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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
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	
  
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)	
  
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	
  
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	
  
Key	
  Process	
  Analysis	
  (KPA)	
  
KPA	
  Results	
  
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)	
  
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	
  
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	
  	
  
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
Women	
  &	
  Children	
  AnalyEcs	
  
Pre-­‐39	
  Week	
  ElecEve	
  InducEons	
  
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	
  
MAJOR	
  LEARNING:	
  

FOLLOW	
  THE	
  PLAN!	
  
	
  
.	
  	
  

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	
  
Professional	
  Billing	
  ApplicaEon	
  
 	
  Professional	
  Billing	
  ApplicaEon	
  
	
  
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	
  
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	
  
CAUTI	
  ApplicaEon	
  
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	
  
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.	
  	
  
Thank	
  You!	
  

	
  
Please	
  submit	
  your	
  QuesEons	
  
and	
  Answers	
  

24	
  

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Changing Healthcare Using Data

  • 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. 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. 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. 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. 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  
  • 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. 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. 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. 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. Women  &  Children  AnalyEcs  
  • 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. MAJOR  LEARNING:   FOLLOW  THE  PLAN!    
  • 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  
  • 18.    Professional  Billing  ApplicaEon    
  • 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. 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  
  • 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. 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. Thank  You!     Please  submit  your  QuesEons   and  Answers   24