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Third-Party Payer Track, National Rx Drug Abuse Summit, April 2-4, 2013. Using Analytics to Track, Monitor and Reduce Costs presentation by Anne Kirby, James Masingill, Joe Anderson and Dr. Robert …

Third-Party Payer Track, National Rx Drug Abuse Summit, April 2-4, 2013. Using Analytics to Track, Monitor and Reduce Costs presentation by Anne Kirby, James Masingill, Joe Anderson and Dr. Robert Hall

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  • 1. Using  Analy+cs  to  Track,  Monitor,   and  Reduce  Costs     Anne  Kirby   Chief  Compliance  Officer  and  Vice  President,  Medical   Review  Services,  Rising  Medical  Solu+ons     James  Masingill   Vice  President,  Claims  Opera+ons,  Market  First  Comp   Insurance  Company   Joe  Anderson     Director  of  Analy+cal  Services,  Progressive  Medical   Dr.  Robert  Hall   Medical  Director,  Progressive  Medical    
  • 2. Learning  Objec<ves  •  Iden+fy  warning  signs  of  misuse  and  abuse  and  how   claim  managers  can  take  ac+on.  •  Tell  how  payers  can  use  effec+ve  analy+cs  to  iden+fy   relevant  trends.  •  Explain  how  Pharmacy  Benefit  Managers  can  use   analy+cs  with  strong  clinical  programs.  •  Describe  the  role  and  benefits  of  predic+ve  analy+cs  in   the  workers’  compensa+on  industry.  
  • 3. Disclosure  Statement    •  Anne  Kirby  has  no  financial  rela+onships  with   proprietary  en++es  that  produce  health  care  goods   and  services.  •  James  Masingill  has  no  financial  rela+onships  with   proprietary  en++es  that  produce  health  care  goods   and  services.    •  Joe  Anderson  has  no  financial  rela+onships  with   proprietary  en++es  that  produce  health  care  goods   and  services.    •  Robert  Hall  has  no  financial  rela+onships  with   proprietary  en++es  that  produce  health  care  goods   and  services.     3
  • 4. Using  Analy<cs  to  Track,   Monitor,  and  Reduce  Costs   Anne  Kirby,  RN  Chief  Compliance  Officer/VP  of  Medical  Review  Services   Rising  Medical  Solu+ons  
  • 5. Accepted  Learning  Objec+ves  1.  Iden+fy  warning  signs  of  misuse  and  abuse   and  how  claim  managers  can  take  ac+on.  2.  Tell  how  payers  can  use  effec+ve  analy+cs  to   iden+fy  relevant  trends.  3.  Explain  how  Pharmacy  Benefit  Managers  can   use  analy+cs  with  strong  clinical  programs.  4.  Describe  the  role  and  benefits  of  predic+ve   analy+cs  in  the  workers’  compensa+on   industry.  
  • 6. Nothing  to  Disclose  
  • 7. Challenge  for  Claims  Claims  with  long-­‐ac+ng  opioid  Rx  cost    9.3  +mes  more  than  claims  without      (Journal  of  Occupa+onal  &  Environmental  Medicine)  •  Very  manual  process    •  Case  selec+on  not  always  on  target  •  Trea+ng  physicians  and  pain  mgmt   peer  reviewers  used  drug  names     inconsistently  •  If  a  person  was  taking  1  or  2  opioids,   it  was  likely  they  were  taking  upwards     of  7  or  8  other  drugs  
  • 8. 5  Key  Problems  1.  Difficult  to  iden+fy  claims  with  ques+onable     drug  use  before  cases  turn  into  large  losses  2.  Too  +me  consuming  for  adjuster     to  find  at-­‐risk  cases  3.  Not  enough  to  have  a  pharmacist     contact  a  trea+ng  physician  4.  Data not  comprehensive  enough – need integrated approach  5.  Viewing  opioids  in  a  vacuum  –  need  to     look  at  other  constella+on  of  drugs      
  • 9. Addressing  the  Problems    Rx  Intelligence  Analy+cs  1.  Expedites  file  iden+fica+on    2.  Flags  poten+ally     problema+c  claims  early    3.  Adds  another  level  of     interven+on  4.  Looks  beyond  just  opioids    5.  Uses  data  to  intervene  
  • 10. Rx  Intelligence  Analy+cs  Sample  Dashboard  
  • 11. Demonstrated  Impact  Effect  of  successful  peer-­‐to-­‐peer  conversa+on  (between  pain  management  physician  and  prescribing  physician)   Fills  before   interven<on   Fills  aFer   interven<on  
  • 12. Demonstrated  Impact   •  Decreased  Rx  Refills  within  6-­‐8  months  of     Peer-­‐to-­‐  Peer  Review       65%    Claims   •  Decreased  Opioid  Rx  Refills   71%    Claims   •  Decrease  of  All  Injury  Related  Drugs   •  Opioids,  Muscle  Relaxants,  Hypno<cs  &     57%     An<-­‐Anxiety  meds  Claims  
  • 13. Connec+ng  the  Dots   Where  do  we  go  from  here?   Treati ng Pain Mgmt Physi PeerClai cian Reviewerms UR NursePerson PATIPharmac TCM ENTy Benefit NurseMgr Clinical Pharmaci st
  • 14. Using  Analy<cs  to  Track,  Monitor,  and  Reduce  Costs   Jamey  Masingill   Vice  President  of  Claims   Markel-­‐FirstComp  Insurance  
  • 15. Accepted  Learning  Objec+ves  1.  Iden+fy  warning  signs  of  misuse  and  abuse   and  how  claim  managers  can  take  ac+on.  2.  Tell  how  payers  can  use  effec+ve  analy+cs  to   iden+fy  relevant  trends.  3.  Explain  how  Pharmacy  Benefit  Managers  can   use  analy+cs  with  strong  clinical  programs.  4.  Describe  the  role  and  benefits  of  predic+ve   analy+cs  in  the  workers’  compensa+on   industry.  
