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 approach...
Computer science and big data
Hype or a new way of business. . .
3COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
Consumer and Boomer revolution
Impact on Healthcare Delivery
•  New generation of health care users entering
the system, 7...
•  45% annual growth in
consumer and healthcare
data
•  Explosion of healthcare
mobility and telemetry
solutions
•  95%1 o...
The ideas that drive new analytic
approaches. . .
•  Use all available data to improve population and
individual health
– ...
Analytic solutions framework
7
Descriptive
Diagnostic
Predictive
Prescriptive
Hindsight Insight Foresight
Generates insigh...
Big data approach
How does it work and why is it different?
•  Big Data comes in the form of clinical, administrative clai...
Illustrative external data sources
Public, Consumer, Financial, Social Media
Public Healthcare
•  Medicare, Medicaid
•  Po...
Analysis approach and process
10
Customized files and reports with actionable
insights
•  Support operations
•  Support bu...
Background on Machine Learning
How does it work and why is it different?
•  Predictive patterns in the data are discovered...
Machine learning is optimized for ‘Big Data’
predictive analytics
12COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED...
Genetic Algorithms (GA)
13
125 models per
generation in 10
seconds
10,000
generations
performed
1.25 Million equations
eva...
The genetic algorithm advantage
•  Superior accuracy through the evaluation of far more data attributes and
combinations o...
Case Example
23
Overview
3 State study of Medicaid Recertification
Identify health plan members likely to:
•  Lose Medicaid eligibility by...
Data sources
17
Altegra – derived time series data, member recertification and disenrollment,
date of birth & age, race, g...
Analysis cohort
18
Popula'on	
  
	
  
Total	
  Members	
  
	
  	
  
Members	
  who	
  were	
  enrolled	
  as	
  of	
  Augu...
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 pe...
Descriptive analytics:
Recertification failure by county
20
For geographic areas with at least 100 members.
Florida Texas
...
Model predictors
Consumer variables
•  Charitable giving – areas where 75% or
more of individuals contribute to charities
...
Three state recertification failure model validation
Excellent validation observed
22
0
20
40
60
80
100
120
140
160
180
20...
23
Current Served Populations
•  Historical experience indicates 1/3 of
population at risk of not recertifying
•  With pre...
COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 24
Big Data
Healthcare
Analytics
Machine
Learning
Delivering machin...
Machining learning modeling performance
Accepted assessment model validation – Intervention engagement
25
•  3,677 members...
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Consumer Behavior: Factors Affecting Member Attrition and Retention

  1. 1. Consumer Behavior: factors affecting member attrition and retention March 19, 2014 Prepared for: Partners Summit, Las Vegas
  2. 2. 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
  3. 3. Computer science and big data Hype or a new way of business. . . 3COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  4. 4. 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 4COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  5. 5. •  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 5COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED Consumerization and realization of healthcare Impact on information
  6. 6. 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
  7. 7. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  8. 8. 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 8COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  9. 9. 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. 9COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  10. 10. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  11. 11. 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   11COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  12. 12. Machine learning is optimized for ‘Big Data’ predictive analytics 12COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED •  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
  13. 13. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  14. 14. 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!) 14COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  15. 15. Case Example 23
  16. 16. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  17. 17. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  18. 18. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  19. 19. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  20. 20. Descriptive analytics: Recertification failure by county 20 For geographic areas with at least 100 members. Florida Texas Georgia COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED Systematic issues, County Office performance •  Addressed by Altegra’s Government Affairs Outreach
  21. 21. 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 21COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 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 )
  22. 22. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
  23. 23. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED New Consumers / Exchange Populations •  Integration of consumer behavior, social claiming can identify risk in unknown populations •  Health exchanges •  Assigned capitated populations
  24. 24. 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
  25. 25. 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 COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED

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