Your digital roadmap probably talks about efficiency.
What if it also determined who gets a fighting chance to live?
Stay with us as we explore the moral calculus of deploying AI in healthcare
and the hard lessons every transformation team can apply across industries.
My name is Alexandre Nevski, and this is Innovation Tales.
Navigating Change one story at a time.
We share insights from leaders tackling the challenges of today's digital world.
Welcome to Innovation Tales, the podcast exploring the human
side of digital transformation.
You've heard the mantra: move fast and break things.
In healthcare, however, the think you break could be a person.
That's why I've invited RJ Kedziora, co-founder of Estenda, HIMSS Davies
and Health 2.0 Leadership Award winner and 25 year digital health veteran, to
show how speed and safety can coexist.
At Estenda, RJ sets the technical strategy for solutions
that truly improve lives.
Today he explains how ambient listening Gen AI scribes cut clinicians
dreaded "pajama time", letting them spend more minutes with patients,
while the notes write themselves.
Put simply his blueprint helps anyone tasked with turning AI's promise into
a real world impact no matter where it sits on your transformational roadmap.
Without further ado, here's my conversation with RJ Kedziora.
RJ, welcome to the show.
Alex, thanks for having me.
Looking forward to the conversation.
Thanks for taking the time.
You specialize in digital health, but you also describe
yourself as an aspiring author.
The book you're writing describes your productive harmony framework.
So what's the story there?
What led you to take on this project?
Yeah, thanks.
The book working title is The Productive Harmony Framework, and
it comes from my life experience.
I am an entrepreneur.
I also race triathlons, and as you can imagine, there's a lot of activity,
physical activity that's part of that.
And yes, that aspect is challenging, but one of the reasons I've gotten
into triathlons is because of the mental challenge as well and what
drives both of those components.
As I sat down last summer and started thinking about, okay, I wanna write
a book, it's been a passion in the back of my mind that I write
a lot of white papers and journal articles, presentations, webinars.
What is the next step?
The evolution in sort of my career and what interests me?
And I was like, I want to write a book.
So as I worked through that, it, it just, everything kept coming
back to the idea of energy.
Whether it's physical energy or mental energy to be able to get stuff done
and be productive, and even more so getting it done right the first time.
And what's fascinating is I started looking into this, the first thing
most people think of, the first thing that I really started thinking of
early in my career was time management.
Okay, how am I going to manage my time to get stuff done?
There's only 24 hours in a day.
You can't control time.
And so what can you control?
You can control yourself, your energy.
How do you focus your energy to be able to maximize those time that you do have?
You're probably gonna sleep 6, 7, 8 hours a night if you're lucky.
So that's out the door.
That's you.
You can't use that time on getting work or personal projects done.
That sleep time is very productive though because it gives you the energy to be
able to do all of these different tasks.
So that's what drove the idea of the book, productive harmony.
It was fascinating when I started it and was working with editors.
I really thought I had to come up with everything, all of the ideas, all of the
content, and that was very intimidating.
And it was interesting 'cause they're like, how many words do you want it to be?
That meaning has no concept to me as a first time author or as you
or most of the listening audience.
I read lots of books, but I don't know.
And so we did, we wound up settling on 50,000 words, which was roughly 200 pages.
And I learned that no, the entire concept idea didn't have to come from me.
A good book to be engaging has to be loaded with personal stories,
research articles, first person interviews that I've done with
different people on the topic.
I reached out on different social media platforms and talk to PhD experts in
time management, which is fascinating ' cause I didn't even know people were
researched from that perspective.
So instead of really just a book writing exercise, it became a
research, exercise for me, which I do every day in my, day-to-day life.
So it became a much easier task, but very fascinating to dive into the
idea of time management, and energy.
One of the interesting things that I've discovered in this, we as
humans, humanity have been thinking about time since very early on.
Cavemen had to think about time.
Okay, you only have so many hours, the sun's gonna go down.
We need to get food, we need to have shelter.
In the early 1900s there was a gentleman, his name was Frederick Taylor.
And the science has been called Taylorism now, but he really kicked off that
modern day theory of time management.
Of sitting there and looking at a task and breaking it down into components and using
a stopwatch to figure out how to optimize productivity from a time perspective.
What history has forgotten and what I uncovered in my research was the
idea that after two years, his efforts did not increase productivity, but
led to burnout and he got fired.
