Dr. Karim Galil: Welcome to this episode of Patientless Podcast. Today's guest Brent Clough, founder, and CEO of Trio Health. Thanks for being with us on the show Brent.
Brent Clough, CEO of Trio Health: Thank you for having me. I look forward to our discussion.
Dr. Karim Galil: Brent started his career in the financial sector. He was actually a VP at Goldman Sachs. And then he founded a very interesting company in healthcare: IntrinsiQ, which pretty much built the largest longitudinal patient database at the time and was later acquired.
Brent is now a cofounder of Trio. Trio leverages real word data for commercial and clinical research excellence, and they have a very interesting model on how they are capturing data and how they're ensuring the fidelity of the real world data. Trio obviously is a very established player in a super crowded space. I'm very happy to have Brent on the show today to share with us his story of starting Trio, what is unique about Trio, and how he sees the real world data/real world evidence industry today.
Before we get started, I think my first question is what attracted you to healthcare? I mean, pretty sure it doesn't pay as much as Goldman Sachs, and it's a pretty sophisticated industry, so I would be very interested to hear what was attractive for someone like you to come into healthcare and build a couple of companies, again, in a very crowded space.
Brent Clough, CEO of Trio Health: Yeah, that's a great question. So, I did spend first 16 years of my career in financial services and then switched over to healthcare and have now been in healthcare since 2004. So about half my career is in financial services, the other half in healthcare.
And to answer your question specifically, some friends of mine had invested in a small oncology software and data company called IntrinsiQ, which is outside of Boston, based on a high profile overdosing death that happened at Dana-Farber. And so, the founder of IntrinsiQ was a physician and a programmer as well as an attorney and basically looked at this and said, Jesus, if this could happen at Dana-Farber, in terms of a dosing death, it must be happening in other areas of the country.
And one of his buddies was an oncologist in upstate New York. And so, they started IntrinsiQ, really as a safety solution in terms of that, to align with the, the way that oncology patients were treated and managed in the early two thousands, which a lot of it was infusion. It was weight-based.
The calculations had to be very precise and specific and unfortunately in this situation, the woman that was overdosed and killed, the calculation was incorrect. It was missed by the pharmacist and physicians.
So what attracted me was in terms of what we are seeing in terms of healthcare, in terms of just the lack of technology and sophistication that was being applied to managing these type of patients and this type of workflow as contrasted to Wall Street that, you know, you could sit on a trading desk and pretty much get information at your fingertips and in virtually seconds.
When you contrast it to kind of financial services in terms of data and analytics and technology, relative to kind of where the healthcare industry was 15 - 16 years ago, it was night and day. And so I actually, through some of my friends that were investors, got introduced to the CEO at the time and the founder and ended up striking a cord with them in terms of really trying to bring to bear a lot of my knowledge and context and relationships and trying to think about how it could be helpful in applying that to the healthcare industry.
And so within about 12 months of joining IntrinsiQ, I was promoted to become the CEO of the company and then ran the company for a number of years. And what we did is we sold our application to over 120 sites across the country with about 700 oncologists, both academic and private practice, for them to more safely and better manage their oncology patients.
So we had oncology nurses that would go out and train the physicians and so forth. And then in the old days, because the internet really wasn't as prevalent, we would have the sites effectively phone home once a week and, in terms of transmit over the internet, a de-identified file of the patients, and then our analytics and operations team would un-package that data and put together and build a longitudinal records in terms of looking at how these patients were being treated, and again, what their outcomes were.
And so it was a really novel time in terms of there's a lot of drugs that were being developed: Erbitux, Avastin, Herceptin, a number of big drugs that are blockbuster drugs were just launching in the early, you know, call it 2004 - 2006 period. And so we saw really this transformation in terms of a big, bolus of new drugs that were being launched into the market.
You had people like Michael Milken that were launching the prostate cancer foundation and what he's done in terms of transforming that disease. And then you had, NCCN guidelines and Bill McGivney, who was starting to build the evidence based pathways.
Dr. Karim Galil: There wasn't even electronic medical records. They weren't widely adopted back in the early two thousands.
Brent Clough, CEO of Trio Health: Yeah, exactly. And so it was an interesting time in terms of, like I said, in terms of like being at the beginning part of early stage in terms of seeing this adoption, as well as starting to deploy clinical evidence and pathways to real world patients. And so, we formed Trio Health in 2013.
And like anything in your career, you've got to learn from him, your mistakes, you learn from kind of the shortcomings and said, jeez if you're going to bring the band back together and do it kind of better, how do we do it? We basically took a lot of the knowledge and things that we learned both at our company, as well as just from our peers in the industry and created Trio Health, which is really principled around building a network in terms of having direct relationships with each of the physician practices, as well as all the additional stakeholders that touch the patient.
