0:00

What we were able to do once with Ardy and

0:02

Seqster is we didn't need to go out to all those

0:04

patients' healthcare providers . We already knew who the patients

0:06

were . We could go directly to the patients and ask them if

0:08

they'd like to contribute their data to research , and

0:10

if they consented to that , we were able to gather

0:13

up their medical records so you're

0:15

able to do a study that otherwise just would not

0:17

have been very possible . That's the big

0:19

evolution we've seen . I call it

0:21

patient , you know , patient agency over their medical record

0:23

. Their ability to do so will transform

0:26

the way we execute all

0:29

sorts of research , from pre-registrational

0:31

through post-approval , and we're just starting

0:33

to see some of that .

0:35

Welcome back to the Chilcast , a healthcare

0:37

podcast from Chilmark Research , helping

0:40

healthcare leaders make the best decisions

0:42

for the populations they serve . Welcome

0:50

back to the Chilcast . I'm the managing

0:52

partner of Chilmark Research and your host

0:54

, John

1:01

Moore III . Chilmark Research is a healthcare technology industry analyst firm founded

1:03

in 2007 to provide objective , expert research and guidance

1:05

on those emerging data-driven tools and services with

1:07

the potential to fundamentally improve the experience

1:09

of care for all . If you are new

1:12

to the show or a return listener and you appreciate

1:14

our programming , don't forget to subscribe on your

1:16

favorite podcast service and leave us a review . If

1:19

you have a topic you'd like to hear us discuss on the show

1:21

or questions about how to participate , shoot

1:24

us a message at podcasts at chilmarkresearch . com

1:27

. For this episode

1:29

, we're going back to standard programming after

1:31

our initial six-part miniseries for the Health

1:33

Impact Project . Be sure to check

1:35

those episodes out , because we had some fantastic

1:38

discussions around defining healthcare

1:40

value and what the value

1:42

is of health IT In

1:44

the fall of 2023, . We

1:46

released our first real-world data real-world

1:49

evidence market trends report . I

1:51

will soon be following that up with the accompanying

1:53

buyer's guide report that goes into more detail

1:55

about the differentiators between solution developers

1:58

, how customers can think about defining

2:00

their specific use cases , to narrow the field

2:02

of options , best practices

2:04

and pitfalls to avoid , and thorough vendor

2:06

profiles . This will

2:08

be our second podcast on the topic of

2:10

RWD and I am pleased to be joined

2:12

by Aaron Berger and Ardy Arianpour

2:14

for this discussion about the evolving nature

2:16

of clinical research and therapeutics development

2:18

.

2:19

Hi there , it's great to be here . Thanks , john , for

2:21

inviting us on the podcast .

2:23

Hi everyone , so nice to be here

2:25

. Happy Friday .

2:27

My pleasure . I'm really looking forward to this conversation

2:29

. So for some background

2:32

, aaron Berger is the Senior

2:34

Vice President and Head of Evidence Development

2:36

Solutions for UBC

2:38

that designs

2:40

and executes modernized solutions for

2:43

leveraging forward-leaning technology and

2:45

research designs that deliver fit-for-purpose

2:47

evidence on the safety and effectiveness

2:50

of treatments for patients . Mr

2:53

Berger joined the UBC team in 2003

2:55

and has since served in a range of diverse

2:58

roles , leveraging global capabilities in

3:00

the execution of PERI and post-marketing

3:02

product development programs and real-world

3:04

evidence generation . He is

3:07

responsible for the development of real-world data

3:09

and real-world evidence technology architectures

3:11

and decentralized research solutions to

3:13

drive greater insight into the safety , profile

3:15

and value proposition of medicines . Ardy

3:19

is the CEO and co-founder of Seqster

3:21

, a healthcare technology company breaking down

3:24

health data silos at scale . Its

3:26

enterprise operating system aggregates disparate

3:28

health data sources into a single 360-degree

3:31

view of a patient in real time , producing

3:34

research-ready longitudinal health records

3:36

for use by pharma providers and payers

3:38

. Ardy is a serial

3:40

entrepreneur in life sciences and digital

3:42

health . Prior to starting Seqster

3:44

, Ardy

3:50

launched several clinical and consumer-based genetics tests as chief commercial

3:52

officer of Pathway Genomics and SVP and GM of Genomics at Ambry Genetics , which

3:54

sold to Conica for $1 billion in 2017

3:57

. As a key player

3:59

in the landmark SCOTUS decision scrapping

4:01

gene patents , which would be 2013's

4:04

Association for Molecular Pathology versus

4:06

Myriad Genetics . Ardy played an instrumental

4:09

role in expanding genetic testing access

4:11

with the launch of BRCA testing , and

4:13

benefiting patients and family members across

4:16

the country . He has been recognized

4:18

with many awards over his career , most

4:20

recently as a MedTech World

4:22

Rising Star in 2022 and

4:25

won a Pharma Vo's 100 Inspiring Leaders

4:27

in Life Sciences in 2023

4:29

. All right , so , aaron

4:31

, thank you for being here with us today for this very

4:34

timely and important conversation

4:36

. First off , let's frame

4:38

the whole area a little bit . What

4:40

does your work consist of as UBC's

4:42

Head of Evidence Development Solutions ? Yeah , john , so you kind of mentioned it in the

4:44

intro you did for me .

4:45

Thanks for that . What does your work consist of as UBC's Head of Evidence Development

4:47

Solutions ? Yeah , john , so you kind of mentioned it in the intro . You did for me . Thanks

4:49

for that . You know what we're here

4:52

to do is answer important questions about

4:54

the safety and effectiveness of

4:56

medicines , and oftentimes we're

4:58

doing that within the context of the post-approval

5:01

requirement that's , from a regulator

5:03

, from FDA , from EMA post-approval

5:06

requirement that's from a regulator , from FDA , from EMA . Other times we're doing it within the

5:08

context of the sponsor , of the manufacturer's desire

5:11

to learn more about the

5:13

safety profile and the

5:15

value proposition of their medication

5:18

.

5:19

Can you share either

5:22

yours or UBC's definition of

5:24

real-world data and evidence for our audience

5:26

?

5:28

Yeah , absolutely so . In

5:30

2018 , FDA released

5:32

a real-world evidence framework , and that's

5:34

kind of what the industry has aligned around

5:36

the definitions

5:39

and standards that are outlined there , and

5:42

that document frames it like this Real-world

5:44

data are any data relating to

5:46

the patient's health status and delivery

5:49

of healthcare . They're routinely collected

5:51

through a variety of sources

5:54

, and examples of

5:56

real-world data include electronic health records

5:58

, claims data , data

6:01

that may come from registries or

6:03

data from digital health technologies

6:05

. Real-world evidence

6:07

is the evidence that's

6:10

curated and generated about the usage

6:12

of , or potential benefit of

6:14

, a treatment for a patient that you derive

6:16

from analysis of that real-world

6:18

data .

6:20

And what are some sources of real-world data

6:23

and examples that you can share ? And what are some sources

6:25

of real-world data and examples ?