So amazingly fascinating to me that what's driving us all day to day
is this time management and getting stuff done in, in that early theories
didn't quite pan out as we thought.
So I think energy management today in the 21st century is a much more effective way
of looking at how to plan out your day and maximize those hours that you do have.
For me, I guess the, first things I think of when I hear managing my
energy and increasing my productivity is avoiding context switches.
I dunno if it's the same for other people, but they are extremely
expensive in terms of energy.
Changing between one task and a completely different task takes
just so long and I really cannot afford to do that 20 times a day.
Complete burnout at the end of the day.
And this is unsustainable.
So I wonder, can you give us a, a teaser of what energy management
means for you in the book?
It is about focusing and looking at how energy affects me day to day.
So in, in my particular case, I do get up early in the morning, 4:30, 4:45 and
train for an hour and a half to two hours.
That actually maybe counterintuitively gives me energy
to be able to take on the day.
The days that I do rest and relax, I, there's maybe even some level of
anxiety of oh, am I doing enough?
Am I doing training?
But I need less coffee, less caffeine those days that I train
because it gives me energy.
And I know I need to focus on tasks that need a lot of thinking, a
lot of focus early in the morning.
Through the midday, I focus on administrative types of activities, which
don't need as much time and attention kind of thing, but definitely have to get done.
And then late afternoon I get a burst of creativity where I try and focus
on more of our marketing activities and what am I gonna do next for this
book and the next chapter in the book.
The creativity really peaks there.
So when you do think about a energy, think of your day and how does
that flow, throughout the day.
The one thing too, to your point of not doing this context, switching.
It is, that's horrible for using your energy, for being engaged
in a task, staying on target.
we all have our smartphones.
We all have our cell phones.
And yes, you turn off notifications, but for most people it just sits next to you.
And what's fascinating, by putting the phone in another room, made a
significant difference of not having it front of mind and not distracting me.
It's almost like fear of missing out.
Okay, the phone's there, but I'm not touching it.
I'm not focused, I'm not getting the, it's not dinging, not getting the
distractions, but it's still there.
You just want to pick it up and look at it real quick.
So having it in another room made a world of change.
And the other thing a lot of people talk about is AI these days.
I do have a chapter in the book on the use of AI for productivity.
And that's what I think the biggest general use case, as we talk
about generative AI these days, is a boon for society in terms
of being much more efficient.
Do you see that also as a, like a tool to manage energy?
I can imagine that sometimes we might choose, instead of doing a task the old
fashioned way, ourselves with our own brain, we might take it easy a little bit.
and, We frame the task, but the AI does the heavy lifting.
Is that how you see it?
Yes, absolutely.
So I think I do use AI a lot in my everyday world in terms of marketing,
product development, data analytics.
We're implementing it in various capacities on some of our digital
health projects that we're engaged with.
And, as an example, I had to do user interviews with PhD
experts in smell testing.
Something that was a new area for me as an individual, but I interviewed dozens,
if not hundreds of people to figure out, what a specific product should be.
What should the business rules be?
How will the system work?
So I can do that, but it typically takes a couple hours
to sit down and plan that out.
Whereas you use any of the Gen AI systems today, you just ask it, create me a user
interview and then you give it a lot of background and context about what you
want to do and what you're trying to accomplish, and it generates 10 questions
and you just, okay, generate me 10 more, generate me 10 more, generate me 10.
And within minutes, you have 50 questions that you can use to
structure a user interview to figure out, what makes them tick and, how
can we help them with an application focused on the idea of smell testing.
It's very much an 80% solution.
So I still go into those questions.
Okay, what is a good flow as part of the conversation to
make that work very effectively?
But something that would normally take me a couple hours, took minutes.
Yeah, and from an energy perspective, having a thinking buddy it's obviously
the idea would be to have an actual human to brainstorm through, but that's
limited to the most important use cases.
And it's nice to have the ability to tap into a thinking buddy research
assistant that helps us go through whatever tasks we need to do.
But I did want to pick your brain more generally now since you've branched
out into the digital health space.
And that's your specialty, of course.
I would like to understand like what are the use cases that
have already seen wide adoption?
So on the traditional side of things, I think there is quite already a
lot of machine learning algorithms used to augment and complement
radiologists or detecting tumors.
