Data Liquidity for Clinical Trials: Patient Autonomy and the Rise of AI with UBC and SEQSTER

Join us on this episode of the Chilcast as we discuss use cases for real world data and the future of medical research. We're joined by industry experts Aaron Berger, SVP of Evidence Development Solutions at the clinical research organization UBC, and Ardy Arianpour, CEO and Co-Founder of SEQSTER, the first one-click, patient-mediated  platform for clinical data aggregation.

Hear how these two have worked together to enable greater data liquidity in the clinical trials process, reducing physician administrative burden (and cost), creating new opportunities to accelerate the development process by expediting recruitment and the collection of medical histories. As health data continues to be digital gold for the companies that are able to effectively leverage it, learn how one partnership around RWD has had far-reaching implications about the future of trial design, post-market surveillance, and more.

Be sure to follow us on LinkedIn and take a look at our research to stay up to date on the health IT industry.

Transcript

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Introduction

Aaron Berger 0:00

What we were able to do once with Ardy and Seqster is we didn't need to go out to all those patients' healthcare providers . We already knew who the patients were . We could go directly to the patients and ask them if they'd like to contribute their data to research , and if they consented to that , we were able to gather up their medical records so you're able to do a study that otherwise just would not have been very possible . That's the big evolution we've seen . I call it patient , you know , patient agency over their medical record . Their ability to do so will transform the way we execute all sorts of research , from pre-registrational through post-approval , and we're just starting to see some of that .

John Moore 0:35

Welcome back to the Chilcast , a healthcare podcast from Chilmark Research , helping healthcare leaders make the best decisions for the populations they serve . Welcome back to the Chilcast . I'm the managing partner of Chilmark Research and your host , John Moore III . Chilmark Research is a healthcare technology industry analyst firm founded in 2007 to provide objective , expert research and guidance on those emerging data-driven tools and services with the potential to fundamentally improve the experience of care for all . If you are new to the show or a return listener and you appreciate our programming , don't forget to subscribe on your favorite podcast service and leave us a review . If you have a topic you'd like to hear us discuss on the show or questions about how to participate , shoot us a message at podcasts at chilmarkresearch . com .

John Moore 1:28

For this episode , we're going back to standard programming after our initial six-part miniseries for the Health Impact Project . Be sure to check those episodes out , because we had some fantastic discussions around defining healthcare value and what the value is of health IT In the fall of 2023, . We released our first real-world data real-world evidence market trends report . I will soon be following that up with the accompanying buyer's guide report that goes into more detail about the differentiators between solution developers , how customers can think about defining their specific use cases , to narrow the field of options , best practices and pitfalls to avoid , and thorough vendor profiles . This will be our second podcast on the topic of RWD and I am pleased to be joined by Aaron Berger and Ardy Arianpour for this discussion about the evolving nature of clinical research and therapeutics development .

Aaron Berger 2:19

Hi there , it's great to be here . Thanks , john , for inviting us on the podcast .

Ardy Arianpour 2:23

Hi everyone , so nice to be here . Happy Friday .

John Moore 2:27

My pleasure . I'm really looking forward to this conversation . So for some background , aaron Berger is the Senior Vice President and Head of Evidence Development Solutions for UBC that designs and executes modernized solutions for leveraging forward-leaning technology and research designs that deliver fit-for-purpose evidence on the safety and effectiveness of treatments for patients . Mr Berger joined the UBC team in 2003 and has since served in a range of diverse roles , leveraging global capabilities in the execution of PERI and post-marketing product development programs and real-world evidence generation . He is responsible for the development of real-world data and real-world evidence technology architectures and decentralized research solutions to drive greater insight into the safety , profile and value proposition of medicines .

John Moore 3:19

Ardy is the CEO and co-founder of Seqster , a healthcare technology company breaking down health data silos at scale . Its enterprise operating system aggregates disparate health data sources into a single 360-degree view of a patient in real time , producing research-ready longitudinal health records for use by pharma providers and payers . Ardy is a serial entrepreneur in life sciences and digital health . Prior to starting Seqster , Ardy launched several clinical and consumer-based genetics tests as chief commercial officer of Pathway Genomics and SVP and GM of Genomics at Ambry Genetics , which sold to Conica for $1 billion in 2017 . As a key player in the landmark SCOTUS decision scrapping gene patents , which would be 2013's Association for Molecular Pathology versus Myriad Genetics . Ardy played an instrumental role in expanding genetic testing access with the launch of BRCA testing , and benefiting patients and family members across the country . He has been recognized with many awards over his career , most recently as a MedTech World Rising Star in 2022 and won a Pharma Vo's 100 Inspiring Leaders in Life Sciences in 2023 .

Advances in Real-World Data and Evidence

John Moore 4:30

All right , so , aaron , thank you for being here with us today for this very timely and important conversation . First off , let's frame the whole area a little bit . What does your work consist of as UBC's Head of Evidence Development Solutions ?

Aaron Berger 4:47

Yeah , john , so you kind of mentioned it in the intro you did for me . Thanks for that . You know what we're here to do is answer important questions about the safety and effectiveness of medicines , and oftentimes we're doing that within the context of the post-approval requirement that's , from a regulator , from FDA , from EMA post-approval requirement that's from a regulator , from FDA , from EMA . Other times we're doing it within the context of the sponsor , of the manufacturer's desire to learn more about the safety profile and the value proposition of their medication .

