Outcomes Rocket_Ivan Jarry: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Saul Marquez:
Hey everybody! Saul Marquez with the Outcomes Rocket. Thank you so much for joining us today. I am privileged to host an incredible healthcare leader today. His name is Ivan Jarry. He is an amazing healthcare leader, and since taking the helm at ObvioHealth as the CEO and co-founder, Ivan has grown the company 13-fold. He leveraged advances in digital technology to tackle the inefficiencies in healthcare clinical research, designing one of the first patient-centric apps for decentralized clinical trials. Since then, he’s put agile teams and processes in place to quickly scale the business. It’s going to be a great discussion today. So privileged to have Ivan with us. Ivan, thanks for joining.
Ivan Jarry:
Thank you very much for having me.
Saul Marquez:
It’s our pleasure, Ivan, and look, there’s been so much discussion around decentralized clinical trials. COVID, I think, also helped accelerate some of those efforts, and we’re in a new world. So I’m excited to dig into what ObvioHealth is doing and what you and your team are up to, but before we do that, I’d love to learn more about what makes you tick? What inspired your work in healthcare?
Ivan Jarry:
I am loving changing things that are kind of broken and fixing them. So I like to look at places where you can improve processes and use technology to improve the way things are. And in healthcare and the space where we play, it’s a very antiquated industry, very regulated, where I figured that even if I’m not a Ph.D. and I have no scientific background, I could use my skills to try to improve the processes around a clinical study.
Saul Marquez:
I love that, and you know what? We need leaders like you, companies like yours to make our health system better. Talk to us a little bit about the business. How are you guys adding value to the healthcare ecosystem?
Ivan Jarry:
So we play in the clinical research space. So, from the moment a drug is being developed by a manufacturer, to the moment it gets authorized by the FDA or any regulatory body and goes in the ends or mouths of patients. So we’re playing this last mile of delivering research to its end goal being getting approved. So what we’re doing is really helping the manufacturer of the drugs to run studies in a more efficient way so that they can get to a status of whether the drug is safe and efficient faster. And if that’s the case, that means that the patient will have the drug available for their use earlier and at a cheaper cost, and this is the space where we’re playing.
Saul Marquez:
That’s fantastic, and there are so many steps in the research process, from design setup to recruitment. Talk to us a little bit about what you guys are doing to make that whole process simpler.
Ivan Jarry:
So in general, we’ve looked at the process from end to end. So, from the moment you need to identify, recruit, enroll randomized patients all the way until you publish your study report with the findings of the study, and we looked at the inefficiency in that process. The main issue in a clinical trial is recruitment. Clinical trials is usually delayed because they can’t find enough patients on time to enroll into their studies, and those patients then do not finish the trial. They drop out during the trial. The reason, the main reason, being in a traditional setting that you rely heavily on a site that has a location, physical location. So you will go to see that site and you say, Hey, Dr. X, we’re going to run that study. How many patients do you think you have that would be interested? And Dr. X believes that is going to have 100 patients interested, but when you start calling them out, it’s actually only 50 that are here, and then you start the study and some go on vacation and miss that visit. So there’s a lot of issue with having a physical site and asking patients to bring a long time to come on a regular basis to an office visit, which is usually during a weekday. It takes, it’s 30 minutes to an hour to drive there, you have to wait in the waiting room for an hour, and you get to visit fifteen minutes, and another drive. So it’s Alpha Day or a whole day that’s going to be taken out of your schedule, and it’s not possible for a lot of people. The way we’ve done it is, when possible, we do the assessment at home. So whether it’s through connected device, whether it’s through your smartphone answering a questionnaire, whether it’s a telehealth visit, whether it’s sending a nurse to your home, which makes the experience for the participants a lot easier. And so you have more people willing to participate and you have more people staying in the trial because if their son is sick and they need to stay at home, we can still go to their house. They don’t need to drive to a site and miss a visit. So that’s what we’ve tried to do. So from the beginning of recruiting people anywhere, because we don’t need to be around the site, to making the experience for the participant a lot easier, which helps us recruit faster, better, and keep people engaged during the entirety of the trial.
