Bridging Medicine and Technology: A Conversation with Dr. Leon Henderson-MacLennan

[00:00:01] [SPEAKER_01]: Welcome to the Chalk Talk Gym podcast where we explore insights into healthcare that help

[00:00:07] [SPEAKER_01]: uncover new opportunities for growth and success.

[00:00:11] [SPEAKER_01]: I'm your host, Jim Jordan.

[00:00:21] [SPEAKER_01]: Welcome back to Chalk Talk Gym.

[00:00:23] [SPEAKER_01]: Today's episode features Dr. Leon Henderson-MacLennan.

[00:00:28] [SPEAKER_01]: He's a distinguished physician and a geneticist with a background in internal medicine and

[00:00:33] [SPEAKER_01]: clinical genetics from Cornell University and Cedars-Sinai Medical Center.

[00:00:38] [SPEAKER_01]: Dr. Henderson-MacLennan has seamlessly transitioned from clinical practice to a pioneering role

[00:00:43] [SPEAKER_01]: in biotech, pharmaconsulting, and healthcare services research.

[00:00:47] [SPEAKER_01]: His journey as a testament to the power of data analytics and innovation in healthcare

[00:00:51] [SPEAKER_01]: and his work in predictive modeling and data-driven decision making has not only

[00:00:56] [SPEAKER_01]: advanced medical practices but also has revolutionized how businesses operate in

[00:01:02] [SPEAKER_01]: the biotech and pharmaceutical industries.

[00:01:05] [SPEAKER_01]: In this episode, we'll explore his transformative insights and the stories behind his groundbreaking

[00:01:10] [SPEAKER_01]: contributions.

[00:01:11] [SPEAKER_01]: Please join us as we delve into the intersection of commerce and medicine and discover how

[00:01:16] [SPEAKER_01]: a strong analytical foundation can drive impactful change in a very complex industry.

[00:01:22] [SPEAKER_01]: So tell me in the audience a little bit more about yourself.

[00:01:25] [SPEAKER_00]: Dr. Leon Henderson-MacLennan Absolutely.

[00:01:26] [SPEAKER_00]: And thank you for having me.

[00:01:27] [SPEAKER_00]: I am a physician by training.

[00:01:30] [SPEAKER_00]: I was practicing until about a year and a half ago, and I lived in the worlds of both

[00:01:38] [SPEAKER_00]: biotech and pharmaconsulting and clinical medicine for the longest of times.

[00:01:43] [SPEAKER_00]: I'm an internal medicine specialist as well as a clinical geneticist.

[00:01:47] [SPEAKER_00]: I did my training at Cornell University and did an internship and two fellowships

[00:01:54] [SPEAKER_00]: at Cedars-Sinai Medical Center here in Los Angeles.

[00:01:57] [SPEAKER_00]: One fellowship was in medical genetics and the other was in a domain that we don't hear

[00:02:03] [SPEAKER_00]: about too much, and that's health services research.

[00:02:07] [SPEAKER_00]: I first was recruited by Fidelity Investments to provide an overview of just how their

[00:02:15] [SPEAKER_00]: very innovative monoclonal antibody might be valued.

[00:02:20] [SPEAKER_00]: It helped me pay back my loans with absolutely no problem.

[00:02:24] [SPEAKER_01]: I'm sure you underestimated the project and its succeeded expectations for everybody.

[00:02:28] [SPEAKER_00]: Dr. Leon Henderson-MacLennan It just blew everybody away and I was extremely happy with

[00:02:33] [SPEAKER_00]: and thought, gee, this is something that I would really enjoy doing as I complete

[00:02:39] [SPEAKER_00]: two fellowships and go out into the world and become a geneticist.

[00:02:44] [SPEAKER_01]: Dr. Leon Henderson-MacLennan So tell me how health services research fits in the continuum of health care in general.

[00:02:49] [SPEAKER_00]: Dr. Leon Henderson-MacLennan It's a broad based field that contributes to multiple spheres

[00:02:56] [SPEAKER_00]: at the intersection of commerce and medicine.

[00:03:01] [SPEAKER_00]: Cedars-Sinai and UCLA had all of these data that are being generated in clinical trials

[00:03:07] [SPEAKER_00]: and it was unnatural to combine that with not only something that was valuable for our institution,

[00:03:14] [SPEAKER_00]: but also valuable for the public.

[00:03:18] [SPEAKER_00]: That's how I got into consulting on a larger basis and to make a long story short,

[00:03:23] [SPEAKER_00]: I was chided by a friend of mine who said, Leon, you are shockingly undervaluing yourself.

[00:03:29] [SPEAKER_00]: And I said, what are you talking about?

