start	end	text
0	7200	First of all, thank you so much to the organizers for having me here today live from home to talk
7200	12260	to you today about some of my work with the All of Us Research Program. My name is Michelle Holko.
12260	17380	I'm currently a Presidential Innovation Fellow, detailed to the NIH, to work with the program,
17380	21560	specifically with digital health technologies. I'm going to talk to you about one of the projects
21560	26920	I've been working on there since about December last year. So I'm going to give you first an
26920	31220	introduction to the All of Us Research Program. I'm going to run pretty fast through those slides
31220	35540	so that we can get to the digital health technologies portfolio, but more broadly in
35540	40220	the program. And then I'm going to spend the most time talking about the Fitbit, bring your own
40220	44380	device data analysis and participant demographics, which is the area that I'm going to be talking
44380	49740	about most today. And then we'll close with some opportunity and considerations for digital health
49740	55000	technologies in the All of Us Research Program. So just again, to give you a kind of an introduction
55000	59600	to the program, in case you're not familiar with it, the All of Us Research Program grew out of
59600	64460	the Precision Medicine Initiative, which was founded several years ago in the previous administration
65000	70620	and has $1.5 billion of congressional dollars to get the program started and to continue
70620	74900	running. And the mission really for the program is to accelerate health research
74900	80100	and medical breakthroughs to promote individualized prevention, treatment, and care for
80100	86060	all of us. There are three core values to the program that are really important to us.
86060	90600	The first is to nurture relationships with one million or more participant partners from all
90600	96740	walks of life for decades. And again, diversity is really critical to the program in that. To deliver
96740	102480	the largest, richest biomedical data set ever, as well as making it easy to use to a wide range
102480	109880	of researchers and to catalyze a robust ecosystem of researchers. So the All of Us Research
109880	116720	Program truly is an innovative and ambitious research effort. And here are some reasons why
116720	122040	really the diversity at the scale of a million people or more and focusing on, you know, capturing
122040	128280	the diversity that makes America, America. Focusing on participants as partners. So not just, you know,
128280	132820	taking data from them, but also giving back to them and continuing to engage with them throughout
132820	138100	the length of the program. The longitudinal design of the program means that we have the ability to
138100	144340	recontact participants, which we've already done in some cases around COVID. And multiple
144340	149520	data types. So diversity in participants, but also diversity in data, including electronic health
149520	156280	records data, survey data, baseline physical measurements, biospecimens, genomics, and
156280	159600	digital health technologies, which is what I'm going to be talking about today.
160160	165080	Creating a national open resource for all, which is broadly accessible to all researchers with
165080	170960	open source software and tools. And building in security and privacy safeguards for all participants.
171120	177380	So some current progress. We opened the doors nationally on May 6, 2018. So far, we've enrolled
177380	183940	over 271,000 participants that have not only completed the first initial enrollment step,
183940	189000	but have also completed what we call the full protocol. And this represents individuals from
189000	195900	all 50 states. Furthermore, greater than 80% of these participants represent one or more of
195900	200640	the underrepresented in biomedical research categories, which includes racial and ethnic
200640	206320	minorities, you know, living in a rural region, and there are many more. I'm going to go through
206320	211380	those in more detail a little bit later. And greater than 50% of the participants are racial
211380	216960	and ethnic minorities. We built a significant infrastructure to support the program with over
216960	224060	100 plus academic groups working with us, the VA, the FQHCs, which are the federally qualifying
224060	229600	healthcare centers, technology and community partners. We have over 320 clinics enrolling
229600	234800	participants, and that number is still expanding. We also have a bilingual website. So the website
234800	239740	is available in English and in Spanish, as well as the participant portal app and call center.
239760	246320	The biobank at the Mayo Clinic supports 24-hour shipping and processing with capacity for 335
246320	251080	million plus files. And we also have an interactive mobile exhibit that travels the country. The
251080	255720	picture is on the bottom right of the screen. Certainly not traveling during right now, but
256840	261900	usually it is, and it's actually wonderful because it can reach, you know, more rural areas where we
261900	270200	don't have partners. So here is just a snapshot of enrollment to date. So you can see enrollment
270200	274560	over time in the graph on the right, and then the bubbles kind of tell you where we are right now.
