Changing Healthcare Using Data

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As the Chief Medical Officer of North Memorial Health Care, Dr. Kevin Croston’s ultimate objective is to improve healthcare by driving variation out and improving cost efficiencies at North Memorial Healthcare. Core to his success has been a fundamental culture shift with physicians who are now using data to drive care optimization.

During this webinar, you’ll learn: 1) how to shift to a data-driven decision making culture, 2) how to make the data meaningful so providers can make better decisions, and 3) examples of successes and challenges, including how North Memorial has reduced unnecessary pre-39 week inductions, improved cardiovascular care and uncovered a substantial revenue cycle process issue.

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Changing Healthcare Using Data

  1. 1. Changing  Healthcare  Using  Data:     A  Case  Study  of  One  Small  Health  System's   Odyssey  To  Achieve  Material  Improvements   North  Memorial  Health  Care   J  Kevin  Croston,  MD  FACS   CMO, President  -­‐  Physician  OrganizaEon
  2. 2. Poll  QuesEon  #1   What  is  your  primary  area  of  focus?   q Physician/clinical  care  provider   q Quality   q InformaEon  system   q Finance   q AdministraEve  execuEve   q Other   2  
  3. 3. ObjecEves   You  will  learn:   –  How  to  shiQ  to  a  data-­‐driven  decision  making  culture   •  KPA   –  How  to  make  the  data  meaningful  so  providers  can   make  beTer  decisions   •  Permanent  processes  and  teams   –  Examples  of  successes  and  challenges   •  Pregnancy  –  ReducEon  of  pre  39-­‐week  unnecessary   inducEons   •  Cardiovascular  care   •  Revenue  cycle  process  –  professional  billing   •  Catheter  associated  urinary  tract  infecEons  (CAUTI)  
  4. 4. About  North  Memorial   •  Minneapolis-­‐based  two-­‐ hospital  health  system   •  Provides  full  conEnuum  of   services   •  Level  I  Trauma  Center   •  CommiTed  to  developing   clinical  effecEveness   guidelines  to  deliver  the   highest  quality  care  at  a   lower  cost   StaEsEcs  (2012)   Number  of  Licensed   Beds   648   Annual  InpaEent   Admissions     33,718  (includes   nursery  4,852)   Emergency  Room   Visits     87,684   InpaEent  Surgeries     8,722   OutpaEent  Surgeries     19,181   Providers  in  MulE-­‐ Specialty  Clinics   300   Total  FTEs   4,281  
  5. 5. North  Memorial  SituaEon       Challenges   •  Tough  regional  compeEtors   •  Declining  payment  stream   •  Data  created  confusion   “data  rich  -­‐  informa/on   poor”   •  Clinicians  and  execuEves   clamoring  for  answers   •  Hospital-­‐centric  decisions   (not  enterprise  based)     Opportuni@es   •  Strong  improvement  and   quality  culture   •  Insighiul  and  supporEve   leadership   •  Recognized  substanEal   changes  were  required  for   survival  
  6. 6. Key  Process  Analysis  (KPA)  
  7. 7. KPA  Results  
  8. 8. North  Memorial  Resources   Consumed   Key  Findings:       50%  of  all  in-­‐pa@ent  resources  are  represented  by  7  Care  Process  Family   •    80%  of  all  in-­‐pa@ent  resources  are  represented  by  18  Care  Process  Family   •  80%   CumulaEve  %   50%   %  of  Total  Resources  Consumed  for  each   clinical  work  process   Number  of  Care  Process  Family   (e.g.,  ischemic  heart  disease,  pregnancy,  bowel  disorders,    spine,  heart  failure)  
  9. 9. Poll  QuesEon  #2   What  percent  of  your  quality  improvement   efforts  are  priori@zed  using  a  similar  varia@on/ resources  analysis?   q 76-­‐100%   q 51-­‐75%   q 26-­‐50%   q 0-­‐25%   q Unsure   9  