[00:03:32] [SPEAKER_00]: Let me have you speak with people who really understand business.

[00:03:36] [SPEAKER_00]: Okay, I'm happy to do that.

[00:03:37] [SPEAKER_00]: I'm always happy to learn and evolve myself and my learning and sure enough,

[00:03:42] [SPEAKER_00]: collaboration with someone who knows a lot more than I do about finance and collaborating

[00:03:49] [SPEAKER_00]: with someone was extremely helpful in generating a business.

[00:03:55] [SPEAKER_00]: We picked up momentum and grew beyond just attracting fidelity and ventured out into other areas.

[00:04:02] [SPEAKER_00]: And we grew from there to other sectors and to the world of biotechnology

[00:04:07] [SPEAKER_00]: and the world of pharmaceuticals.

[00:04:09] [SPEAKER_00]: In short, learning really about the ecosystem that is in a nutshell US healthcare.

[00:04:16] [SPEAKER_00]: And for a physician, it's an eye-opening experience because we're so tunneled

[00:04:23] [SPEAKER_00]: for such a long time in our pursuits that this eye-opening possibility was very appealing to me

[00:04:30] [SPEAKER_00]: because I like to do many things at once.

[00:04:33] [SPEAKER_00]: So I was able to continue in both clinical medicine and in these commercial pursuits.

[00:04:39] [SPEAKER_01]: The American healthcare system is moving towards 20% roughly at 18%.

[00:04:43] [SPEAKER_01]: And the rest of the world is at 10% to 12% healthcare reform.

[00:04:46] [SPEAKER_01]: Whether people like healthcare reform or not, at the end of the day,

[00:04:50] [SPEAKER_01]: it has brought the distance between our gross domestic product annual growth

[00:04:55] [SPEAKER_01]: and the healthcare annual growth into single digits.

[00:04:57] [SPEAKER_01]: So we decide that we need to pay for our healthcare based upon value.

[00:05:02] [SPEAKER_01]: That's the goal.

[00:05:03] [SPEAKER_01]: And we give a good talk and we talk about how we need to get there.

[00:05:07] [SPEAKER_01]: But the fact of the matter is we're not there because we're in silos of data.

[00:05:11] [SPEAKER_01]: So it's been 14 years, we're getting better.

[00:05:13] [SPEAKER_01]: But we're still very silent as your business is trying to bring sense of this

[00:05:18] [SPEAKER_01]: to people making business decisions.

[00:05:20] [SPEAKER_00]: And making sure that those decisions take into account the various players

[00:05:26] [SPEAKER_00]: that you're talking about.

[00:05:28] [SPEAKER_00]: We have a tendency to look at what we can see of the elephant.

[00:05:33] [SPEAKER_00]: If you always want to go back to that analogy, like my goodness,

[00:05:37] [SPEAKER_00]: that elephant is large.

[00:05:38] [SPEAKER_00]: We have to understand that if we remain in silos such as this,

[00:05:43] [SPEAKER_00]: if we remain focused on one part of the elephant,

[00:05:46] [SPEAKER_00]: we're doing a disservice to the industry.

[00:05:49] [SPEAKER_00]: So surrounding oneself with people who can see the lot of the elephant,

[00:05:56] [SPEAKER_00]: we're going to succeed a little bit more.

[00:05:58] [SPEAKER_00]: And we need also to have tools to integrate the vision of these various

[00:06:05] [SPEAKER_00]: extremely intelligent people looking at their portion of the elephant.

[00:06:10] [SPEAKER_00]: And as we do that, each person is able to see a little bit more of the elephant

[00:06:17] [SPEAKER_00]: each time the integration gets better, the process get better.

[00:06:20] [SPEAKER_00]: The deliverables that a client, whether it's a biotech leader or a pharmaceutical

[00:06:27] [SPEAKER_00]: leader receives will have more pertinent information for them to make

[00:06:32] [SPEAKER_00]: decisions that are appropriate for their firm, for their shareholders.

[00:06:36] [SPEAKER_00]: So indeed, as we grow, everyone else grows as well.

[00:06:40] [SPEAKER_00]: And obviously now we're looking at technologies that can bring those

[00:06:44] [SPEAKER_00]: aspects of vision together.

[00:06:47] [SPEAKER_00]: It's an exciting time in that respect because we've tried to do this

[00:06:51] [SPEAKER_00]: consistently ourselves as we grow.

[00:06:54] [SPEAKER_00]: And now with AI and machine learning and other tools at our disposal,

[00:07:00] [SPEAKER_00]: we're able to not only do this faster, but also we have the ability

[00:07:05] [SPEAKER_00]: to sift through it as we interrogate these new tools and separate the wheat

[00:07:12] [SPEAKER_00]: from the chaff, the valid from the invalid.

