Mark Gordon

Mark Gordon

[00:00:00]

Don Davis PhD, MBA: Welcome to the latest episode of the life science success podcast. For those of you who don't know me, my name is Don Davis and I'm a consultant in the life science space and I help companies scale and manage complexity and increased performance. Today, I'm going to be joined by Mark Gordon. Welcome Mark.

Mark Gordon: Hi, Don. How are

Don Davis PhD, MBA: you? Really good. And, uh, thanks. Thanks a lot for being here. I appreciate it. And, uh, I look forward to having a great conversation with you about the drug development process and, [00:01:00] and, uh, ways that companies can continue to, uh, you know, to, to leverage your technology and capability. So, thanks.

Mark Gordon: Well, thanks. Thanks for having me. I'm looking for.

Don Davis PhD, MBA: Yeah. So before we get started, um, and, and we talk about you and your company. One of the things I wanted to talk about a little bit was the drug development, um, you know, process itself. So how does the drug development work its way through, um, you know, from the very start of the process all the way through to the end and kind of those percentages.

And as I have it, um, in phase one clinical trials, about 75% of the drugs, make it through phase one clinical trial, In phase two clinical trials, about 50% of those drugs, make it through. And then about in phase 3 59 per. And then 88%, you know, make it through kind of that NDA or bla portion of the drug discovery process.

And so, I mean, it means that the likelihood is [00:02:00] success. According to my calculations is, is actually, you know, less than 20% or something like that. So is that, is that kind of match what, you know, as well?

Mark Gordon: Uh, I, I really, we, you see lots of statistics. Uh, the, the, the largest publications we've seen, which vandalized, you know, thousands and thousands of drug programs as they go through clinical trials really put the number at more like 10% only.

And sometimes you see even less, uh, 10% of any drug that starts clinical trials, 10% of all of the drugs that start clinical trials get, get all the way through approval. So it's pretty abysmal, pretty rough.

Don Davis PhD, MBA: I would say so. And, and, um, so maybe, maybe as a next introduction, could you talk to us a little bit about, uh, you're the CEO of, of diviner and, uh, can you tell us about what diviner does and, and, you know, essentially what does the technology look to do?[00:03:00]

Mark Gordon: So diviner is focused squarely on that problem, uh, which is. There's gotta be a better way or there's gotta be a way to improve that success rate. So if you could, if you could arm investors in, in drug development and investors in drug development are pharma companies. Of course, big pharma is a huge part of that ecosystem.

Biotech venture capitalist. Uh, when things get further on the line and we're talking about publicly traded biotechs, which as, you know, they, they IPO early in clinical trials, or even before that's public equities funds, hedge funds. So they're all investors in. If you could, if you could arm investors with a way to better pick the winners, improve that probability success, you know, they would do far better.

They'd have better returns and we get better medicines. So that's what diviner is about, is about solving that.

Don Davis PhD, MBA: Yeah. So, um, in, in terms of, um, the, the, you know, [00:04:00] overall perspective of, you know, new clients that come to diviner, um, what do they look like? What are, what are they, what are they looking for and how does the platform work?

Mark Gordon: So it, it's interesting. Uh, you know, it's an interesting question. We are pre-revenue. So we were beginning to, after a, a lot of proof of concept. We're beginning to go to the market and talk to potential clients. What the problems that we're solving are, you know, essentially, um, is this drug going to work?

Should I invest in it? And w we, we, we, we had a hypothesis when we started diviner that collective human intelligence is the solution to the problem backed and supported by AI and analytics, but mainly human cells. And there's really good reasons for that, which, which, which I can get into, but, but to answer your question first, we had a hypothesis that we had a method [00:05:00] that would be better at predicting which drugs were going to work.

And we, we knew that it would be very difficult to get the attention of serious investors in the industry. You know, the pharma companies that, that feces, et cetera, until we had proven that our math work, that our statistics work, that we could take. And give it a probability of success and say, this, this drug is 70% chance likely to work or 30% that we had something to build on.

