The Financial Health of a Wellness Program...2016 Member Months 119,424 2,148 44,616 74,808 121,572 2015 Rx Risk Score (Normalized) 0.980 2.094 0.906 1.024 1.000 2016 Rx Risk Score

The Financial Health of a Wellness ProgramBrent Jensen FSA, MAAA

Consulting Actuary

1 November 2017

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Data Provides Direction

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Data GOOD Data Provides Direction

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Data for a Wellness Program

StrategyProvider

SelectionThe Process Results Action Steps

▪ Background

▪ National employer – grocery and wholesaler chain

▪ Low margin industry

▪ Competitive benefits

▪ Focus on Employee Health

▪ Wanted a wellness program

▪ Improved employee health

▪ Potential H&W savings

▪ Wanted to give an incentive to participate – credit savings to employees

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StrategyProvider


▪ Wellness Vendor

▪ Considered simplicity of program for employees.

▪ Incentive structure for qualifying for lower employee contributions.

▪ Vendor guarantees related to the savings generated by the program

▪ Used propensity score matching methodology

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StrategyProvider


▪ Milliman Review of ROI

▪ Independent review of vendor ROI plus estimating ROI with different methodology

▪ Data expertise in risk adjustment using Milliman Advanced Risk Adjustment (MARA)

▪ Access to integrated claims and wellness data via MedInsight

▪ ROI tied results to actual medical costs for entire group

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StrategyProvider


▪ How to use claims data to measure the ROI of the wellness program?

▪ Need a way to accurately compare participants with non-participants

▪ Claims data housed in MedInsight

▪ Milliman Advanced Risk Adjuster (MARA) run on detailed claims data

Two column list

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Participants Non-Participants

–Typically Higher Cost

–Higher Risk

–Lower Participation Rate that Expected

–Typically Lower Cost

–Lower Risk

StrategyProvider


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StrategyProvider


▪ Two drivers of savings

▪ Reduced Trend (short-term)

▪ Reduced Overall Risk – Health Risk Reduction (long-term)

