Jennifer Carter and the Power of Individualised Cancer Care

Dr. Jennifer Carter says it was watching friends and family members stricken with cancer struggle navigate the complexities of the healthcare system in the early 2000s that inspired her to start a company in the area of precision medicine. At that time, the development of targeted therapies for cancers with specific genetic markers was already offering new hope to patients, but it was also creating new challenges for doctors and patients, who had to digest, manage, and interpret unprecedented amounts of data. The vision of her company N-of-One, she says, was around “how do you create something that could cut across all the different stakeholders and create the knowledge necessary that connected physicians and patients with cutting edge diagnosic and treatment strategies in a way that made it understandable and accessible.” That ended up being “a very good strategy for physicians, patients, and the company,” Carter says—an observation confirmed by QIAGEN’s acquisition of N-of-One in January 2019.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here’s how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac.

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you’ll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled “Ratings & Reviews.”

4. Under one of the highlighted reviews, select “Write a Review.”

5. Next, select a star rating at the top — you have the option of choosing between one and five stars.

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

7. Once you’ve finished, select “Send” or “Save” in the top-right corner.

8. If you’ve never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out.

9. After selecting a nickname, tap OK. Your review may not be immediately visible.

That’s it! Thanks so much.

Transcript:

Harry Glorikian: Welcome to the Moneyball Medicine podcast, I’m your host Harry Glorikian. This series is all about the data-driven transformation of healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious value-based healthcare economy. We look at the challenges and opportunities they’re facing and their predictions for the years to come.

My next guest has said that, for precision medicine to work correctly it is critical to have high quality interpretation of the data that is specific to the patient’s genes and variants, in the context of their disease. And that in turn needs to be linked to clinical and scientific evidence, which is then linked to the appropriate therapeutic strategies. Dr. Jennifer Levin Carter is a precision medicine entrepreneur and executive. She was the founder of NE-one the global leader in oncology molecular decision support recently acquired by Kyochon.

Dr. Carter has a passion for finding solutions to improve patient care. Her particular focus has been the development and delivery of solutions to enable greater patient and physician access to novel diagnostics and therapeutic strategies. Since 2012, she has participated as a presenter, expert panelist and moderator at more than 25 industry conferences, events and symposiums and serves on multiple industry boards. Dr. Carter recently advises companies on their precision medicine strategy including its service offerings, physician engagement methods and growth opportunities.

Prior to founding and leading NF-one, Dr. Carter spent eight years as an investment consulting specializing in biotechnology and life science investments. After obtaining her MD, Dr. Carter practiced Internal Medicine at Mount Auburn Hospital in Cambridge Massachusetts. Dr. Carter has a BS in molecular biophysics and biochemistry from Yale University, an mph from Harvard School of Public Health, an MD from Harvard Medical School and is currently suing an MBA in the executive MBA program at MIT. Jennifer welcome to the show.

Dr, Carter: Thank You.

Harry Glorikian: Jennifer you’ve had a fantastic background and obviously have done, you know some amazing things. How did you, I want to say meander or thrust yourself into this whole area of precision medicine?

Dr.Carter: Well, thanks for including me and your podcast Harry. You know it was an interesting journey actually, I mean I was really inspired by friends and family members who got sick with cancer. And we’re struggling to navigate the complexities of the healthcare system to find the best treatments for their disease, and that’s what really took me down this course. As I started to help them navigate that system based on my background in clinical medicine and in drug development. And I started to realize that, this was back in the early 2000s actually, and it was clear based on some of the advancements in diagnostic technologies and the way that drugs were starting to be developed in terms of targeted therapies back then, that there was going to be a whole new paradigm.

And that at the same time, there were gonna be incredible challenges for getting new diagnostics and treatments to patients at the point of care in real time.

Harry Glorikian: You guys were I mean I remember you know when I had my consulting firm Santi. I mean you guys were; I want to say way ahead of the curve. I mean I can’t think right now of another commercial, not a provider system but a commercial entity that was doing sort of interpretation of genomic variants and helping patients and so on and so forth. So, I felt like it was way ahead of what everybody else was doing. It’s amazing that you know how you came up with the idea and sort of got it going in a time where, I mean, I still feel like precision medicine isn’t anywhere close where it should be back in the day. I think there were only a handful of people out there talking about it.