So we've spent we're, pre-revenue, we've been, we've invested the money we've raised and we've done a lot of bootstrapping and we've run a very large proof of concept to show that we can, in fact predict which drugs will work now, what do we do with that to really think it would be, you know, to your question.

Biotech funds that invest in biotechs would, if they believed that we can help them tell which ones are going to work, they'll pay for those, for those insights, either in the form of a subscription to our [00:06:00] forecasts. Um, in some cases, can you forecast something we're looking at diviner that you haven't looked at?

So it would be a sort of a customer bespoke offering and, uh, our, our assumption really out of the gateway. Uh, after talking to a lot of people in the industry, as we were starting to build the capability that the signals that we're generating are, are the signal that is the hardest to assess in assessing a biotech company.

You know, you look at the management team, you look at the science, but is the drug going to work? That's the big unknown. And if we have a signal that is predictive, then the best way to monitor. At least the best way to prove to the industry that there's something real there is going to be to power investment funds directly with these signals.

So we're looking at some creative ways to monetize, but essentially it is. Should I invest in this company? Should I, should I make a [00:07:00] venture investment? Should I buy the stock? If it's publicly traded? What what's, what are the odds? The drugs are going to work. That's essentially the.

Don Davis PhD, MBA: It's funny that you say that because, uh, so last night I was listening to the Chris yang podcast.

Chris Hayes, a coauthor of a blitz scaling and a with blood scaling, they came up with seven factors that essentially. Point to whether or not, if a, if an organization is ready to be blitz scaled or not. So essentially what they do is they look at a series of our portfolio of companies. And then in this podcast, they kind of, you know, rank whether or not.

You know, suggest to these companies that, Hey, look, you're actually, you know, a good target for blitz scaling, uh, or not. And, um, and, and I just, I think it's an amazing sort of, you know, thought process that you can use this, you know, collective intelligence, but also sort of historical thinking to kind of say, Hey, look, let's influence the way that this process might play out in the future [00:08:00] and use some of our historical capability to, to really, you know, help companies decide whether, whether.

Which companies should actually receive additional funding in the future.

Mark Gordon: Right. Right. I mean, the, the technique you mentioned is I think similar in a lot of ways to how, how you'd rate any complex topic, maybe how an investor would assess a startup company or how, uh, you know, how you decide whether an M and a makes sense, what are the factors to consider?

And let's score them on a, on a number of scales. Uh, when it comes to drug development, you know, those things, you know, that you can define some things that you want to assess. And that's of course what the industry does today, but then there's that big one big unknown thing, which is what's the probability that drugs actually going to work or show efficacy and safety in this trial.

So, right. There's that we've got a known factor that we're focused on knowing that in anybody that's making that's a serious investor in the industry will [00:09:00] absolutely. Score the management team score, the, you know, many other attributes about, about the decision.

Don Davis PhD, MBA: Yeah. And, and so, I mean, in terms of the, the, the way that it's going for you with regards to investment, where are you guys currently at?

Uh, right now from an investment standpoint.

Mark Gordon: So we, we, we raised some friends and family money and then some, uh, early angel money, uh, about a year. And that was enough to carry us through this proof of concept that we ran all last year. Essentially, what we did is we recruited a network of industry veterans, people that have spent their careers in drug development, and we brought their expertise together into our proprietary process.

And we ran a process every week. We assessed a new clinical trial and gave it a probability and the. Waited for the results to measure, measure how good we were. We didn't wait, [00:10:00] we just kept doing more. Um, and so we did that last year with that initial, that initial funding we've now based on the success we've had, the track record is better than we'd hoped for.

Initially, we've been able to, to, to put a around around together that we're getting close to closing, that will take us now through another major set of proof points going to market with paying customers. And, and another one is. Demonstrating that our predictive signals are strong enough to make trades on in the public equities market.

That's another proof point that we believe will give us not only one powerful way to monitor monetize what we're doing, but also an additional proof point. So a little bit of money last year, uh, and now, uh, uh, more substantial, uh, second part of our seed funding for right now.

