October 15, 2025 • 16 mins
Episode 2: In the second episode of On Call, Off Script, host Ruelle Champion sits down with George Rosello, Jackson’s associate vice president for digital transformation, to explore how artificial intelligence (AI) is transforming health care. From faster documentation to smarter workflows, discover how Jackson is using AI to save time, improve care, and keep patient data safe – all while preserving the human touch.
Guest Info: George Rosello, associate vice president for digital transformation, Jackson Health System George Rosello serves as associate vice president for digital transformation at Jackson Health System. At Jackson, he spearheaded the creation of Overwatch, a platform designed to improve population health outcomes and streamline patient appointment management. Additionally, his team recently launched the Jackson digital workforce, an intelligence platform using AI to automate repetitive tasks and improve productivity across departments. Under Rosello’sleadership, Jackson’s data science team also established one of the first local machine learning models during the COVID-19 pandemic, which was used by Jackson and Miami-Dade County to predict COVID surges. Rosello was recognized as one of CIO.com’s “Future Edge 50” awardees for his transformative work in health system information technology, and since then, his team has also been awarded the CIO 100 four times, all for healthcare innovation and digital transformation accomplishments.
With deep expertise and years of experience in enterprise applications, data integration, and clinical IT alignment, Rosello is a driving force behind Jackson’s efforts to harness data, predictive analytics, and system automation to enhance care delivery.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
I might.
I might have.
Your intake form just filled itself,
and your time at the doctor's officehas just been slashed in half.
Welcome to health care that runs smarter.
This is on call off script.
A Jackson Health System podcast.
I'm your host, world champion,
and we are talking all thingsartificial intelligence,
(00:23):
especially how it speeds upthe boring parts of healthcare.
Joining us today is George Rossello.
He's the tech maven behind automationat Jackson George.
Thank you. Tell us more about your role.
Thanks. It's great to be here.
So my role kind of oversees, pretty much everything data driven,
within Jackson Health Systems.
So, from teams that do data engineeringall the way to data science,
(00:45):
integrating data, and trying to make itmore accessible and more useful.
Now, that is a perfect wayto set the scene for a George.
How are we working to make things smarter,not harder, for our people?
Yeah.
So I think one of the biggest challengesin healthcare is that it's a very,
unstructured place.
So when we think about data,we think about things
that are filled out on formsthat are easy to read.
(01:06):
But a lot of the data in healthcareis unstructured.
It's conversations.
It's, documentation, it'stranscriptions from physicians.
So really only about 20% of itis is useful,
and the other 80% is really difficultto, to get.
So and it's really hard to create as well.
Those physicians
spend a lot of time doing documentation,a lot of time reading things.
(01:26):
So anything we can do to to speed that upand make that more
accessible is going to be a huge benefitto the patient care journey.
So one of those things is leveraging AI.
You know, the last few yearswe've seen huge leaps and what I can do,
and most notably, being able to tackle
this unstructured problem ofhow do I get access to this data
that reads like a story instead of,you know, really select data points?
(01:47):
So by being able to leverage
AI tools, we can nownot only draft documents for providers,
but also, read documents that providershave have already done in the past.
We can take years of medical informationand extract meaningful information from it
and make that accessible to a physicianin your most current visit.
So we're takingwhat used to be a really tedious process
(02:08):
that would happen after hours or,you know, before a patient showed up.
So now being able to happen in real time
and something that'sthat's very easy to do and anyone can do.
Now that sounds like it's savingtons of time, right.
So what does that translate tofor the patient.
Right. The patientthey go into an appointment.
They're not thinking about who's readingwhat chart or who's taking what notes.
Have we been able to pinpoint okay.
(02:30):
This is knocking down 30 minutes.
This is knocking down an hour.How much time are we really saving.
Yeah.
So the the average visit might construe oflet's take an outpatient visit,
some of the simplest ways that a patientmay come in
just to check for their PCPor do a follow up.
You know, these visits might be 15,30 minutes of time in an office,
but the amount of documentationproduced for that can be several pages.
(02:51):
Some of it may happen immediately
after the visit,some of it may happen days later.
So these types,these types of visits are very common
and produce a lot of datathat is otherwise very difficult to use.
So by being able to leverage AI,we can hopefully shorten that time
frame down, make sure it's accurateat the time the patient's in the visit.
When they leave,they have a copy of it as well.