Dr, Carter: Right, I mean we were really early, we were definitely one of the first companies to jump into the space and to. And then you know in many ways we ended up defining a whole niche within the space that was this area of knowledge creation if you will and clinical interpretation. I think that early on the observation that I had made about, in general how some of the challenges that patients and physicians face in healthcare is just the ability to digest and manage all the knowledge and the data. And when you see the evolution in how, what the impact of precision medicine was going to mean for that.

The data was going to be that much more intense, that’s much harder to understand and interpret, and it seemed that it would just add to the current state of fragmentation. And so really the vision was around, how do you create something that could cut across all the different stakeholders? And create the knowledge necessary, that really connected physicians in patients with cutting-edge diagnostic and treatment strategies in a way that made it understandable and accessible. And so, we started that way in 2008 and by a little bit of I guess lock it ended up being a very good strategy for patients and physicians and for the company.

Harry Glorikian: So, well I would be missing or leaving something out if I didn’t say a heartfelt and wholehearted congratulations on selling the company to KyaJen. Para-shots is a great guy, I interacted with him a lot when I had the consulting firm, but just so everybody who’s listening and those people who are trying to learn, what did and of one do in in sort of layman’s terms if possible.

Dr.Carter: Sure, so what we did and actually we’re still doing in many ways, today in the context of kayJin is, we did what we called in those days the clinical interpretation of molecular diagnostic data. So, a person with cancer would have their tumor biopsied, and then it could go for genomic sequencing or other types of molecular testing. And then that data which basically could show changes in the tumor DNA, needed to be interpreted. The data is very complicated, there are lots of different changes that occur in the DNA of tumor tissue and tumor cells.

And it’s very time-consuming to process that information, and most physicians don’t have the time or the necessary background for understanding the data. So, what we did was, built a team of scientists and consulting oncologists and we would get the data from the labs, and then we would interpret the data. And basically, figure out which were the relevant variants, which were the relevant genes and variants, what was the combination of variants, which we call the multi variant analysis to understand the impact on drug sensitivity and drug resistance. And then we would compile and essentially curate the clinically relevant clinical and scientific evidence, and match it up with therapies, those that were FDA, approved on label those that were FDA approved off-label and then those in clinical trials.

And we created one of the most sophisticated systems our platforms for matching patients to clinical trials based on their molecular data.

Harry Glorikian: What’s interesting when I think back in the day, I used to be able to you know flip on my computer in the morning and you know I would read about some new gene that had some you know impact on a particular cancer and or disease. And now it’s just impossible to keep up with them one by one, and so I have to imagine that back in the early days we’d be like, oh yeah you there’s this one and there’s that one there’s B-raf there’s K-raf there’s. Now there’s, we’re looking at panels of hundreds of them, if you if you look at what people are running.

Some of them having a very strong relevance and then some of them are being way out on the fringe of not knowing what happens. But I can imagine that was such a learning experience for, you guys and luckily, you guys were there in the early real days.

Dr. Carter: Right, it’s true and we learned a lot you know as we as we went. I mean we basically we’re evolving in many ways ahead of science as it started to emerge. It was very exciting, still very exciting because if science is still really changing every day, what we’re learning is incredible. You know and, in some ways, I think we just scratching the surface, even though the change has been phenomenal, and we’ve seen just recently with Locke being approved for tumors, any tumor -.

Harry Glorikian: Right.

Dr. Carter: With an N-Trek alteration, you know now that’s the true example of a precision medicine. You know that basically, it’s totally based on the biology that’s unusual actually. You know a lot of the biomarkers that we see still are relatively particular for the organ of origin.

Harry Glorikian: Right.

Dr. Carter: But certainly, with Katruda and lots of drug you know; they’re based on a biomarker.

Harry Glorikian: Yeah, I mean I remember talking about this at Applied Bio systems when we were, before we had finished the gene, before we had even started the genome in a sense, right. What we were sort of throwing around ideas like this, and it seems like it’s been a lot, I feel like it’s been a long time, right? And we’re still not anywhere near where I think we should be, right. I think half of that is because medicine is not an outcomes-based payment system, right.