Saul Marquez:
You know, that’s really interesting, Ivan, and this recruitment, I’ve heard it time and again, it’s so hard. And then you try to get a diverse subset of patients as well that includes, you know, something that’s closely representative of the eventual recipients of the drug, critical that we do that. So what I hear you saying is that you’re bringing people process and technology altogether and helping these drug manufacturers make things more efficient.
Ivan Jarry:
That’s correct. The diversity point is very important. For what I explained, you would understand that a person that has a 9 to 5 job Monday to Friday would be, by default, excluded from being able to participate in a clinical study because they can’t be taking time off every Tuesday afternoon to go to site visits. So the individuals that were participating in clinical studies were, whether people that had no other choice, to life or death decision, I have no other treatment, I need to go on a clinical study. And in that case, yes, you find the time and you make the time to go to a site. But for all other conditions, it was only a subset of the population that was living close to a site and that the availability or the flexibility because of that job to be able to do so. So now by doing it at home, at the own reason of the participant, suddenly the entire population can participate. And when I say entire, it’s true. We’ve done study with people over 80 years old and a sponsor was like, will they be able to use a smartphone? Yes, the good news is they have more time. So they’re actually more compliant. They are able to chat with the study team in the morning or in the afternoon. So we’ve seen that it’s a lot more inclusive and we have a population that is way more representative than what we had in the past in the traditional model.
Saul Marquez:
Thank you, Ivan. That’s fantastic to hear. And so, you know, when you think about ways that you guys have made business better or improved outcomes, what some of the things that you guys hear from customers that say to you, man, you know what, Ivan? Thank God you guys exist because you help me solve X. What is it?
Ivan Jarry:
So beyond the processes, which is very important to improve, digitalize, streamline to make the experience easier for the patient, it’s also how you measure those endpoints that you’re going to use to make a judgment on the efficacy of a drug. We’ve noticed that a lot of the tools that were used for 30, 50 years were pretty inaccurate. I’ll give you a couple examples. One very easy that is well known in the industry is parking lots. We know that most of the diaries on the clinical study were filled up on the parking lot just before your site visit. You have a visit, they tell you, hey, you need to fill your diary at home every day, and when you come back in two weeks, you bring back your little diary with your pen and your paper, and I’m going to review that to see if you had adverse events, you felt pain or anything. And obviously, people are humans and they would fill their diary for the first three days or the first five days, and then on day six, they would forget and they would catch up on their seven and then they would forget them day ten and then arrive day 14 and then, oh crap. I haven’t filled it for four days. I’m on the parking lot, you know, I’m seeing my doctor, let me go, and I don’t remember if I had pain three days ago, if I went to the bathroom, how my stool consistency was, if I was feeling anxious, if I slept well, whatever the measurement was supposed to be reported that date. So I’m doing my best to recall, and so the data accuracy of this diary was very poor because people were not completing on time. The fact that we do it through a digital instrument where there’s reminders people can’t lie, they can’t forget, and if they miss a data point, they miss the data point, that’s okay. We know about it, but we know it’s a missed data point. It’s not an inaccurate information that’s going to be filled three days later. So that’s an easy one. The second way we’ve done is looking at what other measurements are inaccurate, and we try to build with our sponsor new tools to measure different elements without the bias that you can have. I’ll give you a very quick example. We’ve done a lot of infants study in which you ask parents how many crying episodes each child had over the last 24 hours. To do so, if you’re a parent is the stress of an infant, you have to calculate and measure how long your baby has been crying because that episode is between five and fifteen minutes. A second episode is defined as a period of five minutes between the first one and the second one of silence and then the second one, very hard to measure. Parents will forget some, and when you ask a parent how long your baby has been crying, they tell you fifteen minutes. But when you actually listen and measure, it’s five. The stress induced by the crying of the baby makes the time go slowly earlier and you think it lasted for a long time. So instead of asking the parents to report in the diary, we put a microphone in the baby’s room. We remove all the sound that is not the baby in any kind of private conversations happening. We just listen to the sound of that single baby, and we can have an accurate measurement of the number of episodes of crying and fussing. That’s one example of many of the research that we’re doing and instruments that we are validating to be able to implement in clinical studies to get better measurements.
