the evolution of predictive analytics in maaged care
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TRANSCRIPT
The Evolu*on of Predic*ve Analy*cs in Managed Care
March, 2014
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The Evolu*on of Predic*ve Analy*cs
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! ‘Unborifying’ Analy9cs ! About Predic9ve Analy9cs ! Our Approach ! Risk Score Model
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ABOUT PREDICTIVE ANALYTICS
Predic*ve Analy*cs ‘Fancy Words’
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! Data Mining ! Big Data ! Machine Learning ! Hypothesis Tes9ng ! Sta9s9cal Significance ! Correla9on ! Regression Analysis ! Goodness of Fit
What is Predic*ve Analysis?
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The process used to iden9fy or predict an unknown event using available informa9on.
What is Predic*ve Analysis?
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Examples from the world of Managed Healthcare: ! Dual eligibility
! Risk score ! Presence of one or more chronic condi9ons ! Likely to NOT recer9fy for Medicaid or dual enrollment
! Likelihood of readmission
What is Predic*ve Analysis?
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Frequently we are unable to obtain the informa9on that is most closely 9ed to the event we are trying to predict. ! Dependent on data availability
! Leverage data proxies
What is a Predic*ve Model?
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𝑌= 𝛽↓0 + 𝛽↓1 𝑋↓1 + 𝛽↓2 𝑋↓2 + 𝛽↓3 𝑋↓3 …+ 𝛽↓𝑖 𝑋↓𝑖 +𝜀
= 𝛽↓0 + 𝛽↓1 𝐴𝑔𝑒+ 𝛽↓2 𝐺𝑒𝑛𝑑𝑒𝑟 + 𝛽↓3 𝑅𝑎𝑐𝑒 + 𝛽↓4 𝑆𝑜𝑐𝑖𝑜𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑆𝑡𝑎𝑡𝑢𝑠+ 𝛽↓𝑖 𝑋↓𝑖 +𝜀
Example: Poten9al Risk Score
Issue: What available informa9on best represents socioeconomic status?
! Income? ! House value? ! Rural/urban? ! Geographic loca9on?
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OUR APPROACH
Collabora*ve & Complementary Approach
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! Mul*-‐disciplinary team • Industry professionals • Clinical professionals • Numbers nerds
! Mul*faceted approach to development and tes*ng • Historic pa`erns & experience • Literature review and external data • Client input and review • Clinical review • Sta9s9cal tes9ng
Altegra’s Predic*ve Models
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! Program eligibility • Dual eligibility • Low Income Subsidy (LIS) eligibility • Failure to recer9fy
! Risk score gaps (RiskView) • Medicare • Medicaid • Commercial
Enrollment and Eligibility Models
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! Dual & LIS specific models • Used to target and drive outreach since 2004
! Two step model design • First step is determining the probability of eligibility • Second step factors in the probability of engagement
! Based on “Big Data” • Latest version uses data from 12+ Million MA beneficiaries • Plan provided data supplemented with census data and Altegra’s outreach data
! Are very accurate • In a recent challenger model test against an industry leading predic9ve modeling firm, our dual model was 96.8% accurate in predic9ng dual enrollment.
Altegra Dual Predic*ve Model Accuracy
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0.1% 0.2% 0.2% 0.8% 2.7%
11.6%
46.0%
69.5%
76.3%
89.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Under 100
100 to 199
200 to 299
300 to 399
400 to 499
500 to 599
600 to 699
700 to 799
800 to 899
900+
Dual Percent Cumula*ve % of Popula*on
Predicted Not Dual (n=90,134) Predicted Dual (n=9,866)
Overall Model Accuracy
96.8%
Actual new duals over 18 months
8,148
Recer*fica*on Model(s)
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! Ini*al model based on MA dual member recer*fica*on • Focused on iden9fying members most likely to fail to recer9fy on their own • Extensible to Managed Medicaid popula9ons • Focused on three states • Model drivers similar across all three states
! Partnered with an industry leading predic*ve modeling firm • Our process leveraged: • Big data /Supplemental data • Machine learning
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RISK SCORE MODEL
Risk Model Dimensions (Sub-‐Models)
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! Sub-‐models without member claims data 1. An external data supplemented predic9ve model
! Sub-‐models with member claims data 2. Tradi9onal coding persistency model 3. Pharmacy claims to condi9ons predic9ve model 4. Medical claims comorbidity/coding based predic9ve
model
! Overall model modifiers • Prevalence rates • Chronicity • Prior provider experience • Provider type • Recoverability
Core -‐> Altegra Dx Condi*on Groups
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Rx Data & Drug Class
Altegra Dx Condition Group
Risk Models• HHS-‐HCC• CMS-‐HCC• CDPS• Etc.
