolution of GenomOncology to this entire space. Solving hard problems, building in Cleveland, the ever changing frontiers of AI and genomics, exponential technology, and a whole lot more. So please enjoy this awesome conversation with Ian Maurer after a brief message from our sponsor. Lay of the Land is brought to you by John Carroll University's Boulder College of Business, widely recognized as one of the top business schools in the region. As we've heard time and time again from entrepreneurs here on Lay of the Land, many of whom are proud alumni of John Carroll University, in this ever changing world of business requires a dynamic and innovative mindset, deep understanding of emerging technologies and systems, strong ethics, leadership prowess, acute business acumen, all qualities nurtured through the Buhler College of Business. With 4 different MBA programs of study spanning professional, online, hybrid, and 1 year flexible, the Bowler College of Business provides flexible timelines various class structures for each MBA track, including online, in person, hybrid, and asynchronous, all to offer the most effective options for you, including the ability to participate in an elective international study tour providing unparalleled opportunities to expand your global business knowledge by networking with local companies overseas and experiencing a new culture. The career impact of a bowler MBA is formative and will help prepare you for this future of business and get more out of your career.
Jeffrey Stern [00:03:57]:
To learn more about John Carroll University's Buller MBA programs, please go to business.jcu.edu. The Buller College of Business is fully accredited by AACSB International, the highest accreditation a college of business can have. So I was thinking about where a good place to start our conversation may be. And whenever I I think about genomics, my mind goes to the exponential nature of technology, which for a long time has been a a curiosity of mine, really stemming actually from the the investing world. But there's this woman, Cathy Wood, who runs an investment firm called ARK, and that's actually not so relevant. But what what is is Yep. So she, I think a lot of people are familiar with, you know, the concept of of Moore's law, which, right, speaks to the whole observation of you can double transistors every 2 years without increasing costs. And and, so you ultimately have this exponential growth of computing over time that that we've gotten to to experience over the last 60 years.
Jeffrey Stern [00:05:03]:
And I I think we'll we'll probably even talk about that as we talk about AI later. But she introduced me to this concept of of rights law, which is kind of a a kindred observation, a corollary to to Moore's Law a little bit that speaks more generally to costs falling constantly for doubling of production. And I always think about genomics when I think about this because when I first was probably aware or introduced to the whole Human Genome Project in the early 2000, I always think about how it, you know, cost about $3,000,000,000 and took 13 years of computing power to to complete on this, like, order of magnitude of capital and resources that is akin to, like, a literal moon mission,
Ian Maurer (GenomOncology) [00:05:45]:
you know Yep. Yep.
Jeffrey Stern [00:05:46]:
At a government level. And it really Ian incredible achievement, but but not at a mass scale. You know, even if you cut that in half and it was 1a half 1000000000, it wouldn't become much more sequence a genome today in under $1,000,000. And in you can now sequence a genome today in under $1,000 Ian in a few hours. And so we've now been able to sequence many millions of of human genomes not just Ian aggregate, but even at an annual, you know, clip now. And so all that is to say, I I've long been interested Ian practice, and what companies can do who are plugged Ian to to what what is now possible. And so I'm very excited to hear your story, more about the work you're doing at GenomOncology. And and that is happening, you know, here in Cleveland, because I I think that that particularly is awesome.
Ian Maurer (GenomOncology) [00:06:47]:
Yeah. I've seen the charts. I think NIH had one where they were showing that Moore's law going down at an exponential you have the costs or whatever per gigaflop or however they were measuring it, And the same thing with the human genome. Right? And they're starting with that 3,000,000,000 price point for the first genome and going all the way down to a 1,000 and eventually a $100 Ian eventually free. Right? Like, that the goal is to have this stuff basically so easy to do that it's basically free to do. Right. And that that actually is the kind of the founding of the company was back in 2012, 2011. At the end of 2011, there were a couple papers in nature where they, you know, they described, you know, how could we use genomic analysis and other, you know, omic analysis to analyze a cancer patient's tumor Ian then figure out what to do based on that information to then help treat the patient.
Ian Maurer (GenomOncology) [00:07:32]:
Unfortunately, it took 6 months to actually do all of the bioinformatics and and the the technical work needed to do that analysis. And so when our founder read that those papers, it triggered, oh, I've been doing bioinformatics Ian the seventies before it was actually even called bioinformatics, and we could go ahead and we could solve that problem Ian here in Cleveland with with folks he knew. So that was really the founding of the and the joke at the time was really, you know, $100,000 genome and $100,000 analysis. Right? So, yeah, great. You can do that. You could great. You can get the a c's, g's, and t's off of a off of a next generation sequencer, which was the devices that kind of emerged in the mid 2000, led by Illumina and Thermo Fisher. But, actually, doing the technical work to understand what those mean, that's a good technical challenge, and we could we could work on that.
Ian Maurer (GenomOncology) [00:08:20]:
And that's really the founding of our company.
Jeffrey Stern [00:08:24]:
So we we were talking before, we we pressed record here about our our mutual friend, Akash, who used to work with you at at Genome Oncology. And I had asked him, you know, what question or topic should I be sure to to ask you about? And, he he brought up your philosophy around explicitly opting to chase and follow the hardest problems. And to me, that felt like a great place to kick off, you know, understanding a bit more about what motivates you and how you came to be doing the the work that you're doing and, you know, as a as a seg to to Genome, which we'll get to, but really in reflection on on your whole journey.
Ian Maurer (GenomOncology) [00:09:04]:
Yeah. That's great. Yeah. The hardest problems are definitely kind of my north star. It's what I enjoy working on. I enjoy on the hardest possible problems. I honestly get bored if it's something that it's kinda like, oh, make another website or what have you. And and so in the 2000, I did, I did do some hard problems around e commerce, which was basically like, how do you get money from a credit card or how do you ship something to a a person? And that those were hard problems in the 2000.
Ian Maurer (GenomOncology) [00:09:29]:
They're not hard problems anymore. Right? Shopify and and Amazon or whatever have kind of solved. So I was getting I was getting a little bit restless. So when, my friend, Manuel, started this company and, told me about it, I I approached him today. I gotta I gotta I gotta join you. I gotta work on this because it sounds like something interesting and something fun to Ian. And so when it comes to building a company, you know, creating value, software is an unbelievable, you know, marginal utility. Right? So you can create something once and and then sell it unlimited amount of time, which is great.
Ian Maurer (GenomOncology) [00:10:00]:
But to sell something in software that's actually useful and something that actually solves a really hard problem, that's how you differentiate yourself.