Don Davis PhD, MBA: And, um, so in terms of, of, uh, next steps after that, where do you see things, you know, kind of, kind of progressing from there?

[00:11:00] What, what would happen next?

Mark Gordon: So there's, there's really, there's, there's a couple of major themes or sets of milestones. One is about, um, continuing to improve and to scale. So, you know, our, our win-loss. Our track record initially out after 21 results to score ourselves on is, is, is we're very happy with it, but we know that our predictive power is still fairly nascent compared to where we believe it can be as we continue to improve our predictive capabilities.

So it's about. Scaling and improving that, which is recruiting more industry veterans into our, into our network of diviners. We call them. And, uh, and by the way, our diviners have an average of 22 years of experience in the industry, heavy PhD, MD, heavy R and D focus. And they tell us they love what,[00:12:00]

and they get to collaborate with people that are peers. Without any of the usual corporate agendas or politics or biases. So they love the process. So it's about scaling, scaling that network more. And then it'll be really important to add in analytics and various forms of AI to improve our predictive power that's on building and improving the engine and then going to market and making sure that.

We can meet customer needs with our solution in, in the ways they want to see them finding our initial customers. We're in conversations now. And so we, we look out, you know, a year and we in the gold world to have been stronger prediction, power, more scale, more forecasts, and, and customers beyond that, there are other use cases where we're focused on pretty narrowly right now, [00:13:00] mid stage clinical trial results.

Uh, where it gets really exciting. Assessing early stage science assessing pre-clinical candidates, for example. So, yeah,

Don Davis PhD, MBA: absolutely. And, um, for anybody that's watching, uh, it looks to me like the, like the videos pausing occasionally, but mark, your audio is coming straight through, so I'm not exactly sure what's happening with the internet today, but, uh, it seems like there's a, there is a little bit of a technical glitches while, but, uh, you know, it seems like your audio is coming through.

You know, just fine. And so I would say we continue and, and, um, so the next question I have to tee up with you is, so in your career you spent a lot of time focusing in, on pharma intelligence, you know, based on my, my sort of judgment of, of looking at, you know, your past, um, how much did that experience shape, you know, what you're doing right now with diviner.

Mark Gordon: Yeah. Um, it had a pretty big impact on it. It is, as you might guess, Uh, [00:14:00] my back originally, um, my background is in computer science. So I spent the first part of my career doing software development and, um, and then a little bit of software sales. I knew I wanted to be an entrepreneur and I got a lot of advice that said you better learn how to sell.

So I did a little bit of that. I found myself, uh, at Eli Lilly and company in 1999. Uh, When Prozac was a couple of years from going off patent, which is always a big corporate scramble in a pharma company when there's that patent cliff coming. And it was an exciting time to be there. I was in a drug scouting team, a biotech scouting team, and my first exposure to pharma, I couldn't spell mechanism of action.

And, uh, I, I, uh, you know, brilliant scientists and business people together in rooms assessing. And they all knew that most of the drugs would fail after they licensed them because most drugs failed. That was shocking to me. So I spent a couple of years doing that [00:15:00] a couple of years then in, uh, inside a business development team at Roche, similar kind of world, looking for the next, you know, the next assets to fill their pipeline.

And then I, uh, you asked about pharma intelligence. I spent seven years at a Thompson writers business unit focused on life science. And then five years in an inform business unit focused on pharma and life sciences. And in both of those cases, the jobs were product strategy and direction. Where do we take our products?

And the products were, um, databases of all of the drugs that are in development by the industry, the clinical trials that are being run. And so I learned. You know, a lot more about how pharma companies and biotech companies look at drug development decisions and what are the data points. They consider a lot about the success rates, the high failure rates in the industry.

There were a lot of analyses that I [00:16:00] came across and the other thing that, so it got me thinking over many years about more and more about this problem. That became the one that, that, that we're focused on with diviner. The other thing is. In product strategy or product management, the objective is make sure you understand the market needs clearly before you build a product in those companies, it was about building a next generation of products because they had the more new add on products and you never build a product until you fully understand what the market wants and is willing to pay.

