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
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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. . .
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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
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• 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
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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
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Performance
Big Data
Advanced Analytics
Speed
Efficiency
Business Insights
Consumer Engagement
Results
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Analytic solutions framework
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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
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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
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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.
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Analysis approach and process
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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
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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
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Machine learning is optimized for ‘Big Data’ predictive analytics
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• 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)
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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 12 Generation Two
Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Generation (n)
Model (n)
Generation One Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 3 Model 4 Model 5 Model 6
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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!)
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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
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Data sources
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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
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Analysis cohort
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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
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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
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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
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Descriptive analytics: Recertification failure by county
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For geographic areas with at least 100 members.
Florida Texas
Georgia
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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
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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
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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
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New Consumers / Exchange Populations • Integration of consumer behavior, social
claiming can identify risk in unknown populations
• Health exchanges • Assigned
capitated populations
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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
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• 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
Engagemen
t Accep
tance Ra
te
Decile
Model Projec:on Actual 2012 Result
The model projection tracks closely with the actual 2012 results
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