Jeffrey Stern [00:10:06]:
That's how you have a moat
Ian Maurer (GenomOncology) [00:10:07]:
of any kind. Right? And so the hard problems are really what I gravitate towards because I know that that's where our clients are. Our Maurer clients are gonna need us. So when we go into a new client and we help them with their existing problems, it's always looking around and talking to them and trying to figure out what are they struggling with, What other challenges do they have that are adjacent to the things that we've already solved for them? Because I know that that's a good direction to take our products so that because if they're having those problems, I'm sure other folks are having those same problems. So let's go ahead and try to tackle those and solve them. Because I also know that all these institutions that are not software development organizations by nature, right, they're they're either cancer centers or reference labs. They're busy solving they're busy doing their work, which is helping patients. We can help them by creating software that's easy to use, integrates within their, their environments Ian makes their jobs better.
Ian Maurer (GenomOncology) [00:11:03]:
It makes their jobs better by letting them do more or do do more at a higher quality.
Jeffrey Stern [00:11:09]:
So I think entrepreneurship almost by definition involves hard problems, but I think you could find hard problems outside of entrepreneurship. And so I'm curious, you know, where your draw to entrepreneurship specifically as a as a means to to tackle hard problems stems from?
Ian Maurer (GenomOncology) [00:11:30]:
I think that that's a patient's problem of mine. I don't I'm not a very patient person, So I I don't do well with bureaucracy. I don't do well with meetings. Like, if I'm gonna be in a meeting,
Jeffrey Stern [00:11:41]:
I wanna be in a meeting with, like, the people that matter for that particular problem and, like, let's solve that problem and
Ian Maurer (GenomOncology) [00:11:46]:
get out of the meeting. Right? Like, so my my mantra at one point was no meetings, no bugs. Right? Like, I've not create bugs because bugs are Maurer demands that cause us our team to slow down. And meetings are once again not they're not useful in their own sense. You do do them as a necessary evil. And when it comes to hard problems, I'm also very focused on essential complexity, not accidental complexity. Right? Essential complexity is, like, this is a hard problem because it doesn't exist in the world. Nobody knows what to do.
Ian Maurer (GenomOncology) [00:12:19]:
Like, that's essential complexity. But once you've solved it, now it's no longer essential. It's now whatever complexity you have left is how do we build the software? How do we test the software? How do we deploy the software? How do we educate the people? All good problems. But you gotta think about it in a way that you can
Jeffrey Stern [00:12:35]:
put the thing to bed. Right? Like, how
Ian Maurer (GenomOncology) [00:12:36]:
do I solve this problem so it never shows up again? And, fortunately for me, I I have a job where I can solve problems once and then they kinda go away. I know that there's other folks Ian other lines of work where it's like you wake up every day and you have to solve the same problem every day. It's just new people or new types of spins on the same thing. I'm just not wired to work in that kind of environment. So entrepreneurship is perfect for me because it's like small teams doing hard things, moving quickly, and I did that in the consulting world. The problem with the consulting world was you do that for 9 months, you have a successful launch with your client, and then they give you a handshake and say, hey. Thanks for your work. Get out of here because you're too expensive.
Ian Maurer (GenomOncology) [00:13:17]:
Like, we want, We're gonna have this done by her internal team or we're gonna offshore it or something. Like, there was no there was no ever re reaping of the reward of the of the value of the work that we created. So with GenomOncology, I build a product in 2012, and we're still working with it 12 years later. And and I know that that that the company is getting value from that that same thing. And so the effort that
Jeffrey Stern [00:13:38]:
I can put in, I've I'm getting the benefit of software,
Ian Maurer (GenomOncology) [00:13:42]:
which is that that marginal utility of write it once and use it forever.
Jeffrey Stern [00:13:46]:
Yeah. I that's real agency, you know, and and ownership and all the all the benefits of
Ian Maurer (GenomOncology) [00:13:52]:
of trying
Jeffrey Stern [00:13:53]:
to solve these hard problems. I wanna do a little bit of of stage setting before we we get into the the heart of it, But but kinda paint a a picture for us maybe of of what precision oncology looks like Ian cancer therapy and and bioinformatics Ian just the the space that that genome oncology is operating within. You know, maybe maybe around the time even just before it was founding and some of the history that, you know, created the opportunity for for this whole undertaking in the first place.
Ian Maurer (GenomOncology) [00:14:21]:
Sure. So, obviously, cancer is a long history. I'm not gonna go into that. What we do for cancer patients at this point, right, are when you have a tumor, right, it's, like, you cut the tumor out, you radiate the tumor, or you poison the tumor. Right? The cutting it out is good. Hopefully, you can cut the whole thing out and not have Maurer you know, the margins are clear or whatever, what have you. Radiation's fine because it's targeted, but once again, you're doing something damaging to your your body to do that. And then chemotherapy is basically a poison Ian your the trade off is I wanna try to poo poison you in a such a way that it poisons the cancer faster than it poisons the rest of your body.
Ian Maurer (GenomOncology) [00:15:00]:
Obviously, not a great trait. The promise and of target what's called targeted therapy, precision oncology, and now immunotherapy as well. Those are all interesting topics subtopics that I can kinda unpack a little bit. Okay. So first, genomics. Fundamentally, right, there there can be nuance to this, but, fundamentally, cancer is, a disease of the genome. Right? You have some breakdown right there. If you sunlight hits your skin in a very specific way and it changes a DNA base pair from a t to an a on a certain chromosome on on, you know, the 7th chromosome at a specific position, it's gonna change the mutation of a given gene, from from basically a e to a, sorry, from a v to an e.
Ian Maurer (GenomOncology) [00:15:46]:
And this is this is a specific type of mutation that's happened. That's gonna cause that cell to continue to reproduce and not die. So now the question becomes, okay, so you take that Maurer, you can actually cut the tumor out Ian then you can actually sequence that that that tissue. You can do the DNA sequencing on it and understand, okay, we do see that change that goes from a v to an e here on that particular chromosome of the the of that cancer cells. Well, there's targeted therapies for that. Right? There's actually FDA approved drugs that will help people with melanoma, fight that that particular type of of cancer. Yeah. So that's the precision part.
Ian Maurer (GenomOncology) [00:16:25]:
Right? You're looking at the data for a specific patient and then you're giving a drug that's targeted at that specific patient's exact disease type, not just class of disease. Another thing to recognize, right, is cancer is not one disease. It's 1,000. Literally thousands of diseases that are in in in these ontologies of of medical ontologies. Okay. So that was targeted therapy. We talked a little bit about sequencing, genomic sequencing, and there are different versions of this. But genomic sequencing can actually look at small areas of the human genome.
