>> It gives me
great pleasure to introduce Kelly Daley,
who's the director of rehabilitation informatics and analytics.
She's clinical informatics program coordinator.
She's going to be speaking with Corina Tamas,
who's a project leader for
a epic the electronic health record
is also going to be joining her.
>> Thank you so much, Dr. Aggarwal and I'm happy to be here.
Karina and I also co-chair
the Johns Hopkins epic clinical informatics council together.
We'll speak to just a little bit high level today on
the electronic health record or
medical record and what it has
to do with the information framework.
Hopefully you can take a few things away from
you with you for your own local areas.
Of course, this is in the context of really pushing the boundaries
that you've heard from the previous speakers yesterday and
this morning on recovery after stroke.
We're looking at multiple points of entry and utilization of data.
We're talking about, and you heard from
clinical experts like painting like in that like Stacy
about how important it was for them to really harmonize and
standardize the measures that are
documented as a standard for SKSI,
and then utilization of
that information for clinical communication and coordination.
In addition, from a data perspective
and the framework that is created,
we're really looking also at
the secondary use of data and requires it to
then go from documentation and clinical use as
primary to a secondary location in the database.
Then from there being pulled to
a repository for reporting science and
precision medicine can really be supported
within this learning health system framework.
Precision rehab for SKSI and
a learning health system has to do with practice to data,
data to knowledge, and knowledge to practice.
Breaking that down into
its foundational components means looking at
those data sources in order to facilitate this cycle.
Those big blocks of components include clinical data, of course,
billing data that goes along with it to help with
analysis of healthcare value and cost.
Then of course, many other additional data sources
that are remotely
collected during real-world care and monitoring.
All of this can then go to
that data lake for the rehab data repository.
Today Karina and I will focus primarily
on the clinical data portion of it.
This is the linkage that you've
heard from the previous speakers this morning,
where they're talking about standardization
of measures and really seeing what's
necessary in the EHR as enhancements in order to
provide a true outcomes database that's explicitly designed.
This is a slide that was used by painting and others to help
really summarize the work
that they had to do clinically to really look at,
take a really close look at capturing all domains,
being able to take measures that include those domains over time.
Because we know the recovery for stroke
starts right away but then can go for many days
that we have measures that
provide sensitivity to change and so on but
it's a standard battery that supports care through the EHR.
Then that's where we can start to really collaborate PHR,
technical teams with our clinical subject matter experts,
a painting and many others really helped to
lead this view of standardizing a battery.
Then was able to identify this barrier or
challenge in the EHR so that they could collaborate with us.
There's an interface that I'd like to share with you.
This concept that might be outside of what
normally do clinically if you're helping to lead this thing.
Where you take an explicit time with
EHR technical teams to look at what would
be feasible EHR entry of clinical measures.
What does that look like for the EHR in workflows?
How can that support care coordination views of
that information across time and disciplines?
We were able to really create a novelist
KI technical build bridges inpatient,
outpatient encounters which can be very
difficult for EHRs to handle.
Our collaboration was really to put
ourselves in the position of clinicians and then also
a technical designers and builders to look
at not just what seems like nice workflow design,
but could be inefficient and instead really
consider our users experience to make it efficient.
In other words, let's actually
sit down with a speech language pathologists,
look at where they're entering measures today for folks who've had
a stroke and how can we bring things
in a more consolidated view that's sufficient,
that's in their workflow where they don't have to
look somewhere else in order to create entry.
In addition, the collaborative design helped us really
look at ways to have formats that were efficient,
not just where they go but what they're entering.
Then that we have guardrails set up to make sure that there's
quality data entry and that we minimize clicks,
that we utilize a selection list works possible.
For instance, if you're entering the AM pack measure,
is there drop-down for the correct ways
to score the sub scores and then calculations,
leveraging the machine or the EHR to calculate those final scores.
I will hand this off to Karina to speak in
more detail about how this design was created,
harmonized with our clinical team, and then implement.
>> Thank you Kelly. Hello everybody.
I will just give you a short glimpse into how
the electronic medical record system
was part of this large project.
We work very closely with
our rehab leadership and clinicians and subject matter experts.
They came to us with
this question and basically how can we make these better,
how can we streamline our documentation
and consolidate so the end-users,
our therapists spend less time trying to
find the data and more climb when patient-care?
>> We had one large overarching project and within that
maybe 20-30 sub-tasks for
individual disciplines and individual build.
But at the end of the day,
the result that we wanted to obtain was a one-stop shop.