274560	279360	Like I said, we have 271,000 that have completed the initial steps of the program, which includes
280300	284340	consenting, agreeing to share your EHR data, completing the first three surveys,
284340	289620	providing physical measurements, and donating at least one bio assessment. We have many more
289620	295100	than that, 353,000 who have completed some part of the program, but they haven't completed all
295100	300020	of those kind of initial steps. This is what the current protocol looks like. The first step is to
300020	305460	enroll, to provide your informed consent, and to authorize your EHR. It's really important that we,
305460	310620	you know, ask for the authorization of EHR up front at this initial step. Right now we're
310620	316600	only recruiting adults who are 18 years and older. We do have plans to expand to children, but right
316600	322020	now we're only recruiting 18 years and older. It's also important to note that at this initial
322020	326800	enrollment, we have in-person enrollment and we have digital enrollment capabilities. Of course,
326800	331940	right now the in-person enrollments have all been paused. So, in that interim, we've still
331940	337900	been able to accept participants joining the program, but only through the digital method.
338180	342120	Then you can answer surveys. There are many different surveys that are already available,
342120	347280	including the basics, overall health, lifestyle, health care access and utilization, family medical
347280	354160	history, personal health history. And we also released a survey around the coronavirus pandemic
354160	359940	called the COPE survey, which focuses not only on signs and symptoms and exposure to coronavirus,
359940	366380	but also, you know, the social and economic aspects of that. The next step, the next two steps actually
366380	370840	kind of usually go hand in hand. And so usually if you're coming to us through one of our health
370840	374660	care provider organizations, you'll provide physical measurements, which are all listed
374660	379060	here. Blood pressure, heart rate, height, weight, BMI, hip circumference, and waist circumference. At the
379060	385300	same time, you'll provide a bio sample. Usually blood, that's the bio sample of choice. However,
385300	390200	if the blood draw is unsuccessful, or if you're coming to us as a direct volunteer digitally,
390200	397260	we can send you a kit to give a saliva sample. Very few participants give your assessments. We
397260	401560	are collecting some of those. And then finally, digital health technologies, which is what I'm
401560	407300	going to be talking about today. We have a bring your own device program already set up with Fitbit
407300	412520	and Apple HealthKit connections. We're also developing integrated apps to track mood and
412520	419660	cardiorespiratory fitness, among other things to come. So now shifting from the participant to the
419660	424600	researcher, and how are we going to make these data available widely to the research community
424600	429780	in a usable way? That sounds very easy, but it's not. So on the left is kind of the traditional
429780	435180	model where a researcher would come to the program, apply for the data, and then they would be
435180	440620	basically shipped out. It would be basically shipped out to them. But with this model,
440620	447340	it certainly discourages sharing of research. It also is a very weak link in terms of security.
447340	452660	You have no control over those data once they leave your facility. You also need a high
452660	458300	infrastructure to be able to support that to pay for multiple copies and all of that. So what we've
458300	463640	decided to do is to create a cloud eccentric approach where we bring researchers to the data.
463640	467680	All of the data are hosted in the cloud, the tools are being built in the cloud,
467680	473080	and then researchers are able to get access to it. And in this way, it facilitates collaboration.
473080	477640	There are centralized security controls. We can make it accessible to all researchers,
477640	482940	and we can even think of ways of making different types of data accessible to different types of
482940	488020	researchers depending on the need. So with this cloud-centric model, you can also think of
488020	495340	providing the data in tiers. We certainly have a public level of data sharing that's happening,
495340	500380	publicly accessible, no login is needed, shared on the website. I showed you the blurbs of the
500380	507040	enrollment over time. That's one of the snapshots. We've also created a registered tier, which
507700	512960	requires identity verification, signing a data use agreement, and completing research ethics
512960	519880	training. There's also the idea of potentially developing a controlled tier of data, which would
520360	528440	give people even more access to the data. So at each step, where the security goes higher,
528440	534600	and we are instituting more and more controls, because you're getting more and more access to
534600	542140	data. So with the registered tier, there are some specific privacy controls that are in place there
542140	546920	that would not be in place in the controlled tier. So we would need an additional trust with
546920	553140	the users of that data. So here's the Researcher Hub website. You can go to researchallofus.org.
