Transforming Healthcare with Artificial Intelligence with Brigham Hyde, Ph.D., President at Concerto HealthAI
Episode 442

Brigham Hyde, Ph.D., President at Concerto HealthAI

Transforming Healthcare with Artificial Intelligence

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Transforming Healthcare with Artificial Intelligence with Brigham Hyde, Ph.D., President at Concerto HealthAI

Episode 442

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Transforming Healthcare with Artificial Intelligence with Brigham Hyde, Ph.D., President at Concerto HealthAI transcript powered by Sonix—the best audio to text transcription service

Transforming Healthcare with Artificial Intelligence with Brigham Hyde, Ph.D., President at Concerto HealthAI was automatically transcribed by Sonix with the latest audio-to-text algorithms. This transcript may contain errors. Sonix is the best way to convert your audio to text in 2019.

Saul Marquez:
Hey everybody Saul Marquez here with the Outcomes Rocket. Are you going to HLTH? That’s HLTH. It’s the largest and most important conference for Health Innovation. HLTH pronounced health is one of a kind of ecosystem event for the health industry and they’re on a mission to bring together 5,000 plus senior leaders to solve the most pressing problems facing healthcare today and actualize the most promising opportunities to improve health. They bring together senior leaders from across across payers, providers, employers, investors, fast growing startups, pharma, policymakers, and innovation centers to ask one question. How do we create the future of health? I’ll be there and I hope to see you there too. If you use outcomesrocketpodcast150 as the promo code that’s outcomesrocketpodcast150. You’ll get a hundred and fifty dollars off your ticket. Looking forward to seeing you there, go to hlth.com to sign up. That’s hlth.com to sign up. Use that promo code outcomesrocketpodcast150 and I’m excited to see you there. I’ll even have a booth recording some podcasts live at the event the MGM in Las Vegas. So, so excited to see you there. If you do sign up don’t be afraid to say hi and we’re going to learn a lot there so go ahead and sign up hlth.com.

Saul Marquez:
Today I have the privilege of hosting Dr. Brigham Hyde. He’s a Founding Partner at Symphony AI. An AI tech company focused on a one billion dollar BCPE. As healthcare partner co-founder at Concerto HealthAI. He also does lots of great work in the healthcare space. As an executive, he led operations including a 10 year partnership with ASCO cancer link for oncology EMR data. He grew the company says three hundred and twenty five employees and led multiple customer relationships in pharma and payers including two major strategic partnerships with Pfizer and BMS. Prior to Symphony AI and Concerto, Brigham was Chief Data Officer and Senior Vice President of Analytics at Decision Resources Group. He joined DRG in 2014 as part of the acquisition of relay technology management and AI drug discovery and an analytics firm he co-founded in 2009. He began his career following a Ph.D in clinical pharmacology from Tufts University as an analyst at Cohen and Company covering Life Science Tools and diagnostics. Brigham holds faculty positions at the M.I.T. media lab and Tufts University School of Medicine, actively publishes on AI Research and RWD topics and is a member of the Ireland special study session at AI and machine learning. So I had the privilege of listening to Brigham’s podcast on Breaking Health podcast. If you haven’t had a chance, I’ll leave a link to that podcast interview. But I wanted to get him on the podcast today because of his thought leadership in AI and healthcare in particular with with pharmaceuticals so Brigham pleasure to have you here today.

Brigham Hyde:
Thanks for having me. I’m excited to be on.

Saul Marquez:
Hey so Brigham tell me what is it that got you to focus on healthcare?

Brigham Hyde:
Yes. An interesting journey I actually began my education career as a chemist and then we appreciate clinical pharmacology is focused on drug development. I think at its core I was always really interested and curious about the mechanisms of disease and the way that we develop cures to those. And that led me down a winding path towards the power of data and techniques like AI machine learning to be used in that space.

Saul Marquez:
It’s so interesting because you really could have done a lot of different things but you really saw the power of data. You started your own business. It got acquired and it’s just been a good ride for you.

Brigham Hyde:
Yeah you know it’s interesting. I think there has been a true north throughout it which has been just to focus on understanding why no curiosity big component that drives me and that’s led me to try and seek more data driven solutions and ultimately techniques like AI machine learning. I think have huge power today but also really exciting where they can take things in the future.

Saul Marquez:
I think it’s a great call. And so if you had to say a hot topic that needs to be on health leaders agendas, what would you say that is and how are you guys addressing it?