[00:07:15] [SPEAKER_00]: This is not a panacea by these tools are not panaceas alone.

[00:07:19] [SPEAKER_00]: We need the human element to help us extract that, which is of greatest value.

[00:07:24] [SPEAKER_01]: And it is a human element, I think, where we find the data to really

[00:07:27] [SPEAKER_01]: understand the problem at hand quite often too, because a lot of it

[00:07:32] [SPEAKER_01]: is our health issues are embedded into family cultures.

[00:07:36] [SPEAKER_01]: Where does your company fit in the continuum of health care today?

[00:07:40] [SPEAKER_01]: You have a new business launching today.

[00:07:43] [SPEAKER_01]: So the first one is the in thought, which is providing data,

[00:07:48] [SPEAKER_01]: the evolved data that you know of existing business systems.

[00:07:52] [SPEAKER_01]: And then there's your labs that is partnering with customers

[00:07:56] [SPEAKER_01]: who are trying to go beyond that, which is known today.

[00:07:59] [SPEAKER_01]: Is that the best way to describe that?

[00:08:00] [SPEAKER_00]: That's a way to look at it.

[00:08:01] [SPEAKER_00]: In thought labs, which is a novel name for inferences,

[00:08:05] [SPEAKER_00]: which was spun out to help clients manage the morass of data

[00:08:12] [SPEAKER_00]: that they're beset with in a sophisticated and efficient way.

[00:08:17] [SPEAKER_00]: Firstly, inferences helped in thought exactly that because we got to a point

[00:08:22] [SPEAKER_00]: where everybody's trying to look at a different part of the elephant

[00:08:25] [SPEAKER_00]: and we're limited by our human capacities.

[00:08:28] [SPEAKER_00]: We needed tools to help us internally

[00:08:34] [SPEAKER_00]: manage the morass of data.

[00:08:35] [SPEAKER_00]: We want to get to the why we want to get to the conclusions.

[00:08:39] [SPEAKER_00]: What is the absolute risk reduction for X drug?

[00:08:43] [SPEAKER_00]: Let's say we're hired by a company that's developing an asset

[00:08:47] [SPEAKER_00]: and they want to understand what their competing assets are generating data wise.

[00:08:53] [SPEAKER_00]: We want to understand whether that competing company

[00:08:56] [SPEAKER_00]: is playing fairly in the sandbox and just cherry picking

[00:08:59] [SPEAKER_00]: the generated data for their benefit.

[00:09:02] [SPEAKER_00]: But we can't get there without having massive data sets.

[00:09:06] [SPEAKER_00]: So we want to better synthesize those data with the tools that are available today,

[00:09:11] [SPEAKER_00]: machine learning, AI, etc., and bring that to bear.

[00:09:15] [SPEAKER_01]: Is there some example that would be not proprietary or public now

[00:09:19] [SPEAKER_01]: that you can look back and say, boy, that was an insight I didn't have

[00:09:22] [SPEAKER_01]: before we did this project?

[00:09:23] [SPEAKER_00]: I'm going to go back to the rheumatoid arthritis and inflammatory bowel

[00:09:26] [SPEAKER_00]: disease landscapes were growing hand in the beginning.

[00:09:30] [SPEAKER_00]: The advent of monoclonal antibodies directed at targets that influence these diseases

[00:09:36] [SPEAKER_00]: is often the speed at which you can help someone wins out.

[00:09:41] [SPEAKER_00]: Recognition that the effect magnitude of a competitor

[00:09:45] [SPEAKER_00]: is greater than the standard of care is where the rubber meets the road

[00:09:50] [SPEAKER_00]: when you're trying to help clients understand what the future is going to look like.

[00:09:53] [SPEAKER_00]: Enbrel had its niche and some was available,

[00:09:57] [SPEAKER_00]: didn't do as well outside of inflammatory bowel disease,

[00:10:00] [SPEAKER_00]: but something comes along.

[00:10:02] [SPEAKER_00]: This is so far in the past that I chose this example

[00:10:05] [SPEAKER_00]: because everybody knows that Abbott and Abvy came along with Humaira.

[00:10:09] [SPEAKER_00]: If you can recognize that magnitude of effect advantage earlier,

[00:10:13] [SPEAKER_00]: and we did, that's a way to have a pretty darn good impact for everybody

[00:10:18] [SPEAKER_00]: that's concerned not only rheumatoid arthritis space,

[00:10:21] [SPEAKER_00]: but also spaces psoriasis psoriatic arthritis.

