Yale Psychiatry Grand Rounds: "Transforming Mental Health Care via Science-Based Digital Therapeutics"

February 02, 2024

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February 2, 2024

Karasu PsychoSocial Lecture "Transforming Mental Health Care via Science-Based Digital Therapeutics" Speaker: Lisa Marsch, PhD, Founding Director, Dartmouth Center for Technology and Behavioral Health

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00:00It's just a, it's just an honor
00:03to be here and to join all of you.
00:05And I have so many wonderful friends
00:07and colleagues at Yale and I see
00:09some familiar names here and very
00:11excited to be with you all today.
00:12So I'm going to go ahead and
00:15share my screen here.
00:19So as you heard,
00:20I'm going to be talking about digital
00:23health as applied to mental health.
00:25And I know several of you on this
00:28call are working in this space and
00:31doing really pioneering work and
00:33exciting work in this space.
00:35And really excited to have a
00:37continued dialogue at the end of
00:39this lecture today and and hear what
00:41questions and comments you have,
00:42but also to learn more about what
00:43you all are building in this space.
00:45And I'm going to talk today about
00:46just sort of where we are in terms
00:48of the state of the science of
00:50applying digital health to mental
00:52health and addiction and give you
00:54a snapshot of sort of the scope
00:56of the science and how we can
00:58implement these tools in real,
00:59real world settings to really
01:01transform models of care.
01:03And then I'll tell you a little bit
01:04more about our Research Center.
01:05We have an Ida funded center as
01:08Stephanie mentioned that's entirely
01:09focused on the application of
01:11digital health to to the realm of
01:13addiction and and mental health.
01:15And we have a lot of resources
01:16and and and opportunities for
01:17collaboration and and if anyone here
01:20is interested in exploring that more,
01:21we would be just delighted to do so.
01:25So in first I want to acknowledge
01:27some of the funding that I'll be
01:28referencing in our work today,
01:30including our center grant from the
01:32National Institute on Drug Abuse as
01:34well as several other NIH grants.
01:36I'll be showing some data from today.
01:40And then I also wanted to acknowledge
01:42that in addition to my academic
01:45affiliation at Dartmouth College,
01:46I also have an affiliation with a
01:48few small businesses that are working
01:50in the digital health space as well
01:52as Burying or Ingelheim that's
01:53working in the digital health space.
01:55And I'll tell you a bit more
01:56about our center at the end.
01:57But you know,
01:58although we are a Research Center
01:59and really focused on bringing
02:01science to the space of digital health,
02:03we are really building out strategic
02:06partnerships to scale the best,
02:08most potent and engaging digital
02:10health tools and doing that in
02:12a strategic way with partners.
02:13And I'll tell you a bit more about that.
02:17So just starting with terminology,
02:20so digital health is a term that you
02:22probably are hearing more and more
02:24of it's it's increasingly a a a key
02:26part of many models of healthcare.
02:27And you know I'm convinced that undoubtedly
02:29it's a a a very key part of the future
02:32of how we're going to see healthcare
02:34delivery particularly in mental health.
02:36So I think it's really critical that
02:38we sort of embrace and understand it.
02:41It's it's going to be a key part of the
02:42work that we also do together as well
02:44as the next generation of clinicians
02:45and and and healthcare providers.
02:48So digital health is a is a term
02:50that often refers to using digital
02:52technology not unlike a a smartphone
02:54that's within arm's reach of most of us,
02:57most of the most moments of the
02:59day to do a few things.
03:00One is to get new insights into people's
03:03health behavior in their daily life.
03:05And then also you can use these types of
03:08digital platforms to provide therapeutic
03:11tools to people anytime and anywhere.
03:14And this is a term called
03:15digital therapeutics.
03:16And I'm going to spend some time talking
03:18about what that means and what these
03:19are and what kinds of clinical effects
03:21we can see with these types of tools.
03:23So it's really around using
03:25digital technology for assessment
03:27and for therapeutic delivery,
03:28which can be quite personalized
03:32and and also widely accessible.
03:35So starting with this term,
03:36digital therapeutics,
03:37this is a term that refers to software
03:43that is effective in preventing,
03:45treating or managing a health condition.
03:47So it's a clinical grade
03:50intervention delivered via software.
03:52So it's it's sort of going above
03:54and beyond what we think of as
03:55health promotion or Wellness apps.
03:57And you're taking the active therapeutic
03:59ingredients of a clinical intervention
04:01and delivering it through the
04:04functionality and the content of software.
04:06So I'll give you any examples in the
04:09talk today, but this could include,
04:10for example,
04:11cognitive behavioral types of
04:13interventions that are entirely
04:15delivered through software.
04:16So this is not telehealth,
04:17but it surely can complement and
04:19extend what we do with telehealth.
04:20But as we know with telehealth,
04:21we have synchronous communication
04:23with the clinician and this is
04:25something that's accessible anytime,
04:27anywhere 24/7,
04:28kind of like a clinician in your pocket,
04:31but you know,
04:33but that is available on demand and
04:36perhaps at at times of greatest need.
04:39And so you're packaging this model of care
04:41into the seamless digital delivery platform.
04:44And there are a number of benefits
04:46of this approach and we've surely
04:48seen this play out in the data.
04:50So first of all,
04:51these types of tools can extend the
04:53reach and the impact of clinicians,
04:55right.
04:56These can be additional tools in the
04:58toolbox of clinicians that can sort of
05:01supercharge our clinician workforce
05:02and extend their reach and provide
05:04resources to people even when they're
05:06not working with their clinicians.
05:08So it can reinforce and
05:10extend the work that you
05:12might be doing with the clinician
05:14and and and I'll show you data,
05:16but we've definitely seen now across many,
05:18many health domains,
05:19particularly mental health.
05:20Much of this work to date has been
05:22in the realm of mental health,
05:24although there are many growing
05:27applications in a wide array of
05:29aspects of preventative health
05:30to chronic disease management.
05:33But we have seen very robust and
05:35replicable effects on all kinds of
05:38health behavior and health outcomes.
05:40We can be assured that these
05:43tools can deliver interventions
05:44with fidelity to best practices.
05:47So this can be treatment interventions,
05:48this can be prevention interventions
05:50and we can really ensure that we
05:53are delivering this with reflecting
05:54sort of state of the science
05:56models of intervention delivery.
06:00And surely we know that not everybody in
06:03the world yet has access to digital devices.
06:06All the all the data, all the trends
06:08show that the majority of the world's
06:11population either has access now to
06:13mobile devices and or is expected to
06:16get access to these mobile devices.
06:18And we we work with all kinds of populations
06:20in this country including some you know,
06:22traditionally underserved
06:23populations in this country.
06:25But we also do a lot of work
06:26in other parts of the world,
06:27low and middle income countries and you know,
06:30we might work with communities that
06:32don't have clean water or you know,
06:33a landline infrastructure but
06:35often have a mobile device.