Brigham Hyde:
Sure. I think there are two really important things happening right now. And the first is really a core for Concerto HealthAI which is the leverage created by using real world data for clinical studies, regulatory studies, and things like synthetic control arms and a touch on the detail. Second the other theme I think which is really exciting is the integration of the power of that data integration through A.I. and email based technology and to the point of care and care decisions and value based decisions. I think our physician community is heavily underserved by information relying on academic research and pharmaceutical research and the clinical trial room not really being empowered by personalized data about their patients or patients like them when making decisions I’m pretty passionate about both topics.

Saul Marquez:
Yeah and it’s interesting so with Concerto, where you guys focus? Are you tackling both of these? both physician…

Brigham Hyde:
So Concerto…

Saul Marquez:
Yeah.

Brigham Hyde:
Yeah sure. So Concerto was founded really around the principle that EMR data and the value that’s locked inside that can create big opportunities for revenue generation and sort of finding new jurors and opportunities and also using that data to develop AI ML based applications. One of the big tailwinds for Concerto and recently published multiple abstracts at this year’s ASCO conference in June was looking at the potential of using real world data to augment classic sort of control clinical trial. So taking the data about patients for this out there in the world and analyzing it in a proper manner where you could actually look at how certain treatments are performing in the real world and the FDA has taken a really progressive stance over the last five years beginning with 21st Century Cures Act and the interpretation of it you begin to try to incorporate that view of the real world. Concerto has a partnership with FDA, works closely with them to develop standards around that. What’s the right way to use EMR data alongside or in combination with classic sort of clinical trial data in order to give a different view. One of the problems that exists with our current clinical trial system is that take oncology there’s just so few patients available to enter these studies. It’s a difficult decision both for the patient and physician to put them on what might be an experimental therapy and a lot of those studies end up smaller than we might like. We also worry that they sort of restrict patients to ones who meet strict inclusion exclusion criteria instead of understanding what’s going to happen when this really hits the real world. And I know the FDA is very focused on getting a better view of what’s going to happen when they approve a drug and release it to the community. The sort of anecdote that gets thrown around is that many clinical trials are performed on very healthy patients at the best institutions with the best doctors right. But when the drug gets approved and it goes out to market there’s great diversity in the patient populations that will be taking those therapies. The health conditions that they may have, cut morbidity is also just the systems of care that were deliberate. and by using real world data, EMR based data, you’re able to get a view of how those patients actually look in the community. And I think that’s one of the real focuses for Concerto for its partner ASCO and ultimately for its clients across healthcare.

Saul Marquez:
Love it. Yeah I’d say it’s a great focus with the growing I mean incidence of cancer and Dallas data locked in the EMR. One of the things that I heard you mention in the previous episode you did was about some data missing right. Like for example the stage of cancer that’s in and so you guys are able to develop an algorithm to more or less accurately guess right and to make it useful.

Brigham Hyde:
Yeah it’s a big challenge and there’s a variety of reasons why this has become the case but they say if you’ve seen one EMR you’ve seen one EMR. They look different between different vendors information stored in different ways the content and quality of that data varies. And then you’ll even see variation of the same EMR system between two different installations that might be down the street from each other. So pulling that all together in a way that is useful and consistent and that can be used to inform clinical decisions and create that type of revenue is really critical. I think what’s emerging is that there are going to be multiple sort of levels or tiers of data that have different use right. We talk a lot about structured versus unstructured EMR content. Structured EMR in kind of think about like the fields that are always filled out are usually filled out in a consistent manner sort of think of it like the structured dropdown tables. That’s when you might pick the unstructured content is stuff like imaging files, notes, scanned, faxes PDX. I mean we’ve sort of seen it all that doctors attach or append to a record and a lot of the at least on ecology. But also I think true in other places a lot of the real value in that. And so you have to have a way to fill in missing this extract value money structure content think the current standard that’s emerging is there’s this concept called regulatory grade data essentially data that comes from both structured and unstructured where there’s a standardized methodology by which that information is extracted. And at this point for Regulatory Studies or critical clinical studies that actually requires human intervention things like EMR duration where you actually have a nurse read over the material and identify the critical clinical elements things like progression disease and other aspects which inform sort of key clinical characteristics. And I frankly think that’s that’s probably a good idea right now if you’re comparing the use of real data to clinical trials it’s really critical that we go looking at one data set that through a base another that’s controlled trial based that you can crosswalk or translate between those datasets and that’s why regulatory grade data and the methodologies around it are so critical. That said there’s other tiers that also have value. And one of the things that Concertos on that’s focused on developing AI and NLP based technology that can look at patterns in data recognize unstructured content and create that consistent structure form and that enables much more scale. We can do a lot more patients that way. And I think than the use of that data may be different than that regulatory group. But still really value for a variety of reasons in particular in the area of value based care. You know I think that’s a critical element. Also patient segmentation and ultimately trying to standardize that information. I’m of the mind you know interoperability gets talked about a lot in the smart community. You’ve been talking about 15 years so biased around the idea that I’m not sure that it’s going to be solved even with innovations like a sudden fire and different standards that are developing. So I sort of assume there’s always going to have to be this layer that you know is using techniques like AI and machine learning or NLP to sort of extract and standardized additional value to a bigger Concerto.