John Moore 5:19

Can you share either yours or UBC's definition of real-world data and evidence for our audience ?

Aaron Berger 5:28

Yeah , absolutely so . In 2018 , FDA released a real-world evidence framework , and that's kind of what the industry has aligned around the definitions and standards that are outlined there , and that document frames it like this Real-world data are any data relating to the patient's health status and delivery of healthcare . They're routinely collected through a variety of sources , and examples of real-world data include electronic health records , claims data , data that may come from registries or data from digital health technologies . Real-world evidence is the evidence that's curated and generated about the usage of , or potential benefit of , a treatment for a patient that you derive from analysis of that real-world data .

John Moore 6:20

And what are some sources of real-world data and examples that you can share ?

Aaron Berger 6:26

So you know , here today we're going to mainly talk about electronic medical records , but you know it goes beyond that . It also goes into claims . It goes into a patient's activity and their health status that you may collect from wearables . It may relate to quality of life or patient reported outcomes that a patient is directly reporting , but the research fundamentally requires that you get access to a patient's medical record in order to answer the questions that we're trying to answer about the safety and effectiveness of treatments .

John Moore 7:06

Okay , yeah , so that you know what's actually happening in the wild , what's happening when you have some awareness of a patient's full medical history , as much as you could collect it . So can you tell me how that information has been collected historically ? How have you guys gone about bringing in , you know , a patient's records and their medical history ?

Aaron Berger 7:29

Yep , definitely .

Aaron Berger 7:30

And that kind of really leads us into how I got introduced to Ardy and how we started collaborating so classically for decades .

Aaron Berger 7:36

The way that you acquire patients' medical records for clinical research or post-marketing evidence generation medical records for clinical research or post-marketing evidence generation is you enlist the healthcare provider .

Aaron Berger 7:48

It's been very healthcare provider mediated , I would say , for decades , and those healthcare providers are enlisted as sites in a clinical research study or in a post-approval study or registry .

Aaron Berger 8:07

The healthcare provider has roles such as a study coordinator , and those study coordinators and investigators literally take data that have been from a patient's chart and they would transcribe it into a case report form that we use for our research purposes . That evolved a couple of decades ago and it evolved into web-based electronic data capture systems and that involves keystroking data from a patient's chart into the case report form , into the study database , if you will , and that's how we've been conducting research , but it always relied upon going through the health care provider . So I've been very interested for a number of years in how do we perhaps disintermediate the health care provider from that process and really relieve them of that burden and get directly to the source of the real world data that we're looking for in this case electronic medical records and do that through the patient's ability to take agency over their own record and decide if they want to contribute it to research .

John Moore 9:09

So it sounds like the old process was very labor intensive and relied a lot on your ability to bring in human capital to actually do that transcription and manual abstraction . Before we get to Ardy and your guys' kind of backstory , I have a couple of quick other questions to kind of frame this conversation and what you've seen in your career . So you've been with UBC since 2003 , which is surprising loyalty to a single company in this day and age . It's also the year that I graduated from high school , which isn't really dating you because you're only a couple of years older than me , but still . I mean just kind of putting it in perspective . It was also the year that the Human Genome Project was completed and we had actually fully sequenced the human genome .

John Moore 9:47

So it was only a few years before that that everyone had been losing their minds about Y2K and clearly a lot has changed in that period of time across many areas that touch clinical research and quite notably the first true global pandemic in the modern era . You know , in a couple generations we haven't seen anything like that . So the way that clinical research is being done and new therapies are being developed has really seen a whole host of technological innovation , scientific innovations and just social disruption that will have touched your career in some capacity . So can you share a few highlights of the most significant changes you've observed in terms of how this is practically impacting the work that you guys do , and maybe some areas where things haven't advanced as much as you would have liked or expected to see at this point ?

Aaron Berger 10:32

Yeah , definitely that's a fun thing to think about and reflect on . So when I started in this industry in about 2003 , I had roles called a CRA Clinical Research Associate . I had roles called a CRA Clinical Research Associate and what that meant was I would go fly around to these sites , those healthcare providers that I talked about who are listed in in a research , in a study , and they would have these big walls , these big columns of paper charts and manila folders and they would roll on tracks and you'd use cranks to get to the medical records you're looking for and the sites would pull these tracks and you'd use cranks to get to the medical records you're looking for and the sites would pull these out and we would literally take the paper , the patient's chart , and I would review what was in the chart and I would review what the site had transcribed into the case report form , which was ultimately became the study database , and to see if those things matched up . Then , if they did , I would take the case report form , which was two part , you know NCR paper , take the top page off and I put all those pages in a FedEx envelope and send it off back to the home office where that data would be either scanned or manually entered into a study database , and that's how we were performing , you know , research and , remarkably , things well , things changed . So then you had an evolution , about three or four years into my career , that the paper went away and you started doing that on a web-based electronic data capture platform . So we're keystroking the data into an EDC , directly into the database , and that was a big leap forward , to be sure , but we're still relying on manual transcription . We're still relying on sites to do this . It has scalability and quality issues associated with it . And then fast forward up to the 21st Century Cures Act and the FDA framework on real world evidence and disruption from technology providers like Seqster , and what happens is we now are gaining the ability to get at those medical records without having to sit somebody down at a console , look at two screens and punch data in from one place to another . And so we're just kind of at the dawn of that .