(03:12):
So they have everything that they knowthey went through and everything
that that was either done to themor if it was a procedure
or if it was a visitthat gave them some advice on
how to change certain thingsto help with a better health outcome,
they'll have all that informationaccessible to them right away.
So it's saving time for providerson the patient side
where we see a lot of time saved is,the build up to the appointment right.
We've all been in that situation
(03:33):
where you get an appointmentand you have to register and it's,
you know, filling out the entire historyof your life in a form,
and then repeating yourself 2 or 3 timesafter you get there.
You know, some of that is to make surethat it's accurate
and give you an opportunity to correcta mistake if you forgot something.
But a lot of timesit just happens because it's you.
It needs to be capturedby multiple systems, right?
Or multiple or differentpeople need to read it.
(03:54):
So if we're able to create a better flowof that information and data
throughout the health system,
from the patient to the registrarto the nurse to the doctor, we're
we're allowing that data to kind of travelwith you more successfully.
And by doing that,we're not just saving you time,
but we're creating a more accurate visitwhere all that information is available
and it stays with you for your next visit.
For a hospital stay,you know, years later
(04:16):
when maybe you haven'thad a visit for years
and you come back in, you know,all that information carries forward.
So the more accessible we can make itand the more digestible,
for clinicians and the easierin, the more it can serve you.
Now, this sounds like one toolthat's doing it all.
Or is it multiple AI tools that areworking hand in hand to do this process?
Is it patients can use voice recognition
(04:38):
software and tools to our providersuse that as well.
Yeah.
So it's a it's a combinationof a lot of tools that accomplish this.
So we do have a lot of voice recognitionthat exists today.
You know, you see this with physiciansthat do transcription.
So like radiology,it's a really popular space.
You also see this for like,surgical operative notes.
So someone having a surgery,
a lot of that informationis also captured verbally.
(05:00):
But what's not capturedis the conversation
between patients and physicians. Right.
So you come into a physician office,you sit down to review the results
of maybe a procedure or a test.
That's a conversation, sameas what you and I are having right now.
No one's recording that, right?
Nothing's being written down.Nothing's being copied.
There'll be some notesthat you'll be given afterwards,
which is the physician's interpretation,and then you'll have your interpretation.
(05:21):
I think one of the biggest takeaways
that we see, around this spacewhen it comes to a patient
receiving informationthis way, is on the drive home.
Right?
So let's say you have somebody come inand one of their kids is with them, right?
Maybe it's an elderly parent.
They bring their younger, sonor daughter with them on the drive home.
What happens?
They say, well,the doctor said I should do A, B, and C,
(05:42):
and then the child says, well, hold on,I actually heard you should be doing it
a little bit differently. Right?
And it creates this, this little spacewhere misinterpretation can happen.
Right.
The discharge documentationmight have the specific steps
they have to take a medicine,a prescription,
you know, maybe some advice in terms of,you know, getting more exercise.
But the key takeaways that happenedin that conversation aren't captured.
(06:05):
So by being able to create some ambientAI tools that can listen and provide that
that agency for both the patient
and the provider, capturing an accurateretelling of that conversation,
that's really the next step.
And being able to capture that visitand make it tangible for that patients
that when they take it home,there's not a debate about what happened.
It's it's able to,
you know, see a transcriptionand know exactly what was said. Yeah.
(06:25):
And now there's there's better caredelivery for the patient because they're
they're able to recite that informationexactly the way it was said.
Absolutely.
Well, accuracy is one thing, but patientsafety in their information is another.
How are we stayingHIPAA compliant with AI tools?
What are some things that a patient
would want to know or need to knowabout the safety of their information?
While keeping information safe is ais a big priority and all these AI tools
(06:48):
that we see coming out, that, you know,everyone has on their phones,
on their tablets, you know, becoming moreand more accessible.
A lot of that informationdoes not reside in the health system.
It goes off into, you know, a cloud.
It goes into an infrastructure
owned by a different companythat has nothing to do with health care.
So, there's a big conversation aroundhow do we keep this safe?
The other part of it, too, ishow do we keep,
(07:09):
how do we track the accuracyof this information?
Everyone's heard about hallucinations.
When an AI gets confused and gives you
the wrong answer to something,but thinks it's the right answer.
You know, one thing that the AI willalways respond with is confidence, right?