  10. 10. How  North  Made  Data  Meaningful   People     •  Formed  permanent  teams     –  Clinical  OperaEons   Leadership  Team  (COLT)   –  Guidance  Teams  (ex.  Women   &  Newborn,  Primary  Care,   Cardiovascular,  OPPE,   InfecEous  Disease)   •  Repurposed  resources   without  adding  FTEs   •  Selected  medical  leadership   to  champion  the  vision  and   process   Processes   •  Data  organizaEon  -­‐  EDW   •  Data  governance     •  OrganizaEonal  team   structure  to  support   outcomes  improvement   processes   •  Ensured  hospitals  and   clinics  were  included  in     consistent  change  while   maintaining  autonomy   •  ArEculated  the  vision    
  11. 11. Pregnancy  (OB)  Team  Structure   Care  Process  Model  (CPM)  Core  Work  Group   Physician Lead Dr. Jon Nielsen Knowledge Manager Bethany Hjelle, R.N. Knowledge Manager Cathy  Anderson, R.N. Nurse Expert Tanya  Thomas, R.N. Nurse Expert Maureen  Ehlers, R.N. Nurse Expert Sally  Walstrom, R.N. Clinical  Director  Lead     Linda  Engdahl  R.N.     Nurse Expert Barb  Pavek , R.N. Key:   Subject Matter Experts Quality/ Work Flow Expert Mike Choi Data Provisioning Outcomes Analyst Ashley Nguyen Data Architect Joel  Zwinger Data Analysis 11
  12. 12. Women  &  Children  AnalyEcs  
  13. 13. Pre-­‐39  Week  ElecEve  InducEons  
  14. 14. Women  and  Newborn       Pre-­‐39  Week  ElecEve  InducEons   “We  wouldn’t  have  had  a  chance  to  do  some  of  the  things  we’ve  done  in  last  18  months  to  enhance  care,  reduce   waste  and  lower  costs  without  Catalyst.  It’s  amazing  how  differently  and  effec/vely  we  can  gather  and  use  data  now.”     -­‐Jon  Nielsen,  MD,  Medical  Director  Women  and  Children’s  Services  at  North  Memorial  Health  Care   ObjecEve   •  •    •    •    Define  exisEng  workflows   and  idenEfy  improvement   opportuniEes   Establish  baseline  metrics   and  measures   Define  evidence  based   standards  for  elecEve   inducEons   Reduce  rates  of  pre-­‐39   week  deliveries  from  1.2%   to  0.6%  to  qualify  for  a   payer  partner  bonus     Health  Catalyst  SoluEon   •    •  Late-­‐BindingTM  Data   Warehouse  Plaiorm   Cohort  Finder   •  Early  inducEon  advanced   applicaEon   •    •  Key  Process  Analysis   applicaEon  (KPA)   •  Results  to  date     •          CollaboraEve  IT  and  clinical   care  workgroups     •    •    •    Adopted  evidence  based   guidelines  and   standardized  workflows     Established  elecEve   delivery  baseline   measurements  to  track   quality  improvement  gains   Established  a  permanent   collaboraEve  team   Reduced  early-­‐term   deliveries  from  1.2%  to   0.3%   $200K  payer  partner  bonus   payment   14  
  15. 15. MAJOR  LEARNING:   FOLLOW  THE  PLAN!    
  16. 16. .     Cardiovascular  Care   Challenges    Lessons  Learned   •  Difficulty  replicaEng  first   clinical  program  success     •  Department  vs  condiEon-­‐ based  issue   •  Difficulty  understanding   importance  of  guidance   teams   •  OrganizaEonal  readiness     •  Physician  leaders  changed   weekly   •  Inspire  knowledge   leadership  and   organizaEonal  readiness   –  Include  the  right  people  in   the  development  of  the  care   model   –  Know  when  you  should  and   shouldn’t  be  involved   –  Require  buy-­‐in  for  the   methodology     –  Focus  of  project  did  not  line   up  with  opportuniEes  based   on  KPA  analysis  
  17. 17. Professional  Billing  ApplicaEon  
  18. 18.    Professional  Billing  ApplicaEon    