Trio actually stands for: physician, pharmacy and payer. So we thought of really those three stakeholders as the stakeholders that could impact the performance of a real world patient.
And so, we developed a technology platform and a business methodology about bringing together all that disparate data, so that we could have what kind of 360° view of the patient. But then what we also recognized just from a technology platform is the inherent deficiencies of trying to record and collect that information from EMRs in different, you know, technology platforms that the stakeholders use that obviously didn't mesh well together, as well as didn't fully encapsulate in terms of all the facets of that care. So in our technology platform, we had to build a two way communication so that we could go in and supplement, adjudicate, and validate information that we couldn't get through the nightly file.
Dr. Karim Galil: Looking at your website, you guys are talking about, the fidelity of the data that you're collecting in comparison to the current methodologies business or technology methodologies in the industry. At Trio, how do you guys define, how real is the Real World Data?
How do you define how good is the data that you guys are capturing compared to several other players in the industry?
Brent Clough, CEO of Trio Health: Yeah. So I think, the way that we look at the landscape is I, I think that there's, there's really kind of two distinct categories: There's a whole group of companies that are focused on very large databases in terms of looking at hundreds of thousands of if not, millions of records in terms of within a specific disease area.
And then there's other companies on the other side. That look more like a registry or that go much deeper, right. In terms of collecting very specific information on the patient. And so when you kind of look at it, it looks like a little bit like a barbell in terms of people are either on kind of one side of the equation or on the other side.
We felt though that we could use both technology as well as almost kind of registry software concept and be a little bit in the middle. And so when we looked at kind of our approach, depending upon the disease coverage, we figured out how could we build a database that had, for example, a hundred thousand rheumatology patients, yet was really on the other side of the barbell, which was very deep in terms of having pharmacy data, information contained from the office, visit notes to labs, to infusions, to kind of all the data that we would want to do.
So that's kind of the role in the niche that we fit. We focus on, in terms of really trying to leverage kind of the value of both of those, in terms of using technology and nightly files, but then also really almost in terms of the registry, which has gained very specific information on very specific fields that we need relative to kind of what are their objectives for that, it's either study or the disease that we're trying to understand and focus on.
Dr. Karim Galil: One of the things that are really exceptional about Trio is that you guys have this broad coverage of different therapeutic areas. You guys are working in rare diseases, you guys are working in rheumatology, you guys are working in hepatitis, and that comes with a lot of complexes. How can you train your team to be able to cover all these therapeutic areas?
How are you going to also be able to build this model where you're able to attract different providers coming from different kinds of specialties and convince them to share data with you? Can we talk more about that? I find that very intriguing about the company that you guys have built.
Brent Clough, CEO of Trio Health: Yeah, it's a great question. So Yoori who co-founded the company with me, her background was really more on the qualitative side in terms of amassing a very large rolodex of key opinion leaders within each specific disease areas. So what's really important in terms of that piece of the business is really, if you think about it, is we start before we enter a disease, as we started in hepatitis C, with the launch of the new DAs in late 2013 and 2014. And we started really with developing a scientific steering committee of key opinion leaders that are across the country that were highly respected by their peers, the manufacturers, and the payers.
And we really use them as our north star to number one: define what is the data that we need to collect on real world patients. These were all physicians that are treating patients. So it was helpful to have kind of realtime insights in terms of: what were they being confronted with on kind of a day to day basis, as well as the evolution of the disease. The third is that we use the scientific steering committee, as I said, to go out and recruit and to build the network. We build every disease organically in terms of one practice at a time, and we signed a business associate agreement and MSA.
The final point that we do is now that we we've used the scientific steering committee for kind of their qualitative expertise, and now we get the quantitative data, we can bring those two important pieces of information together, as well as sit on top of live and active network of physicians that are managing treating patients.
So we have the ability to adapt very quickly. In terms of, to the disease, but also be very responsive in terms of when we start to think about our output and all of our studies that we published today, which is, an excess of over 120 studies all have been authored by the scientific steering committees in collaboration with our statisticians and our analytics people.
And so when you think about the importance in terms of what is the point of the study and what is its relevancy, you're getting an interesting perspective beyond just, an RWD or RWE, company in terms of you're really getting, the physicians that are being respected and are managing, treating the patients so that when we go to submit these studies to medical conferences, they typically are on, you know, obviously forward thinking and really thinking about in terms of what are the specific issues that physicians and patients are confronting on almost a real time basis.