It will always sound like itknows what it's talking about.
So it's up to us to be able to factcheck that.
So when it comes to keepingpatient information safe,
(07:30):
you know, all the AI stuff that we do atJackson stays within the walls of Jackson.
So any anytime we're usingany kind of cloud service, it's always,
you know, separatedfrom that infrastructure
and something that we are doing onlywithin our account.
So it's something that we're ableto keep safe,
keep within the walls of Jackson,keep patient information safe
or we're able to build on that
(07:50):
through our data science teams, our dataengineering teams, business intelligence,
and be able to build on the knowledge
we're gathering from thatto create more accuracy, better tools.
But not sharing anyone's data,with outside agencies.
Absolutely.
Now, let's switchback over to the employee experience.
I'm just picturing myself as a nurse,and I'm
putting in information for a patientwho I'm seeing in their chart.
(08:13):
One thing that I canimagine is overwhelming is being a doctor,
looking at that chart,
not knowing what to look for precisely,or what should stand out the most.
Are there any tools that we're usingthat can give that receiving provider?
Like, hey, you need to look at this first,look at these vitals,
or look at this informationfirst before other pop ups.
Yeah, I mean there's there's a combinationof tools that we use to accomplish that.
(08:36):
So the electronic medical recordshave a lot of rules built in
that will help promptwhen something is is very discreet.
Right.
If a lab value's too high or if,a numeric value isn't where it should be.
But what becomes complicatedis when a series of conversations
or documentation
track data points that aren't necessarilyassociated to themselves.
Right?
So maybe I complained to my primary careabout one ailment,
(08:59):
and I went to a specialistand complained about a different element.
But if I combine those two things,it creates a higher concern.
And that's where we seethe advent of AI being helpful,
where it can it can look through all thisreally dense information,
it can gather it together,and it stays persistent and constant.
You know,every time you go to a new doctor,
you have to retell your story.
You know,what's what's been your patient journey?
What's the history of your health care.
(09:20):
It makes you wonderwhy you even say it in the first place.
If you have to sound like a broken record.
Yeah, yeah,it's it's a it's a big challenging space.
And every time you,
you recite that history, you know,it tends to get shorter. Right.
Because we're not going to gothrough years and years of the past.
We're going to saywhat we think is relevant.
But everything is relevant,especially when we're
dealing with so much datathat persists across our entire lives.
(09:42):
So leveraging toolslike AI allows us to make sure
that conversation stays persistent. Right?
Every time I go see a doctor,if I'm using the same technology
solution, I should be able to continuethat conversation where it left off.
And if I, as a provider, missed something,or there was too much information for me
to be able to track ahead of timeor know where to look, you know, the
AI can push that forward and say,hey, this seems relevant.
(10:05):
You should take a look at this.
So these kinds of toolsare in development, and you see them
acrossdifferent vendors of different spaces.
We're working on our own within Jacksonto try and really use AI as a way to point
people to the most important informationat the time that they need it,
and make sure that that rises to the topso that people spend less time
searching and less time missinginformation that they should be finding.
(10:26):
I was going to be my next question,George, is what are we doing here?
What's going to start here at Jackson?What are we rolling out?
What's the rollout planeven look like for you? Yeah.
So we have a few different,vectors that we want to bring in.
So one thing that we're launching in
which we're really excited aboutis the digital workforce.
So this is really leveraginga combination of AI and automation
to be able to create digital workersin different departments and spaces
(10:48):
that can help out.
So one of them, you know,is to help with documentation, right?
Where we may have, a lot of different,notes that exist for a patient.
Maybe they're it's a long hospital stay,
and they've had a lotof different procedures,
and they've spoken to a lot ofdifferent doctors and clinicians,
and it's hard to put all that togetherinto one concise note.
So being able to leverage AI to facilitatethose conversations, document
(11:10):
that and draft those things for physicianscan save a lot of time.
In other spaces,you know, we're leveraging, in scheduling
to be able to help
with, very, you know, it's very tediousto schedule medical procedures.
You know, some things are easy.
You want to just come in and see a doctorfor an ailment, no problem.
But other things can be really complexand have a lot of steps,
and it can be very confusing for patients.
What steps I need to do.
(11:31):
How far have I completed that process?