6:25

that you can share . So you know , here today we're going to mainly talk about

6:28

electronic medical records

6:30

, but you

6:33

know it goes beyond

6:35

that . It also goes into claims . It goes into

6:37

a patient's activity

6:40

and their health status that you may collect

6:42

from wearables . It may relate

6:45

to quality of life or patient

6:47

reported outcomes that a patient is directly

6:50

reporting , but

6:59

the research fundamentally requires that you get access to a patient's

7:01

medical record in order to answer the questions that we're trying

7:03

to answer about the safety and effectiveness of treatments

7:05

.

7:06

Okay , yeah , so that you know

7:08

what's actually happening in the wild , what's happening when

7:10

you have some awareness

7:13

of a patient's full medical

7:15

history , as much as you could collect it . So

7:17

can you tell me how that information has been collected

7:20

historically ? How have you guys gone about

7:22

bringing in , you know , a patient's records and their

7:24

medical history ?

7:29

Yep , definitely . And that kind of really leads us into how I got introduced to Ardy

7:31

and how we started collaborating so classically

7:34

for decades . The

7:36

way that you acquire

7:39

patients' medical records for clinical research or post-marketing

7:41

evidence generation medical records for clinical research or post-marketing

7:43

evidence generation is

7:45

you enlist the healthcare provider . It's

7:48

been very healthcare provider

7:50

mediated , I would say , for

7:53

decades , and

7:55

those healthcare providers are enlisted

7:57

as sites in a clinical research

7:59

study or in a post-approval

8:02

study or registry . The

8:07

healthcare provider has roles such as a study coordinator , and

8:09

those study coordinators and investigators literally

8:13

take data that have been from a patient's

8:15

chart and they would transcribe it into

8:17

a case report form that we use for our research

8:20

purposes . That

8:22

evolved a couple of decades ago and it evolved

8:24

into web-based electronic data capture

8:27

systems and that involves

8:29

keystroking data from a patient's

8:31

chart into the

8:33

case report form , into the study database , if

8:35

you will , and that's how we've

8:37

been conducting research , but it always

8:40

relied upon going through the health

8:42

care provider . So

8:44

I've been very interested for a number

8:46

of years in how do we

8:49

perhaps disintermediate the health care provider

8:51

from that process and really relieve

8:53

them of that burden and

8:56

get directly to the source of

8:58

the real world data that we're looking for in this case

9:00

electronic medical records and do

9:02

that through the patient's

9:04

ability to take agency over their own record and

9:07

decide if they want to contribute it to research

9:09

.

9:09

So it sounds like the old process was very labor intensive

9:12

and relied a lot on your ability

9:14

to bring in human capital to actually

9:16

do that transcription and manual abstraction

9:18

. Before we get to Ardy and your guys' kind

9:20

of backstory , I have a couple of quick other questions

9:23

to kind of frame this conversation

9:25

and what you've seen in your career . So

9:27

you've been with UBC since 2003

9:29

, which is surprising loyalty

9:31

to a single company in this day and age . It's

9:34

also the year that I graduated from high school , which

9:36

isn't really dating you because you're only a couple of years older than

9:38

me , but still . I mean just kind of putting it in

9:40

perspective . It was also the year

9:42

that the Human Genome Project was completed

9:44

and we had actually fully sequenced the human genome

9:46

. So it was only a few years

9:48

before that that everyone had been losing their minds about

9:51

Y2K and clearly a lot has changed

9:53

in that period of time across

9:55

many areas that touch clinical research and

9:58

quite notably the first true

10:00

global pandemic in the modern era . You

10:02

know , in a couple generations we haven't seen anything

10:04

like that . So the way that clinical

10:07

research is being done and new therapies are

10:09

being developed has really seen

10:11

a whole host of technological innovation

10:13

, scientific innovations and just

10:15

social disruption that will have touched

10:18

your career in some capacity . So can you

10:20

share a few highlights of the most significant

10:22

changes you've observed in terms of how

10:24

this is practically impacting the work that you guys

10:26

do , and maybe some areas where things

10:28

haven't advanced as much as you would have liked or

10:30

expected to see at this point ?

10:32

Yeah , definitely that's a fun thing

10:34

to think about and reflect on . So

10:36

when I started in this industry in about 2003

10:39

, I had roles called a CRA Clinical

10:42

Research Associate . I had roles called a CRA Clinical

10:44

Research Associate and what that meant was I

10:46

would go fly around to these sites , those healthcare

10:48

providers that I talked about who are listed in in

10:50

a research , in a study , and

10:53

they would have these big

10:55

walls , these big columns of paper charts

10:58

and manila folders and they would

11:00

roll on tracks and you'd use cranks to get to the medical records you're looking

11:02

for and the sites would pull these tracks and you'd use cranks to get to the medical

11:05

records you're looking for and the sites would pull

11:07

these out and we would literally take the paper

11:09

, the patient's chart , and I would review

11:11

what was in the chart and I would review what

11:14

the site had transcribed into the case

11:16

report form , which was ultimately became

11:18

the study database , and to see

11:20

if those things matched up . Then , if

11:22

they did , I would take the case report form

11:24

, which was two part , you know NCR paper

11:26

, take the top page off and I put all

11:28

those pages in a FedEx envelope and

11:31

send it off back to the home office

11:33

where that data would be either

11:35

scanned or manually entered

11:38

into a study database , and that's how we were

11:40

performing , you know , research

11:42

and , remarkably

11:44

, things

11:46

well , things changed . So

11:49

then you had an evolution , about three or

11:51

four years into my career , that the paper went

11:53

away and you started doing that on a web-based

11:55

electronic data capture platform . So we're

11:58

keystroking the data into an EDC

12:00

, directly into the database , and

12:02

that was a big leap forward , to

12:05

be sure , but we're still relying

12:07

on manual transcription . We're still relying

12:09

on sites to do this

12:11

. It has scalability and

12:14

quality issues associated with it

12:17

. And then fast

12:20

forward up to the 21st Century Cures

12:22

Act and the FDA framework on real

12:25

world evidence and disruption

12:27

from technology providers like Seqster

12:29

, and what happens is we now

12:31

are gaining the ability to get

12:33

at those medical records without having

12:35

to sit somebody down at a console , look at two

12:37

screens and punch data in from one

12:39

place to another . And

12:42

so we're just kind of at the dawn of that . We've used

12:44

these types of solutions several times in

12:46

partnership with Ardy , and

12:49

what it allows you to do is

12:51

conduct some research that otherwise would not have

12:53

been possible . I'll give you one example

12:55

when you're trying to do research

12:57

in a rare disease , as

12:59

one of our clients was , and

13:02

you would , literally , if you wanted to do this type of study

13:04

it's a chart review or natural history study you'd

13:06

have to go to find 50 patients

13:08

to get quality data on . You'd have

13:11

to go out to 50 different sites , different

13:13

healthcare providers , and ask them to

13:15

execute that enterprise . I just

13:17

told you about public manual data entry and

13:19

sometimes that's just not economically

13:22

practical and the sponsor is

13:24

going to decide not to do that type of research

13:26

in that setting . But what

13:28

we were able to do once with Ardy

13:30

and Seqster is we

13:32

didn't need to go out to all those patients' healthcare

13:34

providers . We already knew who the patients were . We could go directly

13:37

to the patients and ask them if they'd like to contribute their

13:39

data to research , just as they would in any study that

13:41

they would decide to participate in . And

13:43

if they consented to that and enrolled

13:45

through the CIGSTER portal , we were

13:48

able to gather up their medical records . So

13:50

you're able to do a study that otherwise just would

13:52

not have been very possible . So we think

13:54

it's a really important part of research

13:57

into rare diseases and

13:59

small populations

14:01

that are dispersed . Using

14:04

these types of methodologies are gonna be very

14:06

important . It's also gonna be important

14:08

for chronic disease and high volumes

14:11

of patients . But that's the big evolution

14:14

we've seen . I call it patient agency

14:16

over their medical record . Their ability

14:18

to do so will transform the

14:20

way we execute all

14:23

sorts of research , from pre-registrational

14:25

through post-approval , and we're just starting

14:28

to see some of that .