06:37And so it's an it's an amazing opportunity
06:39to harness the widespread availability
06:42and growing availability of these types
06:44of tools to give people resources,
06:47healthcare resources in new ways
06:49through these types of platforms.
06:52It's scalable.
06:52I think that's one of the most
06:54exciting things is that you can
06:56have really widespread reach and
06:57impact with these types of tools.
06:59And as you all know very well,
07:01during the the COVID crisis,
07:03we definitely saw a big surge in
07:05demand for remote models of care and
07:08that definitely included telehealth.
07:09But we also saw,
07:10and I'll tell you a bit more
07:12about this later,
07:13a big growth in demand for
07:15these types of digital tools,
07:17digital interventions.
07:20And as I mentioned,
07:21we see we can get a big impact on on
07:24lots of different health outcomes,
07:25including health costs.
07:27And there's some striking data
07:28including some recently released data
07:30that really showed not only can this
07:32impact people's lives and their functioning,
07:35but can have huge implications
07:38to healthcare expenditures.
07:40So these are some of the the benefits of it.
07:42And this slide is just a snapshot
07:44of sort of the state of research
07:47in the application of digital
07:49therapeutics to behavioral health.
07:50And we've been,
07:51I've been doing this work for decades now.
07:54And but there's, you know,
07:55really decades of of really
07:57robust literature focused on,
07:59you know,
08:00how do you best develop and and
08:02test and implement and sustain these
08:04types of tools to really have value.
08:06And this slide sort of gives you the
08:08big picture of that body of of research.
08:10And So what we generally see in the
08:13literature is that if you develop
08:14these tools, well development's huge,
08:17it's it's really huge.
08:19We could spend a lot of this time just
08:21talking about development in terms
08:22of how do you really develop a tool that is,
08:24you know,
08:25reflective of of the needs and and
08:27cultures and values and and and brings
08:29clinical utility to your target audience.
08:32But if you embrace sort of best
08:34practices and really have something
08:35of value to to your target audience,
08:38we see that these tools can be
08:40highly useful and acceptable to
08:42lots of different populations.
08:44We see we can have a very large
08:46impact on a wide array of health
08:49behaviors and health outcomes.
08:50We also have seen now in many studies
08:53that digital interventions can produce
08:56outcomes that are as good as or better
08:59than clinician delivered interventions.
09:01And you know some people you know
09:02sort of bristle at that and and
09:04worry that we're trying to replace
09:05clinicians with these types of tools.
09:07But as you all know very well in the
09:09work that that you do you know it
09:11we really have a capacity challenge
09:13in many pockets of the world in
09:15terms of you know really having
09:17sufficient mental health workforce
09:19to meet our population level needs
09:21or or addiction treatment workforce
09:22to meet our population level needs.
09:24And so it's it's I,
09:26I think of great value to know that
09:28the data support that these types
09:30of digital tools can really produce
09:32meaningful clinical effects that can
09:35extend the workforce that we have
09:37and can help increase capacity and
09:40reach for prevention and treatment
09:43of various health conditions.
09:45We've also seen you can increase reach,
09:47you can increase personalization of care.
09:49I'll talk a bit more about some
09:50of the economic data,
09:51but now there's a growing body of
09:54literature showing economic benefits and
09:56these don't always have to work the same way.
09:58It doesn't have to be 8 sessions of 1
10:00intervention or 12 sessions of another.
10:02You can really embrace what technology
10:04can do and have very adaptive types of
10:08interventions that can change over time,
10:11that can be very personalized to
10:13whatever an individual's needs
10:14and preferences are in the moment.
10:15But that can again be adoptive in
10:17an ongoing way as people's clinical
10:19trajectories and needs change over time.
10:22And there's a lot of exciting
10:23research in that space,
10:24including in our center and I'll
10:26I'll speak a bit about that.
10:27But I think that's,
10:29I think that we have a lot of
10:31promise for precision interventions,
10:33precision delivery of mental health
10:36interventions delivered anytime and
10:39anywhere through capture of digital
10:41data at the individual level and
10:43then very responsive interventions
10:45provided on digital platforms.
10:49I'm going to talk about, first of all,
10:52I'm going to talk about a
10:53digital intervention that we did.
10:55We developed the first iteration of
10:56a long time ago, actually the first,
10:58first iteration of it was in
10:59the late 90s and it was not,
11:01it was web-based at the time and not
11:03a mobile intervention at the time and
11:05has evolved over that period of time.
11:08But I talk about this as an
11:10exemplar of a digital therapeutic,
11:12just to highlight what one
11:13of these can look like,
11:14but also what kinds of clinical
11:16effects you can see when you
11:18use these tools different ways.
11:20So I'm going to give you examples
11:22of different ways you could
11:23apply this type of approach,
11:25But I know there are many others.
11:26In fact, Yale, you know,
11:27has developed fantastic tools in the space,
11:30including CBT for CBT,
11:31and there are many others for
11:33substance use and for mental health.
11:35So this is just an example.
11:37And briefly,
11:38this is a pretty intensive behavioral
11:42treatment for substance use disorders
11:45that's entirely delivered in this
11:48interactive self-directed way.
11:49Again started web-based and then
11:52morphed into a a mobile tool.
11:54It just briefly I'm not going to
11:57talk about the details of this.
11:59I'm happy to chat more about this
12:00if folks have specific questions.
12:02But this takes one of our very
12:04potent behavioral treatments for
12:06substance use disorders called the
12:08community reinforcement approach
12:09to substance use disorder treatment
12:12and takes the active ingredients
12:13of that therapeutic approach and
12:15delivers it on a digital platform.
12:17So it's very interactive at
12:20the individual level.
12:21It focuses on helping people understand
12:24their specific sort of pattern of
12:27of behaviors of cognitions that
12:29maintain self defeating patterns or
12:30drug taking behavior and how you can
12:33understand and disrupt those patterns.
12:35And and and develop a new skill set
12:36and a new behavioral repertoire that
12:38can help you initiate and maintain a
12:40recovery process and how to leverage
12:43different resources in that process.
12:46And there is an optional piece
12:48to this intervention that is
12:50motivational incentives piece,
12:51which is often called contingency
12:54management where you give people incentives,
12:57prizes, rewards,
12:58contingent on different milestones,
13:00different successes in a recovery
13:01process in this case.
13:02And that that's all automated
13:04in this tool and is an optional
13:06component of this broader platform.
13:09So again, happy to talk about this more,
13:10but just just wanted to give you
13:12a snapshot of what this is it.
13:14It,
13:14it's not just about sort of
13:15enhancing motivation to change.
13:16It really is intended to be an
13:19intensive behavioral treatment
13:20that that really helps people
13:22build the skills and capacity for,
13:25for change,
13:26particularly around substance use.
13:28So what I'm going to do now is just
13:30give you a little bit of data and
13:32some examples of the kinds of effects
13:34you can see with these types of tools.