Saul Marquez:
And I think it’s a good call. It’s a heavy lift to try to achieve that interoperability and having solutions like gears makes a lot of sense. The thing that keeps coming to my mind is what about a country like England for example. You know they’ve got the NIH. Are they a little bit more standardized? And would it be easier to work with them? What are your thoughts there.

Brigham Hyde:
Yeah you mean the NHS.

Saul Marquez:
Lineages yeah.

Brigham Hyde:
A lot of acronyms out there and there. You know it’s it’s interesting. I mean I think when you have a government or federally centric health system that enables the roll out of common technology it certainly helps. But in my experience that hasn’t led to the level of detail put this way there’s still unstructured content in those records and still inconsistency in how that’s recorded. I think there’s the potential to do more but you know this let’s remember I mean the reason that you know these unstructured documents or content get created is because it’s it’s difficult to have a one size fits all EMR. Each practice is a little bit different. The communication patterns are different. So even in the government based systems this is still a challenge although I think there’s still an issues under way in adjusting other countries to begin to standardize that a bit more.

Saul Marquez:
That really is no no easy button to this I mean and that’s why the work that you’ve done in your previous companies and what you’re doing now is so valuable.

Brigham Hyde:
Yeah I wish there was a you know colleagues we sort of sit around and think about how would we do it all over again if we could digitize Phil Spector. I think a fundamental concept that you know has been left out of the discussion is that the EMR companies largely serve the providers like they’re selling software to providers and the wider systems are the customer and the things they create are driven by the processes and interests of that group. That group largely is now responsible for analyzing the data or utilizing it analytically downstream. So we created systems that were customer oriented to people who had diverse views and diverse needs and left out of that conversation with the analytical community. So I invite a wish that would just be that those of us that end up having to analyze this data had a voice at that table and that’s gotten better and there’s several forums for that. But at the end of the day if you’re an EMR company you’re going to change your product based on the customers that pay you not the ones that are utilizing data for other purposes.

Saul Marquez:
Now that’s a very interesting perspective. Yeah. You know when you think about it’s kind of like you guys are the bolt on two systems that are already doing something for the provider. You guys are bolting on technologies to help. So who’s paying for your stuff? Who are the companies interested in this?

Brigham Hyde:
Sure. At Concerto work with many sort of constituencies in the healthcare system although I’d say pharma is sort of the primary partner for concerto and the real driver for that is that the potential of using EMR based data and regulatory grade data to offset, the risk and the costs of clinical development for them is a big factor. If you’re running a clinical trial phase 3 and you have a control arm you’re trying to recruit 500 patients to where control are being they would just receive standard of care and you would use it to compare to your your active therapy arm you know that could take 18 months just to get it recruited stood up and enrolled and the cost on something like that or you know 20 30 50 million dollar range you know when you compare that to taking EMR data on patients that are in market and you know running a similar control arm in that role data the factor of change on cost is significant do it for a lot less money which means you can do larger studies need to be more studies and also the time that 18 month window I gave you that type of study could be done in three to six months using real world data. And that’s potential really changes the equation for outside the farm on and also gives them strategic advantage. There’s been a big push around that oncology may be leading the way but there are other areas for that’s true and so that’s a huge factor in trying to consider this. Also point out in addition to helping pharma we’re also talking about speeding drugs to markets that can help patients. And one of the big things that I find sort of bipartisan about this issue with the politics of it is that the diversity that’s available in EMR data whether we’re talking about racial or socioeconomic or access to care the people in clinical trials tend to be more affluent more white and healthier in general to getting a viewpoint on folks who don’t typically get access to clinical trials but what they actually look like out there. They’re comorbid with other diseases or you know how their interaction with the health system actually works. That’s really valuable. And I think it gives a voice to certain communities that have been underprivileged in clinical studies. Also true women which typically are under enrolled in clinical studies so I really think and pharma likes all of this. Right. And they want their therapies to succeed. When they go to market, they want to have a clear view of you know the personalized aspect which patients their drugs going to work for and this gives everybody pharma regulatory insurers and providers a much better view of that. So I think it’s a really there’s a lot of value created not just for pharma clients but for the whole system.

