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Digital Health- The Mental Side and more · Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast.

0:00
Did you know that even in high income countries,
0:03
something like 50 % of patients don't actually
0:07
get treatment for psychiatric disorders? And
0:10
in low to middle income countries, well, that
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figure can shoot up to 85%. 85%. Quite staggering.
0:16
It really is. That's a massive, undeniable treatment
0:19
gap. The traditional system just isn't bridging
0:21
it for everyone, is it? Clearly not. Welcome
0:24
to the deep dive. This is where we take a stack
0:26
of sources, articles, research, notes you've
0:29
sent us, and we pull out the most important bits
0:31
of knowledge, the key insights. Think of it as,
0:34
well, a shortcut to getting properly informed,
0:38
hopefully with a few surprising facts, maybe
0:40
just enough humor to keep you listening. That's
0:42
good. Today, we're diving deep into a body of
0:44
work exploring the profound impact of AI and
0:48
digital technologies on global health care. We're
0:50
looking particularly at mental health and also
0:53
the future of our hospitals. A huge topic. Absolutely.
0:57
And guiding us through this really fascinating
0:59
landscape, I'm joined by a profmo imam. His ability
1:03
to synthesize all this diverse information, spot
1:06
the patterns, connect the dots. It makes him,
1:08
well, the ideal expert to help us unpack what
1:11
truly matters in this dense but really crucial
1:14
material. It's brilliant to have you here. Thank
1:17
you. It's a privilege to dive into these sources
1:18
with you. So right off the bat. The sources talk
1:21
about a profound reform coming to health systems.
1:24
It seems intrinsically linked to this digital
1:27
transition. If you had to pick one thing, what's
1:29
the single biggest, most fundamental change you
1:31
see happening across the sector based on this
1:34
material? That's a big question. Based on these
1:36
sources, I'd say the single biggest change is
1:38
the radical reshaping of where and how care is
1:42
actually delivered. It's not just about digitizing
1:44
the processes we already have. It's more fundamental.
1:47
It's about a geographical and and functional
1:50
reorientation. Right. The sources strongly suggest
1:53
a future where the high level technologically
1:57
advanced acute care sort of consolidates within
2:00
hospitals. But at the same time, there's this
2:03
massive and necessary push to strengthen care
2:07
much closer to home. proximity, healthcare, primary
2:10
care, home care, this dual movement, this push
2:13
and pull, it fundamentally alters the traditional
2:15
model we're used to. That tension between the
2:18
high -tech hospitals and care closer to home,
2:21
that's really interesting. Now, thinking specifically
2:23
about mental health, these digital tools, things
2:26
like smartphone apps, chat bots, they're obviously
2:29
gaining traction. They are indeed. What's the
2:32
most promising potential application highlighted
2:35
in these sources for genuinely reaching more
2:37
people, especially those who are currently underserved?
2:40
Well, the most promising application, as the
2:42
sources really highlight it, lies in the potential
2:45
for these digital tools to democratize access
2:49
and also to provide continuous objective insights.
2:52
Right. Exactly. Think about smartphone apps enabling
2:55
real -time data collection, data processing.
2:58
They offer a pathway to get clinical insights
3:00
that are relatively cheap, secure, fast, and
3:04
crucially available to individuals who might
3:07
face really significant barriers getting to traditional
3:10
in -person care. Geographic barriers, stigma.
3:13
All of those things. This capability offers a
3:16
real route to extending support way beyond the
3:19
clinic walls. OK. And the sources also delve
3:22
into big data and machine learning AI, essentially
3:24
for predicting health outcomes. Even incredibly
3:27
sensitive things like suicide risk or psychosis.
3:30
What's the fundamental shift that represents
3:32
and how clinicians might actually work day to
3:34
day? This shift is it's really towards what some
3:38
call precision psychiatry. Or you could say truly
3:40
individualized prediction. Precision psychiatry,
3:42
okay. Yes. So instead of clinicians relying on
3:45
these episodic, potentially quite subjective
3:47
reports from patients during infrequent appointments.
3:50
Snapshot view. Exactly. Big data and machine
3:52
learning models. offer the ability to process
3:55
these vast multi -dimensional data streams. This
3:59
allows for continuous objective insights and
4:01
crucially individualized risk estimates. Right,
4:04
for that specific person. Precisely. It's moving
4:08
away from a sort of population average view to
4:11
a much more dynamic understanding and prediction
4:14
of outcomes for the specific individual patient.
4:17
Yeah. It fundamentally changes the information
4:19
landscape that's available to the clinician.
4:20
Okay, let's really unpack this further then.
4:22
We're moving into our first proper deep dive
4:25
segment now, focusing on that broader digital
4:27
transition you mentioned and how these tools
4:29
are reshaping care delivery right across the
4:32
system. You started by talking about profound
4:34
reform driven by demographic changes. What exactly
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did the sources zero in on when they talk about
4:40
this big picture transformation? The sources
4:43
are actually quite explicit here. They link the
4:46
future of the health sector very strongly to
4:48
advances in science and this, well, what feels
4:51
like an inevitable digital transition. Right.
4:53
A major driver they identify for this profound
4:56
reform is the demographic transition specifically,
5:00
increased longevity, particularly in more developed
5:02
countries. People living longer. Exactly. People
5:05
are living longer. And with that comes a greater
5:07
prevalence of chronic diseases. Health systems
5:10
simply must be equipped to manage these effectively,
5:13
often over many years. And this requires a health
5:16
system that's far more integrated than perhaps
5:19
our traditional models have allowed for. So the
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aging population, the rise of chronic conditions.