13:37So one of the really early studies
13:40that we did was I'll just briefly
13:42describe the study and I'll mention
13:43that we have lots and lots
13:45of papers on all of this.
13:46So if anyone has any interest,
13:48please reach out.
13:49I have my e-mail address on the last slide,
13:52Please reach out and I'm happy
13:53to share any and all additional
13:55information that might be useful.
13:57But in the interest of time I'm
13:59just going to sort of give you a
14:01flavor of of some of this work.
14:02So, so this is this particular trial
14:05Nida funded study was a three arm
14:07randomized clinical trial and these
14:09were all adults entering outpatient
14:11treatment for opioid use disorder.
14:14And every every single participant
14:16in the study received medication.
14:19We all know that medication is a
14:21critical component of effective
14:22treatments for opioid use disorder.
14:24This is a sample here that all received
14:28buprenorphine medication as part of care.
14:31But the randomization occurred on the
14:33type of behavioral treatment that people
14:35received on top of the pharmacotherapy.
14:37So if you went into the condition that
14:40is reflected in the blue column here,
14:43you were randomly assigned to a therapist
14:45and you met with this therapist three
14:47times a week in individual sessions.
14:49And this therapist delivered to
14:51you the community reinforcement
14:53approach to behavior therapy for
14:55substance use disorder treatment.
14:57And there were all kinds of fidelity
14:59checks in place to make sure it
15:00was being done in accordance with
15:02sort of state of the science
15:03approach to this therapeutic model.
15:04And so it was a pretty intensive one-on-one
15:08therapeutic approach with a clinician.
15:10If you went into what's
15:12reflected in the red column here,
15:14you had a therapist and you saw them
15:16every other week just to check in.
15:18But your, your therapy,
15:19therapy was offloaded to a
15:22digital delivery platform.
15:23So this is a group that in this
15:26particular study actually went to a
15:28computer lab on site at the treatment
15:30facility and interacted 3 * a week
15:33with a digital version of this
15:36community reinforcement approach,
15:38behavioural therapy approach.
15:39And then if you went into what's
15:42reflected here in the Gray column,
15:43you received what was considered
15:46treatment as usual standard treatment
15:48for opioid use disorders in the
15:50US at the time and it wasn't the
15:53community reinforcement approach.
15:54So basically this slide shows that
15:56even when you offload the bulk of this
15:59therapeutic approach to a digital platform,
16:01you can get comparable clinical
16:03outcomes to what you observe from
16:06exclusively clinician delivered care.
16:08And then both versions of this
16:10you know very effective and potent
16:13behavioral therapy produce better
16:15outcomes than our standard treatments.
16:17And this particular slide is on a
16:20objectively captured data through
16:22urine toxicology testing looking
16:24at abstinence from opioids and
16:26cocaine in the sample.
16:27So.
16:27So that was one example of a way that
16:30you could apply this and and and
16:32you can see the kinds of benefits
16:34you get clinically from that.
16:36But now I'm going to show you
16:37different examples.
16:37So this was a study that we did
16:40in New York City and
16:42these were all adults again with
16:44opioid use disorder that were
16:46entering outpatient treatment.
16:48This was done in methadone treatment
16:51systems and when people came in
16:53to treatment they either received
16:56treatment as usual in methadone
16:58treatment setting which as you
17:00likely know includes daily methadone
17:03medication and some therapy support
17:06from counselors in the system.
17:08So they went either into that
17:10condition or they were randomized to
17:12this condition which was basically
17:15a condition where they they they
17:18they had daily methadone access,
17:20they had a clinician.
17:21But the only difference between
17:23the two conditions is that in
17:24the one reflected here in blue,
17:26those participants had a therapist
17:30but their clinician patient time
17:32was cut in half and the other half
17:35of that time was spent interacting
17:38with the digital therapy.
17:40So let's say instead of doing a 60
17:42minute session with their counselor,
17:43they did a 30 minute session with
17:45their counselor and then they
17:46interacted for the rest of 30 minutes
17:48in this interactive one-on-one
17:49way with this digital treatment,
17:51this community reinforcement
17:53approach treatment.
17:54And what we find is that when
17:56you cut in half patient clinician
17:58contact time and replace it with
18:00this digital intervention,
18:01we had significantly greater
18:04documented abstinence from opioids
18:06in that sample versus the sample who
18:10received standard methadone treatment.
18:13We had the opportunity in this study,
18:14this was Nida funded.
18:16We had the opportunity to track these
18:19outcomes for 12 months per participant
18:22and the differential here you see
18:24persisted for that 12 month window.
18:27We saw this benefit from including
18:29the digital treatment as part of the
18:32care model and there's lots to say
18:33about why we think this is the case.
18:35But you know this is an intervention
18:37that you know is being delivered
18:39with fidelity every time and
18:40it's very responsive to what
18:42people are understanding or not,
18:44what their needs are or not.
18:45So it's very personalized in the way
18:47that it delivers the interventions
18:49to a a particular individual.
18:51And and we know there's a
18:53lot of variability right, in,
18:54in terms of what happens in counselling
18:56in different in therapeutic settings.
18:58And so this is very encouraging that this
19:01can really have this kind of robust effect.
19:05And then one more snapshot,
19:07There's lots more data to
19:08share beyond these studies,
19:10but one more snapshot just to
19:11show you a different way that a,
19:13a clinical setting could embrace
19:15a digital therapeutic.
19:16And that is to say, OK,
19:18we're not going to touch the
19:19underlying model of care.
19:19We're just going to add this
19:21on as a supplement.
19:21We're going to say over and above our
19:23treatment as usual our care model.
19:26What if we offered a digital intervention
19:28as an adjunct to care and that's
19:31what's reflected in this study.
19:33So again,
19:34this is another study with
19:36adults with opioid use disorder
19:38entering outpatient treatment.
19:40This is another study in a
19:42methadone treatment setting.
19:43And the participants either again
19:45receive standard methadone treatment or
19:48as an adjunct to that they were
19:50given when they joined the study,
19:52access to a mobile version
19:54of this digital intervention.
19:56And when you gave folks this
19:58mobile version on top of the
20:00underlying treatment model,
20:01we were able to keep those
20:03people in treatment much longer,
20:05much higher percent of those people
20:07retained in treatment compared to
20:09those who received standard treatment.
20:10So right now we're looking at
20:12what happens in the first three
20:14months of your treatment episode.
20:16And we found much higher percent of
20:18patients who got the app as part of
20:21care were retained in that window
20:22of time versus those who didn't.
20:24And you know, this is really important.
20:26We know that treatment retentions are really.
20:28Important predictor of all kinds
20:30of other clinical outcomes in
20:31substance use treatment.
20:33And if we can bump up and and you know,
20:35increase our retention,
20:36particularly in these early windows,
20:39you know of when dropout can be quite high,
20:42you know this can be a
20:43really meaningful effect.
20:44And the same pattern I'm showing
20:45you here showed up in the urine
20:48toxicology data where if you offered
20:50this mobile tool as part of treatment,
20:52you had more documented abstinence
20:55from opioids than if you didn't.