[00:10:25] [SPEAKER_00]: And you can imagine that that particular example is repeated tens of thousands of

[00:10:31] [SPEAKER_00]: times across various therapeutic areas and across various geographies.

[00:10:36] [SPEAKER_01]: I'm working with a really group of professors.

[00:10:38] [SPEAKER_01]: It's called the Pittsburgh Learning Continuum, and they're focused on how does

[00:10:41] [SPEAKER_01]: the body learn, how do the immune system learn, how does the brain learn

[00:10:45] [SPEAKER_01]: and what data sets we have and what data sets we don't have.

[00:10:49] [SPEAKER_01]: And the step to get to this in salt public,

[00:10:51] [SPEAKER_01]: the step to get to this was if you think of just a normal distribution

[00:10:55] [SPEAKER_01]: you have the tails of it, which are the odd stuff.

[00:10:58] [SPEAKER_01]: And what they discovered is when they're handling their various research questions

[00:11:02] [SPEAKER_01]: in a lab that's very micro oriented,

[00:11:05] [SPEAKER_01]: the information they're interested on is generally within the first few standard

[00:11:08] [SPEAKER_01]: deviations and for them those last pieces are not relevant to what their research is.

[00:11:13] [SPEAKER_01]: But if they could get the tails together and go horizontally across

[00:11:17] [SPEAKER_01]: disciplines, they've actually found that the future hypotheses are very much

[00:11:22] [SPEAKER_01]: within the tails of the discarded data.

[00:11:24] [SPEAKER_01]: Was that what your company is leaning towards in the future business model?

[00:11:29] [SPEAKER_00]: That's an interesting thought process.

[00:11:31] [SPEAKER_00]: I say as a geneticist, let me see if this fits the analogy.

[00:11:35] [SPEAKER_00]: We know that there's coding regions of the genome.

[00:11:38] [SPEAKER_00]: We know that there's non coding regions of the genome called the junk DNA.

[00:11:43] [SPEAKER_00]: We need to understand really what's going on.

[00:11:45] [SPEAKER_00]: And we know now that some of that junk are very important regulatory sequences,

[00:11:51] [SPEAKER_00]: very important sequences that have to do,

[00:11:54] [SPEAKER_00]: perhaps with epigenetic effects if they're wrapped around non coding DNA

[00:12:00] [SPEAKER_00]: and other proteins.

[00:12:02] [SPEAKER_00]: What do we do with the junk is often where a lot of value lies.

[00:12:08] [SPEAKER_00]: We don't want to throw out the information that we're not using today

[00:12:12] [SPEAKER_00]: can be used tomorrow.

[00:12:14] [SPEAKER_01]: Another macro example, they say that a drug costs over a billion dollars

[00:12:18] [SPEAKER_01]: to get to market.

[00:12:20] [SPEAKER_01]: It's not that one drug from beginning to end takes a billion dollars.

[00:12:24] [SPEAKER_01]: It's that when you start one in the lab, there's a 2% probability

[00:12:27] [SPEAKER_01]: it comes out the other end is a successful drug.

[00:12:30] [SPEAKER_01]: So you have to pay for those failures.

[00:12:33] [SPEAKER_01]: If you're going to go into a drug category,

[00:12:36] [SPEAKER_01]: you need to make sure that you're going to make over a billion dollars.

[00:12:40] [SPEAKER_01]: And so drug companies are saying, I know this people with this disease,

[00:12:44] [SPEAKER_01]: but I can't justify the cost of going after it.

[00:12:47] [SPEAKER_01]: So we have the FDA trying to figure out a more cost effective ways.

[00:12:52] [SPEAKER_01]: On the other end of that, I'm working with the Precision Medicine Company

[00:12:55] [SPEAKER_01]: where they have 10 steps to the worst case scenario with this disease.

[00:13:01] [SPEAKER_01]: And so a general practitioner might only see 1% of these patients

[00:13:04] [SPEAKER_01]: out of all their patients that fit this profile.

[00:13:07] [SPEAKER_01]: And when certain events happen for that patient,

[00:13:10] [SPEAKER_01]: it's different than if it were to be you and me.

[00:13:12] [SPEAKER_01]: And so the Precision Medicine says, hey, this patient just walked in

[00:13:16] [SPEAKER_01]: from my office looking at what might not be a material physical event.

[00:13:21] [SPEAKER_01]: But for what they have, it's actually very material.

[00:13:24] [SPEAKER_01]: And that's how the company started.

[00:13:26] [SPEAKER_01]: Now machine learning or artificial intelligence has said

[00:13:29] [SPEAKER_01]: there's something going on with these patients statistically at 1.5.