Saul Marquez:
Yeah that’s outstanding. I mean because that that is one of the knocks you know in the demographic is is narrow it doesn’t cover the community of potential users of the drug. It’s pretty neat that you guys are able to offer that.

Brigham Hyde:
Yeah it’s really measurable and ASCO which is the Concerto’s primary partner. Was a number of studies on this and sort of shown the potential and I think the net effect is that you get a viewpoint around how this new therapy is actually going to do when it gets out there and what are things to watch out for. What are maybe specific risk factors that say somebody shouldn’t be on a drug or maybe the flipside a population that’s really underserved by existing therapy where you know a new therapeutic would really benefit them. So as we talk about things like value based care and personalized medicine to me as a core that is evidence generation driven by raw data.

Saul Marquez:
That’s a great call out. So give us an example, Brigham of a time when things didn’t work out. What do you learn from it? How did it make you guys better?

Brigham Hyde:
In my career in general or in Concerto?

Saul Marquez:
Yeah at Concerto.

Brigham Hyde:
Sure. I mean I think one thing that happens in healthcare is you’re building companies and you’re trying to drive innovation as much as you can. But there are many systems in place that I don’t think it’s not like the intentional thing but to limit that potential. And as we develop things and A.I. and machine learning you know we have to keep an eye on sort of two perspectives. One is this desire to be heavily future oriented right where there’s an A.I. algorithm you know collaborating with your doctor to inform what they should give you. And you know an app on your phone that you could assess risk and benefit a different thing. And you want to develop those things but there’s also the reality is and you know frankly in some cases you know justifiable concerns about safety and security and wanting to do things sort of practically. And so while we’ll publish and we publish a lot on exciting areas the AI applications the ways to actually get those to the point of care are sometimes a lot more practical a lot more sort of off that’s oriented you know. Where can we find a recommendation a simple action somebody can take you know instead of doing something like changing treatment or predicting an outcome? Is there a recommendation or alert that can be sent for? Or a flag to somebody that hey maybe they should roll in a clinical trial or something like that. So balancing innovation and the future with the practicalities of the current healthcare system and trying to find places where you can still deliver value that’s a constant sort of battle number regardless of where you’re focused on healthcare. So I think you know Concerto we’ve had success doing that and continue to evolve this with great partners and trying to be patient about the potential of future will be impractical about some of the current business models that work.

Saul Marquez:
And that’s a tough balance right. I mean well do you have like a something that’s like okay, “this is the thing that would qualify this as as meaningful innovation versus just innovation.”

Brigham Hyde:
Yeah I mean I think all innovation is meaningful to some extent. I think about it in terms of what our customers and partners do as you’re talking to a pharma company if they’re able to see drug development reduce costs get more to market they’re able to optimize processes around clinical element clinical trials and treatment of those things. I think those are all really positive measurable outcomes. Does that sound as exciting and sci fi as having you know AI that predicts your every health outcome. No but but it is right it’s very it’s doing that just in a practical orientation. I think learning to work with the systems in healthcare and you know how to show value through them is a really important factor when you’re developing a business.

Saul Marquez:
Now a great call out. Greg I’m glad you mentioned that. So what would you say the other side of that coin is. What’s one of your proudest experiences you’ve had to date?

Brigham Hyde:
Yeah I mean I think the A.I. machine learning can be very buzz words. You know I don’t know where we are in the hype cycle at the moment. Obviously IBM Watson drove a lot of the interest in this. Their sort of push and marketing. You know there’s been some backlash against them. Some of it very justified some of it a little bit hype oriented and I think one of things that we’re particularly proud about at Concerto that we’re really practical about A.I. are really focused on things like validation and transparency and the ways to really really do it right. Man I think a system we created there around how we develop, build, launch, deploy, retrain and sort of operationalize AI think is really exciting. A lot of the research you’ve published over the last 12 months and by the way you know the young company you’re focused on getting the revenue and sort of making the business work taking time with your research is nice to have at times but I think we’re really proud of a lot of stuff we publish there and the potential of it. And I think just continuous set that bar you know I work with the parties that are out there whether it’s ASCO or the FDA and others and work towards the right path to bring that to patients. I mean the whole reason we’re all doing this on some level of your work and healthcare period but certainly in church is that we’re trying to help cure cancer patients pretty lofty goal. And like you to be careful would be arrogant about that. We were always very focused on being practical. What have we actually proven how can we prove that we’re right. How can we show this has value. One example of something we published in the past is a prediction model that focuses on outcomes in advanced non small cell lung cancer patients you know and we’re getting really strong predictive values for that we’ll get you can read about the quotation out in our ASCO abstracts but are really looking at it. Yeah for advanced non small cell lung cancer.