20:58And again,
20:59I'm just giving you examples of data there.
21:01There's lots of really compelling
21:04literature on the on the utility,
21:06clinical utility of these types of
21:08tools for substance use disorder
21:10but also for other types of mental
21:12health conditions.
21:13And I I just wanted to mention this is
21:16something that if you are a clinician,
21:18if you don't know about this,
21:19you you should because it's undoubtedly
21:21a big part of of what is going to be
21:24a part of our future of healthcare.
21:26And surely you know our our
21:27residents and then the trainees,
21:29medical students should should
21:30surely be aware of this.
21:31So it's the case now in this country
21:34that software can be prescribed by
21:37doctors and there's a there's a
21:39category of medical devices that
21:42the US Food and Drug Administration
21:44calls software as a medical device
21:47where you can go to the FDA with
21:49data from a clinical trial seeking
21:52a label saying this software is
21:54effective in the prevention,
21:56treatment or management of some
21:58disease or disorder.
21:58And you have to meet a lot of
22:00different requirements.
22:01But if you if you meet the
22:03requirements and and you get sort
22:05of authorization for that,
22:07for that claim,
22:08you become what's called a prescription
22:11digital therapeutic and that software
22:13is an eligible to be prescribed
22:16by clinicians in this country.
22:18And the first time that happened
22:20was in 2017 and that happened to be
22:24a this computerized intervention
22:26I just described to you for this
22:29community reinforcement approach
22:31to behaviour therapy for substance
22:33use disorders.
22:33And then the second one which was
22:36FDA cleared in 2018 was a specific
22:39indication of that intervention for
22:41treatment of opioid use disorder.
22:43Now we have a growing array of
22:46digital therapeutics that
22:47are available for prescription.
22:49Most of them are in the mental health space,
22:51not all and one includes a
22:55pediatric indication for ADHD.
22:57So again FDA is not going to look at your,
23:00your your software if it's if it's
23:03a general Wellness app or a health
23:05general health promotion type of tool.
23:08Those are excluded from regulatory oversight.
23:09But it really has to be you know,
23:11software driven,
23:12evidence based and seeking to make a
23:14claim of this is a potent intervention.
23:16Some people call these digital pills.
23:19So you know, there's been a lot of
23:22excitement around this evolution in in
23:25the regulatory space and there are other,
23:27I'll speak later,
23:28there are multiple other paths
23:29to deployment as well.
23:30But what we have seen, you know,
23:32having worked in this space for a
23:33long time and having clinicians come
23:35to us and say I'm excited about this,
23:36I'm excited about the data,
23:37my patients are interested in this,
23:39What should I offer?
23:40If I go to the App Store,
23:42there are huge numbers of mental
23:43health apps out there.
23:44How do I navigate that?
23:45How do I know what's effective,
23:46what's not,
23:47what could be harmful.
23:48And so there are there are multiple
23:50ways to do that.
23:51But one benefit of this particular
23:53pathway is that you know if something
23:56has gotten this designation by
23:58FDA that it's really you know,
24:00been carefully vetted and really you know,
24:03had to meet all kinds of criteria
24:05to say indeed this is you know,
24:07safe and effective in preventing,
24:08treating or managing a health condition.
24:10So this is a growing, growing area,
24:12rapidly growing area.
24:14But I just want to tell you that you
24:16know I I've highlighted some examples
24:18of digital therapeutics in the realm
24:19of substance use disorder treatment.
24:21But we have all kinds of examples
24:23now of robust clinical effects
24:24for lots of health conditions,
24:26heavily mental health.
24:27So we've seen as you see here on the
24:30side decreases in mental symptoms
24:32in things like ADHD, anxiety,
24:35depression, PTSDOCD, schizophrenia.
24:37We've seen you can improve remission
24:40rates in in some types of mental health.
24:43We've done a number of studies with
24:46chronic pain patients and you know,
24:49giving people a digital tool to
24:51help them better manage chronic
24:53pain and to help prevent chronic
24:55pain from ruining their lives,
24:56ruining their relationships,
24:57preventing them from achieving goals
25:00and and giving them resources and to
25:02help them help them in these areas.
25:04And what we find is that you know we
25:06can not only improve pain management
25:08and sort of goal directed activity
25:10among chronic pain patients.
25:12So we've also seen that we can
25:13reduce Ed visits, right.
25:14So when you have something in your
25:15pocket that in the moment can help you,
25:17when maybe you're catastrophizing about pain,
25:20perhaps that tool can help you
25:21instead of you know needing to go to
25:24the Ed as as the response to that.
25:25And so you know surely there
25:28this could reduce cost
25:30of of healthcare utilization
25:32including Ed visits.
25:34And we've also seen some really
25:36compelling data in reducing
25:38healthcare costs for panic disorder,
25:39substance use disorder
25:40and opioid use disorder.
25:41For example,
25:43Mass Health Massachusetts Medicaid
25:47just recently published data with
25:51these tools that I just described
25:53for treating digital therapeutics
25:54for treating substance use
25:56disorder that they used with their
25:58first cohort of patients in the
26:00state who got access to those.
26:02And they showed that they dropped Ed
26:04utilization by 45% with when these
26:07tools were part of care models and
26:09they dropped hospitalizations by 64%.
26:11Very compelling data.
26:12I was very excited to see it.
26:15You know this is in the real world
26:17where things are is in the wild what,
26:18what kinds of clinical effects you
26:20can see but also economic effects.
26:22And we've seen also another
26:24application of these types of
26:25tools is in promoting medication
26:27adherence and also adherence to
26:29various types of medical regimen
26:31and then also functional outcomes,
26:34you know,
26:35really helping people have meaningful lives.
26:37So this is just a snapshot of
26:40what's evolving in this space.
26:41There's a lot of exciting work in
26:43the prevention space and really
26:45you know really robust effects in
26:47in building up protective factors
26:48and reducing risk factors for
26:50lots of health conditions,
26:52mental health,
26:53substance use as well as chronic
26:56disease management.
26:58So I think it's a really interesting
27:00time of opportunity.
27:01I've been doing this for a long
27:03time and you know I have seen a
27:05whole confluence of factors recently
27:07that I'm pretty excited about that
27:09I think positions us as a field to
27:12really envision and help shape I
27:15think a very promising future for
27:17for digital health and digital therapeutics.
27:20So we surely know we've seen growing
27:22demand for remote models of of
27:24care and and intervention delivery.
27:26We also unfortunately no across
27:29the globe we've seen a big surge in
27:33behavioral health needs in this country,
27:35in many other parts of the world,
27:37in youth, in adult populations.
27:40You know the statistics are really,
27:42really alarming and and and we
27:44also sadly know that a lot of
27:47people either won't access mental
27:49health care or can't.
27:51And that's not just true in you know,
27:53low and middle income countries.
27:54That's true in rural America.
27:55That's true in in many communities
27:57as you all likely well know.