Dr. Karim Galil: It seems to me like your scientific committee is at, a foundation of your business model. It's kind of the core of the company and you built business processes, you built technologies, you built different things, but at the very core, your scientific committee is driving the company. I find this to be attractive to a lot of providers and pharma companies, knowing that this is not a tech play, it's a teamwork between clinicians and technologists.
How were you able to assemble your scientific committee? I think that's one question. The other is, what's in it for the sites to sign an MSA with you, share their data, and contribute to the registries that you guys are building?
Brent Clough, CEO of Trio Health: Yeah, I think echoing to your point that you just made, you know, I, we kinda think of, our scientific steering committee is a little bit of a Trojan horse. It really starts and ends with them. Number one, they give us the credibility immediately amongst their peers. Because again, they're backing this. They're supporting this.
The other piece that is important to note is that we file all of our research as investigator sponsored research. Which means that we get sponsorship from manufacturers, but it's really an arms length transaction so that the authors of the study can't be influenced or tainted.
And it really is up to them at the end of the day, in terms of the methodology, the findings, and everything that we come up with, the conclusions related to that study, which I think is important because. What it does is it really provides the integrity at all levels in terms of that really facilitates our business model, which is why physicians want to join the network.
And in some cases, a lot of the practices join and we don't provide them with any type of honoraria or financial payments, but they're really doing it for what's in the best interest of their patients and for the best interest of care. And so we've been very fortunate in the fact that we can build very large data cohorts in terms of having diversity of academic physicians, private practice physicians, and get the geographic diversity.
Because when you look at the leadership of the scientific steering committee and kind of their track record related to their participation in clinical trials and getting the disease state to where it is today, they want to be part of this. And I think of it a little bit in terms of giving back in terms of, to the patient, as well as to promoting, in terms of best practices for the patients within that disease state.
Dr. Karim Galil: So you are a proxy between sponsors and clinical research sites where you enable the sponsor to learn from the care of each patient that went through that site in a digital way where you don't have to recruit an actual patient, talk to them, consent them. This is the theme of our podcast is patientless trials.
What we mean by patientless trials is not necessarily, getting the patient out of the equation. It's actually, the patient is always in the center of it, but rather than the patient contributing in a clinical setting, the patient is contributing in a data setting where they are basically leveraging their data.
How do you define patientless trials at Trio Health and how do you see the clinical research industry moving from a very clinical setting centric kind of an approach to more of like a digital centric approach where data is leveraged in many different ways?
Brent Clough, CEO of Trio Health: It's a great question, a complex question. Our objective is to best represent the patient by having the most comprehensive data set that provides the greatest insight. And I think what we're most proud of at Trio health is a lot of the patient advocacy work that we do.
So when we bring all that disparate data together and it's "patientless", meaning, you know, the patient's actually not involved in terms of providing supplemental information, but really catalyzing all that information on that patient. I've got two examples that I think that we've been very successful about:
One is, the new hepatitis C drugs transform the disease to, as you probably know, cure rates exceed almost 90%, greater than 95%. With a drug that you take once a day for eight weeks with no side effects. And what we found is with our database, in terms of the timeliness of the updates and the pharmacy and the clinical data, is we found this huge disparity across the different Medicaid States in the country.
And we published a study, that received a lot of awards and recognition and it was on over 20,000 patients where we looked across 40 different Medicaid States. And we saw that Ohio Medicaid had a 95% denial rate of patients as contrasted Connecticut that had a 95% approval rating, which is crazy in the fact that we were looking at patients that were cirrhotic.
That again were not high risk patients in terms of patients that, were stereotyped as living under the bridge or being drug users in terms of they were 'high risk'. And what we were finding were these were patients that, again, you know, one woman and we actually publish a book on our website was basically infected with hepatitis C based on a blood transfusion because she was bleeding out during a pregnancy. And in those days, obviously they had not screened the blood well enough, and so she was tainted with the hepatitis C strain.
And so again, what we did is we use that information, we published it, but then we also went to CMS and shared with CMS that manages Medicaid and showing the disparity across the different states and said, this was completely egregious in our book. We titled it Is This Really the United States of America or the United Countries of America?, because how can we be seeing this level of disparity?
The second piece that we did in collaboration with NORD, which is the National Organization of Rare Disorders, which is a not for profit, is we in collaboration with the FDA looked at, all for pro bono, is how we looked at six rare diseases by which there was a diagnosis for it yet, there was no approved treatments.
And so we took all the data that NORD had collected in a registry for their natural history, and we ended up publishing and presenting at the respective different conferences around the world. And we also published a book around trying to create awareness to the investment community, as well as pharmaceuticals, in providing more insight in terms of these types of patients to see if there are things in the pharma portfolios that could be helpful in terms of being potential solutions.
