MODELING IN THEHEALTHCARE INDUSTRY:

A COLLABORATIVE APPROACH

William B. Disch, Ph.D.Director, AnalyticsEvariant

O P E ND A T AS C I E N C EC O N F E R E N C E

BOSTON 2015

@opendatasci

Abstract

Evariant has partnered with, and are using DataRobot for multivariate predictive analytics because it is a flexible, robust, and extremely efficient tool for maximizing our modeling efforts, as well as an example of leveraging high-end data science and tools in the healthcare industry.

… DataRobot helps in automating many routine processes like finding important variables, variable transformation, variable selection, model building and scoring. As a result, we data analysts/scientists have more time for analytical thinking…

Overview of Evariant/Propensity Models

Evariant is on a mission to move healthcare providers to the cloud with the data and analytics required to confidently identify and execute on the most important strategic growth, patient engagement, and physician alignment initiatives.

The primary goal of Evariant’s predictive modeling is to identify and target patients and non-patients who are likely candidates for health services

Patients and non-patients in healthcare markets have differential levels of response propensity for different disease-states and health screening programs

Our predictive modeling is evolving with the healthcare industry to not only capture traditional volume based targeted marketing, but to also incorporate the rapid move to value based marketing initiatives

Optimal modeling can incorporate volume and value based targeted marketing initiatives.

The Healthcare industry in Transition

Why incorporate both volume and value based modeling and analytics? ?

Curve 1: Volume Based/Static Example: Mammography Screenings• Current state• Standard for most healthcare marketers• All about volume • Little incentive for real integration

Curve 2: Value Based/Dynamic Example: Traffic in Women’s Health Center• Current + Future State• Few healthcare marketers taking

advantage• Shared savings program• Bundled/global payments• Value-based reimbursement• Rewards integration, quality, outcomes,

and efficiency

Types of ModelsPatient Model - Which patients are likely to respond to a disease-specific marketing campaign (cross-sell, upsell, retention)

Non-Patient Model - Which non-patients in the market are most likely to respond to a disease-specific marketing campaigns (acquisition, re-acquisition)

Certain individual patients and non-patients in a healthcare market have a higher likelihood of benefitting from different health screening and treatment programs

Multivariate statistical analyses (predictive scores) can optimize the precision in which these patients and non-patients are identified and targeted for marketing purposes

If your recipe for targeted marketing include traditional volume based approaches, limitations include only relying on preselect criteria against “prospect lists” that include sociodemographic, lifestyle, response, transactional or other elements

Propensity models assign propensity scores to patients and non-patients that represent their likelihood to respond to a given campaign, based on a broader set of predictive elements

We have a core set of approximately 130+ disease and health screening models available for your patient population and consumers in your market

Evolving Solutions

Multivariate predictive modeling is employed along the Needs continuum, and incorporates both volume and value based initiatives.

Sample: Model Debriefing Agenda

There are three components to the model debriefing:

1. Overview of Modeling Processes

2. Overview of Tableau Visual Output

3. Overview of Dynamic List Builder (DLB)

Q & A/Next Steps

Model Inputs/Parameters

Multivariate Comprehensive Datasets Include:

Patient demographics Patient visit data history Appended Consumer Data

– Personal Informationo Lifestyleo Sociodemographic/socioeconomico Health behavioro Reported prescription data

– Household Informationo Ailmentso Family size/childreno Income/lifestyle variables (mortgage, dwelling size, location)

Derived and proprietary variables such as behavior profile and comorbidity index

Cardiology Patient ModelSummary Statistics and Scoring Validation

Gender – Cardiology

71

158

0 20 40 60 80 100 120 140 160 180

female

male

Market Value Index

The market value index discriminates target group predictive characteristics from the prospect universe. Index values greater than 120 or less than 80 are indicators of statistically important over or under-value. A value of 100 indicated no over or under-value. For example, an overvalue index of 150 means that the prospects are 1.5x more likely than chance to statistically resemble the target group.

