Hello.
I'm Renita Washburn, a PhD student at the University of Central Florida
in the Modeling and Simulation program.
Today, I' ll be presenting…
Sorry.
I know we have to do it in one session.
Dr. Amon, really quick, do you want me to say mine
and then you say yours and then I do the title?
You could restart.
You could restart if you'd like to.
We've got time.
Sorry.
You can just introduce me to… or whichever works for you.
I can go ahead-
I'll just stop that I'm in the program
and then you can introduce yourself and then I'll keep going.
Okay, sounds good.
After I say in the school Modeling, Simulation & Training at UCF
then you can pick it up, okay?
Okay.
By the way, this will be, like I said, post process,
so we can all edit all of that stuff out.
Okay, sounds good.
Okay, I'm going to go on mute again, and once I do, it's all you.
Hello.
I'm Renita Washburn, a PhD student in the Modeling and Simulation program
at the University of Central Florida.
And I'm Dr. Mary Jean Amon.
I'm an assistant professor at the University of Central Florida
in the School of Modeling, Simulation, & Training.
Today, we will co- present our poster on identifying patterns
in patient experience ratings
with machine learning clustering techniques.
In this poster session, we'll summarize the objectives,
method, and results from the exploration of these patterns
in patient experience ratings.
Patient experience ratings were obtained
from the 2019 Consumer Assessment of Healthcare Providers and Systems,
from here referred to as CAHPS, response data,
and limit it to patients seen by primary care provider.
We use JMP's machine learning clustering and data preparation tools
to identify four patient groups based on their survey responses.
Cluster analysis is a machine learning technique
used in many industries for customer segmentation.
The goal is placing customers into groups
based on similarities within the group and differences between the groups.
In healthcare, exploration of customer segments
provides insights on possible differences in care journeys and experiences,
such as disparities between race, gender, culture, or health status.
Identification of distinct groups
can inform the design of tailored care delivery models.
The project's three objectives were to, first,
conduct a hierarchical cluster analysis on categorical survey response data;
second, identify clusters through visual inspection of dendrogram
and color map partitions based on their journeys,
which was measured by survey questions
related to link the relationship with the provider,
utilization of services, and level of care management;
and lastly,
conduct pos t-hoc analyses to explore differences among clusters
in their ratings of the provider,
their overall health, and overall mental health.
Before we dive into details of the methods and findings,
we'd like to acknowledge the US Agency for Healthcare Research and Quality
and Westat
for providing the identified C AHPS data for this effort.
The CAHPS data is used to gain insight into the healthcare experience
from the patient's perspective.
The 12 selected questions are intended to capture a patient's journey
and interaction with your primary care provider
over the last six months.
The questions again focus on length of relationship with the physician,
how the patient interacts with the physician's office
for routine and urgent care needs,
and the level of care coordination for ancillary services requested.
Prior to initiating JMP's clustering tool, data preparation,
including assigning the appropriate data modeling type
for the survey questions.
The data modeling type was either nominal, yes- no or not applicable,
or null, a [inaudible 00:03:43] scale.
Another data preparation task was reformatting of select questions.
The CAHPS survey use this scale logic.
For example, one question asked in the last six months,
did you make any appointments
for a checkup or routine care with this provider?
If no, skip to next question.
It was determined that the skip questions were relevant to the exploratory analysis.
Therefore, values are recorded from missing to zero,
which JMP refers to missing not at random.
The last step of preparations that we highlight
is related to the missing values.
Instead of addressing this prior to modeling,
we use JMP's built-in missing value feature to impute,
to replace with estimates those missing values.
This is an option selected from the clustering menu.
Given the Likert scale questions,
we hypothesize that the data was hierarchical
with likely subgroups between the data.
Hierarchical clustering with the ward distance method was applied,
and the output was limited to four clusters
for ease of interpretation.
The ward method was appropriate for the categorical data
as it did not require pure measure of distance.
Instead, it builds clusters based on an analysis of variants like in Innova.
A color map was added to the dendrogram output
to aid visual comparison on response differences across the groups.
Unique patterns within and differences between clusters
were summarized based on low, medium, or high maintenance.
Meaning how much access to care was used by the patient
such as frequency of routine and urgent office visits
or contacting the office during or outside of regular hours,
as well as how well patients believe the office was managing their care,
which was weak, higher, sufficient,
defined by ratings and follow- up for lab and prescription needs.
The cluster output was saved and assigned to each response for the ad hoc analysis.
Now, the primary focus of the project was comparing clusters
on three key ratings related to the provider,
their overall health, and overall mental health.
However, with JMP, you can use the cluster assignments
to explore the distribution of demographic data
as well as other question responses between the groups.
For future analysis, we recommend exploring differences in age
or race distributions
between the maintenance management- based clusters.
We were interested in understanding
if there is a relationship between the cluster assignment
and the patient's ratings of the provider, their overall health, and mental health.
Visual inspection of the mean scores for each of these three variables
suggested that there may be significant differences based on cluster.
For example, high health maintenance patients
who utilize more healthcare services but also have satisfactory ratings for lab
and prescription management
also appear to have higher ratings of overall health and mental health.
If we go to the next slide,
these observations were further examined using JMP's contingency analysis,
which is a method for examining the relationship
between two categorical variables.
We identify statistically significant differences
in provider, overall health, and mental health ratings
based on the patient cluster,
which further highlights the utility of our clustering approach
in identifying meaningful patient groups.
Overall, understanding the relationship between each group's care journey
and overall experience and health ratings
can inform the design of health care practices
such as enhanced communication channels during non- regular office hours
or care navigation services
to aid with follow- up of lab and prescription management.
Thank you for viewing today's session.
We welcome your questions and comments.