So this allows us to really helpfacilitate that conversation
a little bit better, identifythings that are missing,
you know, be able to predictif a patient might no show
because they're not further aheadin the process,
than they should be, especially based onwhen their appointment date is,
and being able to manage manage schedulesa little bit more efficiently.
(11:52):
Last place is tedious work.
So this is where, you know,you have to blend a careful line
because anytime you're bringing in AI,especially today with the conversation
we see, you know, in the mediaand on social media is, you know,
I is coming for our jobs, right?
And,and it's not really coming for your jobs.
The people who know how to useAI are the ones coming for those jobs.
So we need to make sure
(12:13):
our workforce knows how to interactwith AI and use it effectively.
And that means, looking at jobsand identifying tedious tasks
that are things that an AI could dovery safely and consistently.
But we as humans maybe don't like to do,and we can take that and automate it.
And then when we automate that,
that frees that worker up to do thingsthat they actually need to do,
which is that human experiencethat healthcare is most known for.
(12:35):
So by freeing that up,we're able to repackage these jobs
and really focus peopleon that human element,
and take away that stuffthat distracts from that and really use
AI to do that,which thankfully it never gets bored.
It doesn't get distracted by Facebook,you know, it stays, it's going.
To stay on task.
It stays on task.
It it doesn't ask for time off, you know.
So it's a great candidate for that.
(12:55):
But nothing beats human interactionsometimes.
So no, definitely not.
George, when we're workshopping these,these new tools, these new projects
that we want to useto bring Jackson into the future,
what's the feed like feedbackbeen like so far of you?
Do you have work groupsthat you're working
within your own department,or are we working more multidisciplinary
(13:16):
across other sectorswithin Jackson to get feedback?
What's it been like?
Yeah, it'sabsolutely a multidisciplinary effort.
I think one of the challengesand this goes even just beyond
just AI, but just what it health carelooks like in general.
You know, for a long timeit's been a service driven industry where,
you know, we're providing service,we're responding to needs.
But now it's more about partnership.
(13:37):
It's more about identifying, what departments do,
what do they function, you know,what are the things that they deem
a success, you know,how do they measure their success.
And then crafting teams that really worktowards that success with that department,
taking on that missionas if it's their own,
you know, used to be years ago when itif you went live with something, you know,
you launched a new application,you installed some new technology.
(13:59):
The installation itself was a success.
That's not the case today.We really want to see the adoption.
We want to see, the end users embraceand use it successfully.
And we want to see thethe mission statement of that department,
you know, whether itwhether it's healthier patients
or on time schedules or,you know, a more efficient practice.
We want to see those goals be achieved.
And until those goals are achieved,
(14:20):
you know, we don't consider our work doneso that that level of partnership
is working side by side with departmentsnot working in a black box of it
and then eventually bringing it in, but working with them hand-in-hand.
And that also gives them an ownershipstake in what we produce. Right?
They feel it's part of what they crafted,what they constructed.
Right.
You know, it'slike if you were getting a house built,
you know, you may not be the architect,you may not be the contractor.
(14:41):
But when you look at that houseand you and you live in
and you movein, and at the end of the day,
that feels like your house,because you were a part of the process.
So that's really what we try to achieve at
Jackson, is work with our partnersin these different clinical departments
and make sure that they feel likethey own the house.
Right.
So it sounds like every department shouldbe familiar with their IT representative
and maybe want to get close with themas we start to integrate these new tools.
(15:05):
Yeah, absolutely.
Now, George, who's your tough question.
And our final question.
So if you limitless budget no obstacles,you can install a new AI tool
that will help patients, doctors, nurses,everybody in between starting tomorrow.
What would it be? What would you do?
Oh, wow.
I mean, and I think that goes back
to one of the earlier pointsin the conversation.
I, I think the, the biggest thingthat we could do is to take that
(15:30):
80% of data that is still kind ofthe unknown of health
care and find a way to,to make it manageable.
Which I think for the first timeever, using AI where we're able to do,
you know, we talk about, you know, what'sleft to discover as, as a human race.
You know, we think of space,
we think of the ocean, you know,but our bodies are also a mystery.
There's still so many things.
We don't know how to heal, so many thingswe don't know how to discover.
(15:52):
And when you look at everything we've donein health care, for the last 20,
30 years, when computers have beenintroduced, we've accelerated so quickly,
and that's been only using 20% of the datathat's available.
So if we could snap our fingersand unlock the other 80%,
imagine how much we could do.
