(simple bright music)
-: Welcome. Thank you for joining us.
I'm Stacy Barrett, a research scientist
in the Division of Healthcare Quality Evaluation
at The Joint Commission.
I'm here today with Deb Sisco,
manager of patient advocacy and engagement
at the University Health in Kansas City, Missouri.
It's a pleasure to be with you today, Deb.
-: It's a pleasure to be here.
-: Today we will discuss the ways that your organization
uses data to identify disparities across patient groups.
This topic is the focus of one of The Joint Commissions'
new health equity requirements regarding the stratification
of key quality and safety data
using patient socio-demographic characteristics,
social needs, or social determinants of health.
Organizations can then use data
about their patient population to determine
whether there are inequalities in care
and treatment provided to one group compared
to another group.
We understand that some organizations may not be equipped
to perform complex data analyses across multiple measures,
but all organizations should be able to analyze
at least one quality or safety issue,
and to identify potential healthcare disparities
amongst its patient population.
Deb, can you tell me a bit about how your organization
uses socio-demographic, social needs or social determinants
of health patient data?
Are the data stratified so you
can identify differences across patient groups?
-: Sure, I'd be happy to.
I think probably one of the first things I'd
wanna say though, is that there's such beauty in data
in the fact that you don't have to start large and complex.
When I heard you talking about that,
oftentimes, like, we started with a simple Excel sheet.
That can get you moving in the direction
that you actually wanted to go.
So one of the most important things is just
being sure about, what is it that you wanna know?
And so for us, when we were working through processes
and how does this look
in our organization and how are we doing,
we knew that we wanted to be able to take a look
at how many people were we screening in those clinics?
What percentage of the people who came
in for their appointments actually were screened?
How many and what percent were positive?
How many and what percent were negative,
and what were they positive or negative for?
Who actually screened that person?
And really just for monitoring purposes,
let's say it's an MA,
how many people did that MA see that day?
So what percentage did they actually screen of who they saw?
And then when you are keeping Excel spreadsheets
of all those things, you actually,
in our sheets we had the name of the patient,
their MRN number, race, ethnicity, their age.
So, all the bit of real data that you want,
you could just add to that spreadsheet.
So we actually started there.
And I know it's tedious and it's by hand,
but as we matured, and we are still maturing,
we were able to sit down with our IT folks and sit
down with Cerner and say, "Hey, this is what we wanna know.
How do we build these reports
so that we can pull this information as this gets bigger
and we start going into more and more clinics?"
And our IT folks are absolutely wonderful.
Have, you know, worked with us on how we make that happen.
And when we have to modify, you know,
we can talk to them and make those changes.
That takes a while and can be challenging,
but you can get there.
-: Okay. So you start to mention some challenges there, Deb.
Talk a little bit more, if you would,
about some challenging aspects of this process
in terms of using data, to identify disparities.
-: The other challenges in sharing data
and being sure that you know who you're sharing
that data with and why you're sharing that data.
We knew that one thing that was important was
the folks who were actually doing this work
needed to know what this work looked like.
So, Florence who is absolutely wonderful,
Florence Adegoke, our data guru, I call her,
she and I met every month with the care managers
and managers of the clinics that we were working
with to go over that data and be able
to show them here's how many people you screened, you know,
here's what you missed,
here's what your positives look like.
We did have one of our initial reports break when
we were trying to add to it.
And it took a good deal of time to get that back.
That was really tough.
It took a number of months actually to get back
to where we wanted to be.
And so you felt a little bit lost because
you didn't have that constant feedback
on where you were going.
Luckily, we finally did get the fix on that, but you know,
that's the beauty of technology, it breaks.
-: Yeah, I know the Missouri Hospital Association
has really been a great partner
to you in your health equity journey.
Can you talk a little bit about the nature
of that relationship and kind of the type
of support that they've provided to you
and that they continue to provide to your organization?
-: Yeah, Missouri Hospital Association, they're amazing.
And that's a brilliant group
of data folks working over there.
They have been able to put together socio-demographic data
that's just beautiful to take a look at because
we can look at our community
as a whole and do comparative data to look at, you know,
say a chronic condition in a specific area of the city,
and what does that look like by race?
And compare one race to another
and see if we have any disparities there.
We're working with MHA right now.
And I just can't wait because we're working
on hospital level data where they're gonna be
able to take all that information and break that down for us
by our own patients, which will just catapult us as far
as what we're capable of knowing
and doing around things like chronic conditions for folks
and, you know, what's actually making a difference
in the work that we're doing.
Very, very excited about that.
-: Thanks Deb.
I know that other organizations will definitely benefit
from your knowledge and experiences.
And I just wanna thank you for taking the time to talk
with me today about this really important topic,
using data to identify disparities across patient groups.
Thanks Deb.
-: Thank you.
(simple bright music)