[00:13:34] [SPEAKER_01]: Now, it takes physicians and scientists to know what to do about 1.5,

[00:13:37] [SPEAKER_01]: but the data is saying we need to look at 1.5.

[00:13:41] [SPEAKER_01]: Now the drug companies coming back and saying,

[00:13:43] [SPEAKER_01]: you're actually giving us the evidence of 1.5 that it's worth

[00:13:47] [SPEAKER_01]: taking a journey for a drug here.

[00:13:49] [SPEAKER_01]: So what are the challenges in this sort of amalgamation of issues,

[00:13:54] [SPEAKER_01]: macro, micro, data sets, anomalies and a data set

[00:13:58] [SPEAKER_01]: that your organization is facing and developing products and services?

[00:14:02] [SPEAKER_00]: You are hitting the nail on the head with a major challenge

[00:14:06] [SPEAKER_00]: that we're often presented with.

[00:14:08] [SPEAKER_00]: And that's the go no go decision

[00:14:10] [SPEAKER_00]: that is based on present data

[00:14:15] [SPEAKER_00]: and the probability of future data generation.

[00:14:18] [SPEAKER_00]: And that's a lot of times where the past performance

[00:14:22] [SPEAKER_00]: doesn't necessarily equal future performance.

[00:14:26] [SPEAKER_00]: However, in silico modeling can better analyze

[00:14:29] [SPEAKER_00]: the present as if it is X time in the future,

[00:14:33] [SPEAKER_00]: Y time in the future, Z time in the future

[00:14:35] [SPEAKER_00]: and come up with not only the predictive modeling around that,

[00:14:40] [SPEAKER_00]: but also discussion points around that we can go to a company

[00:14:45] [SPEAKER_00]: and articulate the model is outputting

[00:14:49] [SPEAKER_00]: in a way that makes sense to them so that their go no go decisions

[00:14:54] [SPEAKER_00]: are based on something more than, let's say,

[00:14:58] [SPEAKER_00]: an inadequate target product profile.

[00:15:01] [SPEAKER_01]: So this show is about how people can apply these concepts

[00:15:05] [SPEAKER_01]: to different business models in health care.

[00:15:07] [SPEAKER_01]: So my last episode I spoke to an insurance company

[00:15:09] [SPEAKER_01]: who was offering a service to large employers who self insure.

[00:15:14] [SPEAKER_01]: Now, an insurance company makes its profit in two ways.

[00:15:17] [SPEAKER_01]: One through administration and the other is through population risk

[00:15:21] [SPEAKER_01]: and population risk is where the bigger challenge is.

[00:15:25] [SPEAKER_01]: And so let's imagine next year

[00:15:27] [SPEAKER_01]: the percentage of my population would cancel moves from say one to 10 percent,

[00:15:30] [SPEAKER_01]: which is obviously a ridiculous amount.

[00:15:32] [SPEAKER_01]: But that would drastically change the cost

[00:15:35] [SPEAKER_01]: and the profit in your own system.

[00:15:37] [SPEAKER_01]: So when you say to self, why do insurance companies make so much on the bottom line?

[00:15:41] [SPEAKER_01]: Part of it is covering this population risk that they have.

[00:15:45] [SPEAKER_01]: So this company that I'm talking about

[00:15:47] [SPEAKER_01]: is taking the administrative component of insurance

[00:15:50] [SPEAKER_01]: and they're allowing large companies

[00:15:52] [SPEAKER_01]: to use their own population data so that they can take on their own risk

[00:15:56] [SPEAKER_01]: and so that they don't have a pay a profit for somebody else's population risk.

[00:16:02] [SPEAKER_01]: But at this point in time,

[00:16:02] [SPEAKER_01]: the company really can't offer this to smaller groups of employees

[00:16:06] [SPEAKER_01]: because they actually don't know the data to know if it's in their interest to be self insured.

[00:16:12] [SPEAKER_01]: I would imagine that your organization could also help with this kind of data analysis.

[00:16:17] [SPEAKER_00]: It's interesting that you bring up the insurance industry.

[00:16:20] [SPEAKER_00]: I was just talking with colleagues the other day about the merger of minds

[00:16:26] [SPEAKER_00]: of folks dealing in actuarial data.

[00:16:29] [SPEAKER_00]: But I think merging our capabilities

[00:16:33] [SPEAKER_00]: with the type you're talking about could benefit industries.

[00:16:38] [SPEAKER_00]: We're developing a tool that helps us understand at a glance

[00:16:44] [SPEAKER_00]: unmet needs of specific populations.