Imagine what we could accomplish withthe whole 100% of data at our fingertips.
(16:16):
We would accelerate health care at a pacewe've never seen before.
By the time you and I are at retirementage, we would look back on health care
and it would look like it jumped forward300 years. Wow.
So let's make a date 20 yearswhen we back here and see how far
we've come with AI in health care. George.All right.
It sounds good, I appreciate you.
Thank you, George,for joining us here today.
So it sounds like our takeaway isif the task is repetitive, just automated
(16:40):
AI is only as intelligentas the humans behind it.
Thank you for joiningus. On on call off script.
Make sure to stay tuned in
with our social media accountsand keep watch for our next episode.
I might.
I might have.
Your intake form just filled itself,
and your time at the doctor's officehas just been slashed in half.
Welcome to health care that runs smarter.
This is on call off script.
A Jackson Health System podcast.
I'm your host, world champion,
and we are talking all thingsartificial intelligence,
(00:23):
especially how it speeds upthe boring parts of healthcare.
Joining us today is George Rossello.
He's the tech maven behind automationat Jackson George.
Thank you. Tell us more about your role.
Thanks. It's great to be here.
So my role kind of oversees, pretty much everything data driven,
within Jackson Health Systems.
So, from teams that do data engineeringall the way to data science,
(00:45):
integrating data, and trying to make itmore accessible and more useful.
Now, that is a perfect wayto set the scene for a George.
How are we working to make things smarter,not harder, for our people?
Yeah.
So I think one of the biggest challengesin healthcare is that it's a very,
unstructured place.
So when we think about data,we think about things
that are filled out on formsthat are easy to read.
(01:06):
But a lot of the data in healthcareis unstructured.
It's conversations.
It's, documentation, it'stranscriptions from physicians.
So really only about 20% of itis is useful,
and the other 80% is really difficultto, to get.
So and it's really hard to create as well.
Those physicians
spend a lot of time doing documentation,a lot of time reading things.
(01:26):
So anything we can do to to speed that upand make that more
accessible is going to be a huge benefitto the patient care journey.
So one of those things is leveraging AI.
You know, the last few yearswe've seen huge leaps and what I can do,
and most notably, being able to tackle
this unstructured problem ofhow do I get access to this data
that reads like a story instead of,you know, really select data points?
(01:47):
So by being able to leverage
AI tools, we can nownot only draft documents for providers,
but also, read documents that providershave have already done in the past.
We can take years of medical informationand extract meaningful information from it
and make that accessible to a physicianin your most current visit.
So we're takingwhat used to be a really tedious process
(02:08):
that would happen after hours or,you know, before a patient showed up.
So now being able to happen in real time
and something that'sthat's very easy to do and anyone can do.
Now that sounds like it's savingtons of time, right.
So what does that translate tofor the patient.
Right. The patientthey go into an appointment.
They're not thinking about who's readingwhat chart or who's taking what notes.
Have we been able to pinpoint okay.
(02:30):
This is knocking down 30 minutes.
This is knocking down an hour.How much time are we really saving.
Yeah.
So the the average visit might construe oflet's take an outpatient visit,
some of the simplest ways that a patientmay come in
just to check for their PCPor do a follow up.
You know, these visits might be 15,30 minutes of time in an office,
but the amount of documentationproduced for that can be several pages.
(02:51):
Some of it may happen immediately
after the visit,some of it may happen days later.
So these types,these types of visits are very common
and produce a lot of datathat is otherwise very difficult to use.
So by being able to leverage AI,we can hopefully shorten that time
frame down, make sure it's accurateat the time the patient's in the visit.
When they leave,they have a copy of it as well.
(03:12):
So they have everything that they knowthey went through and everything
that that was either done to themor if it was a procedure
or if it was a visitthat gave them some advice on
how to change certain thingsto help with a better health outcome,
they'll have all that informationaccessible to them right away.
So it's saving time for providerson the patient side
where we see a lot of time saved is,the build up to the appointment right.
We've all been in that situation
(03:33):
where you get an appointmentand you have to register and it's,
you know, filling out the entire historyof your life in a form,
and then repeating yourself 2 or 3 timesafter you get there.
You know, some of that is to make surethat it's accurate
and give you an opportunity to correcta mistake if you forgot something.
But a lot of timesit just happens because it's you.