553140	558360	I encourage everybody to do that. From there, you can see the data snapshots, the data browser,
558360	563880	and the survey explorer. The data browser that's available publicly does not let you drill down
563880	570000	to individual level data. To do that, you have to move into what's on the right here, the Researcher
570000	575760	Workbench. So this is just how the Researcher Hub components fit together. On the left, you have
575760	580860	the data browser, data snapshot, survey explorer. And then in the middle is this kind of data
580860	585380	passport model, which is how we are making the data available to researchers. We don't require
585380	592000	you to ask or tell us what specific project you're going to be working on. We just verify your
592000	598920	identity and, you know, kind of build a level of trust with you as a researcher, and then give you
598920	604240	access to the data. In the Researcher Workbench, it includes the data dictionary, concept sets,
604240	610080	cohort builder, notebooks, and help desk. And the one exciting piece of news to share is that the
610080	614580	All of Us Research Program Researcher Workbench is open for beta testing. So it's a very important
614580	619400	moment for us. We want to build this platform together with the research community, but we
619400	625120	absolutely need the input of researchers to make it more robust to learn how well the data tools
625120	630380	and policies are working. So the current components in terms of data include physical
630380	636360	measurements, survey data, some EHR data. Much more is to come here, including genomics and digital
636360	641600	health technologies data. The tools that are included include a dataset builder, a cohort
641600	647620	builder, Jupyter notebooks with R and Python supported, and the help resources are wide and
647620	653060	varied, including FAQs, sample notebooks and workspaces, including code snippets, documentation,
653060	659060	and help desk. So access and security, just a moment here. Again, All of Us employs a data
659060	665580	passport model for access to this researcher tier to grant researchers broad permission to
665580	670740	explore the data for a wide range of studies. Data testing is available to researchers who have
670740	675120	an eRA Commons account. This is what we're using for identity verification. So you have to have an
675120	680600	eRA Commons account. Most researchers do. And if not, you can look into the process for creating
680600	685460	that. And then you also have to have a signed data use agreement with your institution.
685460	690700	So the institution that houses the data resource center finds an institution with your institution,
690700	694900	and then we verify your identity through the eRA Commons account, and then that's how you're able
694900	700260	to get access. So again, to facilitate collaboration, the researcher workbench is
700260	705920	hosted in a cloud-based system. Data downloads onto your local machine are not at all allowed.
706000	710360	And during the beta testing phase, another really nice perk is that researchers will receive free
710360	716780	credits to cover computing storage costs. So here's just a peak look at what the workspace
716780	720740	looks like when you come to the researcher workbench. First, you create a workspace, and this
720740	727280	is where everything kind of will be all collected. And then you can create cohorts, concept sets,
727280	732940	cohort reviews, and notebooks to run your analyses. This is just a look at what the tools look like
732940	740320	in terms of the cohorts, data sets, and concept sets. And then just one more summary about, you
740320	746480	And I have to say, the support features are really extensive, so anyone who is not a coder
746480	751460	coming to this should be able to even get up and running. So there's the user support hub,
751460	756060	researcher workbench, featured workspaces, reusable code snippets,
756060	760760	demonstration projects, and then a help desk form that can give you kind of one-on-one help.
761380	766040	So just to summarize this section, we are providing a robust data ecosystem
766840	771840	with survey data, physical measurements, structured EHR data, with plans to add additional
771840	776780	surveys, digital health technologies data, unstructured EHR data, linkages to other data
776780	784080	sets, additional assays and biospecimens, and genomics. So moving on to the topic of the day,
784080	789360	digital health technologies in the All of Us Research Program. So what is the vision really
789360	794900	for digital health technologies? Well, digital health technologies data from things like fitness
794900	802460	trackers, smartwatches, apps, other types of devices, can capture health-relevant data from
802460	808220	individuals outside the hospital or clinic, which can really supplement current medical information.
808520	814960	Another feature, especially for fitness trackers and smartwatches, is that the data are longitudinal
814960	819860	in nature, and it can really give researchers an unprecedented ability to better understand
819860	826060	the impact of lifestyle, environment, and other factors on health outcomes, and eventually and
826060	831500	ultimately develop strategies for keeping people healthy in a very precise and individualized way.
831620	836080	The program is currently collecting data from participants in a bring-your-own-device model,
836080	839560	so we are not distributing devices to participants, although that is something
839560	844420	that we are planning for. And we currently have connections to Fitbit and Apple HealthKit.
844420	849960	So again, just bring your own device. We are also considering developing pilots of
849960	856460	smartphone-based apps in the future. So what is the vision or impact specifically for the Fitbit
856460	862080	bring-your-own-device data in the Researcher Workbench? So I believe that all of us
862080	867220	really truly have a unique opportunity to release one of the largest and most diverse digital health
867220	873920	technology data sets. We currently have data from over 9,000 participants, some beginning in 2009.