And so again, you go back to this high quality data and this kind of patientless concept. Trio and our scientific steering committee look at and say: "Okay, how can we give back in terms of helping to promote therapies that are going to improve the quality of care for these different patients?" Be it natural history where there really is nothing approved and trying to create awareness on the disease to second, looking at unbelievably transformational drugs in hepatitis C, that are still being denied in United States with massive disparity based on different payers, both commercial payers and in my example the Medicaid States.
Dr. Karim Galil: This was a great example. Disparity is actually something that you cannot capture in a randomized clinical trial in an easy way. I love that example. You have also worked on rheumatology registries and you were able to collect longitudinal and comprehensive data. We at Mendel find it very hard to do what you guys are doing because:
One: Being able to be EMR agnostic is not easy today. And unless you're being EMR agnostic, you have this selective bias where you are biasing your data base on a set of research sites or a set of sites that are using a specific EMR vendor, while you want to achieve this breadth of sites and you want to be EMR agnostic and that's technically not easy.
Two: We see is a lot of the data actually exists in a non-machine-readable formats. 70% of the data today are faxed. The healthcare industry is one of the very few industries that are still using fax as a preferred method of communication. We also see a lot of the doctor narrative being indirect where they're expecting every one who's gonna read this note to be a physician, so they don't have to explicitly describe everything. Those are like some of the challenges. How can you integrate with different EMR vendors? And also, how can you deal with non machine readable formats like faxes and doctors who are not necessarily explicit or structured in how they describe a patient journey?
Brent Clough, CEO of Trio Health: Yeah, I think you're absolutely correct. And we kind of think of it as three different levels, right? There's kind of the, the most basic and common model, which is nightly files. The second is really AI and OCR technology. And I think the third is the good old-fashioned roll up your sleeves with a clinically trained certified person can remote log in and read chart notes or scan documents or things that obviously don't meet the first two criteria, and really start to put together that patient story or build that mosaic. So you can understand it in an example, you know, that, I think in rheumatology to your question, what every manufacturer wants to know, and what every payer wants to know, it's not, you know, we all know what happened, but we want to know why.
So when we look at a rheumatology patient and we say, jeez, you know, they started on Humira and then they switched over to Xeljanz, you know, why did they discontinue Humira? And why did they select Embrel as a, as a second or third line or fourth line of therapy?
And so what we're, you know, we did in terms of, we looked at really using all three levels of that to answer those questions with the third level being, we actually have certified chart abstracters that have been clinically trained to go through, and look at entering the discontinuation reason. And what we uncovered specific to rheumatology, which was interesting, is that a vast majority of the discontinuation reasons is based on patient tolerability.
Where if you go back to kind of oncologists and they go, jeez, this is standard protocol for us to manage patients with pain, nausea, diarrhea, rashes, and so forth. And what the rheumatologists are telling us is, you know, we actually don't do a very good job managing patient tolerability. So what we're trying to do is uncover for rheumatology specifically, going back to your question in terms of these three levels, is how do we bring this unique insight to help advance the disease?
So how could we help physicians understand in terms of the prevalence of a particular category of patient tolerability? And then how could you potentially work with the patient hubs and support paths from the different manufacturers to do almost realtime triage? And so now, you know, if I was going to discontinue because of a GI abdominal pain, the question is, is there a way to help mitigate that? In terms of to keep me on that therapy yet manage that derivative or a derived side effect that could be correlated or non-correlated. But at the very least it's the basis for why there's a switch. And so, again, we think that there's a lot of still great opportunities in terms of really taking a comprehensive view.
In space like rheumatology , there's a lot of entrenched competitors. There's a lot of people that have real world data, but it's really trying to think about how you creatively look at bringing together all the resources and capabilities to draw out some unique insight to 'advance the disease state'.
And so that would be an example where we're super excited in terms of some of the work that we're uncovering in rheumatology.
Dr. Karim Galil: How did COVID affected Trio? Is COVID the catalyst for real world data studies or did it slow down or change to where it's more data driven trials? How do you see COVID today affecting the real world evidence industry?
Brent Clough, CEO of Trio Health: I can't speak on behalf of CROs other than I know the clinical trials have obviously been stalled and it's been a difficult environment. I think what's been fascinating going back to the beginning of our conversation around this kind of barbell strategy around real world data companies, in terms of being on kind of one end of the spectrum, you know, I think there's a lot of great work on the large data sets to look at prevalence and looking at different populations in terms of how they're being impacted and what the outcomes are.
And then I think if you look at kind of what we're looking at on the Trio Health side is, is really going down 10 or 15 different levels.