Ian Maurer (GenomOncology) [00:16:55]:
Just look at specific hot what are called hot spots, specific areas of of interest based on cancer that we know. There are genomic tests or, you know, where you actually just look at the genes of specific cancer diseases. And then there are whole exome where you're looking at the protein encoding sections of the genomics, and then the whole genome processing. Eventually, we're gonna get to the point where we're just do testing everything because it'll be so cheap, and we might as well just test it all. And then by testing it all, you also have a a baseline where you can actually say, okay. That person's, you know, germline's here. And now as things evolve over time, we'll we'll be able to learn how your genome and transcriptome and all these other types of omics evolve over time. We'll be able to measure Ian, and we'll be able to help treat patients, more effectively.
Ian Maurer (GenomOncology) [00:17:41]:
And then the the last part is the immunotherapy. Immunotherapy is really the idea of, you know, can we you know, cancer is good at tricking your body. Right? Not only is it tricking, you know, causing it itself to grow, but it also tricks your immune system to not fight it. There are ways of, like, kinda amping up your immune system or giving it information to then have it more effectively fight the cancer. So that's another tool that has has really evolved over the last 10 or so years. And so genome oncology is really in the business of of helping providers, know what the options are. Providers are, you know, clinical oncologists and molecular pathologists help their patients, better. And I can I can tell you more about the ecosystem of our tools and and knowledge that we have in our system?
Jeffrey Stern [00:18:28]:
No. Absolutely. I'd love to hear about those maybe as a a way to approach those. I'd love to understand though, at the onset, you know, what were it it feels like the the possibility the the breadth of things that you could have possibly chosen to tackle is enormous Mhmm. At at the beginning under, you know, this this umbrella problem space. How did you know which problems to to start with, on the analysis side?
Ian Maurer (GenomOncology) [00:19:00]:
Yep.
Jeffrey Stern [00:19:00]:
And then what and what did that actually look like at the beginning?
Ian Maurer (GenomOncology) [00:19:02]:
Yeah. So what it looked like at the beginning was what data was available. So we actually were able to there was a project called the 1000 genomes project. We downloaded all that data, which was basically the full genomic, you know, makeup of a 1000 patients. And and then we actually built a prototype solution that we called our research application. Ian, basically, what we were able to do was in 2012, on a Mac Mini, Ian these Mac Minis are way better than they were 12 years ago, on a little Mac Mini, you know, the 4, $500 machine you can buy at an Apple Maurer, we're able to ingest the 1,000 genomes and build a research application that lets you, you know, not only look at each of the genomes that were in there, but do these crazy, you know, what are called set theory type analysis where you're like, okay, show me all the variants that are in this subgroup of folks but not in this subgroup of folks. And, you know, in and of itself, that research app on top of, you know, open source data wasn't that interesting. But we were able to show that to folks at different you know, these conferences that people go to.
Ian Maurer (GenomOncology) [00:20:02]:
And and eventually, we were able to land 3 engagements as a small, you know, 4 person company where we were behind firewall working with the teams at Ohio State and Roswell Park in Buffalo and and Pitt in Pittsburgh. And the theory was, okay, we'll get in there. We'll work with really smart key opinion leaders, and we'll help them solve their problems. Like, we'll just figure out what their problems are and we'll help them solve them. And then, hopefully, over the next couple of years, we believe that people will start thinking about this stuff earnestly and how to use this information to help treat cancer patients. That was kind of the that was kind of the bat or the approach was, let's go do something that we know how to do, which is, you know, analyze data quickly because, you know, we've got that skill set. And then use that to to basically make friends. And then we had friends Ian and those friends would then tell us, you know, what they were really struggling with.
Ian Maurer (GenomOncology) [00:20:53]:
And the theory was that, you know, people would start using genomic information in, like, 2015 or 2016 to actually start making reports and treating patients, that actually happened way faster than we expected. So we ended up building our first commercial product in 2013, which is what we call our clinical workbench or pathology workbench, which is really a high performance reporting engine. And what it does is it basically at these labs, then we've we've got about 50 labs across the country that work with us. They have these devices from Illumina and Thermo Fisher where they're sequencing the tumor of patient data, which generates the DNA of that information, which is all just a c's g's and t's. What our software then does is take that information and help them quickly interpret it and report on it. So the interpretation Ian, first, validating the quality of the Ian. Right? Did the sequencer do its job? Did it actually understand the ACGs and Ts and and did it report because these things aren't perfect. Did it report the information accurately? Yes.
Ian Maurer (GenomOncology) [00:21:56]:
It did. Great. Next step, for each variant, is this a good variant or a bad variant? Is it benign or is it pathogenic? And we have a database that we curate and and and augment that has that information. And then once they've made those decisions, then they then it uses our knowledge base again to then say, okay, what are the therapies? What are the clinical trials? What are other information Ian we tell this doctor or this pathology pathologist to about that that particular patient in their case. So that was the that was the goal. So at the very beginning was, let's build something that's useful, make friends, and then try to figure out where their next problems are Ian hopefully be ready, to take this thing, and make it, useful Ian in clinically helping patients?
Jeffrey Stern [00:22:41]:
It's such a a sound strategy. You know, it I feel like people over complicate the the process a lot. But, yes, if you if you ask people what their problems are Ian they tell you, and then you help solve them, and you build trust.
Ian Maurer (GenomOncology) [00:22:55]:
Correct. Yep.
Jeffrey Stern [00:22:57]:
That was, that resonates as as my as part of my own journey, as well. That's that's where it came from. It's just asking people.
Ian Maurer (GenomOncology) [00:23:06]:
For sure. Yeah. Ian Paul Graham I mean, Paul Graham I used to read the Paul Graham essays all the time, and it's just about getting out there and talking to people. I you know, I'm a software developer. I'm more introverted than most Ian, you know, going out and talking to folks is is not your not necessarily my first choice on on what to do every day, but that's the best way to learn. It's like find out what people what their problems are and then try to help them with their problems. And that's how you can actually make something that's commercially viable.
Jeffrey Stern [00:23:30]:
With that being said, did you feel there was a an over riding vision at the beginning of what Genome could become, kind of the the north star beyond, you know, your personal one of of finding the hardest problems to solve, but of what the kind of company Genome could become and the impact it could have could be?
