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Underwriting with Prescription Histories
Texas Wide Underwriting Conference
April 4, 2011
Mark Franzen, Ph.D., FSA
Milliman IntelliScript
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Agenda
How Rx Histories work
Myths / Realities
Measuring Value• Client Studies
• RGA mortality study
Interpreting Rx History Reports• Red / Yellow / Green
• Rule Engine
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How does it work?
1. Obtain the authorization
2. Submit the query
3. Review the results
Data Source
Data Source
Data Source
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Pharmacy Benefit Manager (PBM)
PBM
Health Plans
Retail Pharmacies
PBMs administer Rx benefits
EmployersGov’t
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Prescription Histories have become a standard
In use for 10 years IntelRx founded in 2001
• Acquired by Milliman in 2005 – renamed IntelliScript
• 100% focus on Rx-history solutions
Widely adopted in individual health, LTC, life Milliman:
• More than 150 insurance company clients
• 3.4 million queries in 2010
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What’s returned on an Rx History?
Prescription• Brand and generic name
• Dosage, quantity
• Date of fill
• Underwriting significance indicator
Physician • Specialty
• Contact info
Pharmacy• Contact info
Dates of eligibility
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Fully Compliant with HIPAA and FCRA
Applicant authorizes access to pharmacy records Authorization elements specified by HIPAA All Insurers using Rx Histories are audited
Applicants may request a free copy of their Rx report May dispute inaccuracies directly with Rx provider Insurer must provide FCRA notice when an adverse decision is
based on the Rx report
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Myths
“Prescription Database”
“My company is not subject to HIPAA”
“Recent legislation threatens use of Rx Histories”
“Hit Rates are too low”
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Measuring value
Insurer studies 35+ proprietary insurer studies
Results owned by insurer
RGA Mortality Study
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Insurer Studies
Two ways insurers measure benefit to cost:
Retrospective Study Select sample of recently issued cases “Re-underwrite” using Rx histories Track decision changes
“Live” Pilot Underwriters track decision changes while using Rx Histories
• Online survey and reporting
In either case:
Value of Rx = Sum of actuarial value of decision changes
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RGA Rx Mortality Study – March 2009
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RGA Rx Mortality Study
1.1 million insurance applicants from 2005-2007• All with Rx histories at time of application
21 million distinct prescription fills
2,530 deaths between 2005-2007 (from Social Security Death File)
Relative mortality ratios calculated for Red / Yellow / Green and other subgroups based on prescription history.
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Mortality Study Results
40%
60%
80%
100%
120%
140%
160%
180%
No Eligibility Green Only Yellow, noRed
Red
Hit Only Rx Found
Rela
tive M
ort
ality
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28.7%19.5% 8.7%
34.8%
8.3%28.7%
19.5% 8.7%
34.8%28.7%19.5% 8.7%Exposure
Proportion
No Hit Slightly worse than average
Eligibility Only About average
Green/Yellow Only Significantly better than average
Red Significantly worse than average
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Red Fill Frequency
0%
50%
100%
150%
200%
250%
300%
350%
400%
No Hit EligibilityOnly
Rx Found, NoRed
1 Fill 2-25 Fills 26-50 Fills >50 Fills
Red Rx Found
Rel
ativ
e M
ort
alit
y
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28.7%
19.5%
43.4%
2.7% 4.8% 0.6% 0.2%Exposure
Proportion
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Tools for Interpreting Rx Histories
Red / Yellow / Green Indicator
Rule Engine
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Red / Yellow / Green Classification System
Every drug mapped to an underwriting significance
• Significant (e.g. Digoxin)
• Potentially Significant (e.g. Norvasc)
• Likely Not Significant (e.g. Amoxicillin)
Separate mappings for Life / Health / LTC
Insurers use mappings “out of the box” or customize
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Why might you consider an Rx rule engine?
Large volume of applications
Large number of Rx fills
Inconsistent Rx expertise
Inconsistent interpretation
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What is RxRules?
Rx Guidance Conditions Severity Decision
IntelliScript Rx data RxRules
Rule variables: Indication / Therapeutic class Drug combinations Red / Yellow / Green Fill timing (date or duration ranges) Fill counts / patterns Dosage / quantity Physician specialty / count Gender / Age Eligibility
Confidential for
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RxRules. . .
400+ Rules• Identify condition
• Provide underwriting guidance
Transparency• All rules visible / editable
Guidance tailored to match insurer’s policies / underwriting guidelines
Confidential for