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

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96 PD Predictive Modeling: Now What?


Kara L. Clark, FSA, MAAA


Philip Fiero

Syed Muzayan Mehmud, ASA, FCA, MAAA Prashant Ratnakar Nayak, ASA, MAAA


Advanced Predictive Modelling

Phil Fiero, Vice President, Predilytics Inc. June 2014



Predilytics is a

healthcare analytics

company that

generates insight from

big data


• Improve quality of care

• Coordinate care

• Attract and retain members

• Manage reimbursement and shared savings

• Reduce costs

We use the latest

machine learning technology


computer science to identify and


opportunities at

both the

population and individual member level.

This approach

enables use of our expansive



on over

225M lives

and our customers’ structured


unstructured clinical and financial data

to optimize the

power and economics of predictive modeling at the

individual level.

Who is Predilytics?

Predilytics serves health, services, and risk bearing entities:

• Health plans • Health systems • Providers • Health services • Medical device Manufacturers At both the member


• 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


– 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

The ideas that drive new analytic

approaches. . .

SOURCE: IDC; US Bureau of Labor Statistics; McKinsey Global Institute analysis, May 2011 Big data: The next frontier for innovation, competition, and productivity


Rx Mental



Provider Office Visits Labs Required Highly recommended Recommend



Member Enrollment Details Claim History EMR/Clinical Including Notes CMS Files MOR/MMR HRA Call Center Logs & Details External Data Census Voting Consumer Social

Expansive use of data:

Demographic, administrative, operational, clinical

Incorporate data in any format, structured or unstructured

Approach maximizes data intake to drive highest order


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


Presence of the words: “SON” or “DAUGHTER” maps to the concept

of family involvement and changing situation


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

Clinical visits

Call center notes


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 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


Machine Learning background

Machine learning is a technology in which

software evaluates a data set


combinations of data sets

millions of times

to learn and predict data


Machine learning is capable of exploring

more data, faster

and more


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


used in the


services and marketing

industries for enhanced business success


Genetic Algorithms (GA)


models per


in 10





1.25 Million


evaluated with



to next generation


Fitness Accuracy Scale


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)


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 Populations

• Integration of consumer behavior, social

claiming can identify risk in unknown populations


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


 Integrate directly into client operations systems and processes


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 .92 1 2-IP-HCC85-CHF GAP-CCSPx44-Lipid


GAP-GC3- Beta-Adrenergic Blockers

Person B .81 2 1-EM-HCC22-Morbid

Obesity Poor LDL-C Control GAP-Annual EM Visit

Person C .97 1 History-CABG GAP-GC3- Ace inhibitors

2-Visits-Cardiologist-Last 30 days


Big Data

Healthcare Analytics Machine


Delivering patented machine learning healthcare data analytics to generate meaningful insight to solve healthcare industry challenges


#96: Predictive Modeling – Now What?

Syed M. Mehmud

Director and Senior Consulting Actuary

Wakely Consulting Group


Risk Score Optimization

Continuing from session 33…


Risk Score Optimization

WNRAR Project


Risk Score Optimization

3R Predictive Analytics




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