Milliman IntelliScript Underwriting with Rx Based Models · 2018-06-04 · Mortality Study Timeline 2009 Milliman / RGA study 1M exposure years 2,500 deaths 2012 Milliman study ...

Eric Carlson

June 8, 2018Actuaries Club of the SouthWest

Milliman IntelliScript

Underwriting with Rx Based Models

Agenda

Rules Engine and Mortality

Simplified Issue Findings

Predictive Model and Mortality

Prescription Data and Mortality

Combining Predictive Models and Clinical U/W

Proprietary and Confidential

The Future of Underwriting …

Electronic requirements (Rx, Medical Data, MIB, MVR, Credit …)

Decision engines driven by data

Predictive Models

Automation

Increasing

APS, Labs

Cycle times

Costs

Decreasing

Better Customer Experience


Multiple Levels of Data

Obtain the authorization

Submit the query

Review the results

Clients

Health Plan

PBM

Clearing

House

Retail

Pharmacy

Data Sources


Prescription Histories

1PrescriptionBrand and generic name | Dosage and quantity | Date of fill

2PhysicianSpecialty | Contact information

3PharmacyContact information

4Dates of eligibilityWith or without prescriptions

5Underwriting significance indicatorRed, yellow, green


Mortality Study Timeline

2009

Milliman / RGA study

1M exposure years

2,500 deaths

2012

Milliman study

21M exposure years

45,000 deaths

Began to validate

and expand Irix

2015

Milliman study

53M exposure years

231,000 deaths

Created Risk Score

2017

Milliman study

104M exposure years

469,000 deaths

Update Risk Score

model


Rx: Relative Mortality by Maximum Drug Priority

0%

5%

10%

15%

20%

25%

30%

35%

40%

20%

40%

60%

80%

100%

120%

140%

160%

180%

No Hit Eligibility Only Green Yellow Red

Exposure

Rela

tive M

ort

alit

y

What is a rules engine?

Irix™UW Guidance

Conditions

Severity

Decisions

Rule Variables

Indication / Therapeutic class

Drug combinations

Fill timing (date or duration ranges)

Physician specialty / count

Gender / Age

Diagnosis / Procedure combinations

Drug / Diagnosis combinations

Other variables

Data Input

Rx

Medical Data

Application Data

Other (MIB, MVR …)


Irix – Insulin Matters

Diabetes

165% relative mortality

Non insulin dependent

128%

Insulin Dependent

269%


Irix – Context Matters

Zofran (ondansetron)

200% relative mortality (RM)

Pregnancy

OB/GYN

58%

1 isolated fill

1 Fill

160%

CINV

Oral Steroid

751%


Irix – Drug Combinations Matter

Spironolactone


Thiazide Diuretics (177%)

Ace / Angio II (ARBS) (119%)

Beta Blocker (137%)

With 2 out of 3 of:

338%

Thiazide Diuretics (177%)

Ace / Angio II (ARBS) (119%)

Beta Blocker (137%)

Without 2 out of 3 of:

169%


Irix – Morphine Equivalence Matters

* MED = Morphine equivalent dosage

Opioids


Low MED*

109%

High MED*

346%


What is an Rx-based predictive model?

Holistic Multi-variate Rx Model of Mortality Risk

Predicts Relative Mortality of a Life or Group of Lives

Delivered via a Rules Engine


0%

100%

200%

300%

400%

500%

600%

700%

800%

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0+

Rel

ativ

e M

ort

alit

y

Live

s

Relative Mortality and Lives

Risk Score 2.0 - Lives Risk Score 2.0 - RM

Models can predict relative mortality accurately.

142017 Milliman Mortality Study: 25M lives, 15M Rx hits, 469K deaths Proprietary and Confidential

Predictive Model Benefits

Evidence based and data driven

Stratify risk within a given medical condition

Detect unintuitive patterns

Quickly and consistently interpret large amounts of data

Easy to test, implement, use, and update


Predictive Model Challenges

Limitations of data or lack of data

How to reflect low-incidence risks?

Operational challenges

Change in underwriting

Field underwriting more difficult

Client / agent communication challenges


Risk Score stratifies platelet inhibitor risk.

0%

50%

100%

150%

200%

250%

300%

350%

400%

0

15,000

30,000

45,000

60,000

75,000

90,000

105,000

120,000

x < 1 1 ≤ x < 1.5 1.5 ≤ x < 2 2 ≤ x

Risk Score Range

Very Serious Platelet Inhibitor (Plavix)

Lives

Relative Mortality


Risk Score stratifies risk within conditions.

19

0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0.00 < x < 0.50 0.50 < x < 1.00 1.00 ≤ x < 1.50 1.50 ≤ x < 2.00 2.00 ≤ x < ∞

Rela

tive

Mo

rta

lity

Liv

es

Diabetes Third Line with Insulin

Risk Score 2.0 Lives Risk Score 2.0 RM


SI Case Study Background

Mostly auto-decision via Irix

Risk Score as of time of underwriting

Have deaths on issued and declined cases


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f Li

ves

Risk Score

Risk Score Distribution by UW DecisionSI Case Study - Hits Only

Lives - Issue Lives - Decline

SI Case Study – Distribution of Lives

Issue

Decline

Average Score (Hits Only)

Issue 0.96

Decline 1.52


0%

100%

200%

300%

400%

500%

600%

0.00 ≤ x < 1.00 1.00 ≤ x < 1.50 1.50 ≤ x < 2.00 2.00 ≤ x < 3.00 3.00 ≤ x

Rel

ativ

e M

ort

alit

y

Risk Score Range

Relative Mortality by Risk Score and UW DecisionSI Case Study - (Hits Only)

Issue Decline

SI Case Study – Relative Mortality

Decline

Issue


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f Li

ves

Risk Score


Thresholds can be adjusted to achieve desired business results.

Low ThresholdHigh Threshold


Some issued premium now gets declined

Equal amount of declined premium now gets issued

Set Risk Score threshold to issue the same amount of business.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps

New Issues New Declines

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps


Threshold

83%

Before Risk Score

75%

After Risk Score

Issued Cases Relative A/E

Same amount of business issued

$4 Million increase in profit

9% Mortality improvement


Set Risk Score threshold to maintain the same mortality A/E.

Some issued premium now gets declined

More declined premium now gets issued

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 > 4.0

# o

f A

pps


Threshold

$56.1 M

Before Risk Score

$66.0 M

After Risk Score

Premium Issued

Same mortality A/E

$2.9 Million increase in profit

18% More issued business


Optimal solution combines clinical underwriting with the power of predictive modeling.


Paradigms

Clinical Underwriting

Condition based

Univariate

Uses clinical expertise

Predictive Model

Statistical basis

Multivariate analysis

Single risk metric for each case

Underwriting decision

Thank you!

Eric Carlson, Principal and Life Actuary

[email protected], 262-641-3537

mailto:[email protected]

Milliman IntelliScript Underwriting with Rx Based Models · 2018-06-04 · Mortality Study Timeline 2009 Milliman / RGA study 1M exposure years 2,500 deaths 2012 Milliman study ...

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