14:30

Yeah , no , it's fantastic seeing how

14:33

actually having this all digitized and then moved

14:35

to the cloud is opening up new ways for patients

14:37

to have that autonomy and that ownership

14:39

over their own health histories . I'm

14:42

seeing a few different companies that are doing

14:44

various aspects of the real world data solution

14:47

development as part of this research effort that

14:49

I'm currently engaged in , but very

14:51

few are taking that patient forward

14:54

, patient centric approach where they're the ones driving

14:56

the ship and then they also get something out of it on the back

14:58

end . So , Ardy

15:01

, that kind of brings you back into the conversation

15:03

. The first time we spoke

15:05

, you and I bonded around our mutual past

15:07

lives working benchside in genetics labs . You

15:10

have me beat by a few years as well , but we're practically

15:12

contemporaries and have both ended up here in the

15:14

brave new world of modern healthcare informatics

15:16

. Can you give us the quick backstory

15:19

of how your early career in genetics research

15:21

led you to found Seqster later on ?

15:28

Yeah , first off , thanks so much , John , for having

15:30

myself and our wonderful partner , UBC , Aaron Berger , here . You

15:33

know I started out when I was 16

15:35

years old and I was lucky enough to grow up

15:37

in the beautiful and finest

15:39

city in America called San Diego

15:41

, and if you know anything about San Diego

15:44

, we have some prestigious

15:46

, amazing universities and

15:49

there's lots of different institutions

15:51

that bring innovation

15:53

out , because of the

15:56

marine biology , I guess , and the

15:58

great , you know , surf that exists in

16:00

La Jolla . Here . La Jolla and Torrey

16:02

Pines has been known as a

16:04

science mecca for quite some

16:06

time . I know we were talking about genomes

16:10

in 2003 . I'm

16:12

one of the first 50 people to actually

16:14

in the world to have my CLIA

16:17

clinically whole genome sequenced . It's

16:19

my 10 year anniversary . There

16:21

you can see my actual flow

16:23

cell where I have

16:25

my blood and my DNA sequenced

16:29

at 100x over 10 years

16:31

ago and that was actually March

16:33

of 2014, . As you can see on the plaque

16:36

here , growing up in San Diego , I was really

16:40

fascinated with science

16:42

and discovery , having

16:44

that first job at the Salk Institute when

16:47

I was 16 years old in a gene

16:49

expression lab probably one of the most prestigious

16:51

gene expression labs with Dr

16:54

Ron Evans , who's won every single

16:56

award other than the Nobel Prize . That's

16:58

when I got introduced to Sanger sequencing

17:00

in the late 90s and

17:02

then from there when I was pre-med at

17:05

UC Irvine and didn't make

17:07

it through and got into commercial

17:09

sales and marketing of biotech

17:11

in my 20s and then became an executive

17:14

in biotech and

17:16

taking next-gen sequencing in the clinic

17:18

with some companies in my late 20s

17:20

. That really opened up my

17:22

eyes to data and

17:25

how data was being siloed . And

17:27

because of my fast track career I was just

17:29

fortunate enough being at the right

17:31

place at the right time and

17:34

seeing where sequencing

17:37

was interoperable . From

17:39

ATCG standpoint , it didn't

17:42

matter what sequencer John

17:44

it came from . Atcg

17:46

standpoint , it didn't matter what sequencer John it came from . What we didn't know is there's

17:49

4,000 plus major healthcare systems and 150,000

17:52

plus providers across our nation

17:54

that are not interoperable

17:57

even if they are on

17:59

the same vendor , such

18:01

as Epic , allscripts or Cerner

18:03

the big three because there's various different

18:05

versions . And so Seqster

18:08

was born out of the fact

18:10

that we can sponsor

18:12

sequencing and stir

18:15

all the other data and

18:17

do some great work for

18:20

both patients and researchers . What

18:22

we didn't know is that we were

18:24

falling on solving

18:26

healthcare's number one problem

18:29

, which is interoperability

18:31

, and what we didn't know also

18:33

was that we were innovating at the same

18:35

time patient centricity

18:38

with what I call patient

18:40

centric interoperability . That's

18:43

what Seqster has pioneered

18:45

and that's our biggest differentiator

18:47

. That's why folks like Aaron

18:49

Berger and UBC have

18:52

solved some problems that they hadn't

18:54

been able to solve before , and

18:57

it's because our operating system

18:59

was built for not

19:01

just patients , but also

19:04

for researchers to have research-ready

19:07

data .

19:09

All right , thank you for breaking that all down for us . I

19:11

can definitely relate to the

19:13

need for that , given my own history in this

19:16

area and working for one of the preeminent

19:18

genetics researchers on the East Coast

19:20

for my first career and learning

19:22

about the importance to try

19:25

to intervene early with a lot of conditions when you can diagnose

19:27

it genetically . So

19:29

, as is the case for many of the entrepreneurial

19:31

minded , you mentioned that you

19:33

lucked out with timing when you were doing the genetics work

19:35

, but I think , based on some of our discussions

19:38

, your initial vision for Seqster was

19:40

, and maybe even is , too early for

19:42

where the market was and might still

19:45

be at the current time . So

19:47

I already know a little bit of this , but could you share

19:49

some background on the original core

19:52

idea of what the company was going to do and some

19:54

of the pivots you've taken along the way to keep the company

19:56

independent and growing

19:58

?