So, people move it at the pace that they want to. But can you share some of you like key lessons that you learned in applying precision medicine to patients in your experience at NF-one or in and around that experience?

Dr. Carter: Sure I mean, I think that one of the key lessons I think the, I think there’s several. I think that you know if you read literature now, you know there’s a lot of debate. What is precision medicine? Is the impact great enough? Are we impacting enough patients? You know I can tell you that when there is an impact based on what we’ve seen that it’s pretty, it can be quite phenomenal and can be incredibly valuable for patients.

I think that what I have seen is that we, that technology is incredibly powerful and it, but there is a lot of data and the data needs to be interpreted in the right way. And that it’s not just about having a mutation in a gene, it’s about understanding exactly what that mutation is in the context of the particular disease. Because you can have our mutation in B-raf as you mentioned in melanoma for example, and that same beautiful cancer is not treated the same way. You can have certain alterations in you know in any gene, and the implications of those have to really be understood within the context of the patient and with the context of their disease.

I think the other thing that we have really believed since the beginning, but I think it’s absolutely playing out more and more better understanding of it now is, the need for different types of technologies to really understand the biology of cancer in particular. So, I think we need to think about how different types of technologies are going to help us really impact our understanding of disease. So, I think you know genomic sequencing is one piece of the puzzle, it’s a really important piece of the puzzle. I think when we look at other types of technologies there’s different you know we really need better biomarkers around the immunotherapies, for example.

And so how are we going to look at those types of biomarkers, is genomic sequencing the only option or there, I know of other technologies that are looking at ex vivo testing to better understand that tumor micro-environment. Because that plays a very important role in immune mediation and control of cellular growth. I think, thinking about it in you know cancer in a in a broader way and even all types of disease. So, go ahead yes -.

Harry Glorikian: But thinking about this sort of backing up for a second. So, you and I are always or a lot of times in a luxurious position of being able to look at a bunch of stuff. It’s what we look at it sort of the world that we live in it’s the people that we hang out with.

Dr. Carter: Right.

Harry Glorikian: And we do not necessarily need to make a decision tomorrow or in an hour or ten times a day like a physician or provider would need to. So, I about this as an evolutionary way, I mean first of all I always think like isn’t it time that we just reorganized oncology around at least this core technology and assign a computer science group to help them think it through. That I mean, that’s one thing I think needs to happen. The other is I don’t think we can come to answers or find clues as easily because the N as you say an N of one. But the N of everything that we do is siloed in each institution and that sort of needs to come together not just with you know patient information but outcomes.

Dr. Carter: Absolutely.

Harry Glorikian: Right, and I think that’s almost a IT project as opposed to you know a pure science project to look for at least a low-hanging fruit from a pattern perspective. So, I mean where do you see this evolving, patients aren’t going to get this if it doesn’t get implemented.

Dr. Carter: Right, I totally agree. So, I mean one of the things while we were successful is a very successful, around how do you take that knowledge and deliver it to physicians quickly, and in a digestible way. Where they can get to an answer and use that information quickly while they’re helping their patients. To your point about computer scientists you know of course AI is you know, people are looking at AI and how it can transform healthcare and certainly precision medicine. And I think that AI can be a great enabler of that, by being able to call through the literature more quickly, look for pattern recognition, be able to pull out data that around variants more quickly.

But AI is not the full solution, because there’s so many shades of grey right now, the complexity of the data, you know the way AI works is its pattern recognition. But if there’s a lot of shades of grey and the data is uncertain, then you can’t get to you know, you can’t get to a range of answers that might be most relevant for the patient. On the other hand, it can make, it more efficient, it can change the workflow, it can change the way information is delivered. So, I agree with you the combination of the technology the people skills and the N AI can really help drive this forward.

One thing though I will say about you know all that, the sort of new wearables and the new technologies and the data is that you need really good data to input into the system for you know, for any of you know that kind of automated interpretation or automated understanding to be really impactful from a patient perspective. Because it has to be a good answer.

Harry Glorikian: Right, well you talked about changing the workflow that’s almost heresy. I mean when you know this is the way we do it sort of thing.

Dr. Carter: Exactly.