Ian Maurer (GenomOncology) [00:23:50]:
Yeah. I mean, we've always had the vision of, you know, medicine is going to be is gonna be completely revolutionized by molecular data. Right? The fact that we don't use molecular data in almost every decision is is kinda silly at this point. And one of the fundamental problems right now is that the EHR, the the medical health record systems, they're mostly very good at, like, billing, and they're very good at, like, reimbursement type stuff and and insurance tracking. But to make them useful in a clinical decision setting, they have to be more omic scale. They have to actually scale to handle data like GenomOncology does. So, fundamentally, we believe, you know, health care is gonna be molecular. Being able to do the the basic, analysis of that information is is, you know, it's a tricky computer science problem, which makes it kinda fun.
Ian Maurer (GenomOncology) [00:24:45]:
And so you need to be able to design your system so that they scale. And then from there, then need to then figure out what are the different pieces parts that are gonna be needed for Ian, and solution. Ian, you know, the thing that wasn't necessarily obvious at the beginning was how much knowledge was gonna play a part Ian to this. And then we'll get into the AI stuff later, but the knowledge that we've curated into our system, which we've spent at least a 100000 man hours, I don't know exact number of hours of, you know, building a knowledge base that powers these reports that we do. We run these things called tumor boards, and then just a lot of other different use cases where, you know, understanding with high fidelity and high accuracy what are the best next steps for a patient? That's really what the GenomOncology system is designed to do.
Jeffrey Stern [00:25:33]:
Well, I'd love to you you had mentioned doctors and the administrative burden, if you will, of of the health record systems.
Ian Maurer (GenomOncology) [00:25:42]:
Mhmm.
Jeffrey Stern [00:25:42]:
To to me, it seems like perhaps the biggest risk would be that doctors maybe get lost in in all this and and kind of the inundation of Ian information. You know, from from my time at at my own company, you know, you come across the stat, but it's it's very much grounded in a reality that doctors spend, you know, maybe 2 thirds plus of their time on some administrative task and not clinically ultimately ultimately unacceptable, to me, especially given this period of distress that specifically practitioners have been under since the the pandemic. But I I'm curious how doctors are supposed to keep up with the vast amount of information that, you know, you've mentioned related to all these things that they need to keep track of from the the GenomOncology, to the clinical trials, to new drugs, you know, in the wake of of thousands of different types of cancers and mutations. Can you speak a little bit to the world of of doctors Ian where Mhmm. Genome oncology has positioned itself best to navigate, you know, actually serving patients
Ian Maurer (GenomOncology) [00:26:50]:
who are
Jeffrey Stern [00:26:50]:
who are going through this stuff.
Ian Maurer (GenomOncology) [00:26:52]:
Yeah. So I described our pathology workbench, which is our first product. And one of the key things we do when we sign a new client up is help them design their first their report. Right? So they're doing this test. They're gonna test 500 genes or 800 genes or the whole exome or whole genome. Really, at the end of the day, their product, the thing that they're actually putting their label on, which is a white label system, we don't put genome oncology all over the report, it's their report. The first thing they got to do is figure out what's the report look Ian. And, there's many considerations, like, one consideration is just completeness of information Ian regulatory compliance Ian other things that they need to do, and that's why the reports can be like 20 pages or 80 pages long, right? But, really, the most important thing is what do you put in that first page? Because the first page is actually what the doctor's definitely gonna look at.
Ian Maurer (GenomOncology) [00:27:35]:
Right? They might dig through it, but they got they're only gonna they might only have time Ian they might only have understanding of like how to read that first page. So really designing that first page is critical for those new engagements. Ian In GenomOncology, I actually spent a few years back. We spent a lot of time building our first, you know, redesigning our kind of default report. Every one of our clients gets their own copy of our software, own copy of our report. We've designed our system in a way that it's, like, automatically deploys Ian and it handles all these customizations that I just talked about even though it's, you know, at all these different clients. But they can all have their own report, but we have a default report that they can accept. And I think I think, like, half of the clients just take our Maurer default report Ian they kinda just had their colors and logos and stuff to it.
Ian Maurer (GenomOncology) [00:28:16]:
And the reason why they do is because we put a lot of thought into making that first page really good. And that first page has to be really good from, like, you know, what are the most critical information? Well, most critical information is what biomarkers did I find? What biomarkers of interest that I found? And then, the other thing that's really interesting is what biomarkers of interest that I not find? Because there are specific biomarkers that people are looking for because they know, oh, I can go give this immunotherapy or this targeted therapy. So you gotta tell them upfront, you know, they didn't have this gene. This gene was clear. So making sure you know that about your client and what their, you know, what their goals are and their intent is is really critical. You don't want them having to read an 80 page report to figure out what didn't what didn't they find. And so that that that's a great lesson. Right? Because it because it gets back to the whole goal of, you know, once again, making this stuff as as fast as possible for the ordering oncologist so they can help patients.
Ian Maurer (GenomOncology) [00:29:06]:
Same thing on the molecular pathology side. For the folks using our software to make this report, we've really worked really hard to understand, you know, what slows them down, what what information do they want at their fingertips so they can make the important decisions. Because at the end of the day, we don't make any decisions. We're decision support software. We are not decision making software. Our clients are the ones that do, through their electronic signature, are the ones actually making the final decision and and signing out the report. And so that's that's a good thing. So then your other part of your question was, you know, how do we reduce the burden on doctors? So we've had this whole EHR thing over the last 20 years now, this experiment that started Ian good with good intention.
Jeffrey Stern [00:29:48]:
Yep.
Ian Maurer (GenomOncology) [00:29:48]:
And the problem is it now, you know, you go to the doctor Ian you sit there and you can see that the doctor is spending half the time looking at the computer and typing. Right? That's the worst possible solution. And I think everybody recognizes that. And there's there's even more burnout from doctors, lots of lots of articles in the Atlantic and New Yorker about how doctors are all burned out. So I think AI has a lot of opportunities there. I am not in this space
Jeffrey Stern [00:30:11]:
at all, this part of AI space. And that part of the AI space is
Ian Maurer (GenomOncology) [00:30:14]:
in what's called Ian technology, right? So basically listening to the doctor and the patient having a conversation, summarizing the information and turning that into some notes. And then that those notes can then be edited, right, Ian draft from draft mode, edited by the doc Ian and sent into the EHR, which is a great thing. And then there's other, honestly, other, like, billing and other types of of push ups these docs have to do where instead of, like, having to write a, letters of of medical necessity or whatever the letters are called, you know, having them write those themselves, they can, you know, basically ask chat gpt. Okay. I have this patient. Here's the situation. Write me a letter. Right? And here's the letter for you Ian I send it off.
Ian Maurer (GenomOncology) [00:30:53]:
So that's another, you know, another area. But now we got the now you're gonna have the the bots Ian both sides of the thing, you know, basically reading and rejecting.