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They're basically forcing this change. Precisely.
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And to manage this effectively, the sources call
5:30
for significantly greater investment in what
5:32
they term proximity healthcare. Proximity healthcare.
5:35
Making care closer. Yes. Making care more accessible,
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closer to where people actually live and need
5:40
it most. And as I mentioned just now, they articulate
5:43
this fascinating tension. The need for this high
5:46
-level, technologically supported acute care
5:49
within hospitals has to coexist and be effectively
5:52
integrated with long -term care delivered in
5:55
primary care settings and home settings. The
5:57
sources seem to see hospitals increasingly focusing
5:59
on the complex, acute technological interventions
6:03
solidifying their role as centers for specialized
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procedures, while at the same time the role of
6:09
primary health care and home care is vastly strengthened.
6:12
It's not just an organizational tweak, you see.
6:15
It's presented in these sources as a fundamental,
6:18
geographical, and functional reorientation of
6:21
the entire health system. That perspective brings
6:24
us neatly to the concept of smart hospitals,
6:27
which is mentioned in the source material. What
6:29
actually defines a smart hospital according to
6:32
this body of work? What are the core characteristics?
6:35
Well, the sources conceptualize smart hospitals
6:37
with a, I'd say, paramount focus on two key things.
6:41
patient safety, and genuine patient -centeredness.
6:44
Patient -centeredness. Yes, they even refer to
6:46
it as achieving a true patient -centricity model.
6:48
The aspiration seems to be for pan -European
6:51
and international health services to really prioritize
6:53
this transformation. And is there a driver for
6:56
this beyond just wanting things to be better?
6:58
Oh, yes, the urgency isn't purely theoretical.
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The sources cite empirical evidence, including
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an EU survey which found that over a quarter,
7:07
a quarter of respondents believe they or a family
7:10
member had experienced an adverse health care
7:13
event in hospitals. Wow, over 25 percent. Indeed.
7:17
That stark statistic alone really underscores
7:20
the vital need for the kind of safety improvements
7:22
that digital transformation might facilitate.
7:24
That figure is quite sobering, isn't it? So it's
7:27
about safety. It's about the patient experience.
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How do the sources suggest we actually measure
7:31
success and drive this complex change towards
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smarter hospitals? Because presumably it's not
7:37
just about buying shiny new equipment. Absolutely
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not. No, the sources are very clear that governance
7:42
and sophisticated change management strategies
7:44
are critical pillars here. They call for the
7:47
design of an evidence -based framework. specifically
7:50
for measuring both intermediate and long -term
7:52
outcomes that result from hospital planning,
7:55
design, and management interventions. An evidence
7:57
-based framework. Yes, and this framework needs
7:59
to be robust, obviously drawing on data, but
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also customizable to different national or federal
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contexts. Furthermore, they really stress that
8:08
developing, disseminating, and, crucially, sustaining
8:11
these best practices requires extensive partnership.
8:15
Partnership with whom? Well, they mentioned specific
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international organizations. the European Society
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of Quality Health Care, ESQH, the European Hospital
8:23
and Health Care Federation, HAP, the WHO Patients
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for Patient Safety Program, the International
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Alliance of Patients Organizations, IAPO, and
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the European Patients Forum. A lot of acronyms
8:35
there. Quite. But the point is, collaborating
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with these bodies, alongside national coalitions,
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health professional bodies, universities, policymakers,
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it's presented as absolutely essential for success.
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It's a deeply collaborative vision, not some
8:49
sort of top -down technology mandate. It certainly
8:51
sounds like it requires broad alignment, a lot
8:54
of people working together. The sources also
8:56
use some language that, well, it might sound
8:58
a bit industrial, maybe manufacturing -like when
9:00
applied to healthcare. Terms like processes and
9:03
flow. Can you help us understand that perspective?
9:06
It's an interesting perspective, isn't it? It
9:08
draws on principles often found in production
9:12
management, as referenced in the material. Concepts
9:15
articulated by thinkers like Modig and Alstrom,
9:18
for example. Okay. Processes are viewed as structures
9:20
or perhaps channels through which units move
9:23
or flow. In this view, flow and process aren't
9:27
really separate things, but sort of complementary
9:30
ways of looking at the same phenomenon. Right.
9:33
When you translate that to health care, the flow
9:35
units are fundamentally the patients themselves,
9:39
along with all their associated information,
9:41
their medical data, their history, their journey
9:43
through the system. Ah, I see. The sources describe
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patient journeys through the health system, whether
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that's within a single hospital or across different
9:51
care settings, as being managed by interconnected
9:53
functional units. Functional units, like teams.
9:56
Exactly. These units are typically multi -professional
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teams or what is sometimes called microsystems
10:01
within a health care setting. So the patient
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is the unit moving through the system of teams.
10:06
That's the core idea. And within this model,
10:10
care activities are described as being co -created
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with the patient. Co -created? Yes. The goal
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of this co -creation is to continuously refine
10:18
value, which, in the context of health care,
10:21
obviously means improving the patient's health
10:23
and their well -being. Process models then become
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these vital tools for describing the structure
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of this flow. They outline specific roles and
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responsibilities within the teams. They define
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how management and continuous development activities
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are organized. And crucially, they detail how
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various vertical functional units like specific
10:41
departments or clinics need to interconnect.
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And why is that interconnection so important?
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Well, this interconnection is absolute key to
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ensuring a smooth, efficient patient journey
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and avoiding what the sources call suboptimization.