19:59

Well said there , john , and

20:01

it still is the same vision . Actually

20:03

, we haven't changed their vision and mission . It's

20:06

to put the person , the patient

20:08

, at the center of healthcare , to disrupt

20:11

and break down their data

20:13

silos , bringing together their episodic

20:15

electronic health records

20:17

, combining it with their baseline

20:20

genetic material and

20:22

data , as well as adding any

20:24

type of other pieces such as claims

20:27

data , pharmacy data , social

20:29

determinants of health data , medical

20:31

devices , wearables . We're

20:33

connected to 400 plus . We

20:36

have nationwide access 327

20:38

million patient records that we have access

20:40

to with one-click records . If

20:43

you're familiar with fintech

20:45

and finances and

20:48

your net worth , no matter if

20:50

your net worth is a dollar or a

20:52

hundred billion dollars , like Bill Gates

20:54

and others , it doesn't matter . Everyone has a

20:56

network . Well , we created the

20:58

mintcom of healthcare

21:01

and life sciences with that same

21:04

approach by bringing together your

21:06

MD Anderson data , your

21:08

wearable data from Garmin and Apple Watch

21:10

because they don't talk to another , but through Seqster

21:13

it can Four

21:15

different other providers from UCSD

21:17

to Stanford to Emory to NYU

21:20

. We don't know where patients have

21:22

data , and what's important is

21:25

to offer a digital

21:27

front door for both

21:29

patients and researchers to

21:31

connect the dots to

21:33

data . We also pioneered the patient

21:35

mediated method , where we are

21:37

the leaders in that . It's

21:40

not just HIE data that we're bringing

21:42

. We have non-HIE data . It's not just

21:44

FHIR data , fast healthcare interoperability

21:47

resource data it's non-FHIR

21:49

data . We've standardized and harmonized

21:51

every single ICD-9 and ICD-10

21:53

code , rx norm

21:55

and SNOMED codes

21:57

and we built the data refinery

22:00

on the backend . We also have access

22:02

to 90 million plus claims

22:04

data through our partnership with United Healthcare

22:07

Group and we're one of the only

22:09

companies that actually is working

22:11

at that sort of level . We

22:14

have partnerships with six out of the top 10

22:16

pharma companies . What's

22:18

so exciting is that

22:21

Seqster saves lives , and

22:23

one thing I do want to touch base on

22:25

is that it saved my dad's life . We

22:27

ran a tumor board in six hours , got

22:30

his exact sciences Cologuard results

22:33

, got his non-invasive liquid biopsy

22:35

test from Garden Health as well as

22:37

his screening test from Grail , so

22:39

genomic data that is siloed

22:41

, not within the EMRs . He

22:44

had four different health systems . We

22:46

were able to get his data

22:48

on a Saturday , run

22:51

a tumor board with Dan Van Hoff , the

22:53

most prestigious pathologist out

22:55

of TGen in Arizona , with

22:58

his colleagues , and get second opinion

23:00

letters to rush him into surgery

23:02

at Kaiser in six business days

23:04

. If I wasn't the CEO of Seqster

23:06

and if I didn't have access to

23:08

such technology , maybe he wouldn't

23:11

be alive . To be honest with you , he wouldn't

23:13

be alive because how , if you know anything

23:15

about cancer and how cancer metastasizes

23:17

time to intervention is

23:20

key , and that's the essence

23:22

of real world . Data is key , and that's the

23:24

essence of real-world data

23:26

, that's the essence of real-world

23:28

evidence , that's

23:32

the essence of medicine and everything that UBC and Seqster are doing , from a medical record

23:34

release standpoint to utilizing our one-click records

23:37

technology to make

23:39

and accelerate clinical

23:41

trials , decentralized trials , hybrid

23:44

trials and so forth . We couldn't be more

23:46

excited that our technology is

23:48

not being siloed , that is being

23:50

used by , you know

23:52

, incredible people that have , you

23:54

know , decades of experience , like

23:56

Aaron Berger , that understand the

23:59

patient journey but also the researcher

24:01

journey .

24:04

Okay , thanks for telling us that personal story , Ardy

24:06

. I was going to get to that in a more

24:08

specific question because that was something I wanted to make sure

24:10

you had an opportunity to tell , because

24:12

, you know , making that personal connection always makes

24:14

these founder narratives a lot more compelling

24:16

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

24:19

know , for people to survive in this

24:21

industry and doing this work , you really have to have

24:23

a real motivation

24:25

, like a personal motivation , to stick

24:27

through all the BS that healthcare throws

24:29

at everyone . I

24:31

don't think I've met a single founder that's been in the industry

24:34

long enough to actually see success , that doesn't have

24:36

a really intense personal reason for doing

24:38

this work . So

24:40

this next question is for both of you , now

24:42

that we've set the foundation how did you two cross

24:44

paths and start working together , and

24:46

what was the problem that you were solving

24:49

? That catalyzes collaboration .

24:52

The way our paths crossed was we

24:55

were attempting to solve a research

24:57

problem for one of our clients , who

25:00

wanted to answer some important

25:03

questions about the safety and effectiveness of

25:05

treatments

25:07

in a certain category of patients

25:09

with the disease , it

25:13

was very difficult to identify where

25:15

these patients received healthcare , so

25:17

that we can enlist their healthcare providers to give

25:19

us the data the real-world data that we needed

25:21

from their electronic medical records . We

25:23

were not getting much traction in

25:26

that respect , and so

25:28

, actually , that particular study

25:30

was about to be canceled , and

25:33

already I had come across

25:35

a podcast , I think or it was maybe

25:38

not a podcast , it was a interview

25:41

that Ardy did . It was online , and

25:45

I heard him describe his solution

25:47

and I

25:49

was thinking you know , that's exactly what we need

25:51

in order to make a study like the one we're

25:53

doing and the others that I envision in the future will be

25:55

necessary come to life

25:57

. I need to be able to get access

25:59

to the patient's medical records when

26:01

that patient decides to participate in

26:03

research and contribute it without having

26:06

to go through that patient's healthcare

26:08

provider . And so

26:10

Ardy and I had had a

26:12

dialogue , we were talking about this and

26:15

we brought the idea to our

26:17

client . We said , hey , we think we

26:19

can do this study . Still , we

26:21

know who the patients are already

26:23

. We can ask them , invite them to participate

26:25

, invite them to consent , and

26:27

we're going to use this new solution

26:29

where they'll go into a portal . It's

26:32

called the Seqster solution . We introduced

26:34

them to Seqster , we had them explain it and

26:36

they said , yes , let's give it a shot . And

26:39

by doing that we were actually able to get

26:41

about 50 patients charts , we

26:43

were able to produce some analysis

26:45

tables and figures and a

26:48

study report and we

26:51

were able to perform some research that we would not

26:53

have otherwise . I've got a couple other examples

26:55

I'll share later and

26:57

where I see this going in the future , because what we

26:59

want to do next is deliver this at

27:02

scale in chronic conditions

27:04

over longer periods of time . But

27:06

that's kind of the origin story of how we met

27:08

and got started .

27:10

Okay . So it was a very clear problem

27:13

and a very specific call

27:15

to action from one of your clients to make this a more

27:18

efficient process . So

27:20

, other than EMR data , what other data sources

27:23

does Seqster bring in that have proven valuable

27:25

to your clients and users ? I know that RD

27:28

rattled off a few of the different ways that they ingest

27:30

and kinds of data that they bring in , but what

27:32

have you seen beyond the EHR

27:34

information ? That's been really valuable .