Jeffrey Stern [00:31:01]:
Is it bots working?
Ian Maurer (GenomOncology) [00:31:02]:
Automated notes. Yes. For sure.
Jeffrey Stern [00:31:06]:
Lay of the Land is brought to you by Impact Architects and by 90. As we share the stories of entrepreneurs building incredible organizations in Cleveland and throughout Northeast Ohio, Impact Architects has helped Ian of those leaders, many of whom we have heard from as guests on this very podcast, realize their own visions and build these great organizations. I believe in Impact Architects and the people behind it so much that I have actually joined them personally in their mission to help leaders gain focus, align together, and thrive by doing what they love. If you 2 are trying to build great, Impact Architects is offering to sit down with you for a free consultation or provide a free trial through 90, the software platform that helps teams build great companies. If you are interested in learning more about partnering with Impact Architects or by leveraging 90 to power your own business, please go to ia.layoftheland.fm. The link will also be in our show notes. So I'd love if, you know, I I definitely wanna unpack the the artificial intelligence side of this with you and and maybe where a lot of this is headed, but take us through kind of the the evolution of of GenomOncology as a as a company throughout throughout this kind of product offering.
Ian Maurer (GenomOncology) [00:32:22]:
So we started in 2013 with pathology workbench, and we grabbed some early adopters. Right? And we saw real quick uptake, signed a lot of new deals. And I think once we actually hit the the, you know, Clayton Christensen, whatever the the value is between your early adopters and and your late adopters, I I forget the those those terms from that book. You know, we we had a little pause there in the middle of of the 20 Ian, and we started looking for other opportunities, like, how how else can we take this technology to market and and where else could we be of use? And so their cancer center was a was an obvious next stop. So one of the key things that I ended up doing so we built this pathology workbench. It does reporting. There's a knowledge base that's part of that. So we actually have a system to let them curate or let our team curate knowledge to then populate that report.
Ian Maurer (GenomOncology) [00:33:11]:
We had folks that were coming to us and saying, hey. We we just want that knowledge, and we don't actually wanna do a we're not a pathology lab. We don't need to do reporting. Can you just give us access to that knowledge? And I realized, oh, there's another product here. Like, we can't help them right now, but what's that product? So the first product I built was what we call our API or API Suite application programming interface, which means a computer now can just talk to our knowledge base and ask questions to it. And what that unlocks is our clients can now build solutions that now have the genome oncology inside of it. Right? The intel old intel inside, Mantra where they can actually use our knowledge base using whatever bioinformatics, you know, database they have or knowledge that they have or whatever use cases they have. They can now use our knowledge base to power whatever solutions they Ian.
Ian Maurer (GenomOncology) [00:33:58]:
And then, Ian have quite a few clients that do that And then as well, we have now also built our own, solutions on top of that API. So we have a solution for what's called a tumor board, and this came out, you know, in the late 20 tens. The tumor board is what happens at a, you know, large cancer center where there that really hard cases are discussed maybe every week or every month or so depending on the size of the the cancer center. And so what happens at a tumor board is usually it's a cross disciplinary, group, surgical, pathology, that kind of thing, all kind of all on Ian Webex or one Zoom, and they're all talking about hard cases. Well, our software helps because it can basically do all the Google searching for everybody, right? Rather than having everybody on their phones Google searching, our software is there driving the presentation, you know, dealing with the information, making high level, you know, recommendations of content and then the the folks, the experts in the room can then look at that information, dismiss information, highlight, select information, and make a report, make a recommendation for the treating oncologist. And then we also have treating, tools for treating oncologists and clinical trial folks. Less than 5% of cancer patients end up on clinical trials, and that's bad for a couple of different reasons. Ian, that means we're not furthering along the knowledge collectively on what which of these drugs could could be helping cancer patients quickly.
Ian Maurer (GenomOncology) [00:35:26]:
And also there's an access problem. Right? The the the folks who do get on clinical trials more often than not have, you know, privilege, you know, relative to the folks that don't get on clinical trials. So we need to open up access to to these clinical trials. Our software can actually help with clinical trial, with co commercial clinical trial matching Ian recruitment and enrollment Ian accrual through a variety of different ways. Matching a patient to a trial is actually a very difficult task because it's not only is the trial open, but is the patient have the right disease type? Is the patient healthy enough? Does the patient have these specific biomarkers? Has the patient not had or has had some prior treatment? All those types of criteria need to be evaluated and match made against the actual trials themselves and you can have more than one trial at a time. Keeping that information in your head is not really possible. What our software does is it actually does that problem. It actually can solve that problem, so we can actually and we can do it at scale.
Ian Maurer (GenomOncology) [00:36:26]:
Meaning, if you have a, you know, cancer clinic where you're, you know, taking in a 100 patients each week and doing the genomic testing, our software can be running in the background analyzing patients and identifying potential matches. Hey, this, you know, we have 20 trials ongoing. We got these Ian patients of these, you know, whenever 2,000 combinations. Let's go ahead and we've highlighted, you know, 15 things to go to go look at. So rather than to have a human do that manually, they can use our software to kind of do that matching. And Ian, that was the next evolution of our software is really getting into the clinic and using our knowledge base to power these different, use cases. And, you know, there's we're continuously challenged by clients with, you know, new things that they're working on and and how molecular information and biomarkers can, be used more effectively to help patients and care and care of the patients.
Jeffrey Stern [00:37:19]:
How do you think about efficacy in in outcomes? Like, ultimately, you know, relative to where a doctor might be without the ability to use genome oncology, what is the effect of introducing what you guys have built into the equation?
Ian Maurer (GenomOncology) [00:37:39]:
Great question. Problem is we don't have access to all that data. Right? So the that's the challenge. So it's so we've done, you know, trials or, I guess, not necessarily the right words. Not not typically a clinical trial, but it's basically a research initiative with clients, you know, using our software and understanding, you know, how does our software help in the ecosystem of decision support. And and it does. Right? It's you know, makes their jobs doable to a degree. Right? We've we've done backwards analysis where it's like, oh, we, you know, we did clinical trial matching for a year before bringing in, genome oncology.