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Suboptimization. Yes, that's when one part of
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the system might operate very efficiently. isolation,
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but inadvertently creates bottlenecks or issues
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for the overall patient flow. You know, one clinic
11:05
runs perfectly on time, but the handover to the
11:08
next stage is chaotic. Right, I understand. It
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messes up the whole journey. Exactly. It's about
11:13
optimizing the whole journey, not just the individual
11:15
steps within it. That reframing of the patient
11:18
journey as a flow managed through interconnected
11:21
processes, that's actually really insightful
11:24
for understanding system design, isn't it? I
11:26
think so, yes. Let's shift gears now and dive
11:28
into a major theme that weaves through the sources.
11:32
Digital mental health. We all carry smartphones.
11:34
The sources clearly see immense potential there.
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They absolutely do. The smartphone is presented
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as a, well, a remarkably powerful tool for real
11:44
time data gathering and processing. Real time?
11:47
Yes. And this capability is enabling this concept
11:50
of digital phenotyping. Digital phenotyping?
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What exactly is that? It refers to the ability
11:55
to quantify aspects of the human phenotype, basically.
11:59
How an illness manifests in an individual using
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data that's passively or actively collected from
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their personal digital devices. Think about the
12:07
limitations of traditional clinical appointments.
12:09
They're snapshots in time, aren't they? Relying
12:12
on patients or caregivers recalling symptoms,
12:15
which can be subject to recall bias. Or it might
12:18
only capture variations between those infrequent
12:21
visits. Right. You only see what's happening
12:23
on that particular Tuesday morning. Precisely.
12:26
Whereas digital phenotyping offers a way to get
12:28
a continuous picture. So it fills in the gaps
12:31
between appointments. Exactly. Unlike those intermittent
12:34
appointments, computers and smartphones can potentially
12:37
monitor continuously and in real time. This opens
12:41
the door to developing what are called digital
12:43
biomarkers. Digital biomarkers? These are objective,
12:47
quantifiable, physiological, or behavioral data
12:50
points. They can be collected either actively,
12:52
maybe through app -based questionnaires, prompting
12:55
symptom checks. Like, how are you feeling today?
12:57
Sort of, yes. or passively through data generated
13:01
simply by using the smartphone or perhaps wearable
13:04
sensors. Okay, what kind of passive data? Well,
13:06
this could include things like screen time, patterns
13:08
of social media interaction, heart rhythm data
13:10
from a smartwatch, sleep duration and quality,
13:13
or physical activity levels tracked by the phone's
13:16
accelerometer. It provides a much richer, more
13:19
dynamic, continuous stream of data to understand
13:23
an individual's illness trajectory almost moment
13:25
to moment. That ability to get a continuous objective
13:29
view of someone's state seems incredibly powerful,
13:32
especially in mental health where symptoms can
13:34
fluctuate so much. It certainly holds that potential,
13:36
yes. The sources also strongly link this to empowering
13:40
patients and democratizing access. Yes, they
13:43
do. They highlight the smartphone as a great
13:45
instrument for empowering patients to actively
13:47
manage their own health on a daily basis. It
13:50
offers the potentially cheap, secure, and fast
13:53
approach to obtaining clinical insights that
13:55
can be used by both the patient and their clinician.
13:58
Right. And crucially, as we touched on earlier,
14:01
it offers a pathway to reach individuals who
14:03
might face those significant geographical, financial,
14:06
or stigma -related barriers that prevent them
14:08
accessing the traditional health system. So it
14:10
expands reach. It aligns very strongly with the
14:13
goal of putting patients first and expanding
14:16
access to care beyond those traditional boundaries.
14:19
What about more structured digital interventions?
14:23
What specific technologies are discussed in this
14:26
digital mental health space within the sources?
14:29
The sources discuss several key ones. Chatbots,
14:32
for instance. Chatbots. AI conversations? Essentially,
14:36
yes. Digital systems designed to interact using
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natural language processing. Their use for mental
14:41
health promotion and basic care is increasing.
14:44
Right. And the sources see them as potentially
14:46
democratizing access to psychotherapeutic care,
14:49
making some level of support available to 3 .47.
14:52
Right. Well, they also note that this area is
14:54
still in quite early development. And there's
14:57
a current lack of standardization in how studies
14:59
evaluating them are conducted, which makes it
15:01
hard to compare results effectively. OK. What
15:03
else? Telepsychiatry is another key technology
15:06
mentioned, which of course saw a massive rapid
15:09
uptake during the COVID -19 pandemic when face
15:11
-to -face consultations just weren't available
15:13
for many. Yes, huge shift then. Huge. The sources
15:16
mention its recognition across several cultures
15:19
now as a valid substitute for in -person sessions.
15:23
Reviews cited in the material suggest success
15:25
in terms of patient and clinician satisfaction.
15:28
decreased no -show rates because those logistical
15:31
barriers like travel are removed. Makes sense.
15:34
And it's considered well established now for
15:36
specific populations like older adults, children,
15:40
and adolescents. Furthermore, some reviews suggest
15:43
it seems broadly equivalent to traditional therapy
15:46
regarding the therapeutic alliance. The therapeutic
15:48
alliance, the connection between therapist and
15:51
patient. Exactly, which is of course a critical
15:53
component of successful outcomes in therapy.
15:55
So with all these tools, the continuous monitoring,
15:59
the chatbots, telepsychiatry, does this material
16:02
paint a picture of a future digital clinic? Is
16:05
that where we're heading? Absolutely. The sources
16:07
explicitly paint a picture of a potential digital
16:10
clinic that could leverage these capabilities.
