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