Identifying patterns of course with traditional machine learning.
And on the Gen AI side of things, I think the first thing that comes to my mind in
healthcare is assistance that doctors can use to reduce the amount of paperwork they
need to do after the clinical setting.
So when you have to go into the office and just write up everything that
instead they can talk to the AI as they're doing the procedure or whatever
they're involved in, and get most of that documentation done on the fly.
First of all, before we move on to other use cases, are those, in
your opinion established already or is that still quite early?
I am glad you mentioned machine learning because I think of that
very often as the forgotten AI.
It has been around for a while and is making an impact.
And the use of it in image analysis is probably the top use case from
a machine learning perspective.
'cause you and I, a trained expert, can look at, a breast image, a brain image.
We do a lot of work in diabetic retinopathy, which is looking at the
retina of your back of your eye for detection of diabetic retinopathy.
It's the leading cause of preventable blindness.
So if you find evidence of it, then you can make a difference
in that person's life.
And so that's the first use case fairly well established from a
scientific computing perspective.
The FDA, here in the us, which is a regulatory body that has to approve
these algorithms, these machine learning systems, I think it's close
to 900 now over the last decade or so.
The vast majority of them, probably 80, 85% are related to
like machine learning and imagery.
It's very well used, established use case.
They can do better than the practitioners and they don't get tired.
Call back to that idea of energy.
If you're looking at a hundred images over a couple hours, you are gonna get tired.
You're human.
The com the computer doesn't.
Now, if we look at generative AI, which is the latest craze today, it's barely, two
years old, ambient listening is the area where it's being most quickly adopted.
And it is to your use case of taking notes and writing those charts, and
doctors and nurses talk about pajama time.
My daughter happens to be a physical therapist and she called on her way
home from work last night and she's like, yeah, I need to sit down and write
notes for 25 patients that I saw today.
I'm like, but now you know you're done work.
And she's like, no, I gotta do these notes now.
So she doesn't, unfortunately, she doesn't have the ambient
listening technology in place.
But the idea is let the AI system listen to the conversation that
you're having in the room and take the notes to make the overall process,
the encounter much more efficient.
And what I like about that approach is it lets the doctor, the medical practitioners
get back to why they got into medicine.
If they got into medicine to talk to people, to help people engage with them,
not to type notes on a computer keyboard.
A lot of the doctors you hear stories that they just have their back
turned to a patient because they're typing away on their keyboard.
That's not why they got into medicine.
They wanna help that individual.
So this opportunity, in a non-invasive way, allows the patient medical
practitioners to talk to each other, have that engaged conversation, and the
AI can pick up and generate those notes.
Then from an efficiency perspective, it could also get into medical coding from
a billing perspective and help out there.
So it is the big use cases in healthcare today are really around the idea of
how do we gain efficiency from an administrative backend perspective.
In some of your posts you were mentioning digital therapeutics.
Is that also an area where the AI is enabling new ways of using it?
A digital therapeutics is an interesting term 'cause it is relatively new.
I think of it as a special class of digital health applications or
systems where you walk into a drug store, pharmacy and buy all sorts of
medications, vitamins, things over the counter without a prescription.
Think of that as your general digital health application.
There's probably 300,000 of those out there, depending on how you count.
A digital therapeutic takes that to the next level.
It's a comparison to an actual drug or medication that went
through extensive clinical trials and clinical regulatory approval.
Here, in the US would be the FDA.
So it's an elevated system.
It has that evidence and it's prescribed just like a
medication would be to a patient.
It is prescribed to a patient to improve them.
Mental health is a huge area for this.
Some new migraine systems were just developed.
So instead of taking a drug which has lots of side effects.
You know I'm sure you've seen those commercials on tv, hear them where it's
just like, who would ever take this drug?
for five minutes they've gone off on all the potential side effects.
A digital health app doesn't have those same issues kind of thing.
You're not taking a physical drug at that point.
And they can make a difference.
And that's the idea of a digital therapeutic.
Unfortunately, it's a little challenged right now from a reimbursement
perspective here in, in the US.
January 1, just a couple months ago, there was some new approvals and
methods to get them to be paid for.
So the idea is the doctor prescribes them like a drug to the patient, but
the insurance company is covering them.