27:59And so there's this big population
28:02level need and and and you know how can
28:05we scale up capacity to to achieve that.
28:07And we at the same time have been
28:09seeing a pretty striking growth
28:10in the digital health industry.
28:12It it calmed down a bit in the
28:13last year and a half or so,
28:15but it is been growing at at at
28:19great just exponentially really in
28:21terms of start-ups in the space,
28:24in terms of venture investment in the space.
28:26But also as you may know there are
28:29a number of global pharmaceutical
28:31companies that are heavily investing
28:33in digital therapeutics and building
28:36out digital
28:37health formulas, Digital therapeutics in
28:39their portfolio that sort of complement
28:41extend what they traditionally do in
28:43the medication space and sometimes
28:45that these are these are digital
28:47therapeutics that are intended to
28:49have synergistic effects with some of
28:50their medications and or promote you
28:52know more adherence to medications.
28:54But sometimes these are stand alone
28:56tools for a health condition that can be
28:59agnostic to you know if they're with,
29:01if they're used with or without
29:03various medications.
29:04So there's a lot of interesting growth
29:06in the pharma space that you may be
29:08aware of and glad to talk more about that.
29:11And then additionally,
29:11there's A at the same time all
29:13of this is happening,
29:14we're seeing more paths to deployment, right.
29:16So I talked about one which is
29:18this FDA regulatory pathway.
29:19But we also know that in this country,
29:23you know there are employers
29:24that are building out their own
29:27offering to their employees.
29:28There are pharmacy benefit
29:30managers that are doing that.
29:32There are groups like CVS,
29:34Caremark and and others.
29:35So there's a growing array and there's
29:38even over the counter offerings now as
29:41well as this prescription model I mentioned.
29:45And then the final piece to
29:46this of course is payment.
29:48How does this get paid for it?
29:49This is a huge issue and this
29:51has been evolving as well.
29:53I think that many people overestimated
29:55the pace at wish reimbursement
29:57would kick in for these.
29:59So even if you get approved by FDA
30:02and your prescribable software,
30:04it may not get paid for.
30:05So there are definitely some
30:07private payers paying for this.
30:09They're definitely some employers
30:11paying for this.
30:14There are a number of states that have
30:16Medicaid coverage like I mentioned
30:18Massachusetts as an example for this,
30:20but but it's it's just it's a minority
30:23of states that have Medicaid coverage
30:25and there's no national coverage
30:27framework by Medicare at this point.
30:30There is,
30:30though,
30:31a lot of hope that a pending legislation
30:35right now called the Access to
30:38Prescription to Digital Therapeutics Act
30:42that it may get past timelines unknown.
30:45There was hope it was going
30:46to happen last year,
30:46but I know there's a lot of
30:48advocacy efforts for this year.
30:49And if that gets passed,
30:51then we would have national
30:52Medicare coverage of digital
30:54therapeutics in this country.
30:55And and some colleagues feel like
30:58that would then expedite the pace at
31:01which Medicaid would kick in as well.
31:03So there's a lot happening in this country.
31:06There's a lot happening in other countries.
31:07There's entire countries that have
31:09national coverage frameworks,
31:10like Germany and the UK.
31:12Asia has been exploding in this space,
31:14many countries in Asia.
31:16So it's a really interesting time and
31:19there's a lot still to sort of work
31:20out in terms of like what's out there,
31:21because there's there's unfortunately
31:23a lot of hype,
31:24but there's also a lot of
31:25really potent tools.
31:26And I'm very optimistic
31:27that we're going to get to
31:28a landscape where we really have a much
31:30more widespread access to these tools
31:32that people can use in their daily lives.
31:35So what I thought I would highlight
31:37at this point is a a project that we
31:40were able to do that shows you you
31:42know so going beyond the clinical
31:44trials that I highlighted to you,
31:45you know those are promising effects.
31:46But what if you really wanted to scale
31:49up the use of digital tools and really
31:52think about new healthcare delivery
31:54models that centrally leverage digital
31:56health tools as part of the care
31:59model and what would the impact be.
32:01So I I'm going to tell you about an
32:02example of the study we've done in
32:04this space and this was a project
32:06funded by the US National Institute
32:07of Mental Health and it was a project
32:11we did in Latin America and we
32:14started in the country of Columbia.
32:17Columbia, as you may know,
32:18is a part of the world that has a very
32:21high mental health burden and very limited
32:25workforce capacity to tackle that burden.
32:28So you know,
32:29Columbia had generations of armed conflict
32:32and and if you know all the data show that,
32:36you know every community has been touched
32:39by this very high rates of depression,
32:41very high rates of alcohol use,
32:43soft and accompanied by domestic violence
32:45and so very high mental health need.
32:47But if you look at the
32:48mental health workforce,
32:49you have, you know,
32:50a handful of psychiatrists in Bogota
32:52or in some of the more urban settings.
32:54But if you go into rural farming communities,
32:56you have we you have no,
32:59no access to sort of outpatient psychiatry.
33:01There's really only inpatient psychiatric
33:03hospitals sprinkled throughout the country.
33:06So this is just an example of a part
33:07of the world and there are many
33:09other examples we could think of
33:11where you know you have this problem,
33:13right,
33:13you have this big need and you have
33:15limited capacity to meet the needs.
33:16So what what do you do?
33:18So what this project did is to leverage
33:23an integrated suite of digital health
33:26tools to seek to scale across the
33:29country screening and treatment for
33:32mental health conditions in a way
33:35that could help meet this need.
33:37So it was basically a digitally
33:39enhanced model of care that we did
33:41in partnership with Primary Care
33:43Systems across Columbia.
33:45And so Primary care in the country
33:47of Columbia had never talked about
33:49mental health.
33:49It wasn't part of any routine
33:51screening or included in care at
33:54all before this project.
33:55We know that's not true in
33:56other parts of the world,
33:57but it happened to be true there.
34:00But this,
34:01the benefit is that primary care was much
34:03more accessible in many parts of the country,
34:07unlike psychiatric care.
34:08So there was surely some training of
34:10primary care providers in understanding,
34:12you know what,
34:13what is mental health?
34:14Why is mental health important
34:16in the whole as you
34:18think about whole care models
34:20for the patients you serve.
34:22So it's definitely some clinician
34:24training and and support to primary
34:26care providers around embracing this.
34:28But then basically we we integrated
34:30into primary care across the country
34:33and integrated suite of tools that
34:35first included taking clinically
34:38validated digital screeners for mental
34:41health and alcohol use was also part
34:44of this and deliver that entirely in
34:46a digital assessment way that then
34:48directly fed into a digital clinical
34:50decision support tool that providers
34:52could use when interacting with the
34:54patient in front of them and to help
34:57with a diagnosis and then care models.
35:00And then also every single patient
35:03who met criteria for one of the
35:06conditions that we're screening for
35:07was also given a digital therapeutic.
35:09So that was on top of what happened
35:11in primary care.