873920	879420	In addition to current survey, EHR, and physical measurements, future data releases will also
879420	883720	include genomics and other data sets. So when you think about the power of combining all of
883720	888940	these different data types together, it's truly amazing. And then this can promote research to
888940	893160	develop new and improved disease prevention capabilities, remote patient monitoring for
893160	898940	connected care, and other opportunities for high-cost diseases. So the approach that we
898940	902660	are using for the Fitbit data in the All of Us Research Program
903440	909500	is this Minimum Viable Product, or MVP, approach. So first step of this is to obtain the data,
909800	916540	and we have all of the data currently through May 2020 that's going to all be curated and released
916540	922600	in the next data release. We've also developed scientific research use cases around the Fitbit
922600	927340	data just to make sure that the way that we're curating the data is going to make it useful to
927340	932640	the research community. We are now assessing the data set for the longitudinal nature,
932640	937020	missingness, and trends, or basically doing exploratory data analysis. And then based on
937020	942440	two and three, based on scientific research use cases and the EDA of the data, defining the
942440	946680	priority data elements for inclusion in the Researcher Workbench. And again, the timeline
946680	954900	for that is to release it in the next data release, which is slated for late fall 2020,
955460	960460	currently anyway. And then to develop and implement a Fitbit curation schema coordinated
960460	965920	with the security privacy team and other approvals as needed. So this is what the currently what the
965920	971880	Fitbit MVP looks like. You can see the data elements table over on the right. The data
971880	976080	elements that we are currently pulling from Fitbit include heart rate, minute level heart rate,
976080	981620	heart rate summary data, intraday steps, which is also the minute level, daily activity summary,
982180	986260	daily weight, daily fat, food, and water. And it's important to note that daily weight,
986260	993240	daily fat, food, and water are all user supplied elements. So just by nature of that, there are,
993240	998540	there is much less of those data. The others are derived from parameters that are derived
998540	1005800	from the device. So what we've decided to do is to provide a series of data tables using BigQuery
1006280	1011740	of parsed data tables, including heart rate, summary by zone, heart rate, minute level,
1011740	1015780	activity, daily summary, and activity intraday steps in that level.
1016440	1022820	So to dig into the data a little bit more, as well as some of the participant demographics,
1022820	1029200	which are important for a number of reasons I'll go into. So these are characteristics of the
1029200	1035980	current Fitbit data set in terms of the data elements. So we have 9,255 Fitbit bring your
1035980	1044500	own device participants. And again, this is until March, March, May 2020 timeframe. 8,535 of these
1044500	1052560	have primary and electronic health record consent with activity measures as early as 2012 and heart
1052560	1058780	rate as early as 2015. So that is quite a lot of data, if you think about it. The data elements
1058780	1063600	that we are pulling from Fitbit API are shown on the right with the device derived parameters in
1063600	1070280	green, daily activity, intraday activity, heart rate, and sleep. And the user supplied are in
1070280	1075680	blue, daily fat, daily weight, food, and water. So when you look at the data corpus as a whole,
1075680	1080700	and you look just by data element to see which elements have the most data in them,
1080700	1085500	intraday steps and activity summary are the data elements with the most data. Heart rate and sleep
1085500	1091640	summary have about 40% less, and the user supplied daily weight, daily fat, food, and water intake
1091640	1097640	have greater than 90% less data than the intraday steps. So in terms of what is going to be most
1097640	1105920	relevant or useful, you know, for giving to the research community, definitely the, in my mind,
1105920	1114200	the intraday steps activity summary, as well as heart rate. So then we wanted to try to assess
1114200	1120580	the completeness of each participant's data. So percent completeness was assessed within each
1121220	1129080	of the 8,535 participants. And the way that we did this was to do a theoretical maximum
1129080	1135680	kind of comparison, where if you have to, that's based on the start and end time of donation. So
1135680	1140260	if you have a user that comes to you, and they started donating data, and then they ended donating
1140260	1145500	data, and it was about 100 days, then your theoretical maximum number of data elements
1145500	1152440	for the daily elements would be 100. For the minute level, it would be 100 times
1152440	1158640	the number of minutes in a day, which is 1,440. So then by comparing the number of data elements
1158640	1163640	that you have for each of those data types within a person, by the theoretical maximum, you can get
1163640	1169320	this percent completeness. And we were looking at two thresholds, greater than 60% complete,
1169320	1175800	and greater than 80% complete. So because intraday steps, daily activity, and heart rate are all,
1176360	1182860	they're all device-derived data types, you would assume that they're all relatively similar,
1182860	1188800	right? Because if the person is wearing the device versus not, you wouldn't get steps and
1188800	1192460	not heart rate, right? Unless the device just didn't have that capability. So it's actually
1192460	1197160	kind of a good control to see that these numbers are very similar. So for intraday steps, daily
1197160	1204020	activity, and heart rate, we have about 77 to 79% of the participants that are donating data have
1204020	1209580	more than 60% of the data compared to the theoretical maximum amount, which is pretty good.