So the company creating the digital therapeutic can get
a higher reimbursement rate.
The EU has done a lot better with this, I think.
It's still in its early stages, but they've tied that regulatory
approval to reimbursement pathways.
Here in the US it was like, oh, you're approved.
Now, how do you get paid for it?
In Europe, they tied those things together, which is a much better approach.
I saw some startups in, in the healthcare AI space offering
through Gen AI automated checkups.
That somebody who has been prescribed a certain medication takes the right
dosage, the right schedule, and generally something that I guess nurses would do.
But nurses can only visit so many patients in one day or call
so many patients in one day.
And that enables to augment their work, if I understand correctly, to
help patients follow through with the.
treatment that had been prescribed, in a, in an outpatient environment,
if they're at home or something.
And so is that the next level from that, the digital therapeutics where it's not
just helping with, like prescriptions for chemical drugs, but also encouraging
people to do specific behaviors that then create better health outcomes?
Yes.
that's the amazing aspect of what this Gen AI is going to make capable.
In a lot of cases, is capable today.
You do have to be cautious.
There's notes of caution around using the generic AI is out there, the ChatGPTs,
the Claude kind of thing, because they're not designed for clinical medical use.
They are very capable and they will give you answers that sound really
good, but you do have to be aware of biases and the idea of hallucinations
or that the AI might make something up.
It ultimately, it wants to please you.
You know it, it's not gonna sit there and tell you, I don't know the answer.
Like you or I having a conversation, I can be like, oh, I don't know.
It's gonna make something up.
And it sounds convincing, which is challenging.
But from a mental health capacity, there is a mental health crisis.
Globally.
There are not enough professionals.
To help all those people.
So yes, how can we implement these AI systems to help those people to have
those conversations, to be the companion?
Your use case that you were talking about is, follow patient follow up.
Are they taking the medication?
Are they having complications?
The AI systems today are very capable of having that conversation with an
individual, getting that feedback and then analyzing that feedback, not just
from a content perspective, but also from a tone and an emotional perspective,
which is just another level you or I, if you really know that patient, over time
could develop that type of awareness.
Is this person sad?
Are they angry?
It usually takes a little bit more experience to develop that, but the
AI systems can tag a conversation as this patient, is showing signs of
depression, which then maybe you have a human follow up with them, but the
computer doesn't get tired, can have conversations all throughout the day.
You know, a nurse that's doing follow up on dozens of patients might have
to constrain those calls to three or five minutes to get through all
of those, where the AI doesn't have that constraint . So you think of,
your mother who might be elderly at home, or an assisted living facility.
It's a great opportunity to engage with them and have a conversation.
And so how do you use that AI to even go beyond that?
Hey, have you taken your medications today?
Hey, how are you feeling today?
To engage in, in a much more meaningful conversation.
It does raise so many questions about the nature of care, just generally,
ethically speaking, how do we feel if a close one, one of our family members,
instead of getting the visits of a nurse that they should be, that we, they
used to get now and AI is calling them?
But at the same time, I think what you were saying was more describing
augmentation rather than replacement.
And earlier you were talking about ambient listening and I believe it was
in Fei Fei Li's book, where she was also talking about this technology as like
a few years back when it was just the beginning and how some of the medical
professionals were quite concerned about, this being used as a system to monitor
performance or something like that.
And also raises quite a few ethical questions.
And, you are the expert that deals with people adopting this
technology day in, day out.
How do you think about those ethical questions?
You definitely have to address them.
You definitely have to be aware of them, and there is a lot of fear,
uncertainty, and doubt when it comes to these AI systems today.
So you do have to think around along those ethical guidelines of how are
we going to be using this system?
What do we have to consider?
What are the risks to patient care and wellness?
AI generally stands for artificial intelligence, but you mentioned
it a minute ago, I really think of it as augmented intelligence.
How do we use it to make our systems and processes better than they are today?
And that's a fascinating possibility.
So I don't want it to replace the nurse, if someone's in
an assisted living facility.
That human contact is very important, but it's fascinating because when the
AI systems are compared against human interactions time and time again, they
come back as the AI is more empathetic in that conversation, in that note,
that was sent via email or text message.
A lot of that I think is just we're human, we're tired at the end of the day and
only have so much capacity, energy to apply to every one of these conversations.
So it is an opportunity to improve that relationship.