Harry Glorikian: I mean I even had the same experience when my mom had pancreatic cancer. It was an interesting experience I don’t want to say fighting the system, but it felt like I don’t know, I don’t want to say who’s smarter. But it was a difficult discussion to have as opposed to a collaborative discussion of taking it forward. And I’ve even had oncologists that I’ve spoken to that say that, their colleagues don’t order, molecular tests simply because they don’t understand it.

Dr. Carter: Right.

Harry Glorikian: Which I think is something’s wrong with that, but let’s talk about, I am sure than what you were doing, you know you looked at machine learning AI. How do you see that making a difference either in a company like NF-1 or an institution whatever you’re comfortable talking about, but where do you see that moving the ball forward and up to what limit or within its current embodiment of AI and machine learning? I’m sure it stops at some point in a human takes over.

Dr. Carter: Right, so I think you know actually there was a very interesting article just in Nature Medicine, in January by Eric Tobol, that actually looks across you know multiple areas of healthcare of where AI is being applied you know sort of the interesting data, that’s coming from it and the limitations and challenges. And I think you know, thinking about that in the context of precision medicine and the experiences that we had, I think that you know again it’s sort of what data is going in -.

Harry Glorikian: Right.

Dr. Carter: Really impacts what data is coming out.

Harry Glorikian: Yeah.

Dr. Carter: And to your point about outcomes and being able to know what’s the right data to go in, the challenge right now is we don’t necessarily have enough data, yet to know what all the right data is to really, create really great outputs from you know from AI to make it a fully automated solution in precision medicine. On the other hand, for scanning the literature really quickly for pulling out the right information for being able to assess where there are relevant outcomes and good data. I think it can be an incredible enabler and drive efficiency.

Those pieces of interpretation of you know processing the data processing large amounts of data, you know very time consuming and inefficient for humans to do right and yet the actual analysis of that data and that impact within the context of the patient. I don’t think that AI or you know any of the program’s, right now are even being able to do that.

Harry Glorikian: No, I remember when people would say, we have a point-of-care cancer test, I’m like no, no. That’s not something you want to say to somebody right there on the spot or at a CVS per say. But how do you see the technology, let’s say this machine learning AI and or the you know the combination of precision medicine, and even going beyond oncology. Is, how do you see that changing the practice of medicine,

Dr. Carter: Well, I think it’s good change practice medicine credibly, right. I mean, I think if you think about you know wearables for example digital technologies, the ability to monitor patients in you know in their real-life environments and be able to track them on an ongoing basis. I think is going to create incredible opportunities and challenges. You know in the first place on the opportunity side, I think it will give much more real-world evidence about really what’s going on with the patients, how they’re responding to therapies. You know how they’re going through their daily life what their habits are? What’s really happening with their heart rate their blood pressure their oxygen saturations?

I mean there are many ways that we can use that to better understand. I think we’re already seeing how the FDA is really interested in real world evidence in clinical trials and collecting more real-world evidence of patient reported outcomes. And that’s really enabled by wearables and digital technology and you know the kind of back end – processing of that data. On the other hand, on the challenges related to that is again how do we use all that information and how does it get processed, and how does that become something that actually doctors can look at and use in their daily practice.

Because you can see how there will be generated an overwhelming amount ongoing data you know, and there can be false positives. There can be you know lots of alarm bells going off, which create sort of both you know unnecessary activity but also crowds out the ability to process you know the really important data and what the doctors really need to look at. So, I think what really is going to be important is, as we collect all this data is trying to get to what is the right data on each patient and how do we manage that data in the context of their overall wellness and care.

Harry Glorikian: Yeah, I mean I think it’s funny, I love our medical you know companies right, our life sciences.

Dr. Carter: Right.

Harry Glorikian: But we’re all science geeks right and we love the data and the numbers and the beeps and the bops and the you know, I love it, love it, love it, right. Especially if you’re the hardware guy building it, you just you love this stuff. I always think like the tech guys would come in and say, your graphical user interface sucks right, like we need to make this simpler so the person understands what’s going on. And you know I think, we need a blend of tech guys to come in with our you know medical guys and say, look you know when the when the slope goes this way or when it you know the dial looks like this or when, the you know something that makes -.