  19. 19. Professional  Billing  Efforts   “The  Health  Catalyst  Professional  Billing  Applica/on  has  given  me  what  I  need  to  be  successful.  Now  I  can  finally   accomplish  what  I  was  hired  to  do!”    Nancy  Young,  Manager  Professional  Coding,  North  Memorial  Professional   Services   ObjecEve   •  •  •  •  Ensure  accurate  and   complete  charge  capture  of   professional  services   performed  in  the  hospital     Address  physician  concerns   that  charges  were  not   reflecEng  actual  services   rendered     Health  Catalyst  SoluEon   •  Late-­‐BindingTM  Data   Warehouse  Plaiorm   •  Professional  Billing   applicaEon  to  idenEfy   revenue  cycle  and   educaEonal  opportuniEes     Automated  data  capture   for  efficient  and  complete   revenue  cycle  analysis   •  Reduce  manual  data  pulls   by  professional  coders  to   determine  which  provider   notes  to  review     •  Deliver  provider  educaEon   to  improve  clinical  data   capture   •  Starter  set  value  stream   mapping  to  idenEfy   workflow  process  gaps   IntuiEve  applicaEon  for   professional  coders  to   opEmize  workflow   Results  to  date   •  6%  increase  in  billing  for   notes  that  had  sufficient   clinical  data     •  PotenEal  $5.7M  charges     over  3  years  from  unbilled   services   •  25%  improvement  in   professional  coder   efficiency,  allowing  Eme  for   provider  educaEon     •  Health  Catalyst  delivered   results  in  6  weeks  vs.   consulEng  firm  who  was   unable  to  deliver  data   capture  and  applicaEon     19  
  20. 20. Catheter-­‐Associated  Urinary  Tract   Infec@ons  (CAUTI)   •  According  to  the  CDC  urinary  tract  infecEons   (UTIs)  are  the  most  common  type  of  healthcare-­‐ associated  infecEon   •  Cause  of  450,000  annual  infecEons  leading  to   13,000  deaths   •  Increasing  lengths  of  stay  by  as  many  as  four  days,   and  increasing  healthcare  costs  by  as  much  as  $500   million  per  year  naEonally.     •  CMS  has  proposed  expansion  of  CAUTI  measures   beyond  current  ICU  areas  to  include  medical   units,  surgical  unites  and  medical/surgical  units     20  
  21. 21. CAUTI  ApplicaEon  
  22. 22. CAUTI  Surveillance     “We’re  extremely  strapped  for  /me  in  the  infec/on  preven/on  world  and  CMS  is  coming  out  with  new   regula/ons  every  year.  The  more  we’re  out  there  preven/ng  –  rather  than  measuring  –  infec/ons,  the   bigger  a  difference  we  can  make,  educa/ng  clinicians  and,  as  a  result,  increasing  pa/ent  safety  and   quality.”  ~  Terra  Menier,  R.N.,  Infec/on  Preven/on  Prac//oner     ObjecEve   Health  Catalyst  SoluEon   •  Scalable  CAUTI  soluEon   to  meet  proposed  CMS   regulatory  measures     •  Leverage  NaEonal   Healthcare  Safety   Network  (NHSN)   definiEons  and   calculaEon  algorithms   •  Late-­‐Binding™  Data   Warehouse     •  CAUTI  ApplicaEon                 •  Clinical  Improvement   Services     •  Starter  set  to  idenEfy   workflow  process  gaps   •  ShiQ  clinical  resources   from  surveillance  to   intervenEon       •  Automated  data   capture  for  efficient     hospital  surveillance     Results  to  date   •  50  percent  esEmated   reducEon  in  CAUTI   surveillance  acEviEes     •  PotenEal  to  convert  from   manual  to  electronic  tracking   for  NHSN  required  catheter   days  reporEng     •  Rapid  Eme  to  value  with  10-­‐ week  implementaEon     •  InfecEon  prevenEonists  can   now  focus  on  intervenEon   instead  of  data  provisioning  
  23. 23. Conclusions   •  Spend  a  lot  of  Eme  up  front  with  teams  before  they   start  down  this  quality  improvement  journey.  Working   on  the  fly  comes  with  major  problems.   •  Don’t  ignore  the  warning  signs  (Cardiovascular).   •  Commit  one  physician  to  the  team.  An  outside   champion  may  try  to  prop  up  a  team.     •  SEck  to  the  plan  and  moEvate  people  to  work   together.   •  Communicate  successes  and  explain  reasons  for   success.  Hold  on  to  those  principles  rather  than   jumping  to  the  next  “shiny  object.”   •  Financial  improvements  do  follow  improvements  in   quality  of  care.    
  24. 24. Thank  You!     Please  submit  your  QuesEons   and  Answers   24  

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