Aaron Berger 12:44

We've used these types of solutions several times in partnership with Ardy , and what it allows you to do is conduct some research that otherwise would not have been possible . I'll give you one example when you're trying to do research in a rare disease , as one of our clients was , and you would , literally , if you wanted to do this type of study it's a chart review or natural history study you'd have to go to find 50 patients to get quality data on . You'd have to go out to 50 different sites , different healthcare providers , and ask them to execute that enterprise . I just told you about public manual data entry and sometimes that's just not economically practical and the sponsor is going to decide not to do that type of research in that setting . But what we were able to do once with Ardy and Seqster is we didn't need to go out to all those patients' healthcare providers .

Aaron Berger 13:35

We already knew who the patients were . We could go directly to the patients and ask them if they'd like to contribute their data to research , just as they would in any study that they would decide to participate in . And if they consented to that and enrolled through the CIGSTER portal , we were able to gather up their medical records . So you're able to do a study that otherwise just would not have been very possible . So we think it's a really important part of research into rare diseases and small populations that are dispersed . Using these types of methodologies are gonna be very important . It's also gonna be important for chronic disease and high volumes of patients . But that's the big evolution we've seen . I call it patient agency over their medical record . Their ability to do so will transform the way we execute all sorts of research , from pre-registrational through post-approval , and we're just starting to see some of that .

John Moore 14:30

Yeah , no , it's fantastic seeing how actually having this all digitized and then moved to the cloud is opening up new ways for patients to have that autonomy and that ownership over their own health histories . I'm seeing a few different companies that are doing various aspects of the real world data solution development as part of this research effort that I'm currently engaged in , but very few are taking that patient forward , patient centric approach where they're the ones driving the ship and then they also get something out of it on the back end . So , Ardy , that kind of brings you back into the conversation . The first time we spoke , you and I bonded around our mutual past lives working benchside in genetics labs . You have me beat by a few years as well , but we're practically contemporaries and have both ended up here in the brave new world of modern healthcare informatics . Can you give us the quick backstory of how your early career in genetics research led you to found Seqster later on ?

Ardy Arianpour 15:28

Yeah , first off , thanks so much , John , for having myself and our wonderful partner , UBC , Aaron Berger , here .

SEQSTER: Breaking down data silos

Ardy Arianpour 15:33

You know I started out when I was 16 years old and I was lucky enough to grow up in the beautiful and finest city in America called San Diego , and if you know anything about San Diego , we have some prestigious , amazing universities and there's lots of different institutions that bring innovation out , because of the marine biology , I guess , and the great , you know , surf that exists in La Jolla . Here . La Jolla and Torrey Pines has been known as a science mecca for quite some time . I know we were talking about genomes in 2003 . I'm one of the first 50 people to actually in the world to have my CLIA clinically whole genome sequenced . It's my 10 year anniversary . There you can see my actual flow cell where I have my blood and my DNA sequenced at 100x over 10 years ago and that was actually March of 2014, .

Ardy Arianpour 16:34

As you can see on the plaque here , growing up in San Diego , I was really fascinated with science and discovery , having that first job at the Salk Institute when I was 16 years old in a gene expression lab probably one of the most prestigious gene expression labs with Dr Ron Evans , who's won every single award other than the Nobel Prize . That's when I got introduced to Sanger sequencing in the late 90s and then from there when I was pre-med at UC Irvine and didn't make it through and got into commercial sales and marketing of biotech in my 20s and then became an executive in biotech and taking next-gen sequencing in the clinic with some companies in my late 20s . That really opened up my eyes to data and how data was being siloed . And because of my fast track career I was just fortunate enough being at the right place at the right time and seeing where sequencing was interoperable . From ATCG standpoint , it didn't matter what sequencer John it came from . Atcg standpoint , it didn't matter what sequencer John it came from .

Ardy Arianpour 17:48

What we didn't know is there's 4,000 plus major healthcare systems and 150,000 plus providers across our nation that are not interoperable even if they are on the same vendor , such as Epic , allscripts or Cerner the big three because there's various different versions . And so Seqster was born out of the fact that we can sponsor sequencing and stir all the other data and do some great work for both patients and researchers . What we didn't know is that we were falling on solving healthcare's number one problem , which is interoperability , and what we didn't know also was that we were innovating at the same time patient centricity with what I call patient centric interoperability . That's what Seqster has pioneered and that's our biggest differentiator . That's why folks like Aaron Berger and UBC have solved some problems that they hadn't been able to solve before , and it's because our operating system was built for not just patients , but also for researchers to have research-ready data .

John Moore 19:09

All right , thank you for breaking that all down for us . I can definitely relate to the need for that , given my own history in this area and working for one of the preeminent genetics researchers on the East Coast for my first career and learning about the importance to try to intervene early with a lot of conditions when you can diagnose it genetically . So , as is the case for many of the entrepreneurial minded , you mentioned that you lucked out with timing when you were doing the genetics work , but I think , based on some of our discussions , your initial vision for Seqster was , and maybe even is , too early for where the market was and might still be at the current time . So I already know a little bit of this , but could you share some background on the original core idea of what the company was going to do and some of the pivots you've taken along the way to keep the company independent and growing ?

