Introducing  Health  catalyst  June  11,  2014  

Introducing  Health  Catalyst®  June  11,  204  

[Mike  Doyle]  This  is  Mike  Doyle,  I'm  the  Vice  President  of  Business  Development  for  Health  Catalyst.    I've  been  with  the  company  for  about  a  year  and  I  have  a  great  opportunity  here  to  share  with  you  a  little  bit  at  a  high  level  about  Health  Catalyst  and  I'll  turn  it  over  to  Eric  who  will  go  into  a  little  more  detail.    But  I  just  wanted  to  say  thank  you.    I'm  very  grateful  that  you  guys  can  join  us  today.      

Eric,  go  ahead  to  the  next  slide.  

Our  Story…  

Our  company  started  in  2008.    We're  founded  in  2008  but  our  story  really  began  back  in  1980.    At  that  time,  Dr.  David  Burton  was  an  ED  physician  at  Intermountain  Healthcare  and  he  sustained  an  injury  that  would  prevent  him  from  practicing  medicine.    Around  that  same  time,  Intermountain  was  interested  in  starting  an  insurance  plan.    They  wanted  it  to  be  a  little  bit  different  and  they  wanted  a  physician  to  lead  it.    So  they  asked  Dr.  Burton  if  he  would  be  their  first  CEO  of  what  later  became  Select  Health.    In  that  role,  Dr.  Burton  started  looking  at  how  he  could  help  improve  the  outcomes  and  manage  the  costs  for  the  members  of  that  insurance  plan  and  he  realized  pretty  quickly  that  one  of  the  roadblocks  to  that  was  going  to  be  the  variability  in  terms  of  the  utilization  and  the  costs  to  treat  those  patients.  

So  he  hired  Dr.  Brent  James,  a  physician  with  vast  experience  in  clinical  quality  improvement,  to  help  identify  some  of  the  opportunities  for  reducing  that  variation  and  both  helping  to  improve  outcomes  and  to  lower  costs.    As  Dr.  Burton  and  Dr.  James  started  thinking  about  how  to  do  that,  they  realized  they  were  going  to  need  data.    So  they  turned  to  Intermountain's  IT  Department  and  specifically  a  man  named  Steve  Barlow.    And  Steve  set  about  to  take  approaches  for  data  warehousing,  the  integration  of  data  from  other  systems  within  Intermountain  and  bring  them  together  for  the  purposes  of  looking  holistically  across  the  care  for  their  patients.    Steve  looked  to  these  approaches,  outside  of  healthcare,  these  traditional  data  warehousing  approaches,  and  found  that  through  one  or  two  different  iterations  of  this,  he  really  struggled  to  achieve  the  success  that  he  wanted  to,  based  on  the  complexity  of  healthcare  data.      

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And  around  that  time,  Dale  Sanders  joined  Intermountain  as  an  IS  leader  and  brought  with  him  some  knowledge  of  doing  similar  thing  but  for  his  data  in  the  Air  Force  and  through  his  work  with  the  Department  of  Defense.    And  he  and  Steve  started  looking  at  whether  he  takes  some  of  those  principles,  those  Late-­‐Binding  ™  principles,  and  apply  them  to  healthcare.      

And  to  make  the  long  story  short,  they  were  very  successful  at  doing  that.    And  with  the  addition  of  Tom  Burton,  they  found  successful  ways  to  integrate  with  Dr.  James'  interest  in  reaching  out  to  those  clinical  improvement  teams  to  provide  them  with  the  data  that  they  needed.    

So,  a  couple  years  down  the  road,  there  had  been  a  lot  of  success  at  Intermountain,  integrating  the  data  that  they  have  within  their  organization,  providing  it  to  these  care  improvement  teams.    And  Dr.  James  has  founded  his  advanced  training  program  to  do  that  and  help  train  other  physicians  and  clinicians  across  the  country,  which  many  of  you  probably  know  all  about  and  maybe  have  even  attended.      

One  of  those  organizations  like  you  then  was  Allina  Health  in  Minneapolis,  Minnesota.    And  Allina  attended  the  events  training  program,  they  heard  all  about  some  different  strategies  and  approaches  to  helping  identify  wasteful  variation.    They  came  back  to  Minneapolis  to  attempt  and  apply  those  there.    But  they  struggled  because  they  really  didn’t  have  the  foundation  for  integrated  data  around  quality,  costs,  outcomes,  patient  satisfaction,  that  Intermountain  had  benefit  from  for  years  through  the  work  of  Steve,  Dale,  and  Tom.    So  they  asked  Steve  and  Tom,  "Would  you  guys  mind  helping  us  get  off  the  ground?"    And  that's  really  how  Health  Catalyst  was  started.    Steve  and  Tom  left  their  job  at  Intermountain,  struck  out  on  their  own,  and  they  didn't  really  intend  to  start  a  company  like  Health  Catalyst  but  they  had  a  lot  of  success  with  Allina.    And  Allina  told  others,  and  pretty  soon  we  had  a  network  of  some  of  the  best  healthcare  organizations  in  the  country  as  our  customers.    And  from  those  humble  beginnings  with  about  two  or  three  employees,  we  now  have  a  company  of  about  170  employees  and  we  conservatively  estimate  that  about  30  million  patients  are  impacted  by  the  Catalyst  platform  in  some  way  today.  

So  that's  a  little  bit  about  how  we  got  started.  

What  does  Health  Catalyst  offer?  

Now,  I'd  like  to  talk  a  little  bit  at  a  high  level  about  what  we  do.    At  the  core  of  what  we  offer  is  that  Late-­‐Binding  ™  platform.    It's  been  informed  to  Dale's  work  through  the  best  practices  that  Steve  and  Tom  learned  and  it  helps  to  integrate  and  organize  your  data.    We  also  stand  that  platform  up  really  quickly,  and  we'll  talk  a  little  bit  about  that.      

Then  we  have  three  different  types  of  analytic  applications  that  help  to  integrate  data  from  that  platform  and  accelerate  insight  within  your  customer  base.    Then  two  types  of  services,  installation  services  and  care  improvement  services.  Installation  services  help  to  get  you  up  and  running  quickly.    Care  improvement  services  help  to  ignite  teams  within  your  organization  and  it's  informed  significantly  by  the  clinical  improvement  methodology  that  we've  developed.    We'll  talk  about  some  of  the  success  we've  had  with  that.  

Next  slide  please…  

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High  Level  Timeline  

So  I  mentioned  that  time  to  value  is  one  of  the  things  that  we  think  differentiates  Health  Catalyst  from  other  companies  in  this  space.    And  I  just  wanted  to  share  a  very  high  level  timeline.    Eric  is  going  to  go  into  more  detail  about  how  we  actually  implement  and  how  our  products  work  in  practice.    But  this  is  a  pretty  representative  timeline.    We  get  your  platform  installed  in  about  three  to  four  months  and  that  includes  training  your  IT  staff  and  your  analysts  to  bring  data  into  the  platform,  as  well  as  between  three  and  five  data  sources,  typically  your  electronic  medical  record,  patient  cost  accounting  system,  patient  satisfaction  system.    And  then  the  application  starts  going  in.    And  by  