I still want the nurse checking on my mother, and having that human interaction.
But at some point, it's gonna be irresponsible to not take
advantage of these tools and these technologies because they can do good.
They can make a difference.
So yes, cautionary notes, we need to be aware of the risks, the biases that
are inherent in these technologies.
And the bias is there because it learned on information that we as
humans created, we as humans are biased.
It's much easier to talk to an AI and help identify its biases than you and I.
We're generally not aware of our biases.
That self reflection and introspection is difficult as it's challenging and
do you do it in every moment of the day as you're providing care and treatment,
you have the best of intentions to do the best job you can for every patient
you see as a healthcare professional.
But your history, situations are going to impact that.
And that can be tuned out of the AI systems.
As well as the other thing, obviously mistakes, and we call them hallucinations
these days, but it's basically an AI system making something up.
But doctors, humans, we are not perfect.
There's a reason for second opinions.
And you talk to a colleague, you can also talk to the AI systems to get an
informed opinion, which interestingly can be aware of the vast volumes of research
that are generated each day, each year.
You and I can't keep up with that.
It's getting us to ask very un unexpected questions about our
own cognitive capabilities.
You mentioned our emotional intelligence, our ability to read
other people's emotions, sympathize.
We thought, I guess a few years back that this was purely human.
No way that artificial intelligence can get there it does raise
a number of questions about where do we stand in there.
But maybe that's a conversation for another day.
I really wanted to get your perspective on the future of healthcare.
You're involved in many, projects using machine learning or artificial
intelligence, or other technologies, but where do you see the field a few
years from now, let's say five years?
I think there's amazing potential that we're not even
aware of what's gonna happen.
My favorite quote these days is from Bill Gates that we as humans tend to
overestimate what's possible in two years and underestimate what's possible in 10.
We have no idea what this technology is going to be capable of in 10 years.
There's a lot of fear of it replacing.
I still think it's augmenting.
We still very much want that human touch.
Even if the AI has empathy, we do wanna keep that human touch in it.
It's gonna change it for the better.
It is going to make a difference in the health and wellness of people globally.
There are aspects of, technical equity and can everybody get access to these systems?
Challenges, yes, we have to work on, but it is going to make a difference
in the lives of millions of people.
And as we're about to wrap up, I usually ask a couple of questions at the end.
So the first one is, what is a book, tool or habit that has made the most
impact on you in the last 12 months?
AI is right up there.
I, I can't overemphasize the idea of how much of an impact AI has made.
But one of the books I interesting that I keep coming back to and
I'm starting to refer people to, obviously I'm writing my own book.
But I discovered a book called When from author Dan Pink.
A really good book.
And we all think about why we do stuff or what do we have to do.
But his book does a deep dive into when does it make sense to do something,
whether it's what point, like seasonally, what time of the year or what time of day.
And that's what tied into my energy thinking.
It's like he was talking about, when do you do a task?
And it's okay, I'm basing that decision on my energy.
Wonderful.
We'll make sure to provide the link in the description next,
of course, to your project.
And the final question, mirroring what you were saying just a moment ago.
We've discussed a lot of things that are changing, but do you
see maybe one thing that is gonna remain the same 10 years from now?
What is not gonna change?
I think AI is going to change so much.
I don't know that we recognize the world a few years from now, honestly.
I think of AI as like the printing press, electricity, uh, computing technology,
the internet, smartphones, social media.
These changes change society significantly.
Absolutely super exciting for some of us.
Maybe super scary for some others in our audience, but in any
case, RJ, it's been a pleasure!
Thank you, Alex.
The core points of today's conversation are simple.
Use AI to give time back to people such as with ambient listening digital
scribes that capture clinical notes during the visit so doctors aren't up
late doing "pajama time" paperwork.
And not deploying AI tools that clearly help patients and expand access to
healthcare may be as irresponsible as failing to address bias or safety issues.
If you'd like to see the projects behind these lessons, check out the
case studies RJ shares at estenda.com.
As always, we have more exciting topics and guest appearances lined up, so
stay tuned for more tales of innovation that inspire, challenge and transform.
Until next time.
Peace.
Thanks for tuning in to Innovation Tales.
Get inspired, connect with other practitioners and approach the
digital revolution with confidence.
Visit innovation-tales.com for more episodes.
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