Marital Status – Cardiology

84

121

107

77

172

0 50 100 150 200

Other

Divorced

Married

Single

Widowed

Market Value Index

The market value index discriminates target group predictive characteristics from the prospect universe. Index values greater than 120 or less than 80 are indicators of statistically important over or under-value. A value of 100 indicated no over or under-value. For example, an overvalue index of 150 means that the prospects are 1.5x more likely than chance to statistically resemble the target group.

Age – Cardiology

32

41

59

87

109

146

192

262

0 50 100 150 200 250 300

youngest to 24 years

25-34 years

35-44 years

45-54 years

55-64 years

65-74 years

75-84 years

85 years and older

Market Value Index

The market value index discriminates target group predictive characteristics from the prospect universe. Index values greater than 120 or less than 80 are indicators of statistically important over or under-value. A value of 100 indicated no over or under-value. For example, an overvalue index of 150 means that the prospects are 1.5x more likely than chance to statistically resemble the target group.

Occupation – Cardiology

139

65

93

75

93

69

121

80

105

96

156

94

0 20 40 60 80 100 120 140 160 180

Blue Collar

Blue Collar Infer

Farm Related

Farm Related Infer

Other

Other Infer

Professional/Technical

Professional/Technical Infer

Retired

Retired Infer

Sales/Service

Sales/Service Infer

Market Value Index

The market value index discriminates target group predictive characteristics from the prospect universe. Index values greater than 120 or less than 80 are indicators of statistically important over or under-value. A value of 100 indicated no over or under-value. For example, an overvalue index of 150 means that the prospects are 1.5x more likely than chance to statistically resemble the target group.

Top Mosaics – Cardiology

121

122

128

128

129

131

132

133

138

147

159

166

176

187

193

193

0 50 100 150 200 250

Silver Sophisticates

Wired for success

Fragile families

Birkenstocks and beemers

Small town shallow pockets

Aging in place

Homemade happiness

Footloose and family free

Gospel and Grits

Golf carts and gourmets

Reaping Rewards

Rural escape

True grit americans

Town elders

Senior Discounts

Sett led and sensible

Market Value Index

The market value index discriminates target group predictive characteristics from the prospect universe. Index values greater than 120 or less than 80 are indicators of statistically important over or under-value. A value of 100 indicated no over or under-value. For example, an overvalue index of 150 means that the prospects are 1.5x more likely than chance to statistically resemble the target group.

Sample - Predictive Drivers – Cardiology Patient Model

For the general cardiology patient model, the top three statistical drivers are comorbidity, age (older), and Mosaic groups (mostly including older folks)

Family history of cardiology related procedures, as well as ethnicity (higher risk for African-Americans and Latinos) are also strong predictors

Even though females make up a greater portion of the non-cardio population, males have a higher likelihood of being cardio patients

Sales/service, professional/technical, and blue collar are the three occupational categories most predictive of having cardio services

Rank Variable Name Variable Description Direction

1 COMORBIDITY_MATCH_CLS * FLAG Comorbidity - Cardio  

2 EX_EXACTAGE_CLS * FLAG Age Older individuals

3 MOSAICHOUSEHOLD_CLS * FLAG MosaicMore Mosaics that include older folks

  Segment J Autumn Years  

  Segment QGolden Year Guardians  

  Segment N Pastoral Pride  

  Segment CBooming With Confidence  

  Segment L Blue Sky Boomers  

4 FMLY_HSTRY Family History Increased Family Hx

5 VST_ETHNICITYRACE * FLAG EthnicityIncreased for African American, Latino

6 EX_AWARNS_PRFL_CLS_ALL * FLAG Awareness of Health  

  HEALTHINSTITUTIONCONTRIBUTOR  Higher donating behavior

  MAILRESPONDER  More multiple responders

  FEMALEORIENTEDMAGAZINE  More female-oriented magazines

  BEHAVIORBANKINTERESTINREADING  More general reading behavior

7 EX_OCCUPATIONMODEL_CLS * FLAG OccupationHigher for Sales/Service, Professional/Technical, Blue Collar

8 EX_GENDER_CLS * FLAG GenderMales overpenetrated compared with females

Sample - Predictive Drivers - Cardiology – Consumer Model

For the general cardiology consumer model, the top three statistical drivers are age (older), Mosaic groups (mostly including older folks), and socioeconomic variables