35:12They had this tool that they could
35:14use every day in their daily lives to
35:17support to provide a mental health care.
35:19And so these are some photos of some
35:21of the tools we used for screening for
35:25the clinical decision support that the
35:28providers used and for the digital
35:30therapeutic that the patients use.
35:32This is a digital therapeutic that we
35:35developed based on a couple of decades
35:37of different NIH funded projects
35:39with different populations where we
35:41took you know sort of core sort of
35:42science of behavior change in the
35:44core active ingredients in helping
35:45people initiate and maintain health
35:47behavior changes and and embedded
35:49it in a transdiagnostic platform.
35:50So we could flexibly provide
35:52therapeutic tools to people depending
35:55on whatever combination of needs and
35:57preferences they have in mental health.
36:00And we have lots and lots of data from this.
36:03It was a big project.
36:04We have wonderful partners across
36:06the whole country work on this not
36:09just research partners but you
36:10know really Ministry of Health and
36:13industry payers and patient advocacy
36:15groups and healthcare leadership
36:17really working on this in order to
36:19be that was really critical in order
36:22to really scale this in the way we
36:23were able to across the country.
36:25So in one,
36:26this is a snapshot of a couple
36:28of years right before COVID.
36:30We saw in that couple years we
36:32went from screening no,
36:33No 10 patients for mental health and
36:35primary care to screening over 22,000 people.
36:39And then of that we had 22% and
36:41positive screens and then 8% diagnosis
36:43of depression or unhealthy alcohol
36:46use in this case.
36:47And so you know this is the those
36:508% would have gone undetected.
36:51You know in the traditional models,
36:53these very simple slide here,
36:54but I just want to give you this was
36:56mostly an implementation science study,
36:57but we we were able to track patient
37:00outcomes for one year every single patient.
37:02We had a whole team tracking patients
37:05for a year and to to to document sort of
37:09their experience clinically for a year.
37:10So these are just some examples of
37:12lots of data that we have including
37:15you know showing a market reduction
37:17in depression symptoms over that year.
37:19And that was true even if you had
37:21pretty severe depression at baseline.
37:24We saw even those,
37:25you know with high versus moderate
37:26versus mild depression at baseline were
37:28able to benefit from this therapeutic
37:30approach and reduce depression symptoms.
37:33Same similar pattern I should
37:36say with problematic alcohol use
37:38over that course for the sample
37:40that had high levels of drinking.
37:42And then even for those who had really
37:45high levels of alcohol use versus moderate
37:48to maybe some lower risk drinking,
37:51again we saw some value for
37:53for all of those folks.
37:55We have a lot of data on how
37:56did this impact the functioning
37:58of the healthcare system,
38:00the clinical workflow,
38:01how people spend their time and
38:04these are just some examples of
38:06some data from those analysis.
38:08So we looked at you know from
38:12administrator point of view,
38:12from provider point of view,
38:14you know what do you think about
38:15adopting this in your setting,
38:17is it is this accessible to do so,
38:19is it appropriate for the context,
38:20is it feasible etcetera.
38:21So what we found is in this two
38:24year window I'm showing here even at
38:25baseline and this was after that we
38:27had trained a lot of the primary care
38:29folks about what we're about to launch.
38:31We had pretty high rates of buy
38:33in and you know on on many of
38:35these dimensions at baseline which
38:37persisted post launch and for two
38:39years post launch for for most of
38:41the data that we've got here there's
38:43some new ones to talk about here.
38:45But in the interest of time generally
38:47we we found that people felt like
38:49it was a value we as part of
38:52the data collection did a pretty
38:55detailed costing assessment.
38:57So you all may know this time driven activity
39:00based costing metric that first came out
39:02of Harvard and it gives you this very,
39:04it's,
39:05it's a very lengthy process to do,
39:06but it's very valuable where you
39:08map the cost of every process
39:10in a clinical workflow.
39:12And then you can see when you introduce
39:14some innovation in a clinical workflow,
39:16how does it impact costs,
39:17what's cost difference.
39:18So the bottom line of this very long
39:21process was that after we implemented
39:23this model in primary care in the
39:25country to screen and treat mental health,
39:28the cost per patient per year was $1.89 U.S.
39:33dollars higher than the what the prior model,
39:36sort of the baseline model before we
39:39before we introduce this new model.
39:40So there's a lot of excitement about
39:42that because of the value that
39:44they saw in doing so and and sort
39:46of the limited cost per patient.
39:48And so you know there's there's a
39:51lot of optimism that that that they
39:54could continue to grow capacity in
39:56the region with this type of tool
39:59and and also grow it to embrace
40:01other areas of health and including
40:02other types of mental health but
40:04other preventative
40:05health promoting interventions and and
40:07other chronic disease management tools.
40:09And again this was the country of
40:11Colombia and now we've expanding our
40:13partnership to Chile and Peru, but.
40:15The nice thing is that this is an
40:17exemplar of a part of the world
40:19where you know we could show value,
40:21maybe we could do so in rural
40:23America or other countries.
40:24And you know it's been exciting to see the
40:27interest in in the region in scaling this up.
40:30Now you know this the funding,
40:32the research funding has ended and now
40:34they are are offering it clinically
40:37and seeking to expand it even more so.
40:40So I'm happy to share papers on that,
40:43but I'm going to shift gears just a
40:45little bit in our remaining time and
40:47I'm watching the clock to make sure we
40:49have enough time here for discussion.
40:50But I want to talk a little bit now
40:54about digital health assessment, right.
40:57So I talked a lot about therapeutics,
40:59but we can learn a lot about people through
41:02digital data capture about their daily lives,
41:04about their, you know,
41:06really granular data even day-to-day about
41:08people's needs in in their real world,
41:10right in their daily lives and and and
41:13provide a lot of insights into people's
41:16clinical status and trajectories.
41:17But then that data,
41:18particularly we can build at
41:19the individual level,
41:21predictive models that help us
41:22understand when someone might benefit,
41:24benefit from an intervention,
41:25can help us then push sort of
41:27digital therapeutics to people when
41:29they might most benefit from them.
41:31You know,
41:32when someone's at risk of relapse
41:33or panic attack or psychotic episode
41:35or whatever it is,
41:36could we in that moment give them some
41:39meaningful therapeutic intervention.
41:41So there's a lot of exciting work
41:43in the space.
41:44Most of it is in mental health,
41:47although that's evolving.
41:48But people are looking at digital biomarkers,
41:52things you can capture in vivo again about,
41:57you know,
41:59sort of in people's environment
42:01and neurodevelopmental context.
42:02That can be done through the very
42:05rich array of sensors you've got
42:07on smartphones and or wearables
42:10like smartwatches.
42:12People are even making smart jewelry
42:14and all kinds of different all kinds
42:17of different ways to capture this rich
42:20information in People's Daily lives.
42:22And and some people call this
42:25digital phenotyping.