16:12
What would that look like? Well, it could involve
16:14
real time monitoring of symptoms using smartphones
16:16
and various sensors, as we discussed. This stream
16:19
of complex data would ideally be curated by skilled
16:24
professionals who can translate these insights
16:26
into accessible information. So not just raw
16:29
data overload. No, definitely not. The idea is
16:32
that this allows for data -driven, accessible,
16:35
shared decision making between the patient and
16:38
their clinician. It's about using the technology
16:40
and the data it generates to provide a richer,
16:43
more continuous understanding of the patient's
16:45
state, enabling more informed discussions and
16:48
treatment choices. The technology in this vision
16:50
is meant to enhance, not entirely replace, the
16:53
human element of care. That's a key point. And
16:55
finally, in this segment, the sources call out
16:57
a specific category, digital therapeutics or
17:00
DTX. How are these different from, say, a wellness
17:03
app, and what examples are given? Right. Digital
17:05
therapeutics are presented as a distinct category.
17:07
Unlike general wellness apps, DTX are defined
17:10
as high -quality software programs designed explicitly
17:13
to prevent, manage, or treat a specific medical
17:15
disorder or disease. OK, so they make medical
17:18
claims. Exactly. And they often require regulatory
17:20
approval, much like a drug or a medical device
17:23
would. Oh, I see. The sources note their particularly
17:26
high growth in the mental health space. They
17:29
mentioned the first FDA -approved DTX was back
17:32
in 2017 for substance use and opioid use disorder.
17:36
Right. Interestingly, the COVID -19 pandemic
17:39
actually spurred a more streamlined review process
17:41
for digital health devices addressing psychiatric
17:44
disorders, which helped accelerate the market
17:46
entry for several DTX products. Any examples
17:49
mentioned? Yes. Examples cited in the sources
17:51
include things like an app -guided hardware device
17:54
designed to treat depression. specific products
17:57
for improving sleep, and even a video game platform
17:59
that has been cleared as an adjunct treatment
18:02
for ADHD. A video game as treatment. As an adjunct
18:05
treatment, yes. The sources discuss the potential
18:08
for DTX to become a sort of default tool prescribed
18:11
by clinicians, perhaps similar to how pharmacotherapy
18:14
is used today, particularly if the necessary
18:17
infrastructure, things like digital formularies
18:19
and reimbursement models, develop alongside it.
18:22
But it's not all rosy. No, they do inject a note
18:24
of caution. They state that while the field is
18:26
advancing rapidly, early trials haven't universally
18:29
shown all positive results, which really just
18:33
underscores the need for continued rigorous evaluation.
18:36
That's a really clear distinction then, DTX,
18:39
as regulated medical interventions. It's clear
18:42
there's immense technological potential reshaping
18:45
how care is delivered from that system level
18:48
flow right down to individual patient monitoring.
18:51
Welcome back to the Deep Dive. We've explored
18:54
how digital tools are starting to reshape healthcare
18:56
delivery, especially in mental health. Now we're
18:59
shifting focus a bit to the engine driving much
19:01
of this potential. The power of big data and
19:04
machine learning. The engine room, you might
19:06
say. Exactly. The sources really delve into this
19:09
as foundational. How do they actually define
19:11
big data? And specifically, what are these Vs
19:13
they mention? It sounds a bit technical. It does,
19:15
but it's a useful framework. The sources spend
19:18
time defining big data because it is distinct
19:20
from traditional data sets. They use the widely
19:22
accepted framework of the five V's. Okay, the
19:24
five V's, what are they? Alright, first there's
19:26
velocity. This refers to the increasing speed
19:29
at which data is generated, often in real time,
19:32
from all sorts of connected devices. Right, speed.
19:34
Second is volume. Just the sheer massive amount
19:38
of data being produced, constantly growing. Terabytes,
19:41
petabytes. Huge scale. Huge. Third is variety.
19:45
the highly diversified nature of the data. It
19:47
comes from countless sources, electronic health
19:49
records, EHRs, mobile devices, social media,
19:52
genomics, sensors, and it's in different formats
19:55
too, structured numerical data, unstructured
19:58
text notes, images, audio. Okay, so messy data.
20:01
Can be, yes, which leads to the fourth V veracity.
20:04
This speaks to the reliability, the accuracy,
20:07
the trustworthiness of the data, which is a critical
20:09
factor given the diverse sources and the potential
20:12
for errors or bias. How good is the data, basically?
20:15
Essentially, yes. Yeah. And finally, the fifth
20:17
V is value, the practical applicability and utility
20:20
of the data. Can we actually extract meaningful
20:22
insights and value from this vast, sometimes
20:26
messy pool? Right. Does it actually tell us anything
20:27
useful? Exactly. And this whole picture contrasts
20:31
quite starkly with small data, which typically
20:33
involves static cross -sectional data collected
20:36
from specific population samples. Big data often
20:39
aims for an an all approach, attempting to capture
20:42
continuous information from entire systems or
20:45
very large populations. That framework really
20:48
helps clarify what we mean by big data. So how
20:50
does machine learning, this branch of AI, actually
20:53
leverage this massive complex data? Well, machine
20:57
learning is fundamentally about systems that
20:59
learn automatically from data without being explicitly
21:02
programmed for every single possible scenario.
