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Consumer Behavior: Factors Affecting Member Attrition and Retention
1.
Consumer Behavior: factors
affecting member attrition and retention March 19, 2014 Prepared for: Partners Summit, Las Vegas
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.
Computer science and
big data Hype or a new way of business. . . 3COPYRIGHT © 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED
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.
• 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Case Example 23
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.
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.
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.
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.
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.
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.
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 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.
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.
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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