27:37

So Ardy mentioned something

27:39

that they recently added , which

27:42

was the opt-in claims data

27:44

, and that's something we'll definitely

27:46

be exploring with Ardy how we could

27:48

leverage that . That's an important piece of

27:50

putting together a rich longitudinal

27:53

history and picture of the patient's

27:55

healthcare journey , because it's not

27:57

always just the

27:59

EMR . It's a fundamentally important piece , but

28:02

you often need to fill that in

28:04

with administrative claims

28:06

pharmacy and medical claims so

28:09

classically , what we do for that

28:11

is we will apply

28:13

a process called tokenization and

28:16

that places a unique identifier

28:19

on a patient and you use that same

28:21

algorithm to assign that

28:23

identifier in other

28:25

real-world data sets like pharmacy and medical

28:27

claims . You match the tokens up and

28:30

then you know that this patient is the same as that

28:32

patient in these two different data sets . You license

28:34

the de-identified data and you

28:37

can bring it together while

28:39

respecting data privacy

28:41

considerations . So it's

28:44

very important to bring those types of data

28:46

in sometimes maybe data from

28:48

lab data , sometimes genomic data , and

28:51

we use a lot of tokenization to

28:53

facilitate that . I'll

28:56

let Ardy talk about some of the things that

28:58

are on his roadmap and bringing in additional

29:02

sources into his universe that maybe

29:04

alleviate the need or maybe make it more one-stop

29:07

shop , and that's what we'll be looking to do in

29:09

the future as the

29:11

partnership evolves .

29:15

Okay , thanks for defining tokenization there

29:17

for me too . I was going to put that into the

29:19

questions in my planning for this , but I wasn't sure

29:21

if it made sense to get that technical . So thanks for

29:23

addressing that directly . Ardy

29:25

, did you want to add anything to

29:27

that ? Aaron kind of mentioned that you might have some pipeline

29:30

things that you're ready to disclose or talk

29:32

to or anything else you might just want to

29:34

contribute there .

29:35

Yeah , I think it's really important to know

29:37

that we can connect

29:40

to any data source and

29:43

every use

29:46

case , every disease

29:48

, every cohort , every

29:50

company , every investigator

29:53

, every observational study , to

29:55

preclinical , to phase one to phase four

29:58

. They have their own inclusion exclusion

30:00

criteria and so

30:02

at Seqster we don't decide that

30:04

. But what we do

30:06

really well is

30:09

we can go fetch

30:11

it if it doesn't even

30:13

exist in our operating

30:15

system . For example , if

30:18

there's some new medical device

30:20

that just came out and someone's

30:22

going to run a remote patient monitoring

30:25

use case and they have

30:27

a thousand patients that

30:29

have this new RPM device to track

30:31

XYZ data and

30:34

we're bringing in electronic

30:37

medical record data , which is really the bread

30:39

and butter . But , as both of you

30:41

just stated , it's not just

30:44

EMR , ehr data that's

30:46

important for real world data . Real

30:48

world data really is what

30:51

patients have

30:53

, and every patient is

30:56

different , from rare disease to

30:58

cancer , oncology , to

31:00

autoimmune , to a healthy individual

31:02

that has a lot of trackers . All

31:05

that data is characterized

31:07

by us at Seqster , from our

31:09

opinion , as real world data . But

31:12

our interoperability engine allows

31:15

you to

31:17

actually request a

31:20

specific data source if

31:22

we're not already connected to

31:24

it . I'll use an example . Let's

31:26

just use Jawbone

31:28

as an example , people

31:39

that used to have a jawbone device that's tucked away in someone's drawer , like the old

31:41

palm devices before the iPad came out decades ago . Right

31:43

, it doesn't mean that that data

31:46

is worthless . There's

31:48

some data maybe in

31:50

there that is valuable

31:52

, but no one's collecting

31:54

that data because no one is using it . But

31:56

how awesome would it be to

31:59

look at that data from 2001

32:01

and compare it to Fitbit's

32:04

newest device data . I'm

32:06

just using this as an example where

32:08

you know , we didn't create Seqster

32:11

to be connected to Jawbone and

32:13

we don't know if patient has

32:16

a Jawbone medical device

32:18

. Or are they seeing a dermatologist

32:21

in small town Arkansas with

32:23

a very , you

32:25

know , rare EMR system ? That's

32:27

not one of the top 10 EMRs

32:30

because we're plugged into 28 top EMRs directly . But

32:32

if we're not plugged in EMRs directly , but

32:34

if we're not plugged in , we've

32:36

also built a statistical learning

32:39

tool or

32:46

some people would call it AI . I wouldn't call it AI , but I would

32:48

call it more statistical learning where we can recognize , from

32:51

the request of the individual

32:53

, our patient mediated method to

32:56

bring in any data source and

32:58

fill in the gaps of data , whether

33:00

that be claims data with their EMR

33:03

data , whether that be social

33:05

determinants of health data , whether

33:08

that be you know some clinical

33:10

diagnostic data that just doesn't exist

33:12

, not your regular 23andMe and

33:14

Ancestry data . Maybe

33:16

it's a whole exome file that

33:19

you had run on your

33:21

child that had some sort

33:24

of rare disease and you wanted

33:26

a diagnostic odyssey finding

33:30

of some sort .

33:31

So that's really where that is Fantastic

33:34

. Thanks for going into that detail . That's a lot of

33:36

ways that you can kind of pull in that patient-generated

33:39

information . What about non-biometric

33:42

or non-fitness tracker data ? Do

33:45

you do anything with patient-reported outcomes in

33:48

any capacity ?

33:50

Absolutely yeah , built

34:03

with actually , abbvie , one of our clients , one of the most prestigious ePro modules

34:05

. So we have an ePro module built into the Seqster operating

34:07

system where our partners

34:09

, our customers , can actually

34:12

launch that with one click to

34:15

one patient or millions

34:17

of lives . And what's

34:19

nice about that is , while you're

34:21

ingesting all of this

34:24

data whether it's coming from the

34:26

EMRs or your genome or your

34:28

wearables or your medical devices or

34:30

whatever that may be the

34:32

researchers get a research

34:34

admin portal where

34:37

they are looking at the de-identified data

34:40

. Number one , number two we

34:42

were able to run analytics on that

34:45

and query the database

34:47

of data that they're collecting

34:50

and their patients for that specific

34:52

data lake that they're building for

34:54

whatever study . And , more

34:56

importantly , they

34:59

can customize the

35:01

ePro to fit not

35:03

only their particular study , but

35:06

they can customize it so they can

35:08

launch a brand new study . I'll give you

35:10

a real world example . We

35:12

ran a migraine observational

35:15

study . They wanted

35:17

50 patients . We got

35:19

them 5,433

35:21

. And in 2026

35:24

, they were going to launch some endometriosis

35:26

projects . But because

35:28

of the data that we got and the scale that we

35:30

got it in less than three months and

35:32

with our ePro module , that

35:34

endometriosis study was

35:37

launched within four months and

35:39

and not like three

35:41

years . And the reason being

35:44

is because there's nothing

35:46

more valuable and powerful

35:48

than real time , real

35:50

world data .

35:52

Yeah , I mean that's a pretty clear ROI , being able to expedite

35:54

pipeline and , you know , really speed up that

35:56

trial recruitment process by three years

35:58

. I mean that can be a massive gain

36:01

to pharma because , as we all know , if they have

36:03

a molecule or a new therapy

36:05

that's ready to go into humans , that patent's

36:07

already a couple of years old . So saving them three years on

36:09

a patent could be billions of dollars , depending on

36:11

the indication , you know , many billions of dollars

36:14

, while something's still protected .