Ian Maurer (GenomOncology) [00:38:15]:
Ian with genome oncology, we'd we would have identified, you know, 50 or a Ian more patient than we could identify just by hand. So there are there are, you know, discussion points like that. But unfortunately, it's really difficult at a cancer center to really understand the final destination, right? Tracking progression free survival is is the terminology that they use in a clinical trial without a clinical trial because that's the mechanism by which, you know, these things can get FDA approvals for drugs, for instance. Unfortunately, GenomOncology can't afford to, right now, afford to run a clinical trial at that kind of scale, But we'd certainly be interested in in working with institutions that are looking forward to doing precision oncology based software interventions and doing it in a systematic way. Right? Because you kinda have to do it in parallel where you do here's, you know, here's a 1000 patients that we're gonna, you know, serve this way versus a 1,000 patients that we're not going to. And to actually get that to that raw data and to to do that analysis. But Ian general, you know, we look at our success as being, you know, we don't lose clients. Right? So, you know, 98% retention rate of clients and really high net provider scores, I guess, is what they're called NPS scores.
Ian Maurer (GenomOncology) [00:39:27]:
So we we look for those types of of metrics, to help guide us and make sure we're doing the right stuff. And then we're always also interested in doing, you know, deep studies with with folks, that are interested in that.
Jeffrey Stern [00:39:42]:
So what are the implications of AI on the the work that that you're doing?
Ian Maurer (GenomOncology) [00:39:48]:
Great question. It's where I spend a lot of my time thinking, right, these days. So back in November 2022, right, ChatChippity 3.5 came out. I said, oh, crud. Do I have a business anymore? Like, let me go figure out what this thing actually knows, and I figured out pretty quickly that it doesn't doesn't, it does fall down on on complex stuff like ours, and I'll I'll explain what I mean in a minute. And then March came, and that was when GPT 4 came out. And I had the same same worry, and I I was relieved to see that this thing is not going to put us out of business anytime soon. But I did realize, oh, this is a really good coding assistant.
Ian Maurer (GenomOncology) [00:40:21]:
So I use it every day for coding. And I use now I'm using Claude Anthropic as well, every day for for coding. So that's good. So the question is, in our business, why can't Jet GPT or its equivalent GPT 5. Right? That's gonna come out in a few months, we we think. Why can't it do what genome oncology does? And it really comes down to a couple different problems. First, the way that they're trained. So, GPT, right, generative pretrained model, is created by predicting the next token, which is a really neat trick.
Ian Maurer (GenomOncology) [00:40:57]:
Like, the fact that that works at all is amazing to me. I wouldn't have guessed that. So they do predict the next token on on all the wars in the Internet. Right? Whether they had the copyright right to do it or whatnot, I'll let the courts decide that. And now you've got this this thing that can predict the next token, and that's what it does. It basically has a model of the world and can can tell you what the next most plausible word is based on all the previous words. And through that, it has knowledge incorporated into it, which is, which is cool. The problem is Ian, it's a, it's a black box.
Ian Maurer (GenomOncology) [00:41:31]:
We actually don't know what it actually knows. We don't know what the data was trained on. The people who have created it can't tell you why it does what it does. It hallucinates. Right? It will just make up things. It doesn't know when it's lying because it actually isn't lying. It doesn't know what the truth is. It just knows how to create the next token.
Ian Maurer (GenomOncology) [00:41:50]:
You cannot build a clinical decision support system off of something that's a black box that hallucinates hallucinates the the word that they use for making stuff up. They they have bias encoded into them. Right? The world has bias. The Internet has bias. Therefore, you know, no matter how much they work on trying to kinda clean up the bias, there's gonna be bias in the those solutions. That's understandable. They're not up to date. Right? So they have a cutoff point.
Ian Maurer (GenomOncology) [00:42:17]:
At some point, they stop training it because they have to get it to market. And the way that they work is you can't just be like, oh, let me go add some more stuff. I'm gonna go add February to the January data. It doesn't really work that way. And then it's not genomic scale. Meaning, yes, it has all the text of the Internet, but it doesn't know all the text of the Internet Ian it certainly doesn't know all the variants in the human genome that are possible and what they mean. So at that point, those are the kind of, like, the the foundation of why they don't replace what genome oncology does. But they are still super useful.
Ian Maurer (GenomOncology) [00:42:48]:
And how they're the most useful in my world, the way I'm thinking about them Ian that, you know, this might change. This is April 9, 2024. So I, you know, keep that in mind when you're listening. They are really, you know, one phrase that I've heard is word calculators. Right? They understand what people are saying. They understand my intent. They know what they know and if you can get good at prompting them, which is called prompt engineering by some folks. You You can get good at prompting them.
Ian Maurer (GenomOncology) [00:43:16]:
What is prompting? You're basically telling them clearly and concisely what you have and what you want. And if you can tell them that those two things, it can kind of fill in the middle for you, not even tell you why, but you might wanna actually ask it why. Like, have it explained to you, it's thought process and it will do that and you'll get a better result actually if you do that, and then it will generate an answer. And so, that's called prompt engineering and I've just and by saying what you have and what you want, you're giving it examples Ian the the lingo Ian l m in the AI spaces, few shot learning. You're basically giving it a few shots of of of examples of what you want Ian then you're asking for it to to give you an answer. And by having it explain itself where you have it explain its thought process, you're giving it a chance to kinda work out the kinks of its thinking just like humans do. Right? I'm kinda talking right now and I'm kind of trying to express myself. Well, giving the large language model time to express itself, that's called chain of thought where you're basically it's having it kinda talk out its thought process and then it actually generates a result.
Ian Maurer (GenomOncology) [00:44:20]:
You get much better results when you do those things. So, you know, that's kind of the background. And what I'm doing is I'm actually integrating it with our API. And so what a lot of folks do is they do a thing called RAG or retrieval augmented generation, which is the idea of the chatbot is given text from a database. Right? You basically you know, if someone asks a question, you use that question to go find relevant content from your database, you stick that content in your prompt, and then you have the large language model reason over that text Ian then responds. It just does a better job when you do that. Another technique that you can do is what's called tool usage, which is you give it a tool. You say, okay.
Ian Maurer (GenomOncology) [00:44:57]:
Here's a tool or 5 tools or 10 tools. You can use those tools as you need to. Just remember, large language model. You're not good at math. You're not good at knowing what the weather is. You're not good at knowing what genomics are. You tell it that through prompting what it's bad at Ian then you say, okay, you're bad at those things but you're really good at understanding what the person you're talking to wants. So under try to understand what they want Ian then use these tools to help them.
Ian Maurer (GenomOncology) [00:45:23]:
That's your old goal. And if you explain it to the to the chatbot like Ian, and then you give it the access to the tools, you can actually get something that's that's almost a product. Right? It's still honestly, it's early days. A lot of these things are demos. That's why chatbots are kinda still the the the best product because the human in the loop is responsible for for figuring out what to do with it. And so that's what I'm building. I'm I'm actually building provider, you know, for an oncologist. An oncologist tool, let them have a conversation with the chatbot that has access to our API to both retrieve back, you know, trials, therapies, and other information for a patient.