Wall Street’s figured it out right -.

Dr. Carter: Yeah.

Harry Glorikian: Very complicated sets of data making it sort of easy to understand and then making a decision of that. Some of that we need to somehow adopt into what we do, so one alarm going off, but three data points sort of pointing in the wrong direction. Maybe now it need an intervention sort of thing.

Dr. Carter: Right, and I mean you know AI is you know some of the really interesting places, it’s been you know radiology for example. And you know some of other types of technologies where the algorithms can really enhance the physician’s ability to see things that, they may not otherwise be able to identify. So, there are some really amazing applications of it.

Harry Glorikian: Yeah, when I talk to you know the CEO of Arturis and I talked to Dr. Alban Patel from Kiesinger. And I remember him saying like our system will automatically reorient the scans by severity, so that the radiologist looks at the most important one first, right. It just, I mean you’re not talking about a major, that’s a simple change relatively speaking but the impact is significant.

Dr. Carter: Right.

Harry Glorikian: I also you know did an interview with Massimo Bousema from Italy and he was like you know the machine can look at the raw data and actually see things that you don’t see in the refined data that the human eye can’t. It is designed to see, so there’s a lot of places where I see the technology having a dramatic impact or at least a leap forward. Based on that, how do you see the business model changing. Because I think of the next if you were to start an N of one today or a company like that is, now you might be able to engage a patient much more than because of technology or provide the service in a way that is different than when you first got it off the ground.

Dr. Carter: Right.

Harry Glorikian: And so how do you see the business model of precision medicine changing and therefore affecting the ivory towers.

Dr. Carter: Yeah, so I think that, I think it sort of broadens in my mind it broadens the concept of what precision medicine really is, right. It you know when, I think that you know over the last decade or so precision medicine has really been focused on the concept of genomics and the application of genomics. And I think you know many way and that’s been great, and I think that concept in of itself is expanding, right. Because we’re seeing more inherited risk panels, the implementation of pharmacogenomics which is so necessary. I think that you know understanding how people metabolize drugs.

Harry Glorikian: Right.

Dr. Carter: It is you know should be a critical component everybody’s health history.

Harry Glorikian: Right.

Dr. Carter: And I think that that conversation in of itself is beginning to move forward, you know the FDA has made a you know essentially given 23 and me, you know the go-ahead to do some consumer facing, genetic testing, inherited risk is very limited. And you know I think that those types of tests, you know people need to move on and get more if they you know it’s better in a medical setting for some of those tests. But I also think with some of the pharmacogenomics, one of the great parts of it is consumer awareness and making people more aware that those tests are available and that they should ask their doctors about what the right test to get is. So, that they can get the best testing for their risk factors.

And we can talk more about that being in the consumer space, if you want as well. The other thing though, I think is thinking more broadly about precision medicine from the context of where else you know how do these other technologies you know expand that definition of precision medicine. And that we’re not just thinking about genomics, but we’re thinking about monitoring for some of these other ongoing physical variables as a piece of the puzzle, as the physician evaluates the patient. So, that you can monitor people’s, the use of medications, the way they’re using medicines, their blood pressures, their pulse in on an ongoing basis I think is more extended.

Harry Glorikian: But it’s interesting to me, because I think looking at, you know moving towards a value-based model. I’m not so sure the provider system is designed to communicate with patients, right. That’s designing an episodic method –

Dr. Carter: Right.

Harry Glorikian: You come in, I hope you leave we’re done. And if you don’t come back I assume you’re well, right. So, but I almost think if I’m CVS and Aetna and my job is now to keep you healthy, because I want to cut coupons while you’re paying your premium.

Dr. Carter: Right.

Harry Glorikian: And I don’t want you to use the system if at all possible. And I have a CVS almost everywhere within reach of anybody at least here in the US. They might be in a better position to communicate these capabilities or these systems or tests to a patient. I don’t know, you know if we could ever get to an arm steak or finger steak and not need a phlebotomist and I could just get enough, or a cheek swab or something like that and start to do these proactively. It’s sort of, I don’t want to say, it changes the dynamic with the patient then you have say directly with the provider and telemedicine.

Now makes the provider can be part of that when needed, because you just dial them up.