36:16

Exactly , absolutely , and I think it's

36:18

the fact that you know the drug development process

36:20

we shorten

36:22

significantly because

36:25

of real world data and and the work

36:27

that UBC is doing with their

36:29

customers utilizing Seqster's

36:31

technology is a great example of that .

36:34

So that actually gets us to the next question , which is

36:36

, Aaron , what are some of the early results

36:38

you've seen from working with your clients ? Can you

36:40

share any other specific metrics or outcomes

36:43

beyond the example that Ardy just gave us

36:45

?

36:46

Yeah . So that's a

36:48

great segue because here's

36:50

a good example of how a

36:53

solution like Seqster and collaboration

36:55

and plugging it into clinical

36:57

research can accelerate drug

37:00

development . So at the start of of any

37:02

clinical trial you

37:04

need to identify patients who are

37:06

eligible for that clinical trial . One of

37:09

the ways you're going to uh , but

37:11

it's fundamental to determining

37:13

eligibility is looking at

37:16

the patient's medical record . What medications

37:18

are they taking that may make them ineligible

37:21

for the trial ? What types of things in

37:23

their medical history and confirmations and

37:26

diagnosis do they have that make them

37:28

eligible or not eligible ? You

37:30

have to be able to look at their records . When a patient

37:32

shows up to a site to participate

37:34

in research and that site already has all their medical

37:37

records , that process will go very quickly and they'll

37:39

make a determine of eligibility and that patient will be enrolled or not enrolled

37:41

. But if that patient comes to a site and they do not have all the medical records at their fingertips

37:43

for that patient will be enrolled or not enrolled . But if that patient comes to a site and

37:45

they do not have all the medical records

37:47

at their fingertips for that patient

37:49

, they have to be gathered and

37:52

classically this process can

37:54

take a long time and it's very manual . You're

37:56

talking about signing medical information release forms

37:58

, sending them around to the places where

38:01

that patient receives healthcare , waiting for the records

38:03

to come back . Sometimes those records requests

38:05

are fulfilled in 30 days

38:08

or something like that , and it just slows

38:10

down the entire effort

38:12

of trying to get eligible patients

38:14

into your study . So we see it as

38:16

a really vital solution there that

38:18

can be applied in site-based research could also

38:20

be applied as we look at studies

38:23

that maybe don't have to go through sites and you're going

38:25

directly to the patients and recruiting them , kind

38:27

of like the examples I cited earlier . You still

38:30

have to gather that patient's medical records if we're

38:32

going direct to patient and these

38:34

tools can be used to do that

38:36

. So we see it playing an

38:38

important role in accelerating

38:40

clinical research . We

38:42

also see it in an important role

38:44

in accelerating real-world

38:46

evidence generation . I'll

38:49

give one more example of that . Let's say

38:51

you're a pharma company and you're bringing a new

38:53

treatment to market in ASCVD

38:56

or atopic dermatitis , and

38:58

one of the initiatives that is pretty common

39:01

for a lot of these companies that are doing

39:03

that is that they may want to conduct

39:05

what's called the disease registry and they just

39:07

want to study patients with a certain disease

39:09

and be able to look back at their

39:11

healthcare journey a certain number of years and then follow

39:13

it going forward . And the reason

39:16

they want to do that is they

39:18

want to understand the treatment landscape , the treatment

39:20

patterns , and then , as their new product

39:22

is introduced into the market , publish

39:25

data on different comparisons and

39:27

looking at patients who are taking their product versus

39:29

others . And we call this

39:31

discretionary evidence generation to drive publications

39:33

and convey potential value

39:35

proposition of one treatment over another

39:38

. In order to do this type of research and this is what

39:40

UBC does , a lot of these large

39:42

disease registries Classically

39:44

in the past , you're enrolling hundreds of

39:47

sites across the country and paying

39:49

them to do the things I talked about earlier manual data

39:52

entry . That becomes very economically impractical

39:54

, but it's not necessary in today's world

39:57

. In today's world , we can go direct to patients

39:59

and we can find patients with atopic dermatitis

40:01

or who are at risk of ASCVD and

40:03

we can ask them directly if they'd like

40:05

to join this disease registry , and

40:07

if they do , then we can

40:09

gather their medical records at scale . We can get

40:11

thousands of patients , tens of thousands of patients

40:13

, if we need to , into a study like

40:16

this , and now

40:18

we're generating evidence that we

40:20

could not have otherwise , and

40:23

so this is going to help

40:25

clinicians and patients understand the

40:28

safety profile and the

40:31

potential efficacy profile of a certain medication

40:33

in ways that , in the past

40:35

, would have taken much longer and been much

40:38

more costly to yield

40:40

that type of evidence .

40:42

Yeah for sure . I think that's one

40:44

of the more apparent and

40:47

discussed use cases for this is that

40:49

clinical trial recruitment , in particular A

40:51

lot of the RWD companies I've been talking to , it's

40:54

identifying where there might be pockets of

40:56

individuals with these conditions that

40:59

haven't really been targeted for clinical

41:01

trials in the past , either because they weren't part of

41:03

an AMC or they're too far away from

41:05

a larger urban center where there was just

41:07

a higher density of those types of patients

41:09

. So it's definitely interesting seeing how

41:11

this can , in theory , democratize

41:15

and open up the

41:17

access to clinical

41:19

trials , both on the provider and the care organization

41:21

side , as well as on that patient side

41:24

. You know , giving this a more open

41:27

access , patient driven

41:29

framework . It does create

41:31

a lot of additional opportunity

41:33

to expand research efforts into

41:35

new pockets , which I think is really exciting . However

41:37

, I was recently speaking with somebody in the CRO

41:40

world who was saying that pharma

41:42

as a whole is a very

41:44

conservative group when it comes to decision-making

41:46

and thinking about the future , which

41:49

means they won't be all-in early adopters

41:51

of anything until someone else proves it

41:53

as an accepted standard way to practice

41:55

or regulations force a change

41:57

. Do you have any examples where

41:59

you've seen a

42:01

client or seen somebody taking

42:03

a more aggressively forward thinking or

42:06

innovative approach to this kind

42:08

of work , where that has ended up being a competitive

42:10

advantage .