And, you know, I learned the hard way we had some failures that I could take responsibility for and we had some successes and I learned, you know, about, um, you know, what, what works and in the, in the second job and informa, it was, it was at a more senior level and leading product teams and getting involved in M and a acquiring other data companies and, you know, helping, helping turn a business around.

So I learned tons, but, but back to the bit about [00:17:00] making sure you understand the market needs. Along comes this idea in my head that I think there's a way to solve this failure rate problem. And what we're doing with diviner is we're kind of in a way we're saying we've got a solution that the industry doesn't even believe can exist.

Nobody's been able to figure out how to predict with any probability that's reliable, which drugs are going to work. So there's not a budget for that, right. But it's, it's such a strong signal and that's why we've chosen. We're going to choose the first, the first way to monetize will be indirectly areas where you can trade biotech stocks based on the results, because there are proof points are right there.

And we know that once the proof points are accepted, then. The willingness to be, to pay we'll be there because we know we're solving a problem. So it's a, it's a, it's, you know, it's a bit of a long answer, but it's [00:18:00] learning, learning over the years about, you know, market research and when, and how to assess market needs and willingness to pay.

And then, and then turning a little bit on its head in, in, in the kind of, uh, in a startup. It's solving a tough problem first, knowing that once, once the industry recognizes the value of the willingness to pay will be there.

Don Davis PhD, MBA: Yeah. I remember. I remember with, I mean, I would think at the end of the day, drug companies would also be interested in this as well.

I remember, I mean, this is decades ago, but I mean, I was at GE healthcare and we had, we had shrunk MRI implementation cycle time while I was there. And the. We had one of the big 10 pharma come in and look at exactly what we had done. How did we step through this process and like find a better way to, to solve the problem because they wanted to know, you know, how did they get to market faster than everybody else?

I mean, that was the key question that they were asking and. You know, at the time I could provide them [00:19:00] less information about that. But I do think that, you know, a solution like yours. I mean, if you could help them down select faster, help them sort of focus on the key things that will be successful, um, that would be helpful as well.

Also just wanting to mention really quickly before I turn it back to you, mark, uh, is Clayton Millis came up and said our audio sounds great. So, uh, we sound fine. Uh, but I do think, I do think the video's pausing kind of hearing. Uh, at least that's what I can see from my side.

Mark Gordon: Okay. Well, I never looked great anyway, as long as they sound.

Don Davis PhD, MBA: Right, right. That's why this used to be an audio podcast. Cause I used to tell people nobody wants to see me anyway. That's exactly. All right. So, um, so tell me this, I mean, and you kind of mentioned this along the way, um, why can't artificial intelligence entirely solve this problem? I mean, you're, you're talking about having, you know, experts, you know, that, that are involved in your solution as well.

And so why, why couldn't you turn this [00:20:00] entirely over to artificial intelligence to have it, you know, kick out an answer.

Mark Gordon: Yeah. I love that question. I can, I can really geek out on the answer, but I'll try to try to not go to, and I'm not an AI expert, but, uh, so here's the way I am. And the shorter answer is the data sets are too small for, and it would be machine learning would be the ideal solution to this problem.

If you machine learning for predictive purposes, the idea is, you know, AI machine learning, you turn it loose on a bunch of. And you figure out what are the combination of signals, which are most predictive of success. So you turn machine learning algorithms loose, or you just, you know, create the right algorithms.

You say, here are all the prior clinical trials that have happened. And here are the ones that are successful. What combinations of attributes, dosage, enrollment criteria, the track record of the sponsor, whatever those might be. What combination of attributes are most [00:21:00] predictive of success and that's how machine learning would work on a problem like this.

In fact, I've had some experience collaborating with, uh, an AI ML group that, that did just that. Uh, the problem is the data sets are too small in this case, and they're changing all the time. So, um, if you think about, I love the machine learning app example I love to use is, you know, say Google's image recognition, right?