Dr. Carter: Right, no I think I mean, I think that’s absolutely CVS and as goal right, is that they will shift your care and your primary care and all that into the CVS, into the well and they’ve opened some hubs in Houston to start prototyping that out. And it’s a very interesting model to see how that shifts, and I agree that you know the ongoing interaction and the notion of telemedicine being able to access more primary care. And those types of care within your community will definitely help shift that model, and it’ll be interesting to see about utilization.

I mean, I think that if you look at the demographics that will appeal to demographic shift, that’s sort of happening you know with patient, people moving employers more frequently you know. So, maybe there’s less connection with that so-called primary care doctor and more willingness to be seen you know on more ongoing basis in your community. It’s easier, it certainly decreases travel time, these are big barriers too, I’m going interaction with the healthcare system. And so I think that the over the idea that CVS and Aetna, you know that they’re promoting is actually a really interesting change and shift in how that care is delivered.

Whether they will start recommending, whether they’ll have the skills you know that the background to really implement, precision medicine, genomics in those settings and whether that will be, you know it’s still the level of complexity, that lend itself to that setting, but it may be.

Dr. Carter: I mean, I think there’s there are quite a few pieces of the chain that will lend itself to that, because of cloud back-end, because of telemedicine, because of certain capabilities well you don’t have to have the that expert right there in that building. But you know it, when I tie it all together I actually think the, all this shift happened because of the value-based shift, right.

Harry Glorikian: Right.

Harry Glorikian: Everybody likes to poo, poo the Affordable Care Act, but that if it wasn’t for that none of this would be happening that we’re talking about right now. No matter how much technology evolved.

Harry Glorikian: Right.

Harry Glorikian: Right, if you don’t change the way people get paid, it’s sort of hard to change business models and people’s behavior. But you had mentioned earlier about real time or real-world evidence, and I find that if you start to connect all these dots is the FDA demanding all this is, we’re moving almost to know when something is working, and when something is not. And therefore you’re starting to step into the world of adjudication. So, why would you want to pay for anything that wasn’t working? So, it’s really interesting when you start to tie all these pieces together.

It might take five years or ten years, but you can see the trajectory of where this whole space is going.

Dr. Carter: Absolutely I mean, but I think this has been one of the real challenges to is, getting payment you know how much outcome data is necessary to convince the payers to pay, right. I mean and intellectually you can say that you know these technologies can enable better, care to enable better outcomes, you know can save money. But I think you made the point earlier in our discussion that, until we bring enough data together to demonstrate the impact -.

Harry Glorikian: Right.

Dr. Carter: And demonstrate where there’s an impact, where there’s not an impact and how we think about you know applying the technologies, the broad range of technologies in kind of that rational way, that without that reimbursement that’s gonna be a real. So, that leads to the potential change in how that type of care is provided and paid for.

Harry Glorikian: Right and I’m, every time I’m looking at something these days. I am totally stepping back and saying I need, I don’t want to be in the box, why does reimbursement matter, okay. If it does great, I apply it.

Dr. Carter: Right.

Harry Glorikian: But questioning it, because the implementation you know, you think about the ECG on the Apple watch, you go reimbursement.

Dr. Carter: Right.

Harry Glorikian: Right, they just want to have the ecosystem and have you in the ecosystem. If you think about tech, they’re picking up data on you in more ways than anybody could possibly imagine. And so they have actually a central database with enough data in some areas to potentially enter start dipping their toes into the adjudication box. Whereas I think Pharma medical device, they’re usually limited to one piece of data or an incredibly small data set, on a small number of patients. And so I think we’re gonna see a big change over time.

Dr. Carter: But now you’re talking about, how do you bring all these pieces together. And I think and, I mean because what you’re talking about is, okay so now you’ve got multiple types of data points in multiple different places on the same person.

Harry Glorikian: Yes.

Dr. Carter: And I mean the goal will be to be able how to use all that data on an individual to make the right decisions. So, then that requires a whole new model for that type of -, I mean because you know a lot of the ways that data is being aggregated right now is in large databases.

Harry Glorikian: Correct.

