Leveraging AI to Understand SDOH Webinar

Confidential Information – For intended recipients only. Apexon, Copyright © 2022 Infostretch Corporation. All rights reserved. 1
LEVERAGING AI TO
UNDERSTAND SOCIAL
DETERMINANTS OF HEALTH
MIKEBOESE AND DR.ATIFFARIDMOHAMMADPHD

We enable #HumanFirstDIGITAL
SPEAKERS:
Mike Boese
Vice President,
Data Science, Apexon
Atif Farid Mohammad PHD
Head of AI/ML
R&D CoE, Apexon
Industry leader in data science, prescriptive analytics,
optimization, and leading data science teams delivering
enterprise-scale solutions.
Pioneering technologist with 30 years' experience identifying,
qualifying, and enabling technologies involving AI/ML.
Confidential Information – Apexon Corporation – For intended recipients only. ©2022 Apexon. All rights reserved. 2

SDOH FACTORS MAKE UP 50% OF AN
INDIVIDUAL’S ACTUAL HEALTH
3
Confidential Information – For intended recipients only. Apexon, Copyright © 2022 Infostretch Corporation. All rights reserved.
APEXON – DATASCIENCE FOR HEALTHCARE
• Employment status
• Income, expenses
• Debt, assets
• Financial support
• Literacy
• Education
• Community
• Language
• Diet
• Exercise
• Medication adherence
• Housing
• Transportation
• Geography
• Water quality
• Healthcare coverage
• Provider availability
• Quality of care
• Water quality
• Stress management
• Sleep

COMMON SOURCES OF SDOH DATA
US DEPARTMENT OF HOUSING
US ENVIRONMENTAL PROTECTION
AGENCY
Housing, access to parks and recreation, housing
density, etc.
Environmental hazards, water quality, air quality
US CENSUS
US DEPARTMENT OF AGRICULTURE US DEPARTMENT OF EDUCATION
Employment, median income, adults per household
Food insecurity, access to nutritional foods, etc. Childhood education, average completion levels, % of
population with bachelor’s degree, etc.
US DEPARTMENT OF
TRANSPORTATION
Transportation costs, access to public transportation,
travel time to providers
3RD PARTY DATA
Medical advertising susceptibility, digital health literacy,
personalized marketing
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UNDERSTANDING SDOH
AFFECT ON INDIVIDUALS AND
INDUSTRY HEALTHCARE
• SDOH are unique to each patient; however, patients are
largely treated the same by their providers.
• If patients’ specific SDOH was well known by their team of
healthcare providers, their treatment and care be greatly
improved.
• Healthcare policies determined be government and
regulators are largely influenced by understanding
SDOH.
APEXON – DATA SCIENCE FOR HEALTHCARE
5

HOW AI CAN POWER RICHER
INSIGHTS FOR SDOH
Natural Language Processing (NLP): Making
more SDOH information available as structured
data ready for analytics and AI.
PREDICTIVE ANALYTICS: Unlocking insights
about the impact of specific SDOH on healthcare.
6

PREDICTIVE ANALYTICS
7
Wouldn’t a controlled test be nice?
Unfortunately, they’re nearly impossible to conduct for SDOH.
But if we could, here’s what we’d do:
Control Group Test Group
Would allow for
comparison and insight
of SDOH:
“Access to Providers”

PREDICTIVE ANALYTICS:UNLOCKING
INSIGHTS OF SDOH POWERED BY AI
WITH MACHINE LEARNING MODELS, IT BECOMES POSSIBLE TO SEGMENT THE PATIENTS INTO
GROUPS AND SUBSEQUENTLY DETERMINE THE DIFFERENCES BETWEEN PATIENT GROUPS.
8
• Physical location
• Financial situation
• Education
• Social support
• Housing
• Access to providers
N independent variables
• Logistic regression
• Support vector machine
• Continuous variable
decision tree
• Extreme gradient
boosting random forest
• Naïve Bayes
SDOH Variables Unsupervised Classification Supervised Prediction ML
Models

WORKING WITH SDOH DATA
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VALUE DRIVEN
USE-CASES
• Payers can better forecast patient healthcare costs.
• Providers can better forecast healthcare demand, hospital
beds, nurses, etc.
• Facilities can create alerts for anticipated patient overflow.
• If we know exactly how much healthcare a patient needs
each year, payers can anticipate payer contributions, set
optimal premiums, and set co-pays optimal for payer and
patient.
HYPOTHESIS: SDOH ARE CLEARLY CORRELATED WITH HEALTH.
BIG PICTURE: A WEALTH OF BENEFITS CAN BE ACHIEVED IF SDOH
VARIABLES ARE HARNESSED TO PREDICT PATIENT HEALTH.
10
Confidential Information – Apexon Corporation – For intended recipients only. ©2022 Apexon. All rights reserved.

