96 PD Predictive Modeling: Now What? Moderator: Kara L. Clark, FSA, MAAA

Transcription

1 96 PD Predictive Modeling: Now What? Moderator: Kara L. Clark, FSA, MAAA Presenters: Philip Fiero Syed Muzayan Mehmud, ASA, FCA, MAAA Prashant Ratnakar Nayak, ASA, MAAA

2 TM Advanced Predictive Modelling Phil Fiero, Vice President, Predilytics Inc. June 2014 Prepared for:

3 Who is Predilytics? Predilytics is a healthcare analytics company that generates insight from big data to: Improve quality of care Coordinate care Attract and retain members Manage reimbursement and shared savings Reduce costs We use the latest machine learning technology and computer science to identify and predict opportunities at both the population and individual member level. This approach enables use of our expansive non-clinical data on over 225M lives and our customers structured and unstructured clinical and financial data to optimize the power and economics of predictive modeling at the individual level. Predilytics serves health, services, and risk bearing entities: Health plans Health systems Providers Health services Medical device Manufacturers At both the member and provider level CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 2

4 The ideas that drive new analytic approaches... Use all available data to improve population and individual health 95% 1 of the data wake we all leave annually is not in the healthcare system 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 Individual insight, population impact by deploying interventions with highest probability of success SOURCE: IDC; US Bureau of Labor Statistics; McKinsey Global Institute analysis, May 2011 Big data: The next frontier for innovation, competition, and productivity CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 3

5 Expansive use of data: Demographic, administrative, operational, clinical Incorporate data in any format, structured or unstructured Mental Health Rx Member Enrollment Details CMS Files MOR/MMR Admissions Claim History Provider Office Visits Labs Approach maximizes data intake to drive highest order prospective models Required Highly recommended Recommend EMR/Clinical Including Notes Data Inputs External Data HRA Call Center Logs & Details Census Voting Consumer Social CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 4

6 Unstructured data mining and linguistic analysis Can provide more accurate and predictive model results Claims and membership data often represent the majority of model input data However, specific words and word pairs in the comment fields can increase the predictive lift of the models (natural language engine) Examples of data with free text that can be mined: HRA data Sales force notes Clinical visits Call center notes EXAMPLE: Presence of the words: SON or DAUGHTER maps to the concept of family involvement and changing situation ANALYSIS: When a son (or daughter or other family member) becomes involved, it may be an early indication that the parent is experiencing health issues it can also be an early flag for disenrollment or exploring health plan changes CONFIDENTIAL COPYRIGHT, 2013 PREDILYTICS, INC. ALL RIGHTS RESERVED 5

7 Illustrative external data sources: Public, consumer, financial, social media Matched holistic view of over 220M people 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. CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 6

8 Machine Learning background Machine learning is a technology in which software evaluates a data set and combinations of data sets millions of times to learn and predict data relationships Machine learning is capable of exploring more data, faster and more thoroughly than traditional statistical techniques 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 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 These patented algorithms have been consistently used in the financial services and marketing industries for enhanced business success CONFIDENTIAL COPYRIGHT, 2013 PREDILYTICS, INC. ALL RIGHTS RESERVED 7

9 Genetic Algorithms (GA) Generation One Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Generation Two Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Generation (n) Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model (n) Fitness Accuracy Scale Low High 125 models per generation in 10 seconds 10,000 generations performed 1.25 Million equations evaluated with learning past to next generation CONFIDENTIAL COPYRIGHT, 2013 PREDILYTICS, INC. ALL RIGHTS RESERVED 8

10 Applying analytics to allocate resources 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 New Populations Integration of consumer behavior, social claiming can identify risk in unknown populations Failure to recertify risk COPYRIGHT 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 9

11 Sample Outputs: Creating member target lists Predilytics Prospective Member List Operationalize established models to Analytic Warehouse. Design, review, and develop member extracts. Generation, validation, and delivery of member extracts in the pre-determined format. Integrate directly into client operations systems and processes COPYRIGHT, 2013 PREDILYTICS, INC. ALL RIGHTS RESERVED 10

12 Sample Outputs: Key model drivers Purpose: Identify the top clinical and non-clinical drivers at a member level in order to support intervention operations. This methodology allows end users (providers, case managers, etc.) of to understand key risk drivers in a comprehensive and actionable way. Key drivers may include, but are not limited to, existing/pre-existing conditions, demographic, consumer, utilization, and financial attributes at a member/risk level. This information facilitates identification of the appropriate intervention at a member level, as well as provides an area of focus for those at the point-of-care. Member CHF Hosp. Risk Decile Key Driver 1 Key Driver 2 Key Driver 3 Person A IP-HCC85-CHF GAP-CCSPx44-Lipid Profile Person B EM-HCC22-Morbid Obesity Poor LDL-C Control GAP-Annual EM Visit Person C.97 1 History-CABG GAP-GC3- Ace inhibitors GAP-GC3- Beta- Adrenergic Blockers 2-Visits-Cardiologist- Last 30 days COPYRIGHT, 2013 PREDILYTICS, INC. ALL RIGHTS RESERVED 11

13 Case Studies CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 23

14 Big Data Machine Learning Healthcare Analytics Delivering patented machine learning healthcare data analytics to generate meaningful insight to solve healthcare industry challenges CONFIDENTIAL COPYRIGHT, 2014 PREDILYTICS, INC. ALL RIGHTS RESERVED 13

15 #96: Predictive Modeling Now What? Syed M. Mehmud Director and Senior Consulting Actuary Wakely Consulting Group

16 Risk Score Optimization Continuing from session 33 A brief re-cap 2

17 Risk Score Optimization WNRAR Project Wakely Consulting Group 3

18 Risk Score Optimization 3R Predictive Analytics Where do we go from here? 4