We created an individual occasion
for each of the disciplines, OT, PT,
and speech where they could find
older data that they had to document for SKSI mostly,
functional outcome measures as long as other documentation.
Some of these data already existed,
so we had to find it in
various parts of the char where there were already documenting it.
Some of them was brand new building we had to create.
I think this helps
streamline the workflow or they know they can just go to
this one section and find everything they need to
see. Next slide, please.
One part of this was also harmonizing as long as customizing.
For example, for speech,
they have to follow a very specific decision tree
in their documentation.
We were trying to find a way to make it available based on
usability heuristics so they don't have to recall the steps,
so we opted for
a side-by-side report where along with their documentation,
they can access the guidelines
to guide them in the decision-making.
Of course, we want to look at outcomes.
We want to look at data over time.
We opted for a synopsis view which allows us to pool
documentation for each discipline in their own specific report,
but also it allows them to
flag documentation from other disciplines and pull it into
their own with the ability to select
up to four items to be graphed.
This report has a look-back currently two years,
so it allows them to look at
the continuity of care for that patient.
Of course, a big element
of this build and this implementation was our training team.
Rachel is our star.
She was able to create a quick start guide with
all the SKSI documentation workflows,
required documentation,
and made these available to all our end-users.
Of course, our subject matter expert have
added to that and customized it to their own
individual needs focusing on inpatient and
outpatient of our documentation.
>> Thank you, Karina. Just a few other tips
you may want to bring with you
that you don't normally think about
if you're living in the clinical world,
when to collaborate technically,
is to think about as Karina and folks like Karina
are building in or enhancing measures in the EHR,
understand that each of the sub scores and the final score,
they're discreetly collected to the database.
The important piece here is they have an address,
kind of like what's on your mailbox,
is a place or location that's
the unique identifier for that particular piece of information.
If you ask for those IDs as they're being built,
it's not too much difficulty for them,
they're in the middle of building it anyway,
and you can use that to
more efficiently and effectively advocate for
subsequent builds in the EHR that are
used for reporting and also,
really importantly, to bring into
your database and data lake for science,
for IRB approved science.
Just another note here on the up,
the back end of the learning organization cycle and
its information framework is not only collaboration
with front end clinicians and EHR technical folks,
but on the back-end with folks who are living in
the data lake or data repository environment,
folks like Dr. French,
was we're able to take a look at the improvement
in the data quality that we
were able to facilitate through our build.
An example that she likes to give
is looking at the gate speed field,
which previously had been built in
a sub-optimal way or designed that way many years before,
where what was being entered was not guided or very clear to
clinicians in the field allowed for low value information.
That wasn't clear whether it was number of feet,
whether it was actually the speed in meters per second
or something else in free text
that essentially looked like zero data,
data missing this or data that was not really usable.
Because if its gait speed,
I'm pretty sure they're not walking 50 meters per second.
That allowed us to connect the dots here and
enhance entry understanding for clinicians and
then data value and quality on
the back-end after making the change,
Dr. French was able to take a look and see that
the entries actually in total sum made a lot more sense,
were higher values, much less missingness.
Now we're seeing the bulk of the data points
being somewhere in the norm that you'd expect
between 0.4 to 1.5 meters per second or something like that.
This really helps to close the loop to
leverage this data secondarily for science and for precision.
Data repository, on a shout out
not just to Dr. French but also to Sharon Penttinen,
who's the project manager for our Precision Medicine initiative,
and many other folks on
that team really helped to look at cohort development.
You'd think it'd be easy to define who are
those patients seen with acute stroke in a health system,
but that actually takes
quite a bit of work to make sure you're capturing it
appropriately that we look to
pull then the identified clinical data,
that we talked about into that Data Lake,
and that we included other important information alongside of
it that were carefully and precisely identified for the Data Lake,
including specific patient demographics,
billing information for cost and value analysis,
and other remotely collected data.
Then that data we are able to then project or create
cohorts for specific IRB approved studies for precision science.
It's been an exciting and fun way
to really connect the dots for this framework.
Take Homes then is
really looking maybe beyond what you've done in the past for
strong clinical technical collaboration to truly harness and
leverage that EHR build and data framework behind it,
and that we really need to strike a balance between
feasible clinical measures and what might be needed for
science and/or a learning organization.
Lastly, if you have additional questions or
concerns that we don't address here,
and we'll try to address a few,
feel free to find me on Twitter
and we'll continue this conversation.
>> Thank you very much, Kelly.
I can't reiterate enough how important and instrumental
the whole idea of integrating with
the EHR was for our clinical care,
for extracting the data, for research,
for even talking about whether we're successful or not.
Thank you again.