Consumer Behavior: Factors Affecting Member Attrition and Retention

Consumer Behavior: factors affecting member attrition
and retention
March 19, 2014 Prepared for:
Partners Summit, Las Vegas

Discussion objectives
•  Growing importance of consumer behavior and decision
making in Healthcare
•  Discuss new approaches to identifying consumer trends
–  Using more expansive data
–  Applying new analytics approaches like machine learning
•  Review a case study
–  Failure to recertify in 3 state study
–  Engagement acceptance
2COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
Privacy and security of personal information is first and foremost
Analytic insights must benefit the individual, governed by code of conduct and privacy
and security regulations

Computer science and big data
Hype or a new way of business. . .

Consumer and Boomer revolution
Impact on Healthcare Delivery
•  New generation of health care users entering
the system, 77 Million Baby Boomers
–  Transform industries as they emerge and
engage
–  New behavior and purchasing patterns
•  Government policy shaping future of
healthcare
•  Financial and funding constraints

•  45% annual growth in
consumer and healthcare
data
•  Explosion of healthcare
mobility and telemetry
solutions
•  95%1 of the “data wake”
we all leave annually is
not in the healthcare
system
SOURCE: IDC; US Bureau of Labor Statistics; McKinsey Global Institute analysis, May 2011
Big data: The next frontier for innovation, competition, and productivity
Consumerization and realization of healthcare
Impact on information

The ideas that drive new analytic
approaches. . .
•  Use all available data to improve population and
individual health
–  Individual behavior is best predicted by socio-
economic and lifestyle characteristics and consumer
activities, not typically found in EMR and Claims Data
•  Machine learning and advance computer science
are required to convert massive amounts of data into
actionable insights, by optimizing identification of
targeted events at the actionable cohort
•  Identify individuals, predict engagement and deploy
interventions with highest probability of success
•  Focus analytics efforts on the critical business and
quality issues that drive organizational performance
6
Big Data
Advanced
Analytics
Speed
Efficiency
Business
Insights
Consumer
Engagement
Results
COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED

Analytic solutions framework
7
Descriptive
Diagnostic
Predictive
Prescriptive
Hindsight Insight Foresight
Generates insight from big data to:
q  Improve quality and coordination of care
q  Identify risk and asses opportunity
q  Evaluate program impact

Big data approach
How does it work and why is it different?
•  Big Data comes in the form of clinical, administrative claims,
operating, demographic, workflow, purchasing, provider and
consumer behaviors, etc. Examples include;
•  Electronic Medical Record(e.g. Clinical values, notes)
•  Monitoring devices (e.g. wellness trackers, biometrics,
telemetry)
•  Consumer engagement (e.g. voting, financial, census,
Facebook, smartphones, portal/website utilization)

Big Data is the essence of collecting and storing data, both
structured and unstructured, from as many different sources
as are readily available

Illustrative external data sources
Public, Consumer, Financial, Social Media
Public Healthcare
•  Medicare, Medicaid
•  Population Stats
•  Healthcare Providers, Cost, Quality
•  AHRQ, NIH, CDC
•  Health Outcomes
Consumer
•  Consumer Behavior / Purchasing
•  Ethnicity
•  Social Security / Death Records
•  Voter Registration
•  Legal / Regulatory
Financial
•  Consumer spending
•  Credit risk
•  Public records
•  Real estate indicators
Social Media
•  Facebook Activity
•  Foursquare Check-in
•  Twitter Activity
•  Google Services, ETC.