Ardy Arianpour 19:59

Well said there , john , and it still is the same vision . Actually , we haven't changed the vision and mission . It's to put the person , the patient , at the center of healthcare , to disrupt and break down their data silos , bringing together their episodic electronic health records , combining it with their baseline genetic material and data , as well as adding any type of other pieces such as claims data , pharmacy data , social determinants of health data , medical devices , wearables . We're connected to 400 plus . We have nationwide access 327 million patient records that we have access to with one-click records . If you're familiar with fintech and finances and your net worth , no matter if your net worth is a dollar or a hundred billion dollars , like Bill Gates and others , it doesn't matter . Everyone has a net worth . Well , we created the mintcom of healthcare and life sciences with that same approach by bringing together your MD Anderson data , your wearable data from Garmin and Apple Watch because they don't talk to another , but through Seqster it can Four different other providers from UCSD to Stanford to Emory to NYU . We don't know where patients have data , and what's important is to offer a digital front door for both patients and researchers to connect the dots to data . We also pioneered the patient mediated method , where we are the leaders in that . It's not just HIE data that we're bringing . We have non-HIE data . It's not just FHIR data , fast healthcare interoperability resource data it's non-FHIR data . We've standardized and harmonized every single ICD-9 and ICD-10 code , rx norm and SNOMED codes and we built the data refinery on the backend . We also have access to 90 million plus claims data through our partnership with United Healthcare Group and we're one of the only companies that actually is working at that sort of level . We have partnerships with six out of the top 10 pharma companies .

Ardy Arianpour 22:18

What's so exciting is that Seqster saves lives , and one thing I do want to touch base on is that it saved my dad's life . We ran a tumor board in six hours , got his exact sciences Cologuard results , got his non-invasive liquid biopsy test from Garden Health as well as his screening test from Grail , so genomic data that is siloed , not within the EMRs . He had four different health systems . We were able to get his data on a Saturday , run a tumor board with Dan Van Hoff , the most prestigious pathologist out of TGen in Arizona , with his colleagues , and get second opinion letters to rush him into surgery at Kaiser in six business days .

Ardy Arianpour 23:05

If I wasn't the CEO of Seqster and if I didn't have access to such technology , maybe he wouldn't be alive . To be honest with you , he wouldn't be alive because how , if you know anything about cancer and how cancer metastasizes time to intervention is key , and that's the essence of real world . Data is key , and that's the essence of real-world data , that's the essence of real-world evidence , that's the essence of medicine and everything that UBC and Seqster are doing , from a medical record release standpoint to utilizing our one-click records technology to make and accelerate clinical trials , decentralized trials , hybrid trials and so forth . We couldn't be more excited that our technology is not being siloed , that is being used by , you know , incredible people that have , you know , decades of experience , like Aaron Berger , that understand the patient journey but also the researcher journey .

John Moore 24:04

Okay , thanks for telling us that personal story , Ardy . I was going to get to that in a more specific question because that was something I wanted to make sure you had an opportunity to tell , because , you know , making that personal connection always makes these founder narratives a lot more compelling and a lot more engaging . And you know , for people , personal connection always makes these founder narratives a lot more compelling and a lot more engaging . And you know , for people to survive in this industry and doing this work , you really have to have a real motivation , like a personal motivation , to stick through all the BS that healthcare throws at everyone . I don't think I've met a single founder that's been in the industry long enough to actually see success , that doesn't have a really intense personal reason for doing this work . So this next question is for both of you , now that we've set the foundation how did you two cross paths and start working together , and what was the problem that you were solving ? That catalyzes collaboration .

Aaron Berger 24:52

The way our paths crossed was we were attempting to solve a research problem for one of our clients , who wanted to answer some important questions about the safety and effectiveness of treatments in a certain category of patients with the disease , it was very difficult to identify where these patients received healthcare , so that we can enlist their healthcare providers to give us the data the real-world data that we needed from their electronic medical records . We were not getting much traction in that respect , and so , actually , that particular study was about to be canceled , and already I had come across a podcast , I think or it was maybe not a podcast , it was a interview that Ardy did . It was online , and I heard him describe his solution and I was thinking you know , that's exactly what we need in order to make a study like the one we're doing and the others that I envision in the future will be necessary come to life . I need to be able to get access to the patient's medical records when that patient decides to participate in research and contribute it without having to go through that patient's healthcare provider . And so Ardy and I had had a dialogue , we were talking about this and we brought the idea to our client .

Aaron Berger 26:17

We said , hey , we think we can do this study . Still , we know who the patients are already . We can ask them , invite them to participate , invite them to consent , and we're going to use this new solution where they'll go into a portal . It's called the Seqster solution . We introduced them to Seqster , we had them explain it and they said , yes , let's give it a shot . And by doing that we were actually able to get about 50 patients charts , we were able to produce some analysis tables and figures and a study report and we were able to perform some research that we would not have otherwise . I've got a couple other examples I'll share later and where I see this going in the future , because what we want to do next is deliver this at scale in chronic conditions over longer periods of time . But that's kind of the origin story of how we met and got started .

John Moore 27:10

Okay . So it was a very clear problem and a very specific call to action from one of your clients to make this a more efficient process . So , other than EMR data , what other data sources does Seqster bring in that have proven valuable to your clients and users ? I know that RD rattled off a few of the different ways that they ingest and kinds of data that they bring in , but what have you seen beyond the EHR information ? That's been really valuable .