[00:16:49] [SPEAKER_00]: In this case, individuals that have disease and at risk for disease,

[00:16:54] [SPEAKER_00]: this co-mingling of models magnifying both sets of data's power.

[00:16:59] [SPEAKER_00]: This is something that indeed we can help insurance companies

[00:17:04] [SPEAKER_00]: who deal in actuarial data and the pharmaceutical and biotech industry.

[00:17:10] [SPEAKER_00]: And it's so interesting that you brought that up.

[00:17:13] [SPEAKER_00]: This is the type of process being done to harness the capabilities

[00:17:18] [SPEAKER_00]: has really endless potential.

[00:17:21] [SPEAKER_01]: To appreciate this point,

[00:17:22] [SPEAKER_01]: I'm currently working with a gentleman from a company called Matchright Care.

[00:17:25] [SPEAKER_01]: And that company is focused on a personal health record.

[00:17:29] [SPEAKER_01]: And his story is quite amazing.

[00:17:30] [SPEAKER_01]: He and his wife had a baby who tragically died from a very rare disease.

[00:17:35] [SPEAKER_01]: And through this heartbreaking experience,

[00:17:37] [SPEAKER_01]: they have traveled the U.S. to get the best care for their child.

[00:17:39] [SPEAKER_01]: And there was no connectivity between the systems.

[00:17:42] [SPEAKER_01]: And at worst, the data was not connectable.

[00:17:45] [SPEAKER_01]: And at best, it was fragmented and incomplete.

[00:17:49] [SPEAKER_01]: And this made it impossible for the system to track their data.

[00:17:53] [SPEAKER_01]: Effectively, they had to do with themselves as parents.

[00:17:55] [SPEAKER_01]: When we think about our national health care system,

[00:17:58] [SPEAKER_01]: the cost of the system and its inefficiencies.

[00:18:01] [SPEAKER_01]: A significant issue we face is information management.

[00:18:04] [SPEAKER_01]: Beyond just dealing with diseases themselves,

[00:18:06] [SPEAKER_01]: we need better infrastructure.

[00:18:08] [SPEAKER_00]: That's exactly true.

[00:18:09] [SPEAKER_00]: I just when I was listening to that episode,

[00:18:11] [SPEAKER_00]: I was reminded of literally I had two cases

[00:18:16] [SPEAKER_00]: the same early morning from literally five miles

[00:18:20] [SPEAKER_00]: from the person lived five miles from the hospital.

[00:18:23] [SPEAKER_00]: Their data was about four miles from the hospital here in Los Angeles.

[00:18:27] [SPEAKER_00]: And I had another patient, same condition really.

[00:18:30] [SPEAKER_00]: It was scary that this was happening at the same time.

[00:18:33] [SPEAKER_00]: But obviously there was a story to be told in the ether.

[00:18:36] [SPEAKER_00]: And there I was.

[00:18:37] [SPEAKER_00]: And the other patient was from, I'm going to say,

[00:18:40] [SPEAKER_00]: somewhere around Calgary.

[00:18:42] [SPEAKER_00]: Yes, who's data got there first?

[00:18:44] [SPEAKER_00]: And it was in part due to the disparate systems

[00:18:49] [SPEAKER_00]: that were being used four miles away

[00:18:51] [SPEAKER_00]: versus where this individual had a similar event

[00:18:55] [SPEAKER_00]: two thousand miles away and the data were harbored there.

[00:19:00] [SPEAKER_00]: And suffice it to say that lag time was not in the person's favor.

[00:19:04] [SPEAKER_00]: The outcomes were good in this case,

[00:19:06] [SPEAKER_00]: but we could have done a lot better had we had that connectivity.

[00:19:11] [SPEAKER_01]: With all the excitement about official intelligence,

[00:19:13] [SPEAKER_01]: people don't recognize that you don't have data.

[00:19:16] [SPEAKER_01]: That system can't really do much without it.

[00:19:17] [SPEAKER_01]: And I think garbage in, garbage out.

[00:19:20] [SPEAKER_01]: All right.

[00:19:20] [SPEAKER_01]: Can you tell me of a time when you've had to adapt or shift your strategy

[00:19:25] [SPEAKER_01]: because it's clearly very flexible in how you approach your career?

[00:19:29] [SPEAKER_00]: I feel like I'm adapting all the time.

[00:19:32] [SPEAKER_00]: One thing that's constant is change.

[00:19:34] [SPEAKER_00]: I think being nimble and focused on what does the future look like?

[00:19:39] [SPEAKER_00]: We were a very conference heavy organization,

[00:19:43] [SPEAKER_00]: not only medical conferences, but also investor conferences.