It needs to be capturedby multiple systems, right?
Or multiple or differentpeople need to read it.
(03:54):
So if we're able to create a better flowof that information and data
throughout the health system,
from the patient to the registrarto the nurse to the doctor, we're
we're allowing that data to kind of travelwith you more successfully.
And by doing that,we're not just saving you time,
but we're creating a more accurate visitwhere all that information is available
and it stays with you for your next visit.
For a hospital stay,you know, years later
(04:16):
when maybe you haven'thad a visit for years
and you come back in, you know,all that information carries forward.
So the more accessible we can make itand the more digestible,
for clinicians and the easierin, the more it can serve you.
Now, this sounds like one toolthat's doing it all.
Or is it multiple AI tools that areworking hand in hand to do this process?
Is it patients can use voice recognition
(04:38):
software and tools to our providersuse that as well.
Yeah.
So it's a it's a combinationof a lot of tools that accomplish this.
So we do have a lot of voice recognitionthat exists today.
You know, you see this with physiciansthat do transcription.
So like radiology,it's a really popular space.
You also see this for like,surgical operative notes.
So someone having a surgery,
a lot of that informationis also captured verbally.
(05:00):
But what's not capturedis the conversation
between patients and physicians. Right.
So you come into a physician office,you sit down to review the results
of maybe a procedure or a test.
That's a conversation, sameas what you and I are having right now.
No one's recording that, right?
Nothing's being written down.Nothing's being copied.
There'll be some notesthat you'll be given afterwards,
which is the physician's interpretation,and then you'll have your interpretation.
(05:21):
I think one of the biggest takeaways
that we see, around this spacewhen it comes to a patient
receiving informationthis way, is on the drive home.
Right?
So let's say you have somebody come inand one of their kids is with them, right?
Maybe it's an elderly parent.
They bring their younger, sonor daughter with them on the drive home.
What happens?
They say, well,the doctor said I should do A, B, and C,
(05:42):
and then the child says, well, hold on,I actually heard you should be doing it
a little bit differently. Right?
And it creates this, this little spacewhere misinterpretation can happen.
Right.
The discharge documentationmight have the specific steps
they have to take a medicine,a prescription,
you know, maybe some advice in terms of,you know, getting more exercise.
But the key takeaways that happenedin that conversation aren't captured.
(06:05):
So by being able to create some ambientAI tools that can listen and provide that
that agency for both the patient
and the provider, capturing an accurateretelling of that conversation,
that's really the next step.
And being able to capture that visitand make it tangible for that patients
that when they take it home,there's not a debate about what happened.
It's it's able to,
you know, see a transcriptionand know exactly what was said. Yeah.
(06:25):
And now there's there's better caredelivery for the patient because they're
they're able to recite that informationexactly the way it was said.
Absolutely.
Well, accuracy is one thing, but patientsafety in their information is another.
How are we stayingHIPAA compliant with AI tools?
What are some things that a patient
would want to know or need to knowabout the safety of their information?
While keeping information safe is ais a big priority and all these AI tools
(06:48):
that we see coming out, that, you know,everyone has on their phones,
on their tablets, you know, becoming moreand more accessible.
A lot of that informationdoes not reside in the health system.
It goes off into, you know, a cloud.
It goes into an infrastructure
owned by a different companythat has nothing to do with health care.
So, there's a big conversation aroundhow do we keep this safe?
The other part of it, too, ishow do we keep,
(07:09):
how do we track the accuracyof this information?
Everyone's heard about hallucinations.
When an AI gets confused and gives you
the wrong answer to something,but thinks it's the right answer.
You know, one thing that the AI willalways respond with is confidence, right?
It will always sound like itknows what it's talking about.
So it's up to us to be able to factcheck that.
So when it comes to keepingpatient information safe,
(07:30):
you know, all the AI stuff that we do atJackson stays within the walls of Jackson.
So any anytime we're usingany kind of cloud service, it's always,
you know, separatedfrom that infrastructure
and something that we are doing onlywithin our account.
So it's something that we're ableto keep safe,
keep within the walls of Jackson,keep patient information safe
or we're able to build on that
(07:50):
through our data science teams, our dataengineering teams, business intelligence,
and be able to build on the knowledge
we're gathering from thatto create more accuracy, better tools.
But not sharing anyone's data,with outside agencies.
Absolutely.