So we may only have a database of a hundred thousand rheumatology patients. Yet, you know, we're tracking in terms of patients that have been diagnosed with COVID and then also measuring and looking at their outcome and having the notes and all of those detailed information. And I think the question that we're trying to look at is are some of the rheumatology drugs delaying onset of disease.
There was a webinar that we hosted two or three weeks ago. We're kind of looking at it in terms of, at a very detailed patient-level specific function, versus there's still a lot of value at the macro level, at the epi-side, in terms of doing that. So I think, as it relates to COVID, it presents a unique environment in terms of the clinical trial development.
But then when you look at the real world data companies, I think that real world data companies have evolved a lot in the last 15 years. So I think they play a role, a very important role, but that's kind of the macro level and the micro level, which is going to be highly complimentary to helping us solve these types of complex problems that we're confronted with.
Dr. Karim Galil: How do you see pharma companies and how they perceive real world data? Do you think they perceive it as a vitamin or more of a painkiller? Is it something good to have or something you must have? I mean, obviously you have been in leveraging real world data for more than 15 years, so you can see the adoption curve.
Are we there yet? Are we at a point where they feel like this is a painkiller or we're still in the vitamin stage?
Brent Clough, CEO of Trio Health: I think we've made a lot of progress. I think the biggest problem with real world data for the last seven years up until the last year or so, or two years ago, has been the confusion around how to use the data, understanding that there is no perfect database.
And I think that, it feels like in the last 12 to 24 months, there's really been a lot of progress made in terms of really kind of ring fencing and understanding within each of the different companies kind of what their capabilities and what their best use cases are. Creating that level of clarity, where we can all add value in some capacity, but understanding where we excel and where our weaknesses are, I think is what's critically important.
And I think that's starting to flush out more and more in a, in a more accelerated rate. And I think any time that you get to that level, then I do think that you, you know, using your analogy in terms of, the vitamin or the painkiller, I think in certain situations they both exist right. In terms of it becomes helpful, to programs that they're trying to advance internally.
And then I think it also becomes necessary or required. But again, I think the starting point that we should all be focused on is making sure that our clients understand with complete clarity and transparency in terms of the good, bad, and ugly. What are we good at? What are our deficiencies and what should you not use us for?
And I think, I think when we get to that level of transparency I think it's gonna be best for the entire industry.
Dr. Karim Galil: A lot of our audience are actually executives in the pharma industry and we always get the question: I have sent an RFP, now I have like 10 vendors, and I need to assess where they are (to also borrow your analogy) on the barbell. How comprehensive is their data? What are the right questions to ask?
That's still the industry trying to figure out what are the criteria or the framework where you can evaluate a vendor and understand where do they stand on the breadth and depth of curves when it comes to their data assets. So, what would you advise? What kind of questions? If you are a pharma executive today, what kind of questions are you going to ask?
Brent Clough, CEO of Trio Health: Yeah, I feel like, the pharma industry has evolved a lot in terms of starting to develop those questionnaires and methodology that provides kind of that 'nowhere to hide' for the different vendors or people receiving or responding to those RFPs. And at the end of the day I think it's, at least from the way that we manage our company, we just think it's a mistake to try to misrepresent us because at the end of the day, there's nowhere to hide. And so what you end up is just in a bad situation.
And so from our perspective, we welcome the transparency and it's imperative for us to, for both our clients as well as even our physician partners and networks, in terms of making sure they understand what are our goals, objectives, and what can we do and what can't we do. And over a history of seven, eight years, we we've had to rein back some of our members who are assigned to a steering committee, who get excited, and start talking and say, Oh, we can do this, this, this, and this. And, and we have to rein them back and say, no, no, we can't do that. Right. That's not feasible.
I think that the nice thing is that the industry has evolved, to a point where the level of knowledge is there. I think transparency is now getting to the place where people now understand where they fit and what their strengths and weaknesses are. And I think with transparency, you're going to see a wider adoption and more use cases in terms of how real world data can be applied across a broader spectrum than even exists today.
Dr. Karim Galil: I want to go back to the scientific committee at Trio. You explained to me how you guys run that. Do you have like representation of different specialties? Are they full time employee of Trio or they're more of a scientific advisor or consultant? How are you guys able to build that kind of committee, and keep them engaged with the amount of business that you guys are generating?
Brent Clough, CEO of Trio Health: I would tell you that not one person on our scientific steering committee does it for the money or any type of honoraria. I think that we've always positioned from day one that we have to be the North Star in terms of clinical evidence. We need to pave the way for the disease state, in terms of doing really novel and transformational research, that provides and sheds new insight and that is going to advance the care of patients for that specific disease.
