the evolution of predictive analytics in maaged care

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  

AltegraHealth.com  

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602.300.0646  Jim  Dalen    

 Jim.Dalen@AltegraHealth.com