You can type in cat eating a hotdog and find pictures of cat is eating. Did software figure that out? Well, no. They took millions of images that were tagged as what they were by humans, or they probably didn't have to take millions. Right. They took thousands and then they train algorithms on millions of examples until the algorithms get good enough to figure it out.

We've there are millions of pictures of that. And cat's eating hotdogs always look the same. There are different, right? There's not a change, [00:22:00] but drug development, there are, I don't know, 40, 50 more lately, 60 drugs approved every year. How many drugs are in clinical trials at anywhere between

Don Davis PhD, MBA: and 60 is the success rate, I think annually now, right?

Mark Gordon: Yeah. Yeah. So, uh, there are thousands and over, over 30 years, maybe. You know, a hundred thousand, there are 300,000 trials in trials road, I think in that database. So there's just not enough data for machine learning to be effective. And it's all changing. The way trials are running is changing even before COVID it was changing the way they're being designed and the way regulatory agencies look at approval, the science of course is changing.

So it's just, it's not, it's not a good solution for AI to solve on its own on its own.

Don Davis PhD, MBA: Yeah. I mean, in a way it's, it's, it's kind of a shame in, in one way and that's that, uh, I've kind of highlighted this recently. I know in my newsletter, I've also talked about it on recent podcasts as [00:23:00] well, is that we're sort of seeing kind of an overall.

Market shift from a venture capital standpoint, much more towards COVID related things. COVID diagnostics, COVID treatments and things like that. And those dollars that were used for, you know, cancer research and other things now. Are a bit harder to find, you know, at least what I'm hearing from people that I'm talking to.

And if you happen to have an antimicrobial resistant drug or you happen to have a, um, a drug that, that deals with epilepsy, I know are two good examples that I've had, you know, personally, you know, come across my desk is, you know, fi finding funding for those is pretty darn tough right now. And, and, um, so the leaders of those companies I know are really struggling and, and if.

Let's just say, you know, a solution like diviner could say, Hey, look, this thing has a better chance of making it over the finish line versus some other drug that, you know, would be, you know, better for cancer treatment or whatever that has like, [00:24:00] you know, a lesser chance of making it, then why not, you know, sort of, you know, shift your investment.

Maybe, you know, might be the way to look at it. Either from private equity or venture cap.

Mark Gordon: Yeah. I mean, that's a great point. I mean, you know that many, many, many people talk about the valley of death in, in drug development. So there's some promising early science and there's just not enough funding from that stage where that's something promising to where it's, you know, it shows enough evidence to get, you know, to get an ID, to get into clinical trials.

And what we really want is for. The funding, the float of the most promising early science. And if you could better identify what that is when we look at early science, as opposed to where diviners focus. Now there'll be a lot of AI based and analytics-based filtering first because there's so many things to look at, but then, you know, the, the, the, the, the collective intelligence of, of [00:25:00] human experts can then can then add to that.

But yeah, I, I, we, we, we, we know that. If we can make, if we can get adoption of these techniques and we're starting with demonstrating the proof points that we can make it easier for investors to pick the winners. And that will also attract more capital to the industry. There's a lot of capital globally that would love to be, you know, there are pension funds all over the world.

There's capital that could flow to drug development, but right now, You have to be an expert in it to be able to make the investment choices. Only the big pharma companies and the big biotech VCs by and large, you know, that's the bulk of the investment, regular investors. They, how would they do it? They don't know what to choose.

So if we can give them a FICO score, if you will, or sure, a movie or on a drug, this, this one is promising so that you know, that we believe that will, that will attract more capital to more of that early science. [00:26:00] Well, you know, there's a, there's a, there's a valley of death is they say absolute.

Don Davis PhD, MBA: So mark, there are three questions that I like to ask every guest, what inspires you?