42:11

Yeah , you're absolutely right . You

42:14

know it is a , you know , traditionally

42:16

a very conservative group

42:19

and what

42:21

has to happen is , you

42:24

know , you usually do

42:26

have to be a pilot study , a proof of concept

42:29

, done in small settings , with

42:31

lower stakes , and

42:36

once that's proven , then it can be applied and it's

42:38

slowly adopted by the organization

42:40

, by pharma , and applied

42:42

in other places , the

42:45

. You know I won't go through it again

42:47

, but but it really is the example I

42:49

cited earlier where we were doing a chart

42:51

review , a natural history study , important

42:54

research , but you know we're not

42:56

in that setting . It wasn't a

42:58

pivotal trial for for for registration

43:00

. So it's a different

43:02

stakes in terms of that and and once it's

43:04

proven and it works . And . So so you know different stakes in terms of that and and once it's it's proven

43:07

and it works and we prove , hey , we

43:09

actually were able to get this patient's medical

43:11

records . We didn't

43:13

have to pay this healthcare provider

43:16

to keystroke it all into a

43:18

web-based EDC . Once you

43:20

show that that works , and then

43:23

word starts to spread

43:25

, it then becomes

43:28

, it will become more

43:30

part of what people do , just like my

43:32

earlier example where we were using

43:34

paper for a long time

43:36

, just transcribing pieces of data from

43:38

a patient's paper chart into a piece

43:41

of paper that went into a study database . ultimately

43:43

, paper chart into a piece of paper that went

43:45

into a study database . Ultimately we got the web based CDCs . Somebody

43:47

proved that it worked and then it became the

43:51

universal standard . This is

43:53

already starting to proliferate . We

43:55

are seeing more and more of our pharma

43:57

clients come to us already

43:59

We'll raise the idea if they

44:01

haven't . But they are coming to us and saying

44:03

you know we'd like to go with a solution

44:06

. They call it different things patient mediated medical

44:08

record release , patient authorized medical

44:10

record release , hie medical

44:13

record release . They're saying we'd like to

44:15

apply that technology here

44:17

. We think it can help us in our

44:19

efforts to identify eligible patients for

44:21

a clinical trial . We think it can help us in our efforts

44:23

to enroll a

44:25

massive registry , starting

44:28

to hear about it and understand

44:30

it and ask for it . So the snowball

44:32

is starting to build and grow

44:34

, so to speak .

44:36

That's good to hear . I do think the

44:38

data technologies are becoming more robust

44:40

. Obviously , one of the big issues for a long time

44:42

was just constantly the data quality

44:44

issue and the interoperability problem of trying

44:46

to reconcile disparate data from disparate

44:49

underlying infrastructure would

44:51

cause complications .

44:53

And that's a good point , because that's still that's

44:56

a really good point , because that still is the

44:58

part that needs to be solved a bit . You know

45:00

we're going to get this data

45:02

, okay , but what formats are we

45:04

getting it in ? What level of harmonization do we

45:06

still need to do ? How do we address missingness

45:09

, which shouldn't be a problem in real world

45:11

data ? You know there's , there's certain you know

45:13

procedures that were just not performed , but you have to know

45:15

was it not performed or

45:17

are we missing it and exists somewhere else

45:20

? So there's still a lot of Devils

45:22

in the details to be solved in that

45:24

respect , and that's

45:27

the next step change

45:29

Makes sense .

45:31

So already you know don't want to

45:33

belabor this too much because I kind of mentioned

45:35

it briefly right before , but I think it'd be interesting

45:37

to hear your perspective on

45:39

what you might be seeing at the kind of

45:41

macro level at Seqster around

45:44

the interest in these types of tools for

45:47

increasing access to trials and improving

45:49

equity in trial recruitment , and

45:52

if you actually are seeing any uptick or

45:54

if you're collecting any metrics around that .

45:57

Yeah , so 2016,

45:59

. Nobody wanted to talk to me . 2017

46:02

, we got maybe a couple phone

46:04

calls from investors that

46:06

were interested and we raised some

46:08

funds after

46:10

I put the first big

46:13

chunk in myself because no one wanted to fund

46:15

the dirty engineering . 2018

46:18

, we come out of stealth . Dr

46:20

Eric Topol tweets that he

46:22

brought his data from 1985

46:24

to present four different health systems , four

46:27

different EMRs , his 23andMe

46:29

data , his genetic data , his Fitbit data

46:31

, his MyFitnessPal

46:34

nutrition data . We didn't know that he was

46:36

even tracking that information and

46:38

in less than 24 hours he said step

46:41

in the right direction . Same

46:43

time , lots of folks

46:46

find out about us because Dr Topol

46:48

is so famous and his tweet

46:50

got us 6 million plus hits and broke

46:52

our consumer model . And

46:55

then we meet Bill Gates . Bill

46:57

Gates tells us to build

47:00

an operating system and take an enterprise . I

47:03

listened to him . That was the best advice that I got

47:05

to this day on the business

47:07

still from any investor or anybody

47:09

. And then we

47:12

do some things around Alzheimer's . With

47:14

Boston University , we

47:17

build the research portal , which was known

47:19

as the SRP , which is the Seqster

47:21

Research Portal , which now

47:23

became our admin portal

47:25

or staff portal for researchers

47:28

and pharma sponsors and CROs

47:31

. 2019 , we're

47:33

trying to figure things out . But pharma starts knocking

47:35

on our door . It was the first year . At JP

47:38

Morgan Healthcare Conference , right before

47:40

the pandemic , patient centricity was

47:42

all over the walls in downtown

47:44

San Francisco . At every single event

47:46

that you went to March 2024

47:48

years ago , the pandemic hits . Everything

47:51

shuts down , everything goes decentralized

47:54

, everything goes hybrid kind

47:56

of , but more so decentralized

47:58

trials , dcts kind of take off

48:00

. Pharma starts getting

48:02

interested in how they can bring patients

48:04

and data from home , and home health and

48:07

telehealth explode 2021

48:09

, 2022, . Pharma starts

48:11

partnering with us and finally

48:14

we see the light at the end of the

48:16

tunnel . It took like six years . I

48:18

think I told you when we were showing you the

48:20

Seqster demo , john , how long

48:22

it took six , six and a half years to really

48:24

hit that growth

48:27

stage . Cro started calling

48:29

us end of 2021 . We strike

48:31

our partnership February of 2022

48:34

with Aaron Berger and friends at UBC

48:36

. We've been working with them for over two years

48:38

now on multiple different studies , getting

48:40

them the data that they need to run

48:42

their RWE generation . And

48:53

then you know , 2023 was the breakout

48:55

year . We exploded . I mean , we got two dozen

48:57

deals . Our technology was built by Pharma , mckinsey

48:59

, deloitte , accenture , cros , patients

49:02

, innovators like Dr Eric

49:04

Topol and his friends . It's pretty

49:06

amazing . And now , in 2024

49:08

, I think there's

49:10

more opportunity that we can handle

49:12

, and before we were

49:14

behind the eight ball , the wind was

49:16

always in our face . I'm here to tell

49:19

you today on your podcast this

49:21

is my 64th or 65th podcast

49:23

doing this sort of thing that

49:26

it's the first time I feel

49:28

like the wind is behind our back and

49:31

the only reason is because we actually

49:33

solve not one problem , not

49:36

two problems , not three problems

49:38

. We solve a multitude

49:40

of problems across healthcare and

49:42

life sciences , and it's not just

49:44

about medical record data . We

49:47

have multiple different tools

49:50

that we have embedded into

49:52

our operating system that relates

49:54

to payers , pharma , cros

49:57

, consumer brands and above Super

50:01

excited .

50:02

Yeah , I can tell , I can hear that coming through . Okay

50:06

, so in closing , I have one

50:08

more question that I want to get to , which is getting

50:10

your guys' perspective on the future

50:12

. So where do you see the industry in

50:15

the next five years , the next 10 years

50:17

? And then can you point to any

50:19

specific inflection points on

50:21

the horizon that

50:23

you can foresee , given recent trends

50:25

and regulatory action ?