Aaron Berger 27:37

So Ardy mentioned something that they recently added , which was the opt-in claims data , and that's something we'll definitely be exploring with Ardy how we could leverage that . That's an important piece of putting together a rich longitudinal history and picture of the patient's healthcare journey , because it's not always just the EMR .

Aaron Berger 28:00

It's a fundamentally important piece , but you often need to fill that in with administrative claims pharmacy and medical claims so classically , what we do for that is we will apply a process called tokenization and that places a unique identifier on a patient and you use that same algorithm to assign that identifier in other real-world data sets like pharmacy and medical claims . You match the tokens up and then you know that this patient is the same as that patient in these two different data sets . You license the de-identified data and you can bring it together

The Impact of Real-World Data on Clinical Research and Trial Recruitment

Aaron Berger 28:38

while respecting data privacy considerations . So it's very important to bring those types of data in sometimes maybe data from lab data , sometimes genomic data , and we use a lot of tokenization to facilitate that . I'll let Ardy talk about some of the things that are on his roadmap and bringing in additional sources into his universe that maybe alleviate the need or maybe make it more one-stop shop , and that's what we'll be looking to do in the future as the partnership evolves .

John Moore 29:15

Okay , thanks for defining tokenization there for me too . I was going to put that into the questions in my planning for this , but I wasn't sure if it made sense to get that technical . So thanks for addressing that directly . Ardy , did you want to add anything to that ? Aaron kind of mentioned that you might have some pipeline things that you're ready to disclose or talk to or anything else you might just want to contribute there .

Ardy Arianpour 29:35

Yeah , I think it's really important to know that we can connect to any data source and every use case , every disease , every cohort , every company , every investigator , every observational study , to preclinical , to phase one to phase four . They have their own inclusion exclusion criteria and so at Seqster we don't decide that . But what we do really well is we can go fetch it if it doesn't even exist in our operating system . For example , if there's some new medical device that just came out and someone's going to run a remote patient monitoring use case and they have a thousand patients that have this new RPM device to track XYZ data and we're bringing in electronic medical record data , which is really the bread and butter . But , as both of you just stated , it's not just EMR , ehr data that's important for real world data . Real world data really is what patients have , and every patient is different , from rare disease to cancer , oncology , to autoimmune , to a healthy individual that has a lot of trackers . All that data is characterized by us at Seqster , from our opinion , as real world data . But our interoperability engine allows you to actually request a specific data source if we're not already connected to it .

Ardy Arianpour 31:25

I'll use an example . Let's just use Jawbone as an example , people that used to have a jawbone device that's tucked away in someone's drawer , like the old palm devices before the iPad came out decades ago . Right , it doesn't mean that that data is worthless . There's some data maybe in there that is valuable , but no one's collecting that data because no one is using it . But how awesome would it be to look at that data from 2001 and compare it to Fitbit's newest device data .

Ardy Arianpour 32:06

I'm just using this as an example where you know , we didn't create Seqster to be connected to Jawbone and we don't know if patient has a Jawbone medical device . Or are they seeing a dermatologist in small town Arkansas with a very , you know , rare EMR system ? That's not one of the top 10 EMRs because we're plugged into 28 top EMRs directly . But if we're not plugged in EMRs directly , but if we're not plugged in , we've also built a statistical learning tool or some people would call it AI . I wouldn't call it AI , but I would call it more statistical learning where we can recognize , from the request of the individual , our patient mediated method to bring in any data source and fill in the gaps of data , whether that be claims data with their EMR data , whether that be social determinants of health data , whether that be you know some clinical diagnostic data that just doesn't exist , not your regular 23andMe and Ancestry data . Maybe it's a whole exome file that you had run on your child that had some sort of rare disease and you wanted a diagnostic odyssey finding of some sort .

John Moore 33:31

So that's really where that is Fantastic . Thanks for going into that detail . That's a lot of ways that you can kind of pull in that patient-generated information . What about non-biometric or non-fitness tracker data ? Do you do anything with patient-reported outcomes in any capacity ?

Ardy Arianpour 33:50

Absolutely yeah , built with actually , abbvie , one of our clients , one of the most prestigious ePro modules . So we have an ePro module built into the Seqster operating system where our partners , our customers , can actually launch that with one click to one patient or millions of lives . And what's nice about that is , while you're ingesting all of this data whether it's coming from the EMRs or your genome or your wearables or your medical devices or whatever that may be the researchers get a research admin portal where they are looking at the de-identified data . Number one , number two we were able to run analytics on that and query the database of data that they're collecting and their patients for that specific data lake that they're building for whatever study . And , more importantly , they can customize the ePro to fit not only their particular study , but they can customize it so they can launch a brand new study .

Ardy Arianpour 35:10

I'll give you a real world example . We ran a migraine observational study . They wanted 50 patients . We got them 5,433 . And in 2026 , they were going to launch some endometriosis projects . But because of the data that we got and the scale that we got it in less than three months and with our ePro module , that endometriosis study was launched within four months and and not like three years . And the reason being is because there's nothing more valuable and powerful than real time , real world data .

John Moore 35:52

Yeah , I mean that's a pretty clear ROI , being able to expedite pipeline and , you know , really speed up that trial recruitment process by three years . I mean that can be a massive gain to pharma because , as we all know , if they have a molecule or a new therapy that's ready to go into humans , that patent's already a couple of years old . So saving them three years on a patent could be billions of dollars , depending on the indication , you know , many billions of dollars , while something's still protected .