Gender (higher for males) and general ailments (appended health flags, most related to cardio procedures) are also strong predictors

Note that in the consumer model, without patient data, both the ailment conditions as well as the ailment medications are significant predictors

Proactive health behaviors are negative predictors of cardio prospects

Rank Variable Name Variable Description Direction 1 EX_EXACTAGE_CLS Age Older individuals2 MOSAICHOUSEHOLD_CLS Mosaic More Mosaics that include older folks  Segment JAutumn Years    Segment QGolden Year Guardians    Segment NPastoral Pride  

  Segment CBooming With Confidence  

  Segment LBlue Sky Boomers   3 EX_WEALTH_PRFL_CLS_ALL Economic Index  Median Home Value   Lower and higher home values more predictive  Travel   Travel behavior is a positive predictors  New Market Auto   In the market for a new auto positive predictor

 4 EX_GENDER_CLS Gender Males overpenetrated compared with females

 5 EX_AILMENT_PRFL_CLS_ALL Dx ConditionTop 5 general appended ailments most predictive of cardio patient status

  Osteoarthritis    High Cholesterol      Heart Disease      High Blood Pressure      Sinuses/sinusitis    

 6 EX_BEHV_PRFL_CLS_ALLProactive Health Behavior

Gardening, fitness, and outdoors interests are negative predictors

  GARDENINGFARMINGBUYER      INTERESTINFITNESS      INTERESTINTHEOUTDOORS    

 7 EX_AWARNS_PRFL_CLS_ALL Awareness of Health  HEALTHINSTITUTIONCONTRIBUTOR    Higher donating behavior  MAILRESPONDER    More multiple responders  FEMALEORIENTEDMAGAZINE    More female-oriented magazines   INTERESTINREADING    More general reading behavior

 8 EX_MED_PRFL_CLS_ALL Medication Profile  Increase in top medications related to cardio 9 EX_BUSINESSOWNER_CLS Business Owner  Increase in risk for business owners

Sample: Model Performance and Testing

Sample of Relationship between Lift and “Best Patient Prospects” for Targeted Marketing Campaigns

Once a final predictive model is created, a multivariate predictive score is produced. Each unique record in a given file is scored, then the scores are broken into deciles.

Decile 1 includes the “Best Patient Prospects” and should be targeted first. Prospects in Decile 1 have the highest probability of looking like those in the Event Group having the behavior of interest (e.g., Cardiology Screening).

Looking at the “Lift” Column in the Lift Calculation table, scored patient prospects in Decile 1 are 2.7x more likely (have greater than chance probability) to look like an existing member of the Target Group (cardio targets).

Best Practices

Model Maintenance

• Models are updated regularly – new patients/non-patients added to database, run through model and assigned a score/decile

• Models should be refreshed when there is a significant change in population parameters:

• Large number of people moved in/out

• Organization acquired/sold service location

Modeling Best Practices

• Evariant will review the need to refresh models

• Evariant will assist in synching marketing and modeling calendars

• Models can be merged to maximize campaign impact

• Consider testing + advanced reporting

• Built-in test-controls can be leveraged to assess the efficacy of propensity models, including refining when necessary

Using a Model for Targeted Marketing Campaign:Breast Cancer Screening

Note: All patient and consumer IDs you have access to come from your own facilities and markets.

MODELING IN THEHEALTHCARE INDUSTRY:

A COLLABORATIVE APPROACH

William B. Disch, Ph.D.Director, AnalyticsEvariant

Thank you!Q and A

O P E ND A T AS C I E N C EC O N F E R E N C E

BOSTON 2015

@opendatasci

Q&AAppendix