50:27

Yeah , I can jump in on that and I

50:30

saw in our pre-show notes

50:32

. You really can't do a podcast on

50:34

anything these days without maybe talking a little

50:36

bit about .

50:37

AI so .

50:38

I know that that's on

50:40

your mind , perhaps on the listeners'

50:42

minds as well . You know

50:44

, what we talked about today is a

50:46

pretty straightforward use case , is being able

50:48

to go direct a patient to do

50:50

things like confirm eligibility and collect

50:53

data at scale for evidence generation

50:55

. I've talked a lot about the way we've used it

50:57

and we're talking mostly about structured

51:00

fields that are in the EMR and

51:02

things you can do with that , and then things

51:04

that you can do with the unstructured field , the source

51:07

notes . At this point , you know , rely mostly

51:09

the way we've used it people reading

51:12

those source notes and extrapolating pieces

51:14

of information . So the

51:16

next crank of the wheel and it's already you

51:18

know it's upon us is is applying natural

51:21

language processing to

51:23

reading those source notes and

51:26

moving far

51:28

ahead in terms of what type of evidence

51:30

can we derive by looking at , you know

51:32

, tens of thousands , hundreds of thousands

51:35

of patients worth of source notes

51:37

with a given disease and being able to

51:39

do that at scale , which you

51:41

know natural language processing can do

51:44

that humans cannot , and

51:46

that will really supercharge the

51:50

types of research we're able to perform , the questions

51:52

we're able to ask and answer from

51:54

reading tens of thousands

51:56

of patients worth of source notes

51:59

and just

52:01

generate richer and more valuable evidence

52:03

. So it's using AI , machine learning

52:06

, to

52:08

read those notes and do some of

52:10

this research . We're gonna see that start to

52:12

happen a lot in the future , but

52:14

Seqster is the . We

52:16

need solutions to obtain those medical records

52:18

in the first place before we can apply some

52:21

language processing and LLM

52:24

. So that's how we obtained

52:26

the records .

52:27

Fantastic and Ardy . What about you

52:29

? Any thoughts on the future ?

52:32

Yeah , you know , I've always had this dream of

52:34

bringing

52:36

the Netflix

52:40

, amazon one-click

52:42

experience to healthcare . It's

52:45

kind of why I started Seqster without even

52:47

knowing it , and it took a long time

52:49

working with wonderful people like

52:51

yourselves to understand that we

52:54

are on the brink of

52:56

making that happen . At

52:58

the time , I wanted to empower 7.7

53:00

billion people to collect

53:03

, share and own their data , and

53:05

now there's 8 billion people to the planet

53:07

. So 300 million plus have been added

53:09

since our little journey here in

53:11

eight and three months and

53:13

change running Seqster

53:15

here , but that

53:18

still is , I think , attainable

53:22

. It's a lot closer than what it was because

53:24

we were early , like you stated , john

53:26

, but we stuck at it , and the advantage of

53:28

being early is that we know what not to

53:30

do . I think we're

53:33

getting closer to

53:35

folks really

53:38

understanding their health journey

53:40

in a way that was non-imaginable

53:43

or not just for the innovators

53:45

, and I believe

53:48

that every single person

53:50

let's just take it looking

53:53

at just the United States should

53:55

have access to being enrolled in a clinical

53:58

trial , and I

54:00

think our technology is

54:02

well positioned to make that

54:04

happen for CROs , for

54:06

pharmacies , for payers , for

54:09

pharma companies and all

54:12

the above , because it is

54:14

an experience . It's

54:16

not just data in , data out . This

54:18

isn't just an API . Those

54:21

things are actually worthless to me and

54:23

that's why we focused on the patient

54:26

. The patient centricity

54:28

and the innovation is not going to

54:30

go away and we're

54:32

really excited about the

54:35

next phase of

54:37

where our clients are going to take our technology

54:40

. Of course , ai

54:42

is meaningless without data .

54:44

Very true . I

54:47

mean that's . The other thing with all this data is that healthcare generates more data

54:49

than any other enterprise sector . Right now , I

54:52

think it's growing at roughly 30% a year . Part

54:54

of that's also because it's late to the game , but

54:57

it doesn't change the fact that it's also generating massive

54:59

amounts of new data every year , and

55:02

so , as we

55:04

look at the future of healthcare

55:06

, it has to be data driven . It's the only thing that we haven't

55:08

really been able to do at scale until the modern

55:10

era , and it'll be very . I'm

55:12

just really excited to see what comes of all that , as

55:14

we start to identify new patterns , and

55:16

these new AI tools that we're bringing

55:19

to market are good at pattern recognition

55:22

, right , so they're going to be able to identify the patterns that

55:24

we haven't been able to see before because we haven't had that

55:26

perspective , and so population

55:28

health and public health could have some very

55:30

significant changes to how that's

55:33

perceived in the coming decade or

55:35

two . All right

55:37

, so thank you so much , both of you , for

55:39

joining us today as we explore one of the primary

55:42

use cases for real-world data and

55:44

real-world evidence , which is the clinical

55:46

research process . If any of

55:48

our listeners want to learn more , how should they go about

55:50

reaching out to either of ?

55:52

you ? Yeah , sure , aaron Berger at ubccom is

55:55

probably the

55:57

best way to reach me and

56:00

, yeah , I'm happy to talk . We're

56:02

passionate about , as I

56:04

mentioned in the beginning , helping

56:07

pharma , biotech answer

56:09

important questions about the safety and

56:11

effectiveness of medicines . We're

56:13

always really excited about having

56:16

collaborative thought

56:18

, partnership discussions on what is the best way to

56:20

design a study to do that , which

56:22

technologies should we bring

56:24

in , and

56:27

those are fun discussions . So if anybody would like

56:29

to have that discussion , yeah , definitely reach out to me

56:31

at my email address .

56:33

Okay , and Ardy .

56:34

Yeah , and I'm Ardy

56:36

at Seqster . com S-E-Q-S-T-E-R dot com

56:40

, and you can also connect with me on LinkedIn

56:43

. I'm very active on LinkedIn and

56:45

so I'm a LinkedIn junkie and

56:48

it's probably a better way , while I'm

56:50

traveling , to connect with me if

56:52

you can't catch me on email or if I don't get back

56:54

to you , but I usually get back to people

56:56

and love having any type of

56:58

conversation with anyone that resides

57:01

around data , interoperability

57:03

, trials , innovation and

57:05

all the above . Thanks so much , john , for having

57:07

me , and Aaron .

57:10

Yeah , thanks , john for having me .

57:12

Yeah , this has been fantastic . So thank

57:14

you so much for being part of this and joining us today and

57:16

sharing your expertise with our community . To

57:20

continue following along with these conversations

57:22

, be sure to subscribe to the Chilcast

57:24

on your favorite podcasting app and follow

57:26

our work on LinkedIn and or via

57:28

our newsletter . Feel free to reach out

57:30

with any questions and suggestions for future

57:33

guests or topics to feature on the show

57:35

, and thank you for tuning in .