So, first and foremost, it really starts and ends with the clinical integrity that we bring to the table, as well as the studies and methodologies that we bring forth in terms of each specific disease state. The second piece is in terms of how do we get them.
You know, our goal is always to try to get an oral presentation of merit at a major medical conference. And when you get an oral presentation, as you know, you're the best of the best in terms of your 1 to 2% of all submissions that make that cut. So we're very much focused in terms of applying our skill set in terms of the data, but also remember that the knowledge that exists within the active physician network to try to really think about, you know, what are the issues confronting these patients real time? And how can we look at it from a safety point of view, an efficacy point of view from different patient cohorts, to even as I mentioned earlier in terms of looking at access to care around payer denials, and so forth.
So I think our attraction is number one, that clinical North Star position, but then to your specific question, each disease state, we have a core of typically five to seven key opinion leaders that serve as the foundation but we will bring in different key opinion leaders for subspecialty expertise within that disease state.
So in hepatitis C, we could have an expert that focuses on the co-infected population, which would be hepatitis C plus HIV. Or we could look at physicians that treat high risk patients based on median income, in different zip codes and so forth, that have a lot of different co-morbids. So for us, it's having that foundation in terms of the rigor and making sure that what we're doing is clinically sound, methodology and so forth.
But then also recognizing that, you know, if we're looking at weight gain and HIV based on the aging population, we need to bring in some of the top statisticians that can deal with this very complex issue that may be out of the purview of our core team. So we have no ego, and our scientific steering committee has no ego as it relates to "it's only these five people that are the authors of every study". It's really about how do we best position that analysis that we think is incredibly important in terms of that topic, so that we can get to that] oral presentation level at that medical conference and to create the greatest awareness and the greatest impact has really always been our focus.
Dr. Karim Galil: You guys are not only leveraging reword data. You're leveraging the clinical integrity of the clinicians and the scientific community. And I find this very, very intriguing.
Another question I have: I want to see from your perspective, what is the good, the bad and the ugly of AI in healthcare? Where are we today? What are the challenges? What are we good at? What are we not yet good at, when it comes to AI? Why are you still using human abstractors? I mean, obviously you see a lot of AI companies saying, listen, we have the best AI out there, but still the industry are at a point where almost every real world data company has a core human operation at the very core of its DNA.
Brent Clough, CEO of Trio Health: It's a great point. Back in 2004, I actually hired a number of data scientists and we built machine learning algorithms to predict and see if we could help, in terms of market share and understanding, a number of different oncology products that we were developing for a suite of clients.
It was interesting because you learn from direct experience. And so I go back and I would say to the AI companies, and OCR companies, the same thing that you would say back to me as a real world data company, which is, what is the best use case of your platform in terms of the data and the assets and the capabilities of your team.
And don't misrepresent yourself. I think that again, generically speaking, I think a lot of AI companies said, look, we can solve the world's problem and do it very well and we can solve it. We can, we can basically be a solution for everything. And I think we all know that AI companies can't be a solution for everything, but they can play it very, very important role in terms of different aspects.
And if I go back and look at rheumatology, it may be difficult for an AI company to go through and read notes to the level of a clinically trained person that has to put together lots of different disparate data. And I'm not saying that AI can't get to it, but then I look at a physician, a patient global assessment forum, I look at HackMD. I look at different things. I go, geez, that AI company would be terrific, in terms of a whole bunch of different capabilities that they could be a creative to for Trio Health and other companies.
But I think it goes back to defining, as I say, your swim lane, in terms of where you can best apply AI and some of the proprietary technologies that the AI companies have developed, and making sure that you kind of stay within that swim lane and not overstep your bounds, no different than I would say the same advice to Trio Health, which is: What are we good at? What are we not good at? And where should you go to potentially one of our competitors or a different vendor to answer those questions or to, you know, solve
Dr. Karim Galil: You guys use a lot of the term FDA level which it basically refers to a data set that can be, okay or meets the benchmarks that FDA has for data integrity. When it comes to AI, have you been successful to make any FDA submission using AI only, or is it always has to include some sort of a human curation layer on top of your data processing techniques?
Brent Clough, CEO of Trio Health: Yeah. So it's a great question. Actually, we just signed a partnership with Greenleaf Health last year that has a regulatory advisory group in Washington, DC and they're all former executives of FDA and spent a long time there. And again, where we look at our collaboration with them as being the regulatory experts in terms of knowing what is really regulatory-grade data. Everyone talks about it, it is a widely used term. But at the end of the day, what, what does it really mean? And, we look to Greenleaf. Our partnership with Greenleaf has really been being the experts, since they sat in that chair at the FDA for a good chunk of their careers. And so I would tell you very simply for us, I use this term ability to validate adjudicate and to supplement. And so if I can represent back to Greenleaf or to FDA in terms of the source of every data field how I received it, who gave it to me, how did I verify it and so forth, it is part of that process that we do.