Saul Marquez:
Yeah, that’s great. You know, you take the burden off the parent. You increase the accuracy. You know, it’s frictionless. It becomes frictionless. And, you know, I was doing an interview the other day, Ivan, with CEO of Tampa General Hospital, and he was like, how can we bring the Starbucks and Amazon experience to the consumer? And what you’re doing is an example of bringing that experience to the consumer.
Ivan Jarry:
Yes, and the way we designed the app, for example, that is the patient-facing interface was with that in mind. The people we brought from the US are people that come from a consumer health app where you’re not forcing the participant to use that single app and pay them, but those are coming from people that have designed an app that needs to be working very well and appealing to the user for them to use it or buy it. So that’s what we used, and the app is made in a way that most of the tasks are done with one thumb. And it’s like when you’re going Instagram or Facebook, you just scroll up slightly. It would start nicely to your next task. You see only one task per screen. It’s not confusing. You wake up in the morning, you have a reminder. It just takes you straight to what you need to do. You don’t need to click on any menu anywhere. And so we made that very easy so that everybody can participate, whatever the age, whatever the condition, to make it extremely seamless for them to participate and continue to be engaged during the entire study duration.
Saul Marquez:
Hey, that beats a journal entry in the parking lot, I’ll say.
Ivan Jarry:
Definitely.
Saul Marquez:
Yeah, you know, when you were telling that story, Ivan, I just imagined myself doing one of these entries, and I’m like, oh, so I’m laying there in bed, I forgot, and then I just literally do the intake right before I go to bed and chat with my wife for a little bit, and then I’m done and it’s in there. So I think this is fantastic. Thanks for making some of these examples tangible because that’s super key, right? And these things, Ivan, are available today, right? These aren’t like things that are coming. These are available today.
Ivan Jarry:
Correct, we’ve done 42 studies in 28 countries in the last three years. So we’ve been using those tools over and over. We’ve been collecting a lot of data to make sure that, again, it was fully available, that any age group, any types of population would be able to use it. We would make sure that the regulation of the different countries would allow it. So we’ve been able to build the nuances depending on the countries. In Mexico, e-signature is not authorized, so you need to be able to print and actually sign and scan back. People don’t have a scanner, so can they take a picture from that phone? So we went through all those elements and we built the tools to enable to do this type of research in all the regulatory environments.
Saul Marquez:
Fantastic. Well, listen, you say 32 studies?
Ivan Jarry:
We’ve done 42 studies for 28 countries, yes.
Saul Marquez:
So you guys have been around the block a couple of times. And so, what, you’ve made the investments, you’ve put in the time, what’s been one of the biggest setbacks that you’ve experienced, and a key learning that came out of that, that’s made you guys even better?
Ivan Jarry:
Every single study we’ve done, you know, while it’s a technology for those that are familiar with the clinical trial process, it involves a lot of stakeholders, it’s very detail-oriented and a lot of things go wrong. In every single study we’ve done, we’ve learned something from the experience that, afterward, we say we could have done better, we didn’t know. So I’ll give you a couple funny examples that we’ve learned and put in place. But the first time one of our patients was a pilot and that flying across the US. So he would take his medication on Eastern Time and then the next one an hour later was on Pacific Time, you know. So how you end all changes of time zone for a single subject when they’re traveling in the plane and they lose connectivity during four hours? Interesting technical challenge that we didn’t think about until we had an actual patient that had a weird effect of his second dose, was happening before the first one, because they had changed time zone. A caregiver, the grandmother is now going to bed and the mother is taking care of the baby, but the grandmother didn’t input the information until midnight or ten and the mother is inputting the information at midnight or one. So how you don’t overlap those two? Which data is correct? Is it the grandmother that is late in entering 15 minutes or the mother that is very diligent and does it in minute one? So those conflicts of making sure the caregiver handoff from a technical point of view from one phone to another happens in a second, so you can’t enter data in two different devices at the same time. Those are the, a few examples from a technical point of view that I’ve been learning that we discovered. The good news is we have live data access. So when this is happening, we see it immediately. In a traditional setting, you would have to go audit or monitor the site. A month later, look at the data set, realize there’s an issue is the data. Call back the site to figure out what happened in that visit and reconcile. For us, we immediately see in that moment, oh, there is an issue and we’re able to call the patient and figure out what happened and understand, oh, my mother was still awake and she entered data at the same time that me or hey, I just landed in San Francisco, and that’s what explained the issue was the scheduling of those events.