Saul Marquez:
Interesting.

Brigham Hyde:
You know you really being OSA say like what is the risk of this patient having a fatal event in what time period. And you know helping to focus efforts on patients that have risk or could be intervene differently also opening up some of the components of that model and if we’re making a prediction of what why what is making that prediction, do we use that to inform guidelines and the treatment community and ultimately clinical decision. So you know this is a bunch we have like that we get really specific. We think very carefully about the use case. We focus heavily on validation when the best ways to sort of do it right when applying AI in developing and building assessment.

Saul Marquez:
Yeah very practical.

Brigham Hyde:
Absolutely.

Saul Marquez:
So if you have to say there’s one project or focus that you guys are working on today that you’re most excited about what would you say that is.

Brigham Hyde:
Well concerto announced partnerships with both Bristol-Myers Squibb and Pfizer earlier this spring also with the stylus and there’s a pre-existing partnership with AstraZeneca. But across those partnerships and other customer relationships these are multi-year strategic deal which is really a complement to the vision of those companies that say look we have some needs for real world data today and technology platforms that enable use of EMR but no let’s have a little bit more of a vision. Take out the crystal ball. Where is this going to go. And by partnering with us in that way they really focused on where those moves the future are going to be. And I think that type of vision and sort of side by side partnership allows us to work towards things together and align to their vision. So I’m excited as you work and chatter take those partnerships and how that emerges or evolves and and emerge as new models for pharmaceutical companies. I think increasingly pharmacy companies are starting to think about themselves as digital companies. I mean some are further along than others but I sort of said you know who better in terms of knowledge of patients knowledge of disease the data they have in their own clinical trial repositories. Who better to bring that type of digital evidence to the care community not only for their own therapies but for broader personalized medicine. So I’m excited to see where those go. And I think there’ll be many exciting things come to those partnerships.

Saul Marquez:
Yeah Brigham and that’s kudos to you and your team. I mean the belief in what you guys are doing and the difference that it can make to speed to market or cost of drug development is really symbolic there with these guys jumping in for a long term partnership. So big kudos to you guys man. That’s a big win.

Brigham Hyde:
Well I think it’s like I said it’s confident in the vision of those companies and leaders within it. They know they need to speed our and reduce costs so they can offset some of the pricing pressures that are coming and also really just start to deliver medicines that are developed for patient of one. I don’t think we’ll get to that overnight but they want to do that. They’re just trying to find a way to do it economically and in a way that’s responsible. So I think these partnerships and others that are out there I think represent big steps in that direction.

Saul Marquez:
Yeah I totally agree with that. So getting close to the end here what we have next is a lightning round followed by a book you recommend the listeners. You ready?

Brigham Hyde:
Yeah sure.

Saul Marquez:
All right. What’s the best way to improve healthcare outcomes?

Brigham Hyde:
Generate more evidence from real world data on EMR.

Saul Marquez:
What’s the biggest mistake or pitfall to avoid?

Brigham Hyde:
I think not standardizing and processing data in a way that’s consistent or transparent is a big mistake. It creates doubt about the use of that data. So I think those out there using it really need to pay attention to the methodology and leadership of the FDA and how to use that data.

Saul Marquez:
And I love the point on transparency right. If you know how it’s being processed and use that it really does increase the trust in it. How do you stay relevant as an organization despite constant change?

Brigham Hyde:
I think it’s about a balance of customer centricity. So ask your clients ask your partners where do they want to be next. Have that as an ongoing conversation one that you develop together. And I think the balance of that with an eye on where should we be. What should be the goals we have and sort of balancing those two narratives bringing all the partners to the table is a really critical step.

Saul Marquez:
And I love that. And what’s an area of focus that drives everything at Concerto?

Brigham Hyde:
I think it’s ultimately improving outcomes in oncology patients. You know you get up every day and think about that question. I think you’re doing the right thing.

Saul Marquez:
Love it. And these next two are more on a personal note for the listeners to get to know yet. What’s your number one health habit?