Ardy Arianpour 36:16

Exactly , absolutely , and I think it's the fact that you know the drug development process we shorten significantly because of real world data and and the work that UBC is doing with their customers utilizing Seqster's technology is a great example of that .

John Moore 36:34

So that actually gets us to the next question , which is , Aaron , what are some of the early results you've seen from working with your clients ? Can you share any other specific metrics or outcomes beyond the example that Ardy just gave us ?

Aaron Berger 36:46

Yeah . So that's a great segue because here's a good example of how a solution like Seqster and collaboration and plugging it into clinical research can accelerate drug development .

Accelerating Clinical Research Through Data Access

Aaron Berger 37:01

So at the start of of any clinical trial you need to identify patients who are eligible for that clinical trial . One of the ways you're going to uh , but it's fundamental to determining eligibility is looking at the patient's medical record . What medications are they taking that may make them ineligible for the trial ? What types of things in their medical history and confirmations and diagnosis do they have that make them eligible or not eligible ?

Aaron Berger 37:30

You have to be able to look at their records .

Aaron Berger 37:31

When a patient shows up to a site to participate in research and that site already has all their medical records , that process will go very quickly and they'll make a determine of eligibility and that patient will be enrolled or not enrolled .

Aaron Berger 37:41

But if that patient comes to a site and they do not have all the medical records at their fingertips for that patient will be enrolled or not enrolled . But if that patient comes to a site and they do not have all the medical records at their fingertips for that patient , they have to be gathered and classically this process can take a long time and it's very manual . You're talking about signing medical information release forms , sending them around to the places where that patient receives healthcare , waiting for the records to come back . Sometimes those records requests are fulfilled in 30 days or something like that , and it just slows down the entire effort of trying to get eligible patients into your study . So we see it as a really vital solution there that can be applied in site-based research could also be applied as we look at studies that maybe don't have to go through sites and you're going directly to the patients and recruiting them , kind of like the examples I cited earlier .

Aaron Berger 38:29

You still have to gather that patient's medical records if we're going direct to patient and these tools can be used to do that . So we see it playing an important role in accelerating clinical research . We also see it in an important role in accelerating real-world evidence generation . I'll give one more example of that .

Aaron Berger 38:50

Let's say you're a pharma company and you're bringing a new treatment to market in ASCVD or atopic dermatitis , and one of the initiatives that is pretty common for a lot of these companies that are doing that is that they may want to conduct what's called the disease registry and they just want to study patients with a certain disease and be able to look back at their healthcare journey a certain number of years and then follow it going forward .

Aaron Berger 39:15

And the reason they want to do that is they want to understand the treatment landscape , the treatment patterns , and then , as their new product is introduced into the market , publish data on different comparisons and looking at patients who are taking their product versus others . And we call this discretionary evidence generation to drive publications and convey potential value proposition of one treatment over another . In order to do this type of research and this is what UBC does , a lot of these large disease registries Classically in the past , you're enrolling hundreds of sites across the country and paying them to do the things I talked about earlier manual data entry . That becomes very economically impractical , but it's not necessary in today's world . In today's world , we can go direct to patients and we can find patients with atopic dermatitis or who are at risk of ASCVD and we can ask them directly if they'd like to join this disease registry , and if they do , then we can gather their medical records at scale .

Aaron Berger 40:11

We can get thousands of patients , tens of thousands of patients , if we need to , into a study like this , and now we're generating evidence that we could not have otherwise , and so this is going to help clinicians and patients understand the safety profile and the potential efficacy profile of a certain medication in ways that , in the past , would have taken much longer and been much more costly to yield that type of evidence .

John Moore 40:42

Yeah for sure .

John Moore 40:43

I think that's one of the more apparent and discussed use cases for this is that clinical trial recruitment , in particular A lot of the RWD companies I've been talking to , it's identifying where there might be pockets of individuals with these conditions that haven't really been targeted for clinical trials in the past , either because they weren't part of an AMC or they're too far away from a larger urban center where there was just a higher density of those types of patients .

John Moore 41:10

So it's definitely interesting seeing how this can , in theory , democratize and open up the access to clinical trials , both on the provider and the care organization side , as well as on that patient side . You know , giving this a more open access , patient driven framework . It does create a lot of additional opportunity to expand research efforts into new pockets , which I think is really

Predictions for Data Technologies and AI in Healthcare

John Moore 41:37

exciting . However , I was recently speaking with somebody in the CRO world who was saying that pharma as a whole is a very conservative group when it comes to decision-making and thinking about the future , which means they won't be all-in early adopters of anything until someone else proves it as an accepted standard way to practice or regulations force a change . Do you have any examples where you've seen a client or seen somebody taking a more aggressively forward thinking or innovative approach to this kind of work , where that has ended up being a competitive advantage .

Aaron Berger 42:11

Yeah , you're absolutely right . You know it is a , you know , traditionally a very conservative group and what has to happen is , you know , you usually do have to be a pilot study , a proof of concept , done in small settings , with lower stakes , and once that's proven , then it can be applied and it's slowly adopted by the organization , by pharma , and applied in other places , the . You know I won't go through it again , but but it really is the example I cited earlier where we were doing a chart review , a natural history study , important research , but you know we're not in that setting .