21:05
Learning from experience, like humans. In a way,
21:07
yes. It uses mathematical models that learn relationships
21:10
and patterns implicitly from the data they're
21:13
fed. The sources highlight how this is particularly
21:15
relevant in areas like psychiatry, because these
21:18
models can handle the complex multi -dimensional
21:20
data sets generated by mobile sensors, EHRs,
21:23
genomic data, all those sources that constitute
21:26
big data. They broadly discuss two main categories
21:29
relevant here. There's supervised learning. Yes,
21:32
where the model is trained on a data set that
21:34
includes both the input variables and the desired
21:37
output or outcome. For example, predicting a
21:39
diagnosis based on a set of symptoms. It learns
21:42
to map the inputs to the outputs and then applies
21:45
this learning to predict outcomes on new unseen
21:49
data. Okay. Training it with answers? You could
21:51
put it that way. The other main type is unsupervised
21:54
learning. This involves exploring unlabeled data
21:57
data without predefined answers to find inherent
22:00
structures or clusters within it. For example,
22:03
grouping patients with similar symptom patterns
22:05
together, even without a predefined diagnosis
22:07
in mind. Finding hidden patterns. Precisely.
22:10
This ability to find patterns in complex data
22:12
leads to some incredibly sensitive, but as you
22:15
say, potentially vital, applications discussed
22:18
in the sources, specifically predicting severe
22:21
outcomes like suicide risk and psychosis risk.
22:23
How is big data and machine learning being applied
22:25
in these areas according to the material? The
22:27
sources highlight the, well, the pressing need
22:31
for more individualized prediction when it comes
22:33
to suicide risk. Traditional methods, as we know,
22:36
often rely on population level statistics or
22:39
simple risk checklists. Which aren't always accurate
22:41
for an individual. Exactly. They have limitations
22:45
in predicting risk for a specific individual
22:47
at a specific time. This is where precision psychiatry
22:51
comes back in using that multi -dimensional big
22:54
data, processed by sophisticated algorithms,
22:58
to make more accurate individualized predictions
23:00
of mental health outcomes. And crucially, to
23:03
guide targeted preventative interventions. Right.
23:06
Proactive, not just reactive. Hopefully. The
23:09
courses discuss machine learning models developed
23:11
specifically for high -risk populations, such
23:13
as individuals with mood disorders or bipolar
23:15
disorder. These models use a really wide array
23:18
of data clinical variables from EHRs, demographic
23:21
information, and in some research studies, even
23:24
genomic data. And how accurate are they? Well,
23:26
the sources mentioned some promising accuracy
23:28
rates in specific studies. They cite AUCs. That's
23:31
AREOBER. Under the receiver operating characteristic
23:34
curve, a measure of accuracy ranging from 0 .77
23:37
up to as high as 0 .98 in predicting suicide
23:41
attempts within certain populations in some studies.
23:43
0 .98. That sounds incredibly high. It is very
23:47
high. For this kind of prediction, yes. An AUC
23:50
close to one indicates very high accuracy in
23:53
distinguishing between those who will and won't
23:55
experience the outcome. They also identify key
23:58
predictors that these models consistently highlight
24:00
as significant. Like what? Things like prior
24:02
hospitalizations, the presence of comorbidities,
24:05
other health conditions, psychotic symptoms,
24:08
previous suicide attempts, which is understandably
24:10
a very strong predictor, and the presence of
24:12
a personality disorder. Okay. The sources cite
24:15
machine learning models for suicide risk prediction,
24:18
showing promising accuracy, with AUCs up to 0
24:20
.98 in some studies, identifying key predictors
24:23
like previous suicide attempts and personality
24:25
disorder. This shifts us towards precision psychiatry,
24:29
making individualized predictions from vast datasets.
24:32
Those accuracy numbers, particularly that 0 .98,
24:35
are quite striking for something as complex as
24:37
suicide risk. Moving on to psychosis risk, the
24:41
sources specifically mentioned something called
24:43
a transdiagnostic risk calculator. What exactly
24:47
is that and why is it needed? Right. This is
24:50
a fascinating example of trying to put prediction
24:53
into actual clinical practice. The calculator
24:56
was developed and then externally validated within
24:58
the South London and Maudsley NHS Foundation
25:01
Trust, known as SLAM, one of the largest mental
25:04
health providers in the UK. OK, SLAM. And why
25:07
develop it? The why is crucial. Many individuals
25:09
who eventually go on to experience a first episode
25:11
of psychosis are not identified during the potentially
25:14
high -risk prodromal stages, the early warning
25:17
phase, through traditional clinical routes. They
25:19
slip through the net. Right. This calculator
25:21
is designed for systematic detection of psychosis
25:23
risk within secondary mental health care populations,
25:27
basically by screening routine data that's already
25:29
been collected. Screening existing data, okay.
25:31
What's special about it? Its core characteristics,
25:34
as detailed in the sources, are quite innovative.
25:38
Firstly, It's lifespan inclusive. It can be applied
25:41
to individuals of any age. It uses clinically
25:44
-based predictors, features identified through
25:47
clinical knowledge, as being associated with
25:49
psychosis risk, selected through a rigorous process.
25:53
It's transdiagnostic. Transdiagnostic, meaning?
25:56
Meaning it works across different diagnostic
25:58
categories or presentations. It's not just for
26:02
people already labeled as high risk for psychosis.
26:05
It provides individualized risk estimates rather
26:08
than just a binary yes or no. It's designed to
26:11
be cheap and automated, screening EHRs in the
26:14
background, so it's highly scalable for large
26:16
populations. And finally, it's built for e -health
26:19
implementation, designed to be integrated directly
26:22
into clinical systems. So it's designed to catch
26:24
people who might otherwise slip through that
26:26
net. Has it actually been tested outside of SLAM
26:28
where it was developed? That's exactly the intention.