And I think that, again, we, haven't not had any direct experience yet in terms of using AI in terms of as a submission or 'regulatory grade', but I believe that just because we haven't done it doesn't mean that it doesn't exist. And I think that there clearly is a role and I think it's just, again, for the agency to understand in terms of the process and methodology. No different than developing a research SOP in terms of the analytics SOP, which is okay: You've got a highly curated data set. And then how did you transform it, you know, based on your statistics and approach and methodology. And documenting that process.
And I think AI should and would be an important component of that as long as the agency can understand the methodology, the process, and exactly how you transform or got to the conclusions that you did.
Dr. Karim Galil: Which is very challenging because a lot of the AI techniques today they used are deep learning, which is not really self explanatory systems. It's very hard to explain how was the outcome generated. And I think this is a very challenging, as AI companies in healthcare have to figure out how can you use AI techniques that are still able to explain how they are able to come to these conclusions. But that's a great point that we need to be able to explain things, to achieve this FDA level acceptance.
Brent Clough, CEO of Trio Health: Look, from my previous experience with the data scientists and machine learning, they uncovered some really interesting patterns and trends that obviously we didn't uncover in terms of just with all of our analytics team. And so if you look at that as really the starting point in terms of then using the rest of the process to then manually go through and verify that, I still think AI can be very informative, in terms of spotting things and in terms of early detections and uncovering things that haven't been detected yet, and doing it in a very efficient way versus kind of a human effort. And then the question is, can you couple that with the human efforts as a backstop to go through and verify in terms of bearing out that trend or bearing out that evidence that the AI company came up with.
Dr. Karim Galil: I agree that the machine has to help the human, but it's not in a position to replace the human. I think this is one of the things actually that we strive to do here at Mandela is we try to always build machines that can truly help a human abstractor or a clinician rather than try to replace the clinical role there.
We are at wide adoption when it comes to the adoption curve of real world data and real world evidence. And I was wondering, how do you see 2025 from that perspective. Are you seeing more budgets allocated for real world evidence? Is it going to be matching the budgets that are allocated for traditional clinical research?
Are you going to see clinicians basing a lot of their clinical judgments on evidence that are created from real world evidence? Are you going to see payers now finally adopting value based contracts, or you still think that 2025 is not going to be where we hit that point of wide adoption?
Brent Clough, CEO of Trio Health: I think we've made tremendous progress in the last 12 to 18 months in terms of, and I go back to my earlier point, I think for us as an industry to move forward, we have to have the transparency and the confidence behind it to understand how we can best utilize real world data and real world evidence.
And I think once you have that transparency and you have that understanding, I think then the opportunities really open up, right? So you're now starting to see a lot of the buzz words around value based contracts, value based contracting, that becomes kind of the next iteration based on real world evidence.
I think, as a relates to clinical trials, you're starting to see the FDA opening up and being more receptive in terms of trying to understand, what are some of the use cases that make sense from their perspective. And so you're seeing, you know, expanded labels. You're seeing synthetic arms.
You're seeing a whole bunch of different things that are now starting to permeate within our business units. And I think there's a lot of exciting things. I think you're going to see a massive amount of changes between now and 2025, but I would go back to that change and that adoption has got to have the clear understanding and transparency to know, kind of the good, bad and ugly.
And once you understand that, then you can apply it. So it could be more precision around clinical trial recruitment, which is speed and certainty. It can be getting better data, so you're seeing now disparate data being linked together, right?
So you're getting more of a complete record on the patient, be it from their primary care physician, to their rheumatologists, to their infusions, especially pharmacy and so forth, which is again great. But, that in isolation, I don't think solves the problem, right? Because there's still the AI component and there's still the other component, which is the human element, which is, I just, I got to go back to the physician and I have to have him certify or verify that what I'm seeing is actually true and it makes sense.
And I think the combination of all three of those and how those are stitched together is really exciting in terms of, I think we're now in the growth cycle or the growth curve of our industry in terms of, of the different use cases that we can develop.
And that can be applied between now and 2025.
Dr. Karim Galil: My last question.
If you can Zoom any living person today, who would it be and why?
Brent Clough, CEO of Trio Health: Great question. You know, this may sound off topic, but probably the person that comes to mind is Richard Branson. And the reason I bring up Richard Branson is, I've never talked to him, I've only read articles about him, but he's built companies that aligned with his personality, it feels like.