Now, let's switchback over to the employee experience.
I'm just picturing myself as a nurse,and I'm
putting in information for a patientwho I'm seeing in their chart.
(08:13):
One thing that I canimagine is overwhelming is being a doctor,
looking at that chart,
not knowing what to look for precisely,or what should stand out the most.
Are there any tools that we're usingthat can give that receiving provider?
Like, hey, you need to look at this first,look at these vitals,
or look at this informationfirst before other pop ups.
Yeah, I mean there's there's a combinationof tools that we use to accomplish that.
(08:36):
So the electronic medical recordshave a lot of rules built in
that will help promptwhen something is is very discreet.
Right.
If a lab value's too high or if,a numeric value isn't where it should be.
But what becomes complicatedis when a series of conversations
or documentation
track data points that aren't necessarilyassociated to themselves.
Right?
So maybe I complained to my primary careabout one ailment,
(08:59):
and I went to a specialistand complained about a different element.
But if I combine those two things,it creates a higher concern.
And that's where we seethe advent of AI being helpful,
where it can it can look through all thisreally dense information,
it can gather it together,and it stays persistent and constant.
You know,every time you go to a new doctor,
you have to retell your story.
You know,what's what's been your patient journey?
What's the history of your health care.
(09:20):
It makes you wonderwhy you even say it in the first place.
If you have to sound like a broken record.
Yeah, yeah,it's it's a it's a big challenging space.
And every time you,
you recite that history, you know,it tends to get shorter. Right.
Because we're not going to gothrough years and years of the past.
We're going to saywhat we think is relevant.
But everything is relevant,especially when we're
dealing with so much datathat persists across our entire lives.
(09:42):
So leveraging toolslike AI allows us to make sure
that conversation stays persistent. Right?
Every time I go see a doctor,if I'm using the same technology
solution, I should be able to continuethat conversation where it left off.
And if I, as a provider, missed something,or there was too much information for me
to be able to track ahead of timeor know where to look, you know, the
AI can push that forward and say,hey, this seems relevant.
(10:05):
You should take a look at this.
So these kinds of toolsare in development, and you see them
acrossdifferent vendors of different spaces.
We're working on our own within Jacksonto try and really use AI as a way to point
people to the most important informationat the time that they need it,
and make sure that that rises to the topso that people spend less time
searching and less time missinginformation that they should be finding.
(10:26):
I was going to be my next question,George, is what are we doing here?
What's going to start here at Jackson?What are we rolling out?
What's the rollout planeven look like for you? Yeah.
So we have a few different,vectors that we want to bring in.
So one thing that we're launching in
which we're really excited aboutis the digital workforce.
So this is really leveraginga combination of AI and automation
to be able to create digital workersin different departments and spaces
(10:48):
that can help out.
So one of them, you know,is to help with documentation, right?
Where we may have, a lot of different,notes that exist for a patient.
Maybe they're it's a long hospital stay,
and they've had a lotof different procedures,
and they've spoken to a lot ofdifferent doctors and clinicians,
and it's hard to put all that togetherinto one concise note.
So being able to leverage AI to facilitatethose conversations, document
(11:10):
that and draft those things for physicianscan save a lot of time.
In other spaces,you know, we're leveraging, in scheduling
to be able to help
with, very, you know, it's very tediousto schedule medical procedures.
You know, some things are easy.
You want to just come in and see a doctorfor an ailment, no problem.
But other things can be really complexand have a lot of steps,
and it can be very confusing for patients.
What steps I need to do.
(11:31):
How far have I completed that process?
So this allows us to really helpfacilitate that conversation
a little bit better, identifythings that are missing,
you know, be able to predictif a patient might no show
because they're not further aheadin the process,
than they should be, especially based onwhen their appointment date is,
and being able to manage manage schedulesa little bit more efficiently.
(11:52):
Last place is tedious work.
So this is where, you know,you have to blend a careful line
because anytime you're bringing in AI,especially today with the conversation
we see, you know, in the mediaand on social media is, you know,
I is coming for our jobs, right?
And,and it's not really coming for your jobs.
The people who know how to useAI are the ones coming for those jobs.
So we need to make sure
(12:13):
our workforce knows how to interactwith AI and use it effectively.
And that means, looking at jobsand identifying tedious tasks
that are things that an AI could dovery safely and consistently.