Dr. Carter: And a lot of those large databases are de-identified right. And even whether or not like even if it’s a healthcare provider or you know and ensure where the database is identifying, where you can track to the individual. There’s still a lot of limitations about how do you bring multiple different pieces of disparate data together to impact care.

Harry Glorikian: Yeah, and it’s interesting though if you think about it, right. I mean the biggest impediment in my mind is EHRs, right is EMRs, right. They are, I actually think that the one thing that could break the whole healthcare is the EMR. Because of their absolute you know fighting, trying to do you know share data, right and create an interoperability standard right. They’ve been fighting it for years. Apple comes out and says, no you can download, I mean it’s not perfect. But now on my device I can just push a button and share my health data if it’s there with any app -.

Dr. Carter: Right, and that’s exactly right, and that there are a lot of companies that are popping up to allow for individuals to control their own data and to have access to it. And then of course that gets into the notion of, can you layer in block chain to be able to track that, be able to create your digital ledger and all these other pieces.

Harry Glorikian: Or, what if I just create the dashboard, that accepts the API from these different things, and create something that, oh my when I look at it I can actually make, I’m not saying it’s going to be the you know the world’s best medical decision. But move my health in a much better Direction based on hypertension or diabetes or any one of these, where we’re spending a tremendous amount of money, right.

I just, I think the existing status quo is busy on making money and not seeing that this undercurrent is happening at such a pace, that I’m afraid that they’re not gonna keep up.

Dr. Carter: Well, there you know, there’s a new company in this digital AI data processing, consumer ownership of their data. There’s a new company almost every day, it seems like and you know I think that, you’re absolutely right you know that tea leaves are is that there is going to be a big shift. All the big tech companies are looking at how do they enable you know the consumer, the patient, the person to own their data to control their data. And for them to be able to you know push out relevant information and gather that data. So, something is going to change.

Harry Glorikian: Yep, well I’m highly critical of a lot of the new, you know even the existing and or new companies right. Because the skill set is not falling off a tree, when you try to put the right group together to make it work. The number of people that we’re graduating that have those skills is incredibly low, and everybody is fighting for them right now. So, but let’s jump back to you know two questions. What are the impediments in moving this into clinical practice and helping patients, and the next question would be is where do you see this evolving over the next three to five years? Because I feel like everything I, every time I ask this question, it’s a three to five-year time frame of really making it happen.

Dr. Carter: Right, and hopefully it’s only three to five year that’s what I would say.

Harry Glorkian: Yes.

Dr. Carter: So, I think that you know focusing in, on you know on molecular diagnostics and understanding you know the genomics rather than staying thing separate from the AI and ML and how that will actually be an enabler. But I think that what we’re going to see is a new and different types of ways of processing molecular data, you know of analyzing molecular data, getting molecular data or different pieces. So, looking at transcriptomic looking at proteomics, ex vivo testing to be able to get a broader sense of in oncology certainly of the tumor micro-environment as I mentioned before. I think that the application of a broader range of genomic analysis.

So, not just looking at the isonomic data, but also being able to marry in the germline data and the implications of that. And then I think even getting in more of the pharmacokinetics. I think that having a bigger picture around what molecular biology means, and both from a cancer perspective but then also from well then also you know from a preventative perspective, people’s risk factors. I think that is where we need to go, and so that it becomes part of you know actually from earlier on, you know kind of a part of the decision tree and part of the metrics around how do you provide preventive care, how do you move towards wellness and these types of things.

So, it moves further up in the healthcare system. That’s what I would like to see happen whether that’s going to happen or not, you know in three years, five years. At least I see more of a conversation around it as being part of the dialogue. But you’re absolutely right there’s still a lot of patients who don’t get their tumor tested, and there’s still a lack of understanding of the range of possible solutions out there.

Harry Glorikian: So, want to wrap it up and not take up more of your time, and I really appreciate the time. congratulations again on the acquisition, I’m sure it was a lot of fun going through it, reflecting on my own experience. And I look forward to continuing our conversation in the future.

Dr. Carter: Thanks Harry, it’s been fun me too.

Harry Glorikian: And that’s it for this episode. If you enjoyed Moneyball Medicine, please head over to iTunes to subscribe, rate and leave a review. It is greatly appreciated, hope you join us next time, until then farewell.