OUR PROVEN METHODOLOGY
• Model Data Preparation
• Cleanse and Categorize
Datasets
• Transform Data and
Remove Redundancies
• Select Training and Test
Data Sets
11
DISCOVERY
• Intelligent Data Clustering
• Train and Optimize
Multiple ML Models
• Evaluate Model Test
Performance
DEVELOPMENT
• Package Optimal Data
Model
• Create ML Pipeline
• Optimize ML Pipeline
DEPLOYMENT
• ML OPS & Enhancements
• Business Intelligence
Application and Monitoring
• Metric Reporting &
Solution Integration
TUNING & MONITORING

DATA SOURCES
PUBLIC DATA:
SDOH data from 2013 onward that covers a
wide array of demographic information.
INSIGHTS:
Patterns and findings sourced from the SDOH
data, with insights on students, diabetes, access
to healthcare, and healthcare claims.
2013 Data 2014 Data
2015 Data 2016 Data

ECONOMIC CONDITIONS
AMONG DIVERSE CHILD
POPULATIONS
• The population of children who live in poverty and
their ages are inversely proportional
• The whole population of children who lived in poverty
decreased from 2010 to 2018
• Children between 0 - 4 years of age have the
highest variance and standard deviation, which
means their numbers decreased extremely fast from
2010 to 2018
• The 12 - 17 age group volume decreased slower
than the other two groups
13

SDOH – SUFFICIENT SLEEP
14
• On average, males (blue), tend to get sufficient rest
more often than females (yellow)
• Generally, highschoolers of both genders aren’t
getting enough rest on average.
• Sufficient sleep decreases with an increase in grade
(i.e., freshman (blue) have the highest values while
seniors (red) have the lowest)
• Generally, highschoolers of any grade are not getting
enough rest on average.

SDOH – COLLEGE ATTENDANCE
15
• Student college attendance immediately after high
school rose steadily from 2013 and peaked in 2016.
• In 2017 there was a slight decrease where the error
bar seems to be significant.

SDOH – CONDITION
MANAGEMENT
• Dataset: U.S._Chronic_Disease_Indicators__Diabetes.csv
• Only crude rates of mortalities were used for calculating measures
of central tendency and dispersion (all other values were ignored).
• U.S. has high mortality rate associated with diabetes, with even the
lowest 25th percentile rates being above 50%.
• Mean and median are close, so outliers don’t appear to have
significantly impacted data
• The mode appears to be a useless indicator as it’s not important if
an exact crude rate is repeated multiple times.
• Skewness and kurtosis values are both relatively small. Data seems
to have a fairly normal distribution while being skewed slightly to the
right.
• Coefficient of variation being < 1 means dataset is centered around
mean.
16

A FEW MOEW KEY SDOH
INSIGHTS
17
• Transportation access to providers is correlated
with overall health
• Food insecurity / access is also correlated with
overall health
• Average lifespan is nearly 20 years longer in
high human development (HD) countries than in
low HD countries.
• Mortality reduction in children under 5 is
attributed to 50% of variables outside the health
sector (i.e., SDOH)

CONCLUSION
• Better management of cash flow
• Appropriately staffed medical personnel
• Pharmacy benefits predicted accurately
• Pharmacy supply managed optimally
• Analytics can be leveraged by medical teams to increase their
quality-of-care
• Increased quality-of-care leads to increased patient
satisfaction and improved patient health
BENEFITS OF USING SDOH TO PREDICT
PM/PM AND PM/PY:
18
SDOH factors are clearly correlated with patient health, and patient
health is correlated with payer and patient costs and provider
demand.

INTRODUCTION TO APEXON

ADVANCE ANALYTICS & AI/ML
Traditional techniques and tools are not
suited for new age data and formats
Dealing with multiple
formats of data
Moving from descriptive and diagnostic
to predictive and prescriptive analysis
BUSINESS DRIVERS
BUSINESS DRIVERS & OUR POINT OF VIEW
OUR POINT OF VIEW FOR DATA STRATEGY SUCCESS
Customized Environments and
ecosystem for Data Scientist
(data flow system for analysis)
Machine language use cases
Assessment, Identify Use
cases and requirement for eco
system On-Prem
SaaS based interactive chatbots for
user self service
Ecosystem setup for Analysis of
structured and unstructured data
DATA SCIENCE MACHINE LANGUAGE CLOUD AI NLP
Deliverable: Personalized
Workbenches, Deep Learning
Predictive and other Models
Deliverable: identified
use cases where AI/ML
can be applied
Deliverable: Cognitive
BoTs, Automation
Deliverable: Focused
automated text
analysis of data
Enterprises today have access to enormous
amounts of data, but the ability to put that data to
work to improve business operations and drive
better business outcomes are constrained by
increasing complexity, poor data management
practices, and ill-equipped infrastructure and
tools. These shortcomings can be overcome
through adoption of newer data predictive
techniques and technologies as part of the digital
transformation initiatives.

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Technology
Expertise
Key
Clients
Intelligent, outcome driven
forecasting
Automated identification and
labelling of objects from
images and videos in real and
near-real time
Business Process centric
Knowledge graph, Cognitive
Document Parsing, Social
Listening
Anomaly detection, Pattern
recognition, Definitive and
Predictive Models
Error Scoring, Risk Scoring,
lead scoring, opportunity
prioritization, etc.
Forecasting &
Optimization
Model
Image and
Video Analytics
NLP and Text
Mining
Anomaly
Detection
Prediction
Modeling
OUR CUTTING-EDGE AI ACCELERATORS

ANY QUESTIONS?
Please submit your questions by
using the Q&A feature.
Subscribe to DTV! A digital transformation
Channel on YouTube and Spotify 
22

THANK YOU
EMAIL US
INFO@APEXON.COM
TALK TO US
+1-408-727-1100

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