Brigham Hyde:
I like to road bike gets really into some of the apps around that also incorporated being… incorporating palatine into my life. I’m sort of really excited about the amplification of exercise and some of the behavioral models that exist out there.

Saul Marquez:
That’s cool.

Brigham Hyde:
And I think they’re empathic now.

Saul Marquez:
Yeah. That’s really interesting. I was in France a couple months ago and I was like man I’m gonna hit the gym tonight and I went there and this gym was like futuristic man. They had like a Palatine and they had like virtualized yoga room and so like all the instructors a remote and virtual. All they’re classes.

Brigham Hyde:
So there’s a great app called aaptiv. Wow. So like people haven’t checked that out.

Saul Marquez:
What’s it called an aptive?

Brigham Hyde:
It’s two A’s but they’re basically we’re taking a skeleton model of generating content that you listen to through earbuds. So there’s a you know if you want to go for a two mile run that got a coach talking to you and setting training exercise for you. They’ve also got ones for the gym. So that’s a pretty cool one.

Saul Marquez:
Wow. Okay cool I’ll have to check that one out active. You guys too if you’re looking to get more active. That’s a new idea. And the last question here in the Lightning Round is what is your number one success habit?

Brigham Hyde:
I think more than anything it’s to chase curiosity when you get the you know that twinge of an idea you wonder what that is really chasing that down and using that to drive your activity. You know I think is the critical feature and ultimately that getting in the weeds and chasing down that curiosity leads to lots of exciting things whether it’s on an innovative front or just driving you to get deep on topics I understand that.

Saul Marquez:
I love that. Yeah and I can imagine with your background Brigham, this pharma focus, you could get pretty deep into the weeds on both the tech and the carrot of science.

Brigham Hyde:
Yeah I mean if in reality I wish I was still a bench scientist I did a lot of bench research worked on the mitochondrial genome and mitochondrial dynamics in grad school loved bench science. I really miss a lot of the basic biology but I felt it was too slow to get drugs and the bench to market and understanding that process led me into the data world.

Saul Marquez:
Super interesting. Interesting. What book would you recommend to the listeners?

Brigham Hyde:
Yes I’ve got two. One is just one of my favorite books to read as you’re thinking about innovating and developing new companies in particular it’s called Thinking, Fast and Slow by Daniel Kahneman get a lot to shape my approach to things and sort of self-awareness of how you approach day to day life but also business. And so that required reading as far as I’m concerned that’s something I’m reading right now which is at the cutting edge of the weary eyes going discussion. It’s called The Master Algorithm by Pedro Domingos and I won’t try and summarize a complicated book but what’s interesting to me is that as we’ve innovated in AI and now rain starts to become a question of do we need multiple algorithms or will there be some sort of Master Algorithm that ends up sort of incorporating a lot of the features of health life into it and ultimately drives it. So there’s a bit of a debate the community. Do we need most bloggers or we end up with one sort of Master Algorithm that deep learning driven and able to evolve on its own. So good discussion of that in Pedro’s book.

Saul Marquez:
Man that’s a really interesting idea.

Brigham Hyde:
Yeah I’m not sure what I think of it yet. There’s some future cited but I like thinking about that edge of it and then trying to translate that back to the technology and know what we do in our business.

Saul Marquez:
Interesting. Folks, for a full transcript of our discussion with Mr. Brigham Hyde including the short notes and links to the things we’ve discussed go to outcomesrocket.health in the search bar type in Brigham and you’ll find our interview with him there. Before we conclude I’d love if you could just share a closing thought Brigham and then the best place for the listeners to continue the conversation with you and the company.

Brigham Hyde:
Great. Yeah. Yeah I think it’s about to leave a point and I know your audience is good. You know both medical professionals and those in the industry getting savvy about the data that’s out there how it’s used and even pushing yourself to take a code academy course and send some of the approaches that are taken. I think it’s becoming a requirement that people have that knowledge so I encourage people to do that and dig in and help figure that out.

Saul Marquez:
Love that. And then for the listeners to continue the conversation where should they visit or follow you?

Brigham Hyde:
Sure so concerto can be followed at concertohealthai.com and associated Twitter handles and best way follow me is on linked through Brigham Hyde. I publish often there and also through Forbes but LinkedIn is the best place.

Saul Marquez:
Outstanding Brigham. Hey I really appreciate your your thoughts on AI, making it practical is the way to go and now looking forward to seeing where were you and the company take oncology so really appreciate it.

Brigham Hyde:
Thanks a lot.

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