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StrategyProvider


2016 Risk Adjusted Allowed Trends by Wellness Activity

2016 ROI Analysis

No Condition

Management in

2016

Condition

Management in

2016

No Lifestyle or

Condition

Management in

2016

Lifestyle

Management in

2016

Total

2016 Member Months 119,424 2,148 44,616 74,808 121,572

2015 Rx Risk Score (Normalized) 0.980 2.094 0.906 1.024 1.000

2016 Rx Risk Score (Normalized) 0.998 2.073 0.913 1.048 1.017

2015 Allowed PMPM 479 1,446 436 505 497

2016 Allowed PMPM 527 1,240 511 536 539

2016/2015 Trend 9.9% -14.2% 17.2% 6.1% 8.6%

2015 Risk Adjusted Allowed PMPM 489 690 481 493 497

2016 Risk Adjusted Allowed PMPM 528 598 560 512 531

2016/2015 Risk Adjusted Trend 8.0% -13.3% 16.3% 3.7% 6.9%

Risk Adjusted Trend Difference 0.0% -21.3% 0.0% -12.5%

Only includes members who were present all 2015-2016

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StrategyProvider


▪ Reduced Trend Savings

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StrategyProvider


Lifestyle Management Risk Shifting from 2015 to 2016 by Risk Score Bucket

% of Total Members 2016 Risk Score Bucket

2015 Risk Score Bucket a: Low b: MedLow c: MedHigh d: High Grand Total

a: Low 40% 7% 3% 2% 52%

b: MedLow 7% 9% 3% 2% 21%

c: MedHigh 3% 3% 4% 2% 12%

d: High 2% 2% 3% 9% 15%

Grand Total 52% 20% 13% 15% 100%

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StrategyProvider


Lifestyle Management Risk Shifting from 2015 to 2016 by Risk Score Bucket



a: Low 40% 7% 3% 2% 52%

b: MedLow 7% 9% 3% 2% 21%

c: MedHigh 3% 3% 4% 2% 12%

d: High 2% 2% 3% 9% 15%

Grand Total 52% 20% 13% 15% 100%

Non Participant Risk Shifting from 2015 to 2016 by Risk Score Bucket



a: Low 52% 5% 3% 2% 63%

b: MedLow 6% 5% 2% 2% 15%

c: MedHigh 3% 2% 2% 2% 10%

d: High 2% 2% 2% 8% 13%

Grand Total 63% 14% 9% 14% 100%

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StrategyProvider


▪ Reduced Overall Risk Savings – Health Risk Reduction

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StrategyProvider


▪ Savings on a Per Participant per Month (PPPM) Basis

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StrategyProvider


▪ Savings vs Expenses on a Per Participant per Month (PPPM) Basis

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StrategyProvider


▪ Bottom-line ROI results

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StrategyProvider


▪ Year to year decision of how to adjust and continue

▪ Effectiveness of wellness program, from independent source

▪ Condition management targeting to achieve potential savings from high cost participants

Thank you

Brent Jensen

[email protected]

TURNING DATA INTO ACTION:

EXPLORING INTEGRATED DATA

PREDICTIVE ANALYTICS

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mailto:[email protected]

MONEYBALL MEASURES

TRADITIONAL MEASURES

MONEYBALL AND SABERMETRICS

Indicators of Offensive Success

Home Runs

Batting Average

Stolen Bases

RBI’s

On-Base %

Slugging %

Pitch Data

Expected Future Runs Scored in an inning given certain conditions. (1961-77 data set)

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“People operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage.” Michael Lewis, Moneyball: The Art of Winning an Unfair Game

WHAT IS INTEGRATED DATA PREDICTIVE ANALYTICS?

Applies mathematical & statistical techniques:

▪ Predict future outcomes.

▪ Improve ability to segment a population of data on the basis of

future probability or outcome.

▪ Provides more objective reasoning based on mathematical/statistical techniques.

▪ Leverages internal and external data.

▪ Improves understanding of risk characteristics that influence future outcomes.

An Objective

Approach to

Analyze Risk

▪ Segment low risk and high risk.

▪ Objective guidance for more efficient, consistent decisions and resource allocation.

▪ Creates opportunities to enhance traditional work processes.

A Tool for

Improved

Efficiency &

Consistency

▪ Model itself provides only a relative risk index.

▪ The rest of the value comes from operationalizing model results in business practices and actions.

A Tool…

Not a

Silver Bullet

HIGHER than

Average Severity

LOWER than

Average Severity

Supports important

business decisions

and yields

efficiency.22

INTEGRATED DATA PREDICTIVE ANALYTICS USE CASE

Data Cleansing and Aggregation

Create Variables

Develop Models

Build Reason Codes & Business Rules

Non-

Traditional

External Data

Sources

Non-traditional

data

introduces new

risk characteristics

into model.

INTEGRATED

DATA SOURCES

ADVANCED

DATA ANALYTICSBUSINESS

IMPLEMENTATION

Claims:

▪ Segment high-risk and low-risk

▪ Claim Triaged and Assigned.

▪ Resource Allocation.

▪ Develop consistent approach for managing claim.

▪ Cost-effective use of clinical resources and legal services.

“Right claim, right resource, right time.”

Data

Specifications

Traditional

Data

Sources

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For claims that have never been prescribed or exceeded a specified range of Opioid

consumption, predict the likelihood of specific

consumption ranges in specific time ranges in the

future

▪ Test various dosage ranges and prediction periods

▪ Claim data set - @1 million annually

BUSINESS CHALLENGE

Individuals that are most likely to consume greater than 50

MED

Individuals that are most likely to consume greater than 90

MED

1. Understanding that risk is pervasive

across the enterprise, without

boundaries.

2. Using risk and business intelligence to

drive performance metrics and business

processes is critical.

3. Leveraging and harnessing the power

of “big data” to gain a competitive

advantage and improve decision

making.

KEY TRENDS FOR ORGANIZATIONS

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The main requirement for predictive modeling process is data. This technique requires

extensive data mining.

WHAT SHOULD YOU DO?

Before you begin…

Effectiveness of any modeling is based on data integrity and quality.

Check internal resources to effectively manage data quality control.

The key is to make sure your data is

accurate before you begin!28

The Financial Health of a Wellness Program...2016 Member Months 119,424 2,148 44,616 74,808 121,572 2015 Rx Risk Score (Normalized) 0.980 2.094 0.906 1.024 1.000 2016 Rx Risk Score

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