Big data and machine learning techniques can be useful tools for health care marketers. Big data refers to large and complex data sets that are difficult to analyze using traditional methods. Machine learning allows systems to learn from data to discover relationships and make predictions. Bayes' theorem provides a framework to update probabilities based on new evidence or observations. Marketers can use these techniques to better understand customers, predict behaviors, and inform strategy through data-driven insights. Questions were invited on applications of these techniques for health care marketing.

1. Big Data in Health Care
What Marketers Need to Know
Tim Gilchrist, May 2014
@timgilchrist

2. Session Goals
• Cover
– Big Data
– Artificial Intelligence / Machine Learning
– How to be an Informed Consumer
– Applications for Marketers
– Questions
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3. Big Data
The term for a collection of data sets so large and
complex that they become difficult to process
• Many data sources with different formats
• Data with missing values
• Text / Social Media
• Things that don’t fit in Excel
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4. Artificial Intelligence
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“Pay no attention to the man behind the curtain”

5. Machine Learning
The construction and study of systems that can
learn from data
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6. Bayes
Thomas Bayes (1701 – 7 April 1761) was an English
mathematician and Presbyterian minister, known for
formulating the theorem that bears his name: Bayes'
theorem
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Bayes theorem uses prior probabilities, combined with new observations to
calculate the probability of a hypothesis being true or false
Bayes is a natural fit to health care due to the presence of hypothesis
(diagnosis) and events (tests / observations)

7. Example
You are all doctors who have administered a
critical test to 50 patients
You know the test is:
– 75% Accurate
– 10% False Positives
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– 10 of your patients tested positive
– How many are actually sick?

8. Bayes’ Theorem – Mammogram Example
We can present the data as a decision tree representing the
probabilities confronting doctors and patients
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If we were to take population-level down to the individual level, much more accurate
probabilities would be possible
#1 Mammogram
#2Biopsy#3Time
Observation
Probability

9. What Does this Mean To Marketers?
• Big data is about discovering relationships
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“Can’t beat a man with some insurance.
I need that health plan baaaby!”
• Then using data-driven insights to inform
strategy

10. Big Data Health Landscape
HIE
Member PCP Specialist
Sees PCP Gets X-Ray Sees Specialist Ambulatory
Outpatient
Analysis / Transformation
EMR
Admission
Discharge
PrescriptionClaims
Plan Data Portal
CarePathOutputsDataTypes
Direct
ConnectionWearable
Telemetry
What is Happening What Will Happen
Social PurchasedLocation
Cell

11. Example
Text Mining for Sales
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12. Listening & Collecting
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Signal

13. Training / Processing
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Tweets extracted from
the Twitter Fire Hose with
key words “Health” and
“Plan”
1MM per day
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@CapoeiraBatuque
“What's the best plan thru
affordable health care. Blue
cross? Blue shield? Health
net? #confused #healthcare”
@bluecalgal
Obama says don't listen to Fox,
why? Obama lied about keep
your dr, health plan, cheaper
than cell phone (bs) keep your dr
Create Training databases
for any classification desired.
+ and – outcomes used here
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Weka turns Tweets into
numerical code that can be
analyzed by computer
“String to Word Vector”.
uses Naïve Bayes classifier
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“What's the best plan thru affordable health care. Blue cross?
Blue shield? Health net? #confused #healthcare”
.225.357
.155
.999
ACA
AccessHealth
HealthPlanNow
Pressure
Confused
Dumb
@ Handle
# Followers
# Tweets
Profile
Retweets
Location
Date/Time
Text Analysis
Stemming/Tokenization
Demographic
Analysis
Once classifications are
established, rules can be
applied to new Tweets with
high accuracy ~90+%
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14. Result
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15. Other Uses
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• Model who will be your most
valuable:
• Customer
• Facebook follower
• (Really) Determining Sentiment
• Marketing Mix Simulations
• Consumer Facing Predictive
Technology
• Prod development (HIX)

16. Questions?
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17. Appendix
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18. Discovering Relationships Between Data
We can use machine learning to form
relationships between sets of data that
are seemingly unrelated (Causal
Relationships):
• Making your bed in the morning and job
satisfaction
• Artificial Christmas trees and family “brag
letters”
• What you buy and why
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19. A Causal Diagram Based on Established Relationships for Estimating
the Incidence of Coronary Heart Disease (CHD).
(Source: Comparative quantification of health risks: Conceptual framework and methodological issues)
19© Tim Gilchrist 2013

20. Bayes’ Theorem – Mammogram Example
Problem: Estimates of breast cancer over diagnosis range from 25%–
52%. Physicians often misinterpret their own lab results
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Prior Probability
Chance that a woman will develop breast cancer in her 40s X 1.4%
New Event: Mammography
Ability of mammogram to detect cancer when present Y 75%
False positives Z 10%
Posterior Probability
Revised Probability, given new event (positive mammogram)
xy+z(1-x)
9.6%
A positive mammogram still leaves a 90.4% chance that the test showed something other than
cancer. When biopsies are performed on this age group, 75% are negative. Physicians rarely
consider the other 90.4%

21. What Does this Mean To Marketers?
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22. Other Reading
Why Most Marketers Will Fail In The Era Of Big
Data
8 Marketers Doing Big Data Right
The big-data revolution in US health care:
Accelerating value and innovation
HITLAB Speaks with Tim Gilchrist, Director of
eBusiness Strategy for WellPoint
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23. The State of Big Data
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