[00:19:46] [SPEAKER_00]: J.P. Morgan in the beginning of the year is always a great one to go to

[00:19:49] [SPEAKER_00]: because you can outline things and you can fill in the blanks

[00:19:53] [SPEAKER_00]: as the year goes along.

[00:19:54] [SPEAKER_00]: A few companies noticed that we were thinking about the data

[00:19:58] [SPEAKER_00]: a lot different ways than others and asked us questions

[00:20:02] [SPEAKER_00]: that ended up leading us into more competitive intelligence areas,

[00:20:09] [SPEAKER_00]: business development questions were coming our way more frequently

[00:20:12] [SPEAKER_00]: because of this, which indications are more ripe for us to go in?

[00:20:18] [SPEAKER_00]: What would you do with the sets of data that we have?

[00:20:20] [SPEAKER_00]: We got a lot more business development questions,

[00:20:23] [SPEAKER_00]: a lot more questions about prioritizing internal calendars

[00:20:27] [SPEAKER_00]: and internal structure.

[00:20:29] [SPEAKER_00]: You're often deciding what's going to be the first condition,

[00:20:32] [SPEAKER_00]: second, third, fourth and prioritize those conditions.

[00:20:36] [SPEAKER_00]: So we pivoted quickly from the conference coverage focus

[00:20:40] [SPEAKER_00]: to addressing a lot of these questions for our clients.

[00:20:44] [SPEAKER_00]: And that helped us grow, that helped us bring on board

[00:20:47] [SPEAKER_00]: people that we always wanted to bring on board but couldn't

[00:20:50] [SPEAKER_00]: until we generate the revenue from these more sophisticated questions

[00:20:53] [SPEAKER_00]: with the questions that are going to float up to the C-suite a little faster.

[00:20:58] [SPEAKER_00]: And now, of course, we're pivoting from fully human activities

[00:21:03] [SPEAKER_00]: to those bolstered by AI and machine learning

[00:21:06] [SPEAKER_00]: with the human element available because clients like to speak with us.

[00:21:10] [SPEAKER_00]: We don't go away when we deliver material to them to answer questions.

[00:21:14] [SPEAKER_00]: The best answers should generate 20 more questions.

[00:21:17] [SPEAKER_00]: Boy, there's a lot of smart people in the industry,

[00:21:19] [SPEAKER_00]: so they're out in front of it and they're asking us new questions all the time.

[00:21:23] [SPEAKER_00]: That's pivoting and being nimble in a nutshell.

[00:21:26] [SPEAKER_00]: It reflects me personally as well.

[00:21:28] [SPEAKER_01]: I think historically when we think of products and services,

[00:21:31] [SPEAKER_01]: we think we're selling answers.

[00:21:33] [SPEAKER_01]: But I think in the future, particularly in the service side,

[00:21:35] [SPEAKER_01]: I think some of these data companies in the future

[00:21:38] [SPEAKER_01]: are actually going to be selling questions.

[00:21:41] [SPEAKER_00]: And walking the same path as you, there's no question about it.

[00:21:45] [SPEAKER_00]: My favorite clients are bombarding us with questions and we know,

[00:21:50] [SPEAKER_00]: OK, these folks are paying attention.

[00:21:53] [SPEAKER_00]: Where do you go for keeping up to date?

[00:21:55] [SPEAKER_00]: Plastic Journal Call Value on Health.

[00:21:57] [SPEAKER_00]: That's written by the ISPO organization that's published

[00:22:00] [SPEAKER_00]: by the ISPO organization, which is the leading health service

[00:22:03] [SPEAKER_00]: or research organization.

[00:22:04] [SPEAKER_00]: I did not know that.

[00:22:05] [SPEAKER_00]: You go. That's what I did.

[00:22:07] [SPEAKER_00]: That's what we're looking for.

[00:22:08] [SPEAKER_00]: That's a mantra for me.

[00:22:10] [SPEAKER_00]: Someone's always asking me about

[00:22:12] [SPEAKER_00]: pharmacoeconomics and H-E-O-R,

[00:22:15] [SPEAKER_00]: and that is the Bible.

[00:22:17] [SPEAKER_01]: So what do you see as the biggest threat to this journey that you're on?

[00:22:21] [SPEAKER_00]: Very unique question.

[00:22:24] [SPEAKER_00]: We were always concerned about threats to any innovation.

[00:22:27] [SPEAKER_00]: That which stifles innovation would be a massive threat.

[00:22:31] [SPEAKER_00]: We thrive on not only understanding innovation, but utilizing innovation.

[00:22:35] [SPEAKER_00]: That's where the rubber meets the road is in novelty,

[00:22:38] [SPEAKER_00]: is in meeting unmet needs and the only way to do that.