Aaron Berger 42:57

It wasn't a pivotal trial for for for registration . So it's a different stakes in terms of that and and once it's proven and it works . And . So so you know different stakes in terms of that and and once it's it's proven and it works and we prove , hey , we actually were able to get this patient's medical records . We didn't have to pay this healthcare provider to keystroke it all into a web-based EDC . Once you show that that works , and then word starts to spread , it then becomes , it will become more part of what people do , just like my earlier example where we were using paper for a long time , just transcribing pieces of data from a patient's paper chart into a piece of paper that went into a study database .

Aaron Berger 43:43

ultimately , paper chart into a piece of paper that went into a study database . Ultimately we got the web based CDCs . Somebody proved that it worked and then it became the universal standard . This is already starting to proliferate . We are seeing more and more of our pharma clients come to us already We'll raise the idea if they haven't . But they are coming to us and saying you know we'd like to go with a solution . They call it different things patient mediated medical record release , patient authorized medical record release , hie medical record release . They're saying we'd like to apply that technology here . We think it can help us in our efforts to identify eligible patients for a clinical trial . We think it can help us in our efforts to enroll a massive registry , starting to hear about it and understand it and ask for it . So the snowball is starting to build and grow , so to speak .

John Moore 44:36

That's good to hear . I do think the data technologies are becoming more robust . Obviously , one of the big issues for a long time was just constantly the data quality issue and the interoperability problem of trying to reconcile disparate data from disparate underlying infrastructure would cause complications .

Aaron Berger 44:53

And that's a good point , because that's still that's a really good point , because that still is the part that needs to be solved a bit . You know we're going to get this data , okay , but what formats are we getting it in ? What level of harmonization do we still need to do ? How do we address missingness , which shouldn't be a problem in real world data ? You know there's , there's certain you know procedures that were just not performed , but you have to know was it not performed or are we missing it and exists somewhere else ? So there's still a lot of Devils in the details to be solved in that respect , and that's the next step change Makes sense .

John Moore 45:31

So already you know don't want to belabor this too much because I kind of mentioned it briefly right before , but I think it'd be interesting to hear your perspective on what you might be seeing at the kind of macro level at Seqster around the interest in these types of tools for increasing access to trials and improving equity in trial recruitment , and if you actually are seeing any uptick or if you're collecting any metrics around that .

Ardy Arianpour 45:57

Yeah , so 2016, . Nobody wanted to talk to me . 2017 , we got maybe a couple phone calls from investors that were interested and we raised some funds after I put the first big chunk in myself because no one wanted to fund the dirty engineering . 2018 , we come out of stealth . Dr Eric Topol tweets that he brought his data from 1985 to present four different health systems , four different EMRs , his 23andMe data , his genetic data , his Fitbit data , his MyFitnessPal nutrition data . We didn't know that he was even tracking that information and in less than 24 hours he said step in the right direction . Same time , lots of folks find out about us because Dr Topol is so famous and his tweet got us 6 million plus hits and broke our consumer model . And then we meet Bill Gates . Bill Gates tells us to build an operating system and take an enterprise . I listened to him . That was the best advice that I got to this day on the business still from any investor or anybody . And then we do some things around Alzheimer's . With Boston University , we build the research portal , which was known as the SRP , which is the Seqster Research Portal , which now became our admin portal or staff portal for researchers and pharma sponsors and CROs .

Ardy Arianpour 47:31

2019 , we're trying to figure things out . But pharma starts knocking on our door . It was the first year . At JP Morgan Healthcare Conference , right before the pandemic , patient centricity was all over the walls in downtown San Francisco . At every single event that you went to March 2024 years ago , the pandemic hits . Everything shuts down , everything goes decentralized , everything goes hybrid kind of , but more so decentralized trials , dcts kind of take off . Pharma starts getting interested in how they can bring patients and data from home , and home health and telehealth explode 2021 , 2022, . Pharma starts partnering with us and finally we see the light at the end of the tunnel . It took like six years . I think I told you when we were showing you the Seqster demo , john , how long it took six , six and a half years to really hit that growth stage . Cro started calling us end of 2021 . We strike our partnership February of 2022 with Aaron Berger and friends at UBC .

Ardy Arianpour 48:36

We've been working with them for over two years now on multiple different studies , getting them the data that they need to run their RWE generation . And then you know , 2023 was the breakout year . We exploded . I mean , we got two dozen deals . Our technology was built by Pharma , mckinsey , deloitte , accenture , cros , patients , innovators like Dr Eric Topol and his friends . It's pretty amazing . And now , in 2024 , I think there's more opportunity that we can handle , and before we were behind the eight ball , the wind was always in our face . I'm here to tell you today on your podcast this is my 64th or 65th podcast doing this sort of thing that it's the first time I feel like the wind is behind our back and the only reason is because we actually solve not one problem , not two problems , not three problems . We solve a multitude of problems across healthcare and life sciences , and it's not just about medical record data . We have multiple different tools that we have embedded into our operating system that relates to payers , pharma , cros , consumer brands and above Super excited .

John Moore 50:02

Yeah , I can tell , I can hear that coming through . Okay , so in closing , I have one more question that I want to get to , which is getting your guys' perspective on the future . So where do you see the industry in the next five years , the next 10 years ? And then can you point to any specific inflection points on the horizon that you can foresee , given recent trends and regulatory action ?