42:26And that's just really this very
42:28detailed granular quantification of
42:30these individual level data in the
42:32real world collected through digital devices.
42:34And it could be either passively
42:36collected through sensing like I
42:38mentioned or you can prompt people
42:40to answer brief queries about,
42:42you know, their pain, their craving,
42:44their their mood state, their sleep,
42:46whatever the question is.
42:49And and you can do that through
42:51on digital platforms using these
42:54ecological momentary assessments.
42:56And so the idea is that maybe we
42:58can understand for a given person
43:00what confluence of factors might
43:01predict clinically meaningful events.
43:03And then this sort of some people
43:06call it just in time adaptive
43:08interventions or just in time
43:10delivery of a therapeutic.
43:11There's a lot of exciting
43:12work happening in this space.
43:13I'm going to give you a little
43:15snapshot of first of all what's
43:16happening in substance use disorder
43:18space and then more broadly mental
43:19health and then tell you a bit about
43:21some work we're doing in this space.
43:22So some of the really early work
43:25in looking at these digital
43:28biomarkers in substance use were
43:30heavily with smoking populations.
43:32So, so Shiffman,
43:33you may you may know his work well
43:36we did some early work in this where
43:38where you're tracking people's mood
43:40in a pretty detailed way and they
43:43had a whole line of research here.
43:45It's just one example showing that
43:46lapses to smoking among smokers trying
43:48to quit were associated with increases
43:50in negative mood for many days and
43:52not just hours before a smoking lapse.
43:54So it was this sort of,
43:56you know, sort of this,
43:57a more prolonged negative mood that
43:59seemed to be associated with lapses.
44:01Kenzie Preston who I
44:03understand recently retired,
44:04but there's still a lot of fantastic work
44:07coming out of her former lab at the Nida
44:10Intramural Research Center in this area.
44:12So one of those studies showed that
44:15craving predicted imminent drug use,
44:18but self reported stress was much
44:20less predictive than craving.
44:21So, you know,
44:22they have this whole literature around,
44:24you know,
44:24do these types of data help us
44:27understand more nuance between,
44:28you know,
44:29between different triggers for drug use
44:32including things like stress and craving.
44:34And they also the same lab,
44:37David Epstein's part of that group show that,
44:40you know,
44:41drug triggers,
44:41things that for a given person like
44:43exposure to drug cues or mood changes
44:46increase for hours before cocaine use events.
44:48But we we saw very different
44:49pattern with heroin use events.
44:51So they're also,
44:51you know,
44:52building out a literature suggesting
44:54that this type of data might give
44:56us new insights into different
44:58sort of risk profiles or different
45:00sort of triggers for different
45:02types of substance use in the
45:05personalized intervention space.
45:06Here's some early studies, again,
45:09including again with smoking.
45:10So if you track people smoking risk,
45:13this is what was done in
45:15this particular study.
45:16You know their risk for smoking.
45:18And then and then you trigger a
45:20tailored message responsive to that,
45:22like, you know,
45:22when someone seems like they are at risk,
45:24advise them to a piece of nicotine gum.
45:26Those types of tailored messages were
45:28more engaging and effective than usual care.
45:30Similar study where you give
45:32adaptively tailored advice for
45:33managing withdrawal symptoms
45:34when people are going through
45:36nicotine withdrawal symptoms and
45:38and medication side effects.
45:39We found that that very responsive
45:41in the moment advice was used more
45:43often and was more acceptable among
45:45smokers seeking to quit than usual care.
45:47And then we had a grant from the NIH
45:50Science of Behavior Change Initiative
45:53out of the Office of the Director's
45:55Office and it was on self regulation.
45:57And one piece of that included
46:00developing A momentary self regulation
46:02scale where we can in the moment assess
46:06different aspects of self regulatory
46:08capacity like emotion regulation.
46:10And we found in this study that
46:14digital interventions can impact
46:15momentary self regulation that then
46:17can in turn impact health behavior
46:19across various populations like
46:21people with binge eating disorder,
46:23like heavy smokers.
46:25So very these are just again a
46:27snapshot of of what's evolving.
46:29There's a lot of exciting work
46:30happening in this space now,
46:31including I I I know some folks at Yale
46:33are doing some great work in this space.
46:36We recently finished a study funded
46:39by Nida run on NIDA's clinical trials
46:42network platform that we're part of.
46:44It was in partnership with
46:46Kaiser Permanente and IBM.
46:47And this study was really trying
46:50to understand the utility of
46:52digital data capture with people in
46:55treatment for opioid use disorder.
46:57So we asked outpatients in buprenorphine
46:59treatment for opioid use disorder
47:01if they want to join the study.
47:03And if they did,
47:05we asked them to not only answer questions,
47:09we asked them through these prompts,
47:10these Emas on on a mobile device.
47:12We asked them if we could passively get
47:15data from smartwatches and smartphones.
47:17And we asked them if you have social media,
47:19can we can we take your social media
47:22data and can we look at that data?
47:24And the idea here was to understand,
47:27are some of these data meaningful
47:30in People's Daily lives to help us
47:33understand when people might relapse or
47:35maybe not take their medication today,
47:37for example, right.
47:38We know a lot about you know we
47:40know medication treatment is a
47:42very effective and literally life
47:44saving for opioid use disorder.
47:45And we also know that we
47:48have you know relapses,
47:49we have dropout,
47:50we have non medication adherence and
47:52we have clinical insights into some of
47:55the factors that contribute to that.
47:56But the idea here is if we get
47:58this day-to-day data that's
47:59outside of a clinical setting,
48:01could we get any new insights into
48:03what when someone might be at risk
48:05of these things and where are there,
48:07where is there redundancy
48:08in the data capture, right.
48:10So maybe you know it's just a snapshot
48:12of these data that are really the the
48:14main sort of data to extract that are
48:16meaningful for predicting these things.
48:18That's the big picture of the the
48:20study we we in the passing sense,
48:23passive sensing.
48:24You get all kinds of rich information from
48:27literally not asking people to do anything.
48:29It's just passively collected
48:30in an unobtrusive way.
48:32You can get features that give you
48:34insights into people's activity levels,
48:36into their sleep and quality of sleep,
48:38into their sociability,
48:39into their light exposure.
48:41Lots of different kinds of data you can get.
48:44And then we asked them these brief queries
48:46as you see on the right here about,
48:48you know, their sleep stress,
48:49their pain, craving,
48:52withdrawal symptoms,
48:53etcetera.
48:54And so these are really brief
48:57questions we prompt them to do
48:58on a mobile device.
48:59And then if they had social
49:01media data from Twitter,
49:03Facebook or Instagram,
49:03we could look at those data.
49:05And so we could look at postings,
49:07we could look at sentiment analysis,
49:09we could look at topology of
49:10social networks, the ideas.
49:12Are any of these data really
49:14strong predictors of things like
49:17when someone might relapse?
49:19So this first day was heavily A
49:22feasibility study and we found
49:24that participants in outpatient
49:26treatment for opioid use disorder
49:28carried the phone on 94% of days.