26:31
And yes, the sources do discuss replication studies
26:33
in other UK settings, specifically Canton and
26:36
Islington and Oxford Health NHS trusts. And did
26:38
it work there, too? These studies were important
26:40
because these populations had key differences
26:42
compared to SLAM, for instance. CNI had an older
26:45
average age, fewer males, different prevalence
26:48
rates for things like substance use or anxiety
26:50
disorders. Despite these variations, the risk
26:53
calculator retained acceptable performance. The
26:56
source is, quote, a Herald C statistic of 0 .73.
27:00
Herald C, similar to AUC. Yes, it's a similar
27:03
measure of discrimination. A value of 0 .73 indicates
27:06
the model has a reasonably good ability to discriminate
27:08
between individuals who will and won't develop
27:10
psychosis. No, obviously not perfect. Okay, and
27:13
was it actually used in practice? Yes, an important
27:16
feasibility study integrated this calculator
27:18
directly into the SLAM EHR system. It automatically
27:22
screened over 3 ,700 individuals receiving care,
27:25
and it flagged 115 who were identified as being
27:28
at elevated risk, according to the model. And
27:30
did clinicians act on those flags? Crucially,
27:32
yes. The study tracked the clinical response.
27:35
77 % of clinicians whose patients received an
27:38
alert responded to it in some way. And 55 % of
27:41
those responses resulted in a referral for a
27:43
more refined in -person psychosis assessment.
27:46
And did those people actually develop psychosis?
27:49
Well, the incidence of psychosis within six months
27:51
for the individuals who were detected by the
27:53
calculator and then subsequently assessed was
27:55
12 percent. which is actually comparable to the
27:58
incidents seen in traditional clinically -defined
28:00
high -risk groups identified through other means.
28:03
Wow. So this outcome strongly supports the calculator's
28:06
potential for systematic detection at scale.
28:08
That is genuinely fascinating, taking routine
28:11
clinical data and using it proactively to identify
28:14
risk at a population level within a health service.
28:17
Amazing. The sources also briefly touch on how
28:20
data is providing insights into other human behaviors,
28:23
mentioning gaming and dating apps. What do they
28:26
highlight there? They do touch on these, yes,
28:29
perhaps more as examples of how data can't illuminate
28:31
behavior outside traditional clinical settings.
28:34
They note that gaming deporter is now recognized
28:36
in the ICD -11 classification. The International
28:39
Classification of Diseases. Correct. And it's
28:42
associated with other conditions like depression,
28:44
anxiety, ADHD, and also comorbidities, with things
28:48
like excessive social media use, gambling, and
28:51
substance use. Data from gaming platforms themselves
28:55
can potentially reveal patterns of behavior relevant
28:57
to diagnosis and intervention. Hmm. They mentioned
29:01
data showing a higher prevalence of gaming disorder
29:03
in males, potentially linked to industry strategy
29:06
or perhaps achievement drives, but they also
29:08
acknowledge rising female participation and the
29:11
potential for bias in the research itself. Right,
29:13
and dating apps. Dating apps are also mentioned,
29:16
highlighting their surprisingly long history.
29:18
Apparently computer matched parties existed as
29:20
far back as 1959. Really? 1959. So the sources
29:24
say, and of course their massive growth now,
29:27
studies using data from these apps or surveys
29:29
of users provide insights into user characteristics,
29:32
suggesting users are more likely to be men, young
29:34
adults, and sexual minorities, and also into
29:37
personality traits, with studies using models
29:39
like the Big Five indicating higher open -mindedness
29:41
and extraversion, maybe lower conscientiousness,
29:44
and high sensation -seeking. These points just
29:47
illustrate how our digital footprints across
29:50
various platforms can become sources of data
29:52
for understanding behavior, sometimes behavior
29:55
linked to mental health. It's clear the potential
29:57
of data and AI is immense across so many aspects
30:00
of life and health. But these technologies also
30:03
bring significant challenges, don't they? The
30:05
sources explicitly acknowledge this. What are
30:09
the main ethical issues and privacy concerns
30:11
highlighted as critical hurdles? They are indeed
30:15
presented as paramount concerns, particularly
30:17
mental health, where the data involved is inherently
30:20
so sensitive, so personal. Absolutely. patient
30:23
and participant privacy is stressed as absolutely
30:26
critical. There's significant worry raise over
30:28
the potential misuse of this personal data. Examples
30:31
given include companies potentially branding
30:33
or targeting products specifically at vulnerable
30:36
populations based on their digital data. Exploiting
30:39
vulnerability. Potentially yes. Or the risk of
30:42
data being traded to third parties without explicit
30:45
informed consent. The sources cite some worrying
30:49
findings that many mental health apps currently
30:51
on the market simply don't adhere to clinical
30:53
or ethical guidelines. And their privacy policies
30:56
are often either absent entirely or deeply flawed,
31:00
failing to adequately protect user data. That's
31:03
quite concerning. It is, and this lack of standardization
31:06
and regulation, particularly in contexts like
31:08
the USA, which is mentioned specifically, contributes
31:11
significantly to a lack of public confidence.
31:14
The forces include a figure suggesting only about
31:16
8 % of users are willing to share their health
31:18
data with technology companies. Only 8%. That
31:22
really underscores the depth of this trust issue,
31:24
doesn't it? It certainly does. It's a major barrier.
31:26
And with literally thousands of apps out there,
31:29
how are clinicians or even patients themselves
31:32
supposed to know which ones are safe and effective?