And so it seems pretty amazing that you can be a serial entrepreneur and your whole theme is based on aligning with what seems to be his personality, in terms of the way that he approaches a market, and yet professional way that immediately attracts people. It would be super cool to meet someone like that. He's built his professional career around his personality.
Dr. Karim Galil: It's very interesting. You're inspired by leaders who are true to their personalities. This is a great example of a CEO who has a culture consistent through the way, right? Like the company, the CEO, they're all about the same thing as how can we be true to ourselves, to the data, and to the industry, and also to the clinical community. Richard Branson is a great kite surfer. So. I think as a good start, we need to get you on the board. We have you here in the Bay area and need to get you started on that.
Hey Brent, thank you so much for your time today. I think you shared with us great examples. I love the disparity example that you gave about this patient with hepatitis. And I think this is an awesome use case for real world evidence. I also really, really got to get inspired with your scientific committee, and how you're able to build solid science, but still have a tech and business processes to support that.
Again thank you so much for your time. All the best for you, for your family and for Trio. Stay safe. And I hope see you soon.
Brent Clough, CEO of Trio Health: Likewise, and thank you so much for inviting me. I really enjoyed our conversation today.
We’ve changed our look. Our goal remains the same: make medicine objective. The new site highlights the way our proprietary AI enables organizations to achieve quality and scale when structuring unstructured data. It comes down supercharging your clinical abstraction. We’ve validated that our human in the loop abstraction approach can support a machine that understands medical context like a physician. In our own experiments, the number of variables needing correction decreased by 40%. High quality abstraction = high quality data for cohort selection, real-world evidence, and registries.
The customer, a key player in the genomics space, had a strategic initiative to build a clinic genomic database to support their life sciences customers.
One clinical trial organization was using manual chart review and was looking to reduce the time it takes to find eligible patients.
From the Desk of the AI Team
Organizations that use patient data for internal or external research need to take steps to prevent the exposure of PHI to those who are not authorized to view it. They do this by redacting specific categories of identifiers from every patient document. Once the identifiers are masked, the risk profile of these datasets is significantly reduced. But how do you ensure that redaction engines are working to the highest accuracy?
The Mendel team is still buzzing from our week-long retreat in Cairo. The theme of the retreat was “coming together” and it was the first time the American and other remote employees were united with their Egyptian counterparts. Although there were many adventures–missing flights, seeing the pyramids, haggling at Khan el-Khalili–the highlight of the trip was collaborating together, as one global organization.
Competence via comprehension
Artificial intelligence (AI) is playing an increasingly important role in the healthcare industry. But to fully leverage the potential of AI, it must be equipped with clinical reasoning skills - the ability to truly comprehend clinical data, or in other words, to read it as a doctor would. When it comes to data processing tools, only a tool capable of clinical reasoning can effectively process unstructured clinical data.
Sailu Challapalli, our Chief Product Officer, spoke at a recent Harvard Business School Healthcare panel. The event brought together different healthcare and AI experts to discuss large language models and their impact.
Manually abstracting patient data at scale is an herculean task for humans alone. It is slow, expensive, difficult, and requires extreme precision and accuracy. Organizations have to choose between breadth and depth when it comes to making data useful for decision making. Because of these challenges, the Mendel team created Carbon. Carbon is an easy to use workspace that allows clinical abstraction teams to efficiently curate high quality clinical datasets at scale. The foundation of Carbon is Mendel’s AI. Carbon pulls directly from Mendel’s AI platform to give abstractors a headstart in identifying relevant data elements within a patient’s chart.
Within the real world evidence space, the generally accepted process for creating a regulatory grade data set is to have two human abstractors work with the same set of documents and bring in a third reviewer to adjudicate the differences. These datasets also serve a second purpose - as a reference standard against which the performance of human abstractors can be measured. Although this remains the industry standard, it is expensive, time consuming and difficult to scale.
From the Desk of the AI Team
AI projects have created tangible results for a wide range of industries. Despite the innovation, it is important to remember that AI is not a magic wand that will solve every problem in every industry with a single wave.
Before embarking on any new endeavor or enterprise, certain questions come to mind: How are we going to handle this? Does our team have the expertise, bandwidth, resources, and time to handle this undertaking on our own? When it comes to finding a scalable way to structure your unstructured healthcare data, the answers to these questions will impact when/whether you deliver a top-tier product for your clients.
Human abstraction has long been considered the gold standard for extracting high quality information from EHR data. With the rise of NLP and machine learning, how should we evaluate these new technologies and are human abstractors still the correct comparison?