1209580	1214500	This is a pretty complete set, which is what that means. When you look at 80%, it's slightly lower,
1214500	1221940	but it's still very good. Around 62 to 65% of the participants have more than 80% of the
1221940	1227960	maximum possible data available. So an interesting thing to note, though,
1227960	1235700	is that when we looked at how complete a participant who is only donating data for
1235700	1240200	one year or less versus a participant that's donating several years of data,
1240200	1245140	we noticed that participants with data available for only one year or less had the lowest percentage
1245140	1251840	of completeness, and completeness of participant data increased as duration of BYOD data donation
1251840	1256560	increases. So this just goes to show that, I think, something that we've all noticed that,
1256560	1260160	you know, if you've had a device for more than one year, if you wear it regularly,
1260160	1263700	you know, it kind of becomes integrated into your life, and so the chance of having a more
1263700	1269580	complete data set goes up. So before I move into the next section where I talk a little bit about
1269580	1280180	the demographics of the participants in the Fitbit BYOD program versus all of us participants,
1280180	1285760	I want to first just give you a summary of how we think about diversity in the All of Us
1285760	1290180	research program. Diversity is critical to doing precision medicine research, because when you
1290180	1295420	think about, you know, if we want to be able to deliver healthcare that's specifically for,
1295420	1300980	you know, a specific type of person, we need to have research on all of those various types of
1300980	1306820	people to be able to find those differences. So this is how we think about these groups that
1306820	1311900	we call underrepresented in biomedical research and their definitions. So race and ethnicity,
1312960	1320000	participants are identified as Hispanic, Latino, Spanish, race, or race identified other than white.
1320000	1325860	Age, participant is under 18. Again, we're not doing that in this program yet. So over 65 is
1325860	1330800	really what we focus on. Sexual and gender minorities, participant sex reported is different
1330800	1335880	from that assigned at birth. Or orientation, sexual orientation is something other than straight.
1335880	1341560	Income, the participant lives below the federal poverty level, which is less than 25,000 a year.
1341580	1346380	Education, the participant has less than a high school degree. Geography, you live in a rural zip
1346380	1351440	code. And then access to care, you can't readily access the healthcare system or can't pay for care.
1351440	1355780	And then disability, some type of physical and or mental disability.
1356900	1365660	So just first to start, I wanted to show you that the breakdown of age group in the FYPIT,
1365660	1371140	versus all of us core participants. So the FYPIT BYOD are in the dark bars,
1371140	1377340	and all of us are in the lighter bars. So what you can see here is that the distribution of age
1377340	1383980	is fairly similar. You do lose a little bit of the 75 to 85, and this does fall into that EBR
1383980	1392620	category. You do gain a little bit in the mid-20s to mid-30s. But overall, this is pretty comparable.
1393120	1397720	So in the next chart, in this chart, I tried to highlight some of where we're seeing some
1397720	1404880	of the gaps. And we're not able to capture EBRs in the same percentages with FYPIT participants
1404880	1410500	versus all of us participants. So you can see that the vast majority of FYPIT participants,
1410500	1419280	over 78% are white, whereas with all of us, we only have about 47, 48% are white.
1419280	1428120	And then you have 20% Black or African American, 16% Hispanic or Latino, whereas in FYPIT BYOD,
1428120	1436980	you only have 4 to 5% of those two EBR categories. Another feature of the FYPIT participants is that
1436980	1444780	over 36% have an advanced degree. In all of us, only 18% have an advanced degree.
1444780	1450920	In terms of high school only, we've got about 21% in all of us who have high school only degrees.