  • 16. Nothing  to  Disclose  
  • 17. WC  Combined  Ra+o:  1994-­‐2012F  Call  To  Ac+on…  
  • 18. Priming  the  Pump  by  Extrac+ng  “Old  School”   Thinking  from  the  Claims  Environment  •  There  is  no  right  or  wrong…only  grey  •  Reduce  ac+vity  checks  and  surveillance    •  Targeted  and  directed  case  management  •  Own  your  data   –  Driven  down  to  unit  and  individual  levels  •  Adherence  to  established  best  prac+ces  •  Valida+on  process  
  • 19. U+liza+on  
  • 20. LT  Closing  Ra+o  Triangles   Lost  Time     2006   2007   2008   2009   2010   2011   2012   12   28.00%   22.90%   26.10%   26.00%   28.90%   26.20%   34.70%   24   64.80%   63.90%   69.90%   68.70%   70.20%   72.70%   36   82.80%   84.20%   86.00%   85.40%   88.20%   48   91.30%   92.30%   92.90%   93.30%   60   95.90%   95.20%   96.20%   72   97.60%   97.20%   84   98.30%  
  • 21. Impact  of  Reduced  Claims  Dura+ons  
  • 22. Notes  Only  Presenta+on  Outline:  •  Preparing  the  claims  environment  before   implemen+ng  your  program.    Analy+cs  and  program   will  only  be  effec+ve  if:   –  Extract  “old  school”  thinking  from  claims  processing   –  Reduce  ac+vity  checks  and  inves+ga+ons   –  Redeploy  those  resources  into  added  medical  exper+se  /   interven+on  tools  •  Using  claims  triangles  to  track  and  improve   performance  •  Importance  of  integrated  approach  from  mul+ple   angles  to  effec+vely  tackle  prescrip+on  drug  problem    •  Impact  on  overall  costs  
  • 23. Using  Analy<cs  to  Track,     Monitor,  and  Reduce  Costs  Joe  Anderson,  Director  of  Analy<cs  Robert  Hall,  MD,  Medical  Director  Progressive  Medical,  Inc.  
  • 24. Learning  Objec<ves  •  Iden+fy  warning  signs  of  misuse  and  abuse  and  how   claim  managers  can  take  ac+on.  •  Tell  how  payers  can  use  effec+ve  analy+cs  to  iden+fy   relevant  trends.  •  Explain  how  Pharmacy  Benefit  Managers  can  use   analy+cs  with  strong  clinical  programs.  •  Describe  the  role  and  benefits  of  predic+ve  analy+cs  in   the  workers’  compensa+on  industry.  