Saul Marquez:
That’s fantastic. Thank you for that. Yeah, you know, it makes so much sense. And these learnings, these little nuances over time, have a compound effect in the way that the entire system works, and so, how are you guys doing your research? How are you doing your trials? It might be an opportunity for you to check out the work that ObvioHealth is up to. And there’s always this element, Ivan, of regulatory compliance. How do you guys ensure that while all this is happening, the data privacy, regulatory factors, and security specifically, are withheld and in place?
Ivan Jarry:
Sure, so we obviously, so there’s different things. From a security and IT point of view, the rules are pretty similar around the world, so we have to obey to a certain standard in the industry, and so the way the platform was designed is to meet all those criteria. One of the main elements is validation, which is a very important element to ensure that at any moment we’re releasing a study, that study has been validated from the platform but also from the configuration of the study. So it’s extremely technical, but we have put in place ways to constantly validate the platform so that every time we run a configuration, that configuration is being validated too. From a regulatory point of view, there’s a lot of differences between countries. So we have run in 28 countries. So before we go into a country, we need to contact the regulatory authorities, talk to the …, talk to people that have experience in those countries, and get in touch with them to understand the constraints. As I said, Mexico, e-signature is not yet allowed, at one point it will be. In Australia, the PI needs to co-sign every informed consent. For us, it means we need to create an access for the PI to view a queue of consent and go there. So we’ve been learning with the experience and every time. We have requests now to go to Oman, and Nigeria, and Lebanon, or a proposal we’re working on, so we are currently looking and studying the regulation of those countries where we’ve never run a study to make sure that we will be able to run those. The last point is the data hosting, which is an issue in some countries. In China, as you know, the data needs to be hosted in China, but also whoever views the data needs to be physically located in China. How you can guarantee that to the local authority is a technical element also. So the way we build the architecture of the platform was to be able to enable all those elements that are IT or regulatory constraints.
Saul Marquez:
That’s great. Thank you for that, Ivan. It was the last piece that I had there. And you guys are doing some incredible work. So kudos to you and the team at ObvioHealth. What are you most excited about today?
Ivan Jarry:
I think where we are. I think we are in a place where we’ve run a number of studies, as I said, we’ve learned a lot and I thin1k we’ve been able to apply those learnings into what we have today. So what we’re seeing is a level of knowledge, confidence, and robustness in what we deploy today thanks to these five years of experience that enable us to really be credible at the moment that we advise our client and being more prescriptive in designing successful studies for our clients. I think our role is not just implementing. For many of our clients, it’s the first time they do a DCT and it’s extremely important that they’re guided the right way so they have the right design that’s going to be successful.
Saul Marquez:
Yeah, that’s great. You know, and having somebody there to walk you through, if you haven’t done it before, or even if you’ve done it before and you’re looking for a better way, and maybe you tried and quit, there’s an opportunity here for anybody looking to take a deep dive into making their clinical trials more efficiently, so what an exciting time. I’ve really enjoyed our conversation today. I can’t thank you enough for sharing all the great things you guys are up to. Why don’t you share a closing thought with the listeners and then the best place they could follow you or get in touch with you or the team?
Ivan Jarry:
Yeah, what, so … is, you know, I think we all agree in the industry that again, the system is broken and needs to be fixed. And I think, you know, there’s quite a few companies and people that are trying to do that. And I would love the industry to be a little bit more adventurous and give a chance to us and other companies that are trying to find solutions to fix the problem, and just listen, and get the time to get convinced that this is the way to the future and that we can actually fix the system. And so if you’re interested to have a conversation about how we can run a decentralized hybrid clinical trial, how we can work together in developing more accurate novel digital instruments, you can find us at ObvioHealth.com, which is O B V I O H E A L T H .com.
Saul Marquez:
Outstanding. Ivan, thank you so much. Really appreciate what you guys are doing and thanks for sharing your time.
Ivan Jarry:
Thank you for having me.
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