31:34
That's another significant challenge the sources
31:36
highlight. The sheer number of available apps
31:38
makes evaluation incredibly difficult, both for
31:41
professionals and for consumers. An impossible
31:43
task, almost. Almost. There's considerable variation
31:47
in the evaluation frameworks that do exist, which
31:50
makes comparing apps tricky. The sources explicitly
31:54
call for the development of standardized, robust
31:56
quality measures and clear guidance for clinicians
32:00
on how to evaluate and potentially recommend
32:02
apps. They make it very clear that simply relying
32:05
on ratings or descriptions in app stores is nowhere
32:08
near sufficient for ensuring quality and safety.
32:10
Okay. Beyond just evaluating the apps themselves,
32:14
how do these new data -driven technologies impact
32:17
the clinician's actual workflow, their daily
32:20
practice? It must require a fundamental shift
32:22
in how they work. It absolutely does. The sources
32:25
really stress that clinicians need to fundamentally
32:27
alter their way of working to effectively and
32:30
ethically incorporate these advanced data -driven
32:32
technologies. It's not just learning new software,
32:34
then? No, not at all. It requires them to engage
32:37
directly with technologists, with engineers,
32:40
to truly understand how these tools function,
32:42
what their limitations are, and importantly to
32:44
ensure their use doesn't compromise clinical
32:46
or ethical duties. Right, bridging that gap.
32:49
Precisely. The sources also mentioned the need
32:51
for developing new competencies for health professionals
32:54
in using other emergent technologies like augmented
32:57
reality, simulation, gamification, which are
33:00
also finding applications in healthcare training
33:02
and delivery. Integrating apps into existing
33:06
clinical workflows, providing staff training
33:08
on how to use them and interpret the data, deciding
33:11
on the appropriate level of clinical oversight
33:13
for patient app use, handling the complexities
33:16
of data sharing and interpretation. These are
33:19
all presented as significant practical challenges.
33:22
Yeah, I can see that. And the move towards these
33:24
potential real -time assessment models means
33:27
clinicians will need to actively consider not
33:29
just whether an app exists, but if the data it
33:32
provides is genuinely valuable to clinical decision
33:35
making. And if it actually helps demonstrate
33:37
real progress for the patient, is it useful?
33:39
It sounds like navigating all this requires a
33:42
lot of cross -disciplinary understanding, a lot
33:44
of collaboration. It certainly does. Which brings
33:46
us back to the human element. How do clinicians,
33:50
patients, and even these AI systems like chatbots
33:53
actually interact effectively and safely according
33:56
to these sources? Collaboration and communication
33:58
are highlighted as absolutely essential for successful
34:01
adoption. Introducing advanced technologies into
34:05
healthcare requires a, well... The concerted
34:07
effort from all parties involved clinicians,
34:10
patients, technologists to understand one another's
34:12
perspectives, needs, capabilities, and to co
34:15
-create innovations that are acceptable and genuinely
34:18
beneficial to everyone. Co -creation again? Yes.
34:22
Clinicians, as we've said, need to engage with
34:24
technologists to ensure tools are clinically
34:26
appropriate and ethically sound. Patients and
34:29
clinicians should also engage with data scientists
34:31
to understand how advanced data -driven technologies
34:33
work. allowing them to make informed decisions
34:35
about their use and interpretation. Is there
34:38
willingness for that engagement? The sources
34:40
note that there is empirical evidence indicating
34:42
a general willingness among these groups to engage,
34:45
which needs to be actively encouraged, fostering
34:48
a sense of shared responsibility in how these
34:50
technologies are developed and deployed in practice.
34:53
And specifically with chatbots, where the interaction
34:56
can feel almost human -like sometimes, are there
34:59
unique risks or relational considerations, the
35:01
sources point out? Yes, the sources do raise
35:04
specific concerns regarding the relational aspects
35:07
of interacting with chatbots, particularly in
35:09
sensitive areas like mental health. How so? Well,
35:12
while chatbots interact using natural language,
35:14
internally they are processing text strings.
35:17
They may potentially miss nonverbal cues or nuances
35:20
like, say, urgency conveyed in spoken language.
35:23
Right, the tone. Exactly. Concerns are noted
35:25
regarding the potential for users to form attachments
35:28
to bots. perhaps alluding to codependency, or
35:30
for users to be overly credulous, to unquestioning
35:33
of the bot's responses, especially given their
35:36
often human -like features and that 247 availability.
35:39
Hmm, blurring lines. It can. And this creates
35:43
a difficult dilemma. While human validation and
35:46
monitoring of chatbot responses might be needed
35:49
for safety, particularly given the potential
35:51
for unexpected or even harmful outputs. This
35:55
oversight could simultaneously jeopardize the
35:57
patient's privacy in their communication with
35:59
the chat bot. A real balancing act? It highlights
36:02
the delicate balance needed between safety, effectiveness,
36:05
and privacy in these kinds of interactions. So
36:07
despite all the incredible technology, all the
36:09
data, the importance of the human connection
36:11
and care remains absolutely central. Absolutely.
36:14
The sources reinforce this point strongly. The
36:17
fundamental value in therapy sessions, whether
36:19
conducted online via telepsychiatry or in person,
36:23
is the therapist focused attention on the patient.