  • 25. Disclosure  Statement  •  Nothing  to  disclose  
  • 26. What  Is  Predic<ve  Analy<cs?   Predictive Analytics is making decisions with statistics and data.Company   Goal  of  predic<ve  analy<cs   Result  Target   Iden+fy  new  mothers  as  quickly  as   Delivered  coupons  to  young   possible  to  get  them  in  the  habit  of   mothers  before  their  family  even   shopping  at  Target.   knew  they  were  expec+ng.  Nemlix   Determine  which  movies  customers   Improved  their  predic+ons  by  10%;   will  like  based  on  what  they  have   a  $1  million  prize  was  awarded.   already  rated.  Oakland   Choose  the  best  baseball  players   20  consecu+ve  wins;  the  book  and  Athle+cs   available  for  the  next  season,  with  a   film  Moneyball  are  based  on  this.   limited  budget.  Sources:  Duhigg,  C.,  How  Companies  Learn  Your  Secrets,  The  New  York  Times  Magazine.  2012  February  16  Lohr,  S.,  A  $1  Million  Research  Bargain  for  NeElix,  and  Maybe  a  Model  for  Others,  The  New  York  Times,    2009  September  21  Mahler,  J.,  Smaller  Markets  and  Smarter  Thinking,  The  New  York  Times,  2011  October  14  
  • 27. How  Can  We  Use  It?   •  As  a  PBM,  we  see  some  of  the  data  going  through  the  system,  but  not  all   of  it.   •  Each  company  in  the  industry  can  use  analy+cs  with  their  own  data:   –  Imagine  if  Nemlix  wants  to  know  whether  you’ll  enjoy  the  movie  Moneyball   –  Nemlix  doesn’t  know  if  you  have  read  the  book  Moneyball,  if  you  studied   sta+s+cs  or  if  you’re  an  Oakland  Athle+cs  fan   –  They  do  know  if  you  like  other  baseball  movies,  other     Brad  Pir  movies  and  other  movies  based  on  nonfic+on  books  Image source: http://www.managedcaremag.com/archives/1208/1208.pbm-functions.html
  • 28. The  Problem   A  solu<on  is  needed  that  reduces  prescrip<ons  most  efficiently.  Prescrip<on  Drug  Deaths  and   Time  Constraints  on  Nurses,   Increasing  Costs   Adjustors,  Clinicians  •  More  people  are  dying  from   •  Cannot  examine  or  intervene  on   prescrip+on  drug  use.   every  claim  •  Prescrip+on  drug  prices  are  rising.   •  Cannot  determine  which  claims  will  •  Workers’  compensa+on  in  par+cular   have  high  long-­‐term  costs   has  seen  increases  in  use  of   •  Too  many  “false  posi+ves”  from   prescrip+on  pain  killers.   individual  clinical  triggers  (i.e.  only   10%  of  claims  with  morphine   equivalence  of  90mg  result  in  high   long-­‐term  costs)  
  • 29. The  Solu<on:   Mul<variate  Sta<s<cal  Model     to  Predict  High-­‐Cost  Claims  Our  original  model,  since  refined:   Correlate  early  data   …  with  resul<ng  long-­‐ about  an  injured   term  spend  of  that   worker…   injured  worker.   Workers  injured  in  2007   Resul+ng  pharmacy  costs  in  2009-­‐2010  
  • 30. Data  Used  in  Sta<s<cal  Models 100%   90%   80%   70%   Pharmacy  Behavior:  Medica+ons,   Percent  of   Number  of  Prescribers,  Number  of   Significance   60%   Pharmacies   (Aggregated  across  mul<ple   Injury:  Body  part,  nature  of  injury   variables)   50%   Prescriber:  Demographics  of  trea+ng   40%   prescriber   30%   Geographic  and  Other  Demographics   20%   10%   0%   1   4   6   9   12   18   24   Months  Since  Date  of  Injury  
  • 31. The  Risk  Score  Claim   Risk  Score   Reason  Allison   6.5   Mul+ple  Neck  Injury,  High  Total  Medica+on  Use  (Including   Narco+cs)  Bob   5.4   Con+nued  Medica+on  Use,  High  Risk  Prescriber:  Allergy  and   Immunology  Specialist  Cindy   5.0   Mul+ple  Prescribers  in  Early  Months,  High  Days  Supply  of   Various  Medica+ons  Dwayne   4.5   High  Risk  State  and  Moderate  Injury  Risk:  Dislocated  Disc  Elaine   3.9   Prescriber  Risk:  Pain  Management  Specialist,  High  Narco+cs   Use  To-­‐Date  Frank   3.1   Moderate  Injury  Risk,  Demographic  Risk,  and  Prescriber  Risk:   Pain  Management  Specialist  
  • 32. Predic<ons  Become  Interven<ons  • Types  of  clinical  interven+ons:   •  Claims  Professional  Outreach   •  Physician  Outreach   •  Drug  U+liza+on  Evalua+on   •  Peer-­‐to-­‐Peer  Review    • Interven+ons  should  be  completed  as  soon  as  possible     to  avoid  any  developing  complica+ons.  