Medical Claims Data
Prevalence Rates
Chronicity & Persistency
Recoverability
Altegra Dx Group Examples
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Dx Group
Dx Group Label HCC HCC Label
HCC Subset Code
HCC Subset Label
DX01 HIV/AIDS 1 HIV/AIDS 1Z HIV/AIDS
DX03
Central Nervous System
Infec9ons
3 Central Nervous System Infec9ons, Except Viral Meningi9s
3A Tubercular Central Nervous System Infec9ons
3B Non-‐Tubercular Central Nervous System Infec9ons, Except Viral Mengi9s
4 Viral or Unspecified Meningi9s 4Z Viral or Unspecified Meningi9s
DX05 Cancer
8 Metasta9c Cancer 8Z Metasta9c Cancer
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Lung, Brain, and Other Severe Cancers, Including Pediatric Acute Lymphoid Leukemia 9Z
Lung, Brain, and Other Severe Cancers, Including Pediatric Acute Lymphoid Leukemia
10 Non-‐Hodgkin's Lymphomas and Other Cancers and Tumors 10Z
Non-‐Hodgkin's Lymphomas and Other Cancers and Tumors
Why Use Dx Condi*on Groups ?
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! Per*nent data are not generally available segmented by the individual risk model categories • Prevalence • Chronicity • Recoverability • Rx’s ,Lab’s, Etc.
! This allows us to transfer what we learn working with one risk model easily to other risk models
! It allows us to explicitly consider risk score gaps with quality score gaps • Integrated suspect lists • Facilitates the implementa9on of personalized, targeted interven9ons
Commercial Model Goal and Constraints
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GOAL: To quickly and efficiently determine the probability that a new commercial exchange member has a risk adjus@ng condi@on. ! These are newly insured members so there may not be any medical/Rx claims history to rely on
! It is es9mated that 20-‐30% of exchange members will have a risk adjus9ng condi9on compared to 80%+ for MA plan members
! Because the commercial risk model is concurrent, there is limited opportunity for retrospec9ve record review and an increased reliance on prospec9ve risk score improvement ac9vi9es
Risk Score Model Structure
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Persistency Model
Expected Value Score
Historic Medical
Condi*ons
Supplemental Data
• Demographic • Geographic • Psycho-‐Demo
Pharmacy Claims Data
Documented Medical
Condi*ons
Supplemental Data Expected Value Score
Rx Model Expected Value
Score
Comorbidity-‐Coding Expected Value Score
Member-‐Specific Expected Value Score
• 6-‐10 Variables • Developed Tes9ng 100+ Variables
• 75K+ NDC Codes • MediSpan Mappings • 61 Dx Groups
• Comorbidity mapping • Includes Self Reported Data
• 13K ICD9 Codes • 68K ICD10 Codes • 141 HCCs (including groups) • Includes Self Reported Data
Tradi*onal Persistency Sub-‐Model
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Not Currently Documented Risk-‐Adjusting Condition Category
Un-‐Documented Comorbid Risk-‐
Adjusting Condition Category
Un-‐Documented Higher Severity Risk-‐Adjusting Condition Category
Historic Medical Condition
Persistency Model Expected Value Score
Supplemental Data Based Sub-‐Model
Member Demographic
Profile
Geographic Risk Profile and
Prevalence Rates
Psycho-‐Demographic Profile & Purchasing Profile
Supplemental Data Model Expected Value Score
• Age• Gender• Race • Ethnicity
• Urban/Rural• Regional
Heath trends• Zip Code
Health Profile
• Housing profile
• Income profile
• Purchasing profile
Rx U*liza*on to Condi*ons Sub-‐Model
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Drug Therapeutic Class
Medical Condition
HHS-‐HCC Model Condition Category
Rx Claims Data
NDC Codes
Rx Model Expected Value
Score
Comorbidity & Under-‐Coded Sub-‐Model
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Un-‐Documented Comorbid
Risk-‐Adjusting Condition Category
Un-‐Documented Higher Severity Risk-‐Adjusting Condition Category
Documented Medical Condition
Comorbidity-‐Coding Model Expected Value Score
Model Adjustment Parameters
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! Prevalence Rates • Based on mul9ple inputs • Known Dx prevalence rates • CMS-‐HCC prevalence • CDPS & Other Medicaid model condi9on prevalence rates
! Chronicity • Based on AHRQ Acute/Chronic condi9on model • Adjusted based on Altegra experience
! Code Recoverability • Based on Altegra experience
Coding Persistency Model Score
Rx Model Score
Comorbidity Model Score
Supplemental Data
Model Score
Prevalence Rates
Dx Chronicity
Project and/or Intervention Characteristics
Provider Characteristics
Ranked Suspect List for
Intervention(s) TargetingMember
Characteristics
Weighted Average Member Specific
Expected Value Score
Risk Model Weights
CE Predic*ve Model Score Composi*on
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Heuris*c (learning) Model Characteris*cs
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! Our models will be con9nuously evaluated and refined as data for this new popula9on becomes available
! Ini9al sta9s9cal rela9onships were determined using extensive risk adjustment experience working gained from with Medicare Advantage and Managed Medicaid Plans over the past 10+ years
! The con9nuous improvement process leads to ever-‐increasing model precision