Ian Maurer (GenomOncology) [00:46:02]:
And and and that's the kind of the long term vision of of of where we see, you know, our knowledge base. And I think AI is actually a great compliment for genome oncology Ian that this stuff's hard. Building user interfaces that can deal with all of the clinical information, the bioinformatic information, trials, the therapies, all that stuff. It's very overwhelming. So think about the most complicated user interface you have have to use at work or whatever, and you and you can see you can see what I mean. Having an experience that where that complexity is kinda hidden away from you, but can then be selectively retrieved into chat experience or, you know, eventually, it won't be just chat, it'll be more dynamic and you, you know, UI driven. Having this agent or assistant help you kinda navigate that that world would be so so great for GenomOncology because the thing our our our desired Ian clients, they don't have time to learn another software product. They don't wanna go and learn how to use some complicated, you know, it's like saying, oh, you have
Jeffrey Stern [00:47:07]:
to use Adobe Photoshop to do something,
Ian Maurer (GenomOncology) [00:47:09]:
to do your job. It's like,
Jeffrey Stern [00:47:10]:
good luck. I have never I've tried to use Adobe Photoshop. It's way too complicated
Ian Maurer (GenomOncology) [00:47:14]:
for me. I couldn't I couldn't use it. So doing the same thing with the doctors is really challenging. So I'm excited that, you know, large language models, especially, you know, maybe the next year or so, will really let us let our our clients truly take advantage of all the knowledge that we've curated over the last 12 years.
Jeffrey Stern [00:47:33]:
No. I mean, it resonates very deeply. I mean, ultimately, from any product's perspective. Right? The user is trying to do a job. They're trying to solve a problem, trying to do a task, and their their job is to accomplish that task Ian not to become an expert in in whatever it is that that you've built. And so there's
Ian Maurer (GenomOncology) [00:47:51]:
learn my software, and I don't blame them. I I wouldn't want to learn learn our software either. The molecular pathologist has a very important job, which is, like, make a report in 15 minutes. If they didn't use our software, it would take them 6 hours. So the trade off to them is night and day. Where in the oncology space, it's, well, I could not use learn that software Ian then basically just keep doing what I'm doing, or I could spend 6 hours to try to learn the software and make my and make my decision making a little bit better. Well, that's that's a that's probably a good trade to make sometimes, but you don't know which software to do that with. Right? So
Jeffrey Stern [00:48:26]:
Yeah. If you
Ian Maurer (GenomOncology) [00:48:27]:
basically spend your whole they don't have enough time as it is. Right?
Jeffrey Stern [00:48:30]:
The the job is not to become an expert in the software. The the product must solve a problem. Exactly. And what and what's really interesting to me about about all your your perspective there is I mean, you mentioned at the at the beginning, you know, we talked a little bit about Moore's Law, Wright's Law, the exponential cost declines of this technology, and maybe the the trite saying in in your industry that, you know, may we're down to a $100 sequencing now, but it's 1,000 of dollars for analysis. It feels like the convergence of the the technology from the the sequencing side and the AI, you know, the the confluence of those two things, it might be very powerful here.
Ian Maurer (GenomOncology) [00:49:07]:
Yep. No. Agreed. And and one thing if people are gonna remember one thing, large language models don't search. You feel like they're searching. Like, you're asking a question and it feels like they're searching information to, like, respond to you. That's not how they work fundamentally. They're not searching.
Ian Maurer (GenomOncology) [00:49:23]:
They're predicting the next token. So our system is really a search engine. It's just a very complicated search engine that lets you search by a patient. Like, basically, a patient and all their DNA and all that stuff is basically a query, and now you're querying our knowledge base, bring back the relevant content, and now the large language model can then reason over that content. What that large language model is doing is basically replacing learning how to do our search in our software. Does that make sense? Yeah. Maybe.
Jeffrey Stern [00:49:54]:
Alright. I think so. What what on the horizon in this space is most exciting for you? When when you think about, you know, Genome in the future Ian and and success and the kind of impact that you hope to have looking back in retrospect, what do you see coming, you know, given all these things? And how and how in reflection, actually, you know, you you even mentioned earlier as well, the the progress is is maybe even faster than than we've expected it to be.
Ian Maurer (GenomOncology) [00:50:22]:
Well, you know, I think at the end of the day, all of health care is gonna be molecular. I want genome oncology to be a core part of that, at least from a knowledge perspective, if not from a bioinformatics and analysis perspective. I think we we have the software and the the people and the expertise to help people do that. And then the goal is, how do we make it so the provider? Right? That's the the industry lingo for the oncologist or the treating physician. How do we reduce the administrative burden, the technological burden, etcetera, so that they can spend their time thinking about the patient and bring empathy and reasoning and their judgment to helping that patient as quickly and painlessly and effectively as possible. That's the goal. I I've lost lots of folks to cancer in my life that I love. I want to help be part of that solution to help reduce that pain and suffering in in the world.
Ian Maurer (GenomOncology) [00:51:17]:
So so that's that's the that's the real goal. The way I'm tackling that goal is through what I'm good at. Right? I'm not good at the stuff that oncologists do. I'm good at the stuff I do, which is solving hard technical problems. And I and I think that the things that genome oncology can do is just continue to scale because it's gonna become a scaling issue. There's so much data when it comes to genomics and other other stuff. So you have to you have to have the right algorithms, honestly, to be able to parse and process and and index and search this information. And then how do we leverage these new amazing and sometimes scary artificial intelligence tools to be an amplifier and an augmenter of expertise.
Ian Maurer (GenomOncology) [00:52:00]:
Right? We're all experts at something. Every human, right, is an expert at something. And these things, if designed and used appropriately, can be all our assistance and make us all 10 x better at whatever we are an expert at. It doesn't have to all be about, you know, generating images and silly songs and and other stuff that nobody really wants. It's it's really about helping helping humanity, you know, solve our most difficult problems. And I think, and I think that's that's where, you know, GenomOncology is going to be a Maurer contributor to.
Jeffrey Stern [00:52:35]:
You you had mentioned your your one takeaway for for AI. I'm curious if you were to offer a a similar takeaway for oncology and and cancer. You know, having spent the last decade working in this in this field, what is something that you wish more people understood about it that maybe we don't?