Mark Gordon: Ah, what inspires me, uh, people who, who have something they're passionate about and have the guts and the perseverance to stick with it. And you know, there's, there's a lot of people like that. Uh, Um, you know, there are obvious well-known examples like Elon Musk, you know, electric cars, really people are going to,

uh, or a Steve jobs, right. Or, or, um, um, there's, there's many people like that. They're not all just the most famous ones. My wife for take choosing a new career that she was passionate about when people said, why would you do that? And so I, I was too timid for, for a number of years. So I'm inspired by people like.

And I think I've taken a step with what [00:27:00] I'm doing now. That's a lot of people say, why, why would you, that's not going to work. So it's that, that inspires me sort of people that are, that have conviction and persistence and, uh, you know,

Don Davis PhD, MBA: yeah. Well, thank you for sharing that. Yeah. I mean, I, I completely agree with you.

I, I, I find a lot of, um, a lot of sort of entrepreneurial, uh, energy in the idea that, you know, Lots of people have tough ideas that they're trying to sort of promote in the world. And, um, you're just watching them come over. The finish line is a, is a great thing. I mean, and, and you know, whether it is electric cars or, or, um, you know, getting back to space or, you know, other things, I mean, Elon Musk has definitely done his fair share of things there.

And he's had some failures. I mean, he has he's has, has had some clear and public failures as well. Yeah. And with that, I mean, he just kind of moves on, I mean, what's the next thing? How do I, how do I do that? I mean, you're having a, [00:28:00] you know, I don't know how many rockets blow up on the lawn on the, uh, re landing pad that whenever they were trying to recapture, you know, their spacecraft was, I mean, as public probably as it gets.

And probably a lot of people were saying, you're just crazy, man. Why just send that thing up and let it be part of the space junk.

Mark Gordon: Absolutely. And I feel so lucky to have encountered something that I can go for. Something big that if it works will improve human health. I mean, that's, that'd be amazing.

Right. So, but I've got to make a run at it.

Don Davis PhD, MBA: Absolutely. What concerns you?

Mark Gordon: Oh gosh. Um, yeah, I, uh, Well, as an entrepreneur, a lot with kids, a lot with a family, but, um, and one of the things that really bothers me, I mean, a lot of people are concerned about things like climate change. I'm an optimistic, maybe I'm crazy, but I think technology will help us find solutions [00:29:00] there.

Um, what I'm really worried about is, is the state of, and this is a, you know, this is an American focus view. I think it's pretty bad in other parts of the world, too. You know, this tribalism and the people just not agreeing on the same set of facts, whether it's right or left, you know, it happens on both ends of the political spectrum.

People don't believe, you know, they believe that, you know, social media, the algorithms, I don't think they were intended to do what they've done to us, but people get entrenched and they, they get reinforced by what they get interested in. And I'm concerned. You know, democracy is at stake. If, if, if people can't agree on basic facts, I mean, I would've said I've thought over my whole life, that kind of thing would never happen here in, in, uh, in the United States or in a Western, uh, open [00:30:00] democracy, you know, you can't have that kind of corruption or.

Or something that'll take us totally off, off the rails because the media would expose it and people would, there's so much transparency. Well, we don't have that. Now we have all the transparency in the world with the internet. Right. And we don't have any agreement. So I that's, that's what I worry about that.

Don Davis PhD, MBA: Yeah. I mean, I agree with you on the, the U S portion, but I mean, the shocker for me, I mean, if you watch what's happening right now with Canada in vaccines, and the fact that they're shutting down the border, because people that are crossing back and forth between the border have to be vaccinated. And, you know, the, the trans transport companies essentially are, you know, boycotting more or less.

Um, it's a, it's a real shocker for me, uh, to just see that, that this problem of, you know, Wanting a better world or wanting a place where people are in general protected from, you know, COVID, [00:31:00] um, has become such a political football that it's, you know, people are willing to do whatever they have to do. And if it's shut down, um, you know, borders, then it's shut down borders.