But we as humans maybe don't like to do,and we can take that and automate it.
And then when we automate that,
that frees that worker up to do thingsthat they actually need to do,
which is that human experiencethat healthcare is most known for.
(12:35):
So by freeing that up,we're able to repackage these jobs
and really focus peopleon that human element,
and take away that stuffthat distracts from that and really use
AI to do that,which thankfully it never gets bored.
It doesn't get distracted by Facebook,you know, it stays, it's going.
To stay on task.
It stays on task.
It it doesn't ask for time off, you know.
So it's a great candidate for that.
(12:55):
But nothing beats human interactionsometimes.
So no, definitely not.
George, when we're workshopping these,these new tools, these new projects
that we want to useto bring Jackson into the future,
what's the feed like feedbackbeen like so far of you?
Do you have work groupsthat you're working
within your own department,or are we working more multidisciplinary
(13:16):
across other sectorswithin Jackson to get feedback?
What's it been like?
Yeah, it'sabsolutely a multidisciplinary effort.
I think one of the challengesand this goes even just beyond
just AI, but just what it health carelooks like in general.
You know, for a long timeit's been a service driven industry where,
you know, we're providing service,we're responding to needs.
But now it's more about partnership.
(13:37):
It's more about identifying, what departments do,
what do they function, you know,what are the things that they deem
a success, you know,how do they measure their success.
And then crafting teams that really worktowards that success with that department,
taking on that missionas if it's their own,
you know, used to be years ago when itif you went live with something, you know,
you launched a new application,you installed some new technology.
(13:59):
The installation itself was a success.
That's not the case today.We really want to see the adoption.
We want to see, the end users embraceand use it successfully.
And we want to see thethe mission statement of that department,
you know, whether itwhether it's healthier patients
or on time schedules or,you know, a more efficient practice.
We want to see those goals be achieved.
And until those goals are achieved,
(14:20):
you know, we don't consider our work doneso that that level of partnership
is working side by side with departmentsnot working in a black box of it
and then eventually bringing it in, but working with them hand-in-hand.
And that also gives them an ownershipstake in what we produce. Right?
They feel it's part of what they crafted,what they constructed.
Right.
You know, it'slike if you were getting a house built,
you know, you may not be the architect,you may not be the contractor.
(14:41):
But when you look at that houseand you and you live in
and you movein, and at the end of the day,
that feels like your house,because you were a part of the process.
So that's really what we try to achieve at
Jackson, is work with our partnersin these different clinical departments
and make sure that they feel likethey own the house.
Right.
So it sounds like every department shouldbe familiar with their IT representative
and maybe want to get close with themas we start to integrate these new tools.
(15:05):
Yeah, absolutely.
Now, George, who's your tough question.
And our final question.
So if you limitless budget no obstacles,you can install a new AI tool
that will help patients, doctors, nurses,everybody in between starting tomorrow.
What would it be? What would you do?
Oh, wow.
I mean, and I think that goes back
to one of the earlier pointsin the conversation.
I, I think the, the biggest thingthat we could do is to take that
(15:30):
80% of data that is still kind ofthe unknown of health
care and find a way to,to make it manageable.
Which I think for the first timeever, using AI where we're able to do,
you know, we talk about, you know, what'sleft to discover as, as a human race.
You know, we think of space,
we think of the ocean, you know,but our bodies are also a mystery.
There's still so many things.
We don't know how to heal, so many thingswe don't know how to discover.
(15:52):
And when you look at everything we've donein health care, for the last 20,
30 years, when computers have beenintroduced, we've accelerated so quickly,
and that's been only using 20% of the datathat's available.
So if we could snap our fingersand unlock the other 80%,
imagine how much we could do.
Imagine what we could accomplish withthe whole 100% of data at our fingertips.
(16:16):
We would accelerate health care at a pacewe've never seen before.
By the time you and I are at retirementage, we would look back on health care
and it would look like it jumped forward300 years. Wow.
So let's make a date 20 yearswhen we back here and see how far
we've come with AI in health care. George.All right.
It sounds good, I appreciate you.
Thank you, George,for joining us here today.
So it sounds like our takeaway isif the task is repetitive, just automated
(16:40):
AI is only as intelligentas the humans behind it.
Thank you for joiningus. On on call off script.
Make sure to stay tuned in
with our social media accountsand keep watch for our next episode.
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