[00:22:42] [SPEAKER_00]: You can't emphasize that, which was there before because it's inadequate by definition.

[00:22:46] [SPEAKER_00]: I would always say to my students, be careful with answers.

[00:22:50] [SPEAKER_00]: Answers sometimes lead you to believe that is the answer.

[00:22:53] [SPEAKER_00]: In medicine, in science, the answer usually means

[00:22:58] [SPEAKER_00]: that's what we know at 1251 Pacific Time July 30th, 2024.

[00:23:04] [SPEAKER_00]: These are the answers of today.

[00:23:06] [SPEAKER_00]: The answers of tomorrow need to be different.

[00:23:10] [SPEAKER_01]: There when we think about the actionable data for VINNAB Health,

[00:23:13] [SPEAKER_01]: much of it happens outside of our acute system,

[00:23:16] [SPEAKER_01]: which our health care system is still heavily sent around.

[00:23:20] [SPEAKER_01]: It's in our devices like Apple Watches and the ability to step on a scale

[00:23:25] [SPEAKER_01]: and track changes.

[00:23:27] [SPEAKER_01]: For example, in congestive heart failure,

[00:23:29] [SPEAKER_01]: if someone gains three to five pounds of water weight,

[00:23:31] [SPEAKER_01]: it could be statistically significant

[00:23:33] [SPEAKER_01]: and signal that action needs to be taken.

[00:23:35] [SPEAKER_01]: There's a huge amount of available data

[00:23:37] [SPEAKER_01]: that could help us be more preventative,

[00:23:39] [SPEAKER_01]: but it also is very challenged because it lies outside of the health care system

[00:23:43] [SPEAKER_01]: and we don't have access to it.

[00:23:45] [SPEAKER_01]: It comes down between a clash between privacy and security and interoperability.

[00:23:51] [SPEAKER_00]: I can only think of two words to describe the type of data set

[00:23:55] [SPEAKER_00]: that would have that type of impact bigger than all of us, right?

[00:24:00] [SPEAKER_00]: I believe you're asking what is the next public health advance

[00:24:04] [SPEAKER_00]: because public health advances are the only things that on mass

[00:24:07] [SPEAKER_00]: provide that type of prevention.

[00:24:09] [SPEAKER_00]: Seat belts, no smoking.

[00:24:11] [SPEAKER_00]: The other thing is hand washing.

[00:24:13] [SPEAKER_00]: Washing in general, something that happened at two turns of centuries ago

[00:24:18] [SPEAKER_00]: that affected longevity more so than anything in modern history,

[00:24:22] [SPEAKER_00]: although it probably happened in ancient history, too.

[00:24:25] [SPEAKER_01]: We started at the beginning in our pre-talk

[00:24:27] [SPEAKER_01]: that we ran the risk of talking for five hours.

[00:24:30] [SPEAKER_00]: Oh, I feel like we could.

[00:24:32] [SPEAKER_01]: So I'll just ask, is there anything else you'd like to share with the audience?

[00:24:35] [SPEAKER_00]: I think listening to your podcast,

[00:24:37] [SPEAKER_00]: I know there's a lot of people out there in industry.

[00:24:40] [SPEAKER_00]: Just stay nimble, stay focused.

[00:24:43] [SPEAKER_01]: And I think we talked about today a lot, too,

[00:24:45] [SPEAKER_01]: that's important is getting people that are another verticals

[00:24:49] [SPEAKER_01]: to listen to this conversation and say,

[00:24:51] [SPEAKER_01]: how could I make my business more efficient?

[00:24:54] [SPEAKER_01]: It seems like that your company is a little different than most.

[00:24:58] [SPEAKER_01]: And no one would really think of calling up your company

[00:25:00] [SPEAKER_01]: from a home care model and asking a question.

[00:25:04] [SPEAKER_01]: And these companies exist that can help you,

[00:25:06] [SPEAKER_01]: that might be focused on another area right now,

[00:25:08] [SPEAKER_01]: but they can really help you out here.

[00:25:10] [SPEAKER_01]: Very good.

[00:25:11] [SPEAKER_01]: Thank you so much for being our guest here today.

[00:25:14] [SPEAKER_00]: It's been a pleasure.

[00:25:15] [SPEAKER_00]: It's been wonderful and I thank you.

[00:25:19] [SPEAKER_01]: Thanks for tuning into the Chalk Talk Gym podcast.

[00:25:23] [SPEAKER_01]: For resources, show notes and ways to get in touch,

[00:25:27] [SPEAKER_01]: visit us at chalktalkgym.com.