Ian Maurer (GenomOncology) [00:52:56]:
Well, if you have a loved one that has cancer, here's my number one advice for you. Find an expert. Not to, you know, besmirch folks that are, working in the community. But if your doctor and if you have melanoma and the doctor you saw or seeing just saw a cancer patient before you and a patient with gastro intestinal cancer after you, they are not an expert in your disease. And as of right now, the best thing you can do for yourself is to go find the person who is an expert if you can afford it. Not all the doctors do it. You would think that it would be standard of care, but it's not. Insurance companies don't necessarily pay for it, but there's there are angles around it.
Ian Maurer (GenomOncology) [00:53:46]:
Call the advocacy groups for your disease type. We work with a group called the pancreatic cancer society, and they will help any pancreatic cancer patient using our software, find a clinical trial, find a doctor, and they will help you analyze the information that's that's presented in Maurer, genomic reports. So, you know, you have to kind of you have to be your number one advocate for yourself, unfortunately. And then as far as genomics and bioinformatics and and and that inform and that it's a great it's a great field. I think that that's, you know, I think that there's there's still lots and lots to work on. And so if you're a young person that's that's looking for, you know, a direction in life and you're interested in really hard problems, look into systems biology, look into bioinformatics, look into these different technologies because I think, just being a computer scientist like myself, I'm a computer engineer, I don't necessarily think that that's the, the right idea for the future. I think you're gonna wanna have an intersection with, you know, 2 or 3 other skill sets, whether it's being able to sell or being able to write software or being able to, you know, manage and understand genomic data. Those are all good skills to have.
Ian Maurer (GenomOncology) [00:55:00]:
And being at the intersection of something is a good way to have, you know, kind of, being more distinctive in the marketplace and standing out.
Jeffrey Stern [00:55:09]:
I'll pull on your advice to a a younger self in in the spirit of of that reflection. What would you say is kind of the earned wisdom that you have from the entrepreneurial process and the company building side and the the business side of this?
Ian Maurer (GenomOncology) [00:55:24]:
So many different things. Be patient. Right? Things take a long time. We're still I mean, I still think of myself as being in a start up. We're 12 years old now at this point. We're, you know, we're still I feel still feel like we're a start up. We're very entrepreneurial and and and willing to fight thing fight for for the the next deal and and get stuff done in a very collaborative environment. So don't expect it to to to work overnight.
Ian Maurer (GenomOncology) [00:55:47]:
Your startups only fail when somebody finally gives up. Right? That's that's how they actually technically fail. So I think that that those are good lessons to learn. But I think right now, there's never been a better opportunity. Right? With if you're ambitious and you're, you know, looking to solve hard problems and you're looking to learn new things, that's a great way to go. If you're just interested in making money or what have you, you probably should figure out a big corporate job and that's that's probably a Maurer path. But from an entrepreneurial perspective, there's gonna be that people talk about, like, the the 3 person $1,000,000,000 company. Right? There's gonna be more Instagrams Ian their, you know, mid journeys and all these other small companies that just have enormous scale.
Ian Maurer (GenomOncology) [00:56:29]:
So those are those are those are interesting things. And maybe stay out of health care. Right? Health care is a tough a tough business to crack. So maybe you wanna stay out of health care and and go more b to c or something.
Jeffrey Stern [00:56:40]:
Yeah. Ian unfortunately, because we there's a lot of problems in healthcare.
Ian Maurer (GenomOncology) [00:56:45]:
There's there's a lot. It's it's tough. I mean, the nice thing about when you're in software Ian health care takes a long time to get in the door. But once you're in the door, it's usually harder for them. It's harder for them to kinda, like, say, oh, no. We don't need your name. Right? Like, you're integrated. You're you're you're part of their ecosystem.
Ian Maurer (GenomOncology) [00:57:01]:
You're delivering value. You know? It's a lot it's a lot stickier of a business.
Jeffrey Stern [00:57:05]:
Well, what what do you feel is is left unsaid? I mean, I'm sure there are many things, but, you know, in reflection on your your personal journey and and building genome oncology.
Ian Maurer (GenomOncology) [00:57:17]:
A lot. Cleveland's a great town, so we didn't talk about Cleveland at all. I do love Cleveland, and and I, you know, I hope I hope to see more investment, more interest in technology and innovation within Cleveland's come to the Cleveland and big data meetups. I think that those might be emerging. I I heard a rumor at least. So there's a, you know, good local development community, technologist community. So even if you're not a software developer and you're interested in those topics, look them up on meetup.com. And otherwise, you know, find me on social media and and let me know if you have any questions.
Ian Maurer (GenomOncology) [00:57:52]:
Perfect.
Jeffrey Stern [00:57:52]:
Well, I think we can we can bookend it then with our traditional closing question, which is which is about Cleveland Mhmm. For for a hidden gem in the area, you know, something that that other people should know about that maybe they don't.
Ian Maurer (GenomOncology) [00:58:04]:
So Lakewood in general. So I moved to Lakewood 25 years ago, and I love it. You're I know you're Ohio City, which is which is pretty solid. I might have to might reconsider that. But book book brothers in Lakewood, that's my that's my go to bookstore. So they actually have a it's like a used bookstore on Madison, and he's got he's curated it. Like, the the owner of the the store is definitely curated. You can tell that it's not your average, you know, kinda half price book type store.
Ian Maurer (GenomOncology) [00:58:31]:
It's it's got interesting stuff, interesting reads that you wouldn't find anywhere else.
Jeffrey Stern [00:58:36]:
Oh, I love it. That's perfect. William, I just wanna thank you again for for coming on, for sharing your story. It's, again, it's very it's long been fascinating to me, this kind of work, and, I love that that you guys are are building it here.
Ian Maurer (GenomOncology) [00:58:51]:
That's great. Thanks, Jeff. Really nice to meet you.
Jeffrey Stern [00:58:55]:
If people had anything that they wanted to follow-up with you about, where where is the best place for them
Ian Maurer (GenomOncology) [00:59:00]:
to do it? So I'm on LinkedIn. Last name is Maurer, m a u r e r. I'm also on Twitter. And you can also just email me at Ian anytime. Perfect.
Jeffrey Stern [00:59:12]:
That's all for this week. Thank you for listening. We'd love to hear your thoughts on today's show, so if you have any feedback, please send over an email to jeffrey@layoftheland.fm, or find us on Twitter at podlayoftheland land or at sternjefe, j e f e. If you or someone you know would make a good guess for our show, please reach out as well and let us know. And if you enjoy the podcast, please subscribe and leave a review on iTunes or on your preferred podcast player. Your support goes a long way to help us spread the word and continue to bring the Cleveland founders and builders we love having on the show. We'll be back here next week at the same time to map more of the land.