Um, they were saying though that the, I mean, they were talking about actual, uh, you know, Uh, reactions, I guess, if people were going to prevent people from getting to the hospital in Canada, which, you know, thankfully, I mean, you know, that's one of the things I guess, that they did talked about was you're shutting down the transport routes to, and from hospitals.

Yeah, to me, if you need care and you need a heart, having a heart attack or, you know, you're in an ambulance for some reason, heaven forbid that there's, you know, a blockade, uh, that prevents you from getting there. So I, I just, I agree with you. I mean, the, the, this sort of ability to have, um, a disagreement without that disagreement, you know, potentially causing somebody else's life, uh, you know, it seems to be.

Where we've got to get back to. Um, [00:32:00] somehow, and I don't have the answers there. I, I leave that up to bigger minds of mine. And what excites you? Mark?

Mark Gordon: What excites me? Oh, uh, big transformative ideas. So you know that the concept of Uber, right? Or Airbnb, those kinds of things, when there's a, a trend, a transformative way of doing something differently, that makes so much sense when you see the picture.

And especially if it's hard to get your head around and a lot of people find it challenging to get their head around. Initially, I get, I get excited about that kind of thing. I think that's why I like collective intelligence. Um, seeing something. That can be transformative that really at the heart of it, it's very simple.

Um, but it's a big idea. I love those kinds of things. So, um, I mean, I I'll take an example that [00:33:00] hasn't come about yet, like Airbnb and Uber as, or is still coming about like diviner is a big idea. There's gotta be a better way for healthcare to T to see doctors instead of waiting for. You know, three weeks, get an appointment to get one opinion, and then not knowing if it's the right opinion.

Why can't I see a panel of people at once on video and have discussion and they have, they, you draw on evidence-based medicine to see what my diagnosis is. Of course there's a lot to be done on the evidence-based medicine side, but there's, there's just there's so there are different ways to do things.

And it's those, those big new concepts that those excite me, they get, they get me.

Don Davis PhD, MBA: Yeah, it definitely seems like there's a lot, there's a lot more that could be done with telehealth. And I mean, along that same line, I mean, I, I visit my doctor. I fill out all my paperwork online before ever go and, you know, go, go to see her face to face.

And, um, yet whenever I [00:34:00] arrive, they want me to fill out a paper for. And, uh, I sort of block it that each time it's like, look, I mean, we're in the digital age. I mean, why are these things not digital? And I can't answer it for the doctor's office. I'm sure there's some reason why they can't convert this one last thing to, to the digital world.

But it's, uh, you know, these sort of challenges are the things I look forward to most. I mean, I, I love, you know, kind of where. You know, AI is today. I love where, you know, the personalized medicine journey is today. We're making progress. We're not done, but we are making progress is exciting.

Mark Gordon: Yeah. There, there is a lot of progress.

I mean, I've, I've got some doctors I've gone to see in the last year or so where all the conversation is happening in a little chat session that doesn't require me to log in and. Turned my keys upside down and type type in 47 codes. Like I can just start having a conversation, um, instead of [00:35:00] a phone call and waiting and a fax and the so it's, it's like health records, you know, those things digitized in a standard way will make such a difference to the ability to understand.

And with huge datasets where AI can be really great, because there are millions of millions of records to learn from what combinations of diagnoses and interventions lead to the best outcomes. And that's coming. I mean, we're, it's taken forever just to get the data integration and standardization, so you could even do those things, but we're getting there and that's, that's exciting.

We're going to see big. Big improvements to healthcare. I think, um, well to drug discovery and drug development, when, when scientists in those, considering which drugs to try, can rely on that long-term outcome data to better assess what might work, but then also, you know, in, in delivering health [00:36:00] care, it's, it's going to be an amazing next 10 or 20 years.

Don Davis PhD, MBA: So Mark Gordon, I look forward to continuing to watch your journey with diviner and all the things that you guys are doing there. Thanks so much for being a guest today on the podcast. And thanks for spending some time with me.

Mark Gordon: Thanks, Don. I really enjoyed it. Thanks for having me.

Intro Music: Thanks a lot. .