49:30We've been very encouraged by that,
49:31wore the watch 74% of days.
49:34We had a average response rate to our
49:37questions of 70% and we were actually
49:39quite surprised about this last one.
49:41We thought it'd be lower but 88% agreed
49:43to share their social media data and
49:46then we have a lot of data including
49:47a lot of evolving data from this.
49:49But I'm just going to give
49:50you a little flavor of some
49:52of the things we're finding.
49:53This slide is from our EMA data.
49:56So this is again the these questions
49:58that we asked people to answer and
50:0311 predictor that seemed among the
50:06strongest in predicting next day
50:08opioid use is this momentary self
50:10regulation metric that I mentioned
50:12to you that we this the scale that
50:15we developed and validated and
50:16particularly when you track momentary
50:20risk taking about 24 hours prior seems
50:24to be a a pretty strong predictor of
50:28next day non prescribed opioid use.
50:31That's just one example.
50:33Here's another from some of our
50:35passive sensing data and this is
50:37just looking at two channels.
50:37This is looking at heart rate
50:39data from wearables and smart
50:41smartphone conversation detection.
50:42So not what people are saying,
50:44not the content of what they're saying,
50:45but detecting instances of communication.
50:49And again we're seeing some promising
50:53utility of these passive data channels
50:56in predicting next day opioid use.
50:59We also have seen similar pattern in
51:02predicting stress and predicting craving.
51:04Lots of ongoing analysis,
51:05but this is just to give you a flavor.
51:08So why did Nida fund this?
51:09So Nida was interested in thinking about
51:12you know, you know in clinical trials,
51:13let's say we have these great
51:15clinically validated assessments
51:16we do in an episodic way during
51:18the course of clinical trial.
51:19Maybe we're looking at you know the
51:21effects of a novel pharmacotherapy
51:22or or something else.
51:23Could this type of data be meaningful
51:25to add an outcomes measurement and what
51:27type of data from digitally derived data
51:30capture could be meaningful as part
51:31of outcomes measurement in clinical trials.
51:33So that's a big part of it,
51:35that's a big question.
51:37But then, you know,
51:38surely there's a lot of excitement
51:39around if indeed we can get these
51:41models good enough to understand,
51:43you know, when someone might
51:44be at risk and we can really,
51:47you know,
51:47trial the,
51:48the utility of these very responsive in the
51:52moment interventions to help prevent relapse,
51:54for example.
51:55So I think this is an exciting space,
51:58you know,
51:59from the discovery science space
52:00and looking at digital biomarkers
52:01to really translational science,
52:03I think in terms of informing
52:05intervention models,
52:06the the literature is compelling,
52:07but there's a lot of proof of
52:09concept out there for folks who
52:10are working in this space.
52:11But it's growing.
52:12But I think,
52:13you know,
52:13there's a lot of opportunity for
52:15more rigor in this work right now
52:17and more validation of measures.
52:18Lots of people look at different
52:20features that you extract from
52:21sensing and other things.
52:22We don't have a lot of replicability yet,
52:24reproducibility of results
52:25or control studies as of yet.
52:27We have some and it's growing
52:29particularly mental health.
52:30But it's a it's an interesting and
52:33exciting space and I think that
52:35it's promising for mental health,
52:37but also really just thinking
52:39about behavior broadly and it's in
52:41in sort of transcending disease
52:43specific types of behaviors to
52:45really understanding in new ways
52:47the complexity and interrelatedness
52:48of different clinical conditions.
52:50And so in the last couple of minutes
52:52before we jump to discussion,
52:53I just wanted to mention again
52:56our center and this
52:58is a Nida funded center and we,
53:00we are really devoted to bringing
53:03science to the space, right.
53:05Bringing science to the development,
53:07evaluation and implementation of
53:09digital health tools from prevention to
53:12treatment for heavily for substance use.
53:15And mental health is a critical mass of
53:16our group in terms of the work that we do.
53:18But and we do work you know ranging from
53:21precision prevention of cancer to you know,
53:23chronic disease management and diabetes
53:24and and and lots of other areas.
53:26So lots of you know, rigorous science.
53:29We're housed at Dartmouth,
53:30but we work with partners across
53:31the country and internationally.
53:33But it's not just about the science, right.
53:34It's great to be able to do the
53:36rigorous science and publish on
53:37and share it with your colleagues.
53:39But our our goal is really to have
53:41impact and how do we bring the
53:42science to People's Daily lives?
53:44How do we scale things so that the most
53:47how effective and engaging tools are
53:49what people can access and we have a
53:51lot of resources if you're interested
53:53in this work or doing this work.
53:56Some of the current things we're up
53:58to particularly in our recent center
54:00grant renewal is a focus on some of our
54:03transdiagnostic digital therapeutics,
54:04some of the adaptive digital therapeutics
54:07I mentioned some of our faculty are
54:10are are doing some really pioneering
54:12work in the realm of artificial
54:14intelligence as applied to mental health.
54:16We've we've launched several
54:19partnerships with training programs
54:21for underrepresented minority
54:23scholars who are working with us in
54:26digital in training for for becoming
54:28digital health scholars.
54:29And then as I mentioned at the beginning,
54:32you know we are really working on
54:35strategic partnerships with with
54:37lots of different partners in in the
54:40regulatory space and policy and industry
54:42investors and and and not just about
54:45bringing the science to that community,
54:47but really understanding what's happening
54:50in those in that area and what kinds of
54:53questions and data people want, right.
54:54So when payers decide to pay
54:56for a digital health tool,
54:57what do they care about?
54:58You know when the FDA is looking at data,
55:00what do they care about so that
55:02you know our research community
55:03can also be capturing meaningful
55:05data that are meaningful to a lot
55:07of stakeholders in this space.
55:08So to that end we've done several things.
55:10We've launched in the annual Digital Health
55:12Summit with a whole array of partners
55:14with that goal for shared dialogue to it,
55:16with a goal of together increasing pace
55:19of access to the most effective tools.
55:23We launched a Dartmouth Innovation
55:25Accelerator in digital Health in
55:27partnership with our Magnuson Center
55:29for Entrepreneurship at Dartmouth.
55:31Again,
55:31it's about getting things out there
55:33that work and having a path in
55:36the right partners and knowledge
55:37and expertise to bring to that.
55:38So this is a big priority of ours.
55:41And again, happy to chat about it more,
55:43but I want to pause here so we can have
55:46some discussion.
55:47I included my e-mail,
55:49our website for our center.
55:51We have a pretty lively,
55:52particularly Twitter life.
55:53If you all are interested in this,
55:54please follow us.
55:56And then I had to mention we have some
56:00assistant professor level faculty positions
56:02available in our center right now,
56:05so if anyone has any interest
56:07in learning more about that,
56:08please feel free to reach out.
56:10So anyway, thank you for the
56:11opportunity to share this.
56:12I'm going to stop sharing my screen.