1451060	1456580	And in FYPIT participants, it's only 6%. In terms of living below the poverty line,
1456580	1461900	not surprisingly, we only have 8% of people donating data from that group,
1461900	1470820	whereas all of us participants, we have 31%. So I wanted to really drive this point home that
1470820	1475240	even though we're trying so hard, this program is so focused on diversity, we're trying really hard
1475240	1480500	to capture the diversity, we can't do it in this kind of bring your own device model because this
1480500	1484820	is probably reflecting people that have the devices, right? So we're going to have to be
1485000	1489640	a little bit creative in order to try to capture the data from other groups. So just to summarize,
1489640	1494920	there are specific diversity gaps in the FYPIT BYOD participants when compared with all of us
1494920	1499820	core participants. And skipping down to the bottom and the two bulleted points that are in bold,
1499820	1504660	there's a definite bias towards white versus other race categories, and that FYPIT participants
1504660	1510520	are skewed to higher education and income levels. So why is diversity important? Just one more piece
1510520	1518300	of evidence. This is a very popular paper in Nature from 2016, which surveyed the data that
1518300	1526440	has been used to do GWAS or genome-wide association studies from 2009 to 2016. And what it shows is
1526440	1534320	that in 2009, 96% of GWAS studies were done using data from or using samples from people with
1534320	1540360	European ancestry. That is a tremendous bias, right? I mean, this is something to be really,
1540360	1547360	really careful about. Since then, since this paper was published in 2016, it went down to 81%.
1548120	1554980	And it looks like, you know, in particular, people with Asian ancestry are becoming more represented,
1554980	1560620	but we're doing a terrible job at getting diversity in this data set. And my worry,
1560620	1566460	my concern is that if we're not careful in how we do research, you know, using these digital health
1566460	1571020	technologies data, we're going to be in the same situation. And that's certainly not something
1571020	1577280	that we want, because these types of gaps are what lead to disparities in healthcare.
1577900	1583500	So what is the opportunity and some considerations for DHT in the All of Us Research Program? So
1583500	1588380	the All of Us Research Program truly has a unique opportunity to address some of the current
1588380	1594060	challenges in DHT research due to the following core values of the program. First, the size and
1594060	1599620	diversity of the cohort. Again, we're trying to get to a million. We've currently got more than
1599620	1610680	350,000. And the focus on diversity means that we really do have a need to focus on diversity,
1610680	1614780	not just at the level of the participants, but also at the level of the data type.
1615840	1620580	It's also important to the program to capture data from those new VR groups,
1620580	1625280	and the goal to develop an open access, rich, and complete biomedical data set
1625280	1630360	that's not biased, and make it available to the research community. So that's really key.
1630920	1635940	So because diversity of participants and of data is of critical importance to the All of Us
1635940	1642780	Program, this information that we've learned from the PYOD analysis will be used to plan future
1642780	1649140	digital health technology efforts, potentially piloting, giving out devices to people,
1649800	1654680	to try to mitigate gaps in the traditionally underrepresented in biomedical research
1654680	1661720	participants. So really the question that I want to get to, and that I want people to think about,
1661720	1666920	and to hopefully spark some discussion, is how can the All of Us Program leverage DHT
1666920	1671460	to provide unique opportunities for our participants and for the research community?
1672040	1677460	So just some other key considerations for All of Us and digital health technologies. Again,
1677460	1681940	diversity is key. I can't say it enough. Even in the All of Us Research Program, where there's a
1681940	1687240	huge focus on diversity, it's a challenge to get that diversity in the digital health technology
1687240	1694300	data. But it's critical and key for eliminating bias from results. You know, the All of Us
1694300	1698680	Research Program, because we have the opportunity to release one of the richest data sets and
1698680	1703980	largest data sets with the Fitbit release, it's an opportunity to democratize access to this data
1703980	1708840	type. You know, there are a lot of research studies that have just a very few number of participants,
1708840	1714260	or for the larger studies with lots more data, they're mostly in the private sector.
1714260	1720860	So how can we, you know, in this safely enable a larger number of researchers to
1720860	1724940	get familiar with these data types and start to do innovative things with them?
1725660	1730260	The other thing is that, you know, a lot in a lot of cases, these are novel data types.
1730260	1734740	So a lot of what has been built for privacy and security is unknown until proven, right?
1734740	1741680	And so certainly data egress is something that the program guards against and is critical.
1741680	1746480	And then with privacy, re-identification of participants is also another consideration
1747100	1754980	that we need to make sure that we are protecting for. So I'm just going to show you slides to let
1754980	1759300	you know that it really does take all of us. These are all of the All of Us consortium members
1759300	1764720	beyond the community partners. You may or may not be able to read all of these
1764720	1770380	organizations. It's truly massive. These are the community partner and provider networks.
1771400	1777980	And here's a picture of All of Us in there somewhere. And it really is truly a monumental
1777980	1783840	effort. So please feel free to find out more information, shoot me a note, ask a question,
1783840	1786140	discuss, and thank you so much for your time.