  • 33. Measuring  Effec<veness   Statistical Confidence that Intervention Changes this Outcome100%   90%   96%   80%   70%   70%   60%   50%   55%   40%   30%   20%   10%   0%   Cost  per  Claim   Morphine  Equivalence  per  Claim  Prescrip+ons  per  Claim  
  • 34. Analy<cs  From  a  Provider’s  Perspec<ve  •  Finding  common  ground     with  analy+cs  and  providers  •  Embracing  challenges  that     can  arise  with  analy+cs  
  • 35. Common  Ground  –  Data  Collec<on  •  Personal  medical  history  •  Family  history  •  Social  history  •  Physical  examina+on  •  Diagnos+c  studies  
  • 36. Common  Ground  –  Risk  Assessment   Stroke   Modifiable  risk  factors   Non-­‐modifiable  risk  factors   •  High  blood  pressure     •  Age     •  Atrial  fibrilla+on     •  Gender     •  High  cholesterol     •  Race     •  Diabetes     •  Family  history     •  Atherosclerosis     •  Previous  stroke   •  Circula+on  problems     •  Fibromuscular  dysplasia     •  Tobacco   •  Alcohol   •  Patent  foramen  ovale   •  Physical  inac+vity     •  Obesity    Source: National Stroke Association, Am I at Risk for a Stroke? Stroke Risk Factors. 2013 March 18
  • 37. Common  Ground  –  Outcome  Predictors   Stroke   •  Poor  strength  recovery  predictors   –  Severe  arm  weakness  at  onset  of  stroke   –  No  hand  strength  4  weeks  aLer  stroke   •  30-­‐day  mortality   –  EKG  abnormali+es   –  Brainstem  stroke   –  Elevated  blood  glucose  in  non-­‐diabe+c  pa+ents  Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation BoardReview. New York. Demos Medical Publishing. 2004
  • 38. Common  Ground  –  Outcome  Predictors   Stroke   •  Nega+ve  predictors  for  return  to  work   –  Low  Barthel  Index  score   •  Ac+vi+es  of  daily  living   –  Prolonged  length  of  stay  in  rehabilita+on   –  Aphasia  (language/communica+on  deficits)   –  Prior  alcohol  abuse  Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation BoardReview. New York. Demos Medical Publishing. 2004
  • 39. Common  Ground  –  Language  •  Data  collec+on  •  Risk  assessment    •  Risk  factors  •  Outcome  predictors  •  Interven+ons  •  Behavior  •  Effec+veness  
  • 40. Embracing  Challenges     Avoid  Blame    •  Comprehensive  claim  evalua+on  •  Interven+ons  may  need  to  be  mulNfaceted  
  • 41. Embracing  Challenges     Validate  Success   •  Hill  Physicians  Medical  Group   –  2,200  physicians   –  332,000  pa+ents   –  Predic+ve  modeling   •  Management  of  chronic  diseases   –  Prospec+ve  Risk  Score   •  Likelihood  of  pa+ent  using  physician  resources  in  future   •  RNs  are  assigned  to  call  pa+ents  with  high  risk  scores  Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Qualityand Reduce Costs, The Commonwealth Fund. 2009 March
  • 42. Embracing  Challenges     Validate  Success        0.5  x  In-­‐pa+ent  days  over  last  365  days        In-­‐pa+ent  days  over  last  90  days     +      2  x  ER  days  over  last  365  days          ER  days  over  last  90  days          2  x  (Prospec+ve  Risk  Score  +  adjustment  factor)     = Priority  Score  Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Qualityand Reduce Costs, The Commonwealth Fund. 2009 March
  • 43. Embracing  Challenges     Validate  Success   •  Diabe+c  pa+ents     –  High  Priority  Score   –  Contacted  by  nurse  case  managers   –  Reminders  for  screenings   •  Eyes   •  Kidneys   •  Cholesterol   –  Counseling  with  diabetes  educator  Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Qualityand Reduce Costs, The Commonwealth Fund. 2009 March
  • 44. Embracing  Challenges     Be  Responsive  •  A  provider’s  ques+ons   –  Is  my  prac+ce  style  being  ques+oned?   –  Will  the  care  of  my  pa+ents  be  affected?   –  Where  is  the  evidence?   –  Why  now?  
  • 45. Embracing  Challenges   Reward  Posi<ve  Outcomes   •  Should  providers  be  rewarded?   –  Pay  for  performance   •  Physician  payments  at  the  group  level  (not  individual)   •  Mee+ng  absolute  benchmarks   •  Soon  auer  performance  period   –  Preferred  provider  status   •  Recogni+on   •  Increased  referrals  Source: Gamble, M., GAO: 3 Ways CMS Can Incentivize Physicians Like Private Payors, Beckers HospitalReview, ASC COMMUNICATIONS. 2012 January 7; 2013 March 11
  • 46. Takeaways  •  Common  ground   –  Data  collec+on   –  Risk  assessment   –  Outcome  predictors   –  Language  •  Embracing  challenges   –  Avoid  blame   –  Validate  success   –  Be  responsive   –  Reward  posi+ve  outcomes  
  • 47. Ques<ons?