Hi there. I'm Vernon Tan,
and I lead the Frostbyte industry solution team here at Snowflake.
Today , I'm going to show you an example of how our healthcare customers are improving care management
with a Healthcare and Life Sciences Data Cloud
to perform advanced analytics on social determinants of health (SDOH).
Health providers and health plans today are realizing that focusing solely on clinical
care has only a 20% influence on the length and quality of life for the communities they serve.
But outside of clinical care, 50% of the influence is tied to social determinants of health,
a diverse and complex set of socio-economic factors that affect a patient or member population---
food insecurity, housing, high-stress life events, social support, transportation, and more.
Traditionally, health insurers would only use claims data to derive analytic insight on higher risk members.
But claims data is only one piece of the equation, especially as it relates to SDOH.
Clinical data and third-party data are also needed to ensure that the data engineering and
machine learning processes to calculate SDOH risk factors are able to execute on a broader and more
representative set of features. In this scenario, we will zoom into a specific aspect of SDOH---
predicting and addressing the transportation barriers within a patient and member population.
The objective: To minimize missed appointments and improve patient outcomes with the Snowflake Data Cloud.
All of this starts with a question: How do we identify patients that require targeted intervention?
In this demo ,you're going to see how we bring together operational healthcare data,
harmonize our operational data with third-party non-clinical data from the Data Marketplace,
and finally, take insight and turn it into action for targeted care management.
What you are seeing here is a Snowflake UI. To perform any meaningful analytics,
it's important to start with a good foundation. What we've done is we've
combined patient and member data in one place for us to start exploring.
We can see the distribution of members by gender, further segmented by age range, and
ensure that this data is secured for any end users to access by creating masking policies
that obfuscate the data, depending on the permissions granted to the user.
This means that you can maintain one central copy of data and ensure the highest standards
of patient and member privacy, while retaining the analytic value of the data for end users.
With the data obfuscated, we can now proceed to explore our data in an effort to better understand
our members. We can see how many appointments have been missed across the entire population,
slice it by gender,
and see which patients have missed
30% of their appointments. But why are these people missing their appointments?
Are there factors we may not be aware of that affect these members' ability
to make their appointments? Working with our first- party patient and member data is the first piece.
The Snowflake Data Marketplace is a gateway to access over 1,000
live data listings from data providers across a variety of categories.
In this scenario, we look to the Marketplace to extend our patient and member analysis to include
non-clinical factors, first looking at Acxiom for the demographic data,
and then Safegraph for their points of interest. We wanted to see how demographic
and location-based SDOH factors influence a patient's risk of missing an appointment.
All this data is live and ready to query. Exploring the data, we can convert our member's lat/longs
into a spatial data type, which we can use to cross-reference with the location data from Safegraph.
We can look at any of these points of interest and run distance calculations to see
where any one patient lives relative to a hospital clinic or any other POI.
All this data can be made available to users of all types, in this case for a data scientist to use
SDOH factors to predict a member's likelihood to miss an appointment. This means that a data
scientist can use both first- and third-party data for feature engineering--the demographic
data from Acxiom, which contains 1,700 columns of key lifestyle indicators, and the Safegraph
data set for a complete list of POIs. With a target variable of missed appointments, we can then define
which fields we want to bring in to define a transportation barrier--access to a vehicle
or proximity of any given member to a transit stop , healthcare facility, or other demographic features.
We can see these features ranked by importance with a couple of interesting insights .
An interesting variable we found is frequent voter status. People who frequently vote
demonstrate three things--one, that they're able to physically travel; two, that they have the means to
transport themselves either by car, transit, or biking and walking; and ,three, they're probably
pretty responsible and would also take care of their health. Interest in arthritis mobility
is another one, indicating that an interest in arthritis products online can also be indicative
of someone who may have issues with physical mobility and, thereby, miss their appointment.
These insights are going to be critical to informed care management. But traditionally, it's
been difficult to not only run machine learning at scale but also bring models to production.
With Snowpark, data scientists can package their models and run them natively within Snowflake,
ensuring that the entire organization can leverage these key insights all the way through to this
dashboard, where we can not only see important KPIs, a geographic map of our patient locations,
but also operationalize machine-learning output, generating a list of the 2,248 members who have the
highest risk of missing an appointment for care managers to act on. Even being able to get down to
a member's individual SDOH factors that contribute to their overall risk of missing an appointment.
With all this data at their fingertips, care managers can use this data for targeted
intervention to improve patient outcomes--arranging transportation, employing telehealth strategies, or
community approaches to addressing transportation barriers. The Snowflake Data Cloud is a global
network that is totally cloud agnostic, with capabilities that enable organizations to address
their most complex problems with no barriers.
I hope you enjoyed the demo,
where you saw just a glimpse of the Snowflake Data Cloud in action.
Thank you very much for tuning in.