36:25
The relationship. The relationship. The technology,
36:28
whether it's real -time monitoring data curated
36:31
by a professional or communication tools, should
36:33
ideally enable and enhance this therapeutic focus
36:36
in the human relationship, not aim to replace
36:38
that essential connection entirely. Right. Augment,
36:41
not replace. Precisely. They mentioned the need
36:44
for investment and leadership to develop digital
36:46
clinician pathways, which would involve training
36:49
and infrastructure to support clinicians in leveraging
36:51
these tools effectively while always maintaining
36:54
the centrality of the patient relationship. This
36:56
has been a really detailed and comprehensive
37:00
exploration of a rapidly evolving field. To wrap
37:03
things up, let's do a quick lightning round,
37:06
drawing on some specific details from the sources
37:08
we've discussed. Just quick, sharp answers based
37:10
on the material, if you can. One digital tool
37:13
mentioned that allowed for basic medical exams
37:15
at home, perhaps during a lockdown scenario.
37:18
Tidal Home Remote Exam Kits were mentioned. Okay.
37:21
Beyond just helping with diagnosis, how else
37:23
can digital phenotyping provide value in areas
37:26
like mood disorders? Give me a couple of ways.
37:28
Right. You can assist physicians by collecting
37:30
signs continuously. Process those signs into
37:33
mood and behavior patterns. Compare current data
37:36
to a patient's baseline. Enhance detection of
37:39
initial signs. tailor interventions, even suggest
37:43
context -aware coping strategies, lots of potential
37:46
applications. Okay. After the big surge during
37:49
the pandemic, what's a key challenge mentioned
37:52
for widespread equitable telepsychiatry access
37:55
going forward? Access to the necessary technology
37:58
and adequate training for professionals to use
38:00
it effectively. Those two are key. Right. And
38:03
finally, one core skill needed for senior leadership
38:06
roles in health tech, like a chief clinical information
38:09
officer or CCIO, something that goes beyond just
38:14
being technically savvy, being good with computers.
38:18
Change management, definitely. Understanding
38:20
human factors, a deep understanding of systems
38:23
and organizations, and critically, being good
38:27
with people, not just computers. Brilliant. Thank
38:30
you. Now, let's try and distill some actionable
38:32
takeaways from this deep dive. Key points for
38:34
you, the professional listener, to consider based
38:36
only on the source material we've covered. Okay.
38:39
Based on our discussion, here are perhaps some
38:41
key considerations drawn directly from the sources.
38:43
Okay, first. Firstly, digital transformation
38:45
in healthcare isn't just about adding some new
38:47
tools. It really necessitates a fundamental reform
38:50
of health systems. Right. This requires properly
38:52
integrating care across different levels. hospitals,
38:56
primary care, home care, and significantly investing
38:59
in developing new competencies for health professionals
39:02
so they can effectively and ethically use these
39:05
emerging technologies. Okay. Fundamental reform,
39:07
new skills. What's next? Secondly, digital mental
39:10
health tools covering things like digital phenotyping,
39:13
mobile apps. tell psychiatry, chatbots, they
39:17
hold substantial potential for expanding access
39:20
to care and enabling truly personalized interventions.
39:23
Nice. But their successful implementation really
39:26
hinges on rigorous validation, very careful ethical
39:30
consideration, and finding ways for seamless
39:32
integration into existing clinical workflows
39:34
and practices. It needs to work in the real world.
39:37
Validation, ethics, integration. Got it. Third
39:39
takeaway. Thirdly, leveraging the power of big
39:41
data and AI, specifically machine learning, offers
39:44
powerful capabilities for predicting individual
39:46
risk trajectories, particularly in sensitive
39:49
domains like suicide and psychosis risk prediction.
39:52
The precision psychiatry idea. Exactly. But this
39:56
power is absolutely contingent on having access
39:58
to robust, high -quality data. It demands transparency
40:02
in how the predictive models actually function.
40:05
We need to understand them. And it requires a
40:07
careful balancing act between the automated insights
40:10
and essential human clinical judgment. Data quality,
40:14
transparency, human oversight. Okay, fourth.
40:17
Fourthly, the widespread adoption of health technology
40:20
introduces critical responsibilities as highlighted
40:23
quite strongly in the sources. There must be
40:25
paramount attention paid to ensuring patient
40:27
safety and data privacy. Non -negotiables. Absolutely.
40:31
Establishing clear ethical guidelines for technology
40:33
use and developing standardized evaluation frameworks
40:36
and potentially even new regulatory approaches
40:38
are needed to build and maintain public trust.
40:40
Safety, privacy, trust through standards make
40:43
sense. And finally. And finally, successful digital
40:46
health adoption is perhaps fundamentally driven
40:49
by human fangars and effective change management.
40:52
It necessitates genuine collaboration and open
40:54
communication across all disciplines, clinicians,
40:57
technologists, researchers, patients themselves.
41:00
Yes, to co -create innovation that truly works
41:03
for everyone involved, moving beyond just a sole
41:06
focus on the technology itself. It's about the
41:09
people. and the process. Collaboration, human
41:11
factors, change management, brilliant summary.
41:14
So here's a final thought, perhaps something
41:16
for you to mull over. As data and AI become increasingly
41:20
central to health care, to prediction, particularly
41:23
in these really sensitive areas like mental health,
41:27
how do we ensure that the pursuit of efficiency,
41:31
of precision, doesn't inadvertently erode that
41:34
essential human relationship at the very heart
41:36
of care? A critical question. It's a critical
41:39
tension, isn't it? Something for us all to navigate
41:41
carefully. If you found value in this deep dive
41:43
today, please do rate and share the show. It
41:45
genuinely helps more people discover how to quickly
41:48
become well -informed on topics like this. Profi
41:50
Mom, thank you once again for guiding us so expertly
41:53
through this complex but absolutely vital material
41:55
today. It was my pleasure. Thank you for having
41:57
me. That's all for this deep dive.