Aaron Berger 50:27

Yeah , I can jump in on that and I saw in our pre-show notes . You really can't do a podcast on anything these days without maybe talking a little bit about .

John Moore 50:37

AI so .

Aaron Berger 50:38

I know that that's on your mind , perhaps on the listeners' minds as well .

Wrap up

Aaron Berger 50:44

You know , what we talked about today is a pretty straightforward use case , is being able to go direct a patient to do things like confirm eligibility and collect data at scale for evidence generation .

Aaron Berger 50:56

I've talked a lot about the way we've used it and we're talking mostly about structured fields that are in the EMR and things you can do with that , and then things that you can do with the unstructured field , the source notes . At this point , you know , rely mostly the way we've used it people reading those source notes and extrapolating pieces of information . So the next crank of the wheel and it's already you know it's upon us is is applying natural language processing to reading those source notes and moving far ahead in terms of what type of evidence can we derive by looking at , you know , tens of thousands , hundreds of thousands of patients worth of source notes with a given disease and being able to do that at scale , which you know natural language processing can do that humans cannot , and that will really supercharge the types of research we're able to perform , the questions we're able to ask and answer from reading tens of thousands of patients worth of source notes and just generate richer and more valuable evidence .

Aaron Berger 52:03

So it's using AI , machine learning , to read those notes and do some of this research . We're gonna see that start to happen a lot in the future , but Seqster is the . We need solutions to obtain those medical records in the first place before we can apply some language processing and LLM . So that's how we obtained the records .

John Moore 52:27

Fantastic and Ardy . What about you ? Any thoughts on the future ?

Ardy Arianpour 52:32

Yeah , you know , I've always had this dream of bringing the Netflix , amazon one-click experience to healthcare . It's kind of why I started Seqster without even knowing it , and it took a long time working with wonderful people like yourselves to understand that we are on the brink of making that happen . At the time , I wanted to empower 7.7 billion people to collect , share and own their data , and now there's 8 billion people to the planet . So 300 million plus have been added since our little journey here in eight and three months and change running Seqster here , but that still is , I think , attainable . It's a lot closer than what it was because we were early , like you stated , john , but we stuck at it , and the advantage of being early is that we know what not to do .

Ardy Arianpour 53:31

I think we're getting closer to folks really understanding their health journey in a way that was non-imaginable or not just for the innovators , and I believe that every single person let's just take it looking at just the United States should have access to being enrolled in a clinical trial , and I think our technology is well positioned to make that happen for CROs , for pharmacies , for payers , for pharma companies and all the above , because it is an experience . It's not just data in , data out . This isn't just an API . Those things are actually worthless to me and that's why we focused on the patient . The patient centricity and the innovation is not going to go away and we're really excited about the next phase of where our clients are going to take our technology . Of course , ai is meaningless without data .

John Moore 54:44

Very true . I mean that's . The other thing with all this data is that healthcare generates more data than any other enterprise sector . Right now , I think it's growing at roughly 30% a year . Part of that's also because it's late to the game , but it doesn't change the fact that it's also generating massive amounts of new data every year , and so , as we look at the future of healthcare , it has to be data driven . It's the only thing that we haven't really been able to do at scale until the modern era , and it'll be very .

John Moore 55:12

I'm just really excited to see what comes of all that , as we start to identify new patterns , and these new AI tools that we're bringing to market are good at pattern recognition , right , so they're going to be able to identify the patterns that we haven't been able to see before because we haven't had that perspective , and so population health and public health could have some very significant changes to how that's perceived in the coming decade or two . All right , so thank you so much , both of you , for joining us today as we explore one of the primary use cases for real-world data and real-world evidence , which is the clinical research process . If any of our listeners want to learn more , how should they go about reaching out to either of ?

Aaron Berger 55:52

you ? Yeah , sure , aaron Berger at ubccom is probably the best way to reach me and , yeah , I'm happy to talk . We're passionate about , as I mentioned in the beginning , helping pharma , biotech answer important questions about the safety and effectiveness of medicines . We're always really excited about having collaborative thought , partnership discussions on what is the best way to design a study to do that , which technologies should we bring in , and those are fun discussions . So if anybody would like to have that discussion , yeah , definitely reach out to me at my email address .

John Moore 56:33

Okay , and Ardy .

Ardy Arianpour 56:34

Yeah , and I'm Ardy at Seqster . com S-E-Q-S-T-E-R dot com , and you can also connect with me on LinkedIn . I'm very active on LinkedIn and so I'm a LinkedIn junkie and it's probably a better way , while I'm traveling , to connect with me if you can't catch me on email or if I don't get back to you , but I usually get back to people and love having any type of conversation with anyone that resides around data , interoperability , trials , innovation and all the above . Thanks so much , john , for having me , and Aaron .

Aaron Berger 57:10

Yeah , thanks , john for having me .

John Moore 57:12

Yeah , this has been fantastic . So thank you so much for being part of this and joining us today and sharing your expertise with our community . To continue following along with these conversations , be sure to subscribe to the Chilcast on your favorite podcasting app and follow our work on LinkedIn and or via our newsletter . Feel free to reach out with any questions and suggestions for future guests or topics to feature on the show , and thank you for tuning in .