Analysis approach and process
10
Customized files and reports with actionable
insights
•  Support operations
•  Support business planning
•  Reporting
Create predictive
models and run client
specific cohort(s) to
generate insights
Predilytics supports
implementation of
analytic insights
Consumer Data
Client & Private Data

Background on Machine Learning
How does it work and why is it different?
•  Predictive patterns in the data are discovered and retained
•  The software builds on previous learnings and highly predictive equations evolve
•  Genetic Algorithms (GAs) are a form of machine learning that are highly effective in
spotting subtle patterns in data sets. GA modeling technology and the output are
transparent and more actionable
Software evaluates data and combinations of data sets millions of times
Machine learning is capable of exploring more data, faster and more
thoroughly than traditional statistical techniques
•  Traditional modeling relies on statistical analyses of data, in particular various
forms of regression, which carry with it certain limitations that are not found in
iterative – based learning models
•  The results are more accurate predictive models


Machine learning is optimized for ‘Big Data’
predictive analytics
•  Linear Regression
•  Logistic Regression
•  Time Series
•  Survival Analysis
•  Segmentation
•  Data Valuation
•  Variable Reduction
Machine Learning
Optimize Prediction of X
Start with “Random Walks”
Learns Quickly & Transparently
Automation saves analyst time for
more value-added tasks
Structure Predictive Modeling
Task
X = f (A,B,C | D,E) + e
GA Enhances:
•  Descriptive
Summary
•  Train / Test Samples
•  Univariate Graphs
•  Variable
Transformation
•  Missing Data
•  Candidate Model
Development
•  Lift Chart / ROC
Curve
•  Scoring Code
GA Automatic Features
Traditional Analytics

Genetic Algorithms (GA)
13
125 models per
generation in 10
seconds
10,000
generations
performed
1.25 Million equations
evaluated with learning
past to next generation
Low
Fitness Accuracy Scale
High
Model 7 Model 8 Model 9 Model 10 Model 11 Model 12Generation Two
Model 13 Model 14 Model 15 Model 16 Model 17 Model 18Generation (n)
Model (n)
Generation One Model 1 Model 2 Model 3 Model 4 Model 5 Model 6Model 3 Model 4 Model 5 Model 6

The genetic algorithm advantage
•  Superior accuracy through the evaluation of far more data attributes and
combinations of data attributes (often 15% to 20% improvement vs.
traditional statistics approaches)
o  Changing the economics of analytics – isolates the actionable
segment for intervention
•  Substantially improves the speed and segmentation of models:
o  Decreasing modeling turnaround time
o  Allowing for a proliferation of predictive models… breaks the analytic
bottleneck
•  Optimizes identification of targeted events at the actionable portion of
the distribution, therefore optimizing the models predictive factors for the
targeted event vs. trying to explain errors of the whole distribution
•  Clear, understandable results (No Black Box!)

Overview
3 State study of Medicaid Recertification
Identify health plan members likely to:
•  Lose Medicaid eligibility by not recertifying (e.g. Dual Eligibles)
–  Identify those who fail to recertify, but are still eligible for Medicaid
Optimizing these goals provides enhanced business performance
•  Improve intervention targeting to increase reimbursement and drive
increased value for Altegra’s customers
•  Improve recertification rates, reach and engagement rates and member
retention
16

Data sources
17
Altegra – derived time series data, member recertification and disenrollment,
date of birth & age, race, gender
Predilytics-household level demographics including measures of affluence,
household composition, length of residence, age, ethnicity, gender of head of
household, home values, financial stress predictors (from unemployment stats)
US Census – zip code level data including distributions related to affluence,
heritage, race, age of household members, languages spoken, educational
achievements, employment, and population density, gender mix, veterans,
disabilities, mobility

Analysis cohort
18
Popula'on

Total
Members

Members
who
were
enrolled
as
of
August
2012,
Medicaid
cer:fied,

and
with
ac:ve
plans
across
3
states
(Georgia,
Florida,
Texas)

78,707

Number
of
Unique
Members
in
Household
Data
13,686729

Successful
Match
to
Household
Data
51,170

Match
Rate
65%

Members
who
failed
to
recer2fy
between
September
2012
and

August
2013

19,538

Recer2fica2on
failure
rate
(Failed
recer2fica2on
members
/
total

enrolled
members
as
of
August
2012)

38%

* An active plan was defined as any plan with members enrolled in September of 2013 Analysis
cohort

0
25
50
75
100
125
150
175
200
225
250
1 2 3 4 5
210
133
78
52
27
Consolidated Failure to Recertify
Model Lift
19
Model performance
Average
Members
Projected
Rate of Failed
Recertification
All 38%
Top 10% 87%
Top 20% 80%
Bottom 20% 10%
Rates indicates how likely a member
is of not recertifying for Medicaid
Model Population
Training Population 35,822
Validation Population 15,353
Top 20% of members are 2x times more likely to fail to recertify
1) Three State Model is combination of FL, GA and TX data, August 2012 to August 2013
Quintile
Lift

Descriptive analytics:
Recertification failure by county
20
For geographic areas with at least 100 members.
Florida Texas
Georgia
Systematic issues,
County Office
performance
•  Addressed by
Altegra’s
Government
Affairs
Outreach

Model predictors
Consumer variables
•  Charitable giving – areas where 75% or
more of individuals contribute to charities
are 35% less likely to fail to recertify
•  Party affiliation – individuals who are
unaffiliated with a political party are 2 times
more-likely to fail to recertify.
•  Foreign Made Car ownership –
individuals who own foreign made cars are
nearly 2 times more likely to fail to recertify
than those own domestic built cares
•  Employment Patterns – (% engaged in
Manufacturing) More manufacturing, lower
probability of recertification failure,
indicating lower skill or blue collar job
stability
41%
37%
29% 27%
0%
10%
20%
30%
40%
50%
0 to 25% 25 to 50% 50 to 75% 75 to 100%
Percent of Population (ZIP) That Have Made Charitable
Contributions
41% 40%
25%
22% 20% 20%
15%
0%
10%
20%
30%
40%
50%
Unknown Unaffiliated Other Republican Democrat Green Libertarian
Registered Parties
29%
33% 32%
39%
43%
0%
10%
20%
30%
40%
50%
10 to 9 8 to 7 6 to 5 4 to 3 2 to 1
Likelihood of Owning a Domestic Sedan
(1: Most Likely, 10: Least Likely )

Three state recertification failure model validation
Excellent validation observed
22
0
20
40
60
80
100
120
140
160
180
200
220
240
1 2 3 4 5 6 7 8 9 10
226
195
155
111
85
71
59
44
33
21
229
195
156
111
85
74
57
42
29
22
Recertification Model Validation Lift by Decile2
Training
Validation
Average
LIft
1)  Three State Model is combination of FL, GA and TX data, August 2012 to August 2013
2)  Population study cohort size of 19,538, or 1,954 per decile, split 70% training and 30% validation
Decile

23
Current Served Populations
•  Historical experience indicates 1/3 of
population at risk of not recertifying
•  With predictive analytics “at-risk” individuals
can be identified increase probability of failure
to recertify to 90% likelihood
•  Improve business performance by
appropriately allocating resources to targeted
cohort
Failure to recertify risk
Applying analytics to allocate resources
New Consumers / Exchange Populations
•  Integration of consumer behavior, social
claiming can identify risk in unknown
populations
•  Health exchanges
•  Assigned
capitated
populations

COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 24
Big Data
Healthcare
Analytics
Machine
Learning
Delivering machine learning healthcare data analytics to generate meaningful insight to
solve healthcare industry challenges
Discussion

Machining learning modeling performance
Accepted assessment model validation – Intervention engagement
25
•  3,677 members were selected for assessments in 2012 who were in the
randomly selected member validation group (not used to create the model
equation)
•  To verify the model’s predictive power, the model equation was applied to
this group as they appeared on the file in June 2012
0%

10%

20%

30%

40%

50%

60%

70%

1
2
3
4
5
6
7
8
9
10

Engagement
Acceptance
Rate

Decile

Model
Projec:on

Actual
2012
Result

The model projection
tracks closely with the
actual 2012 results

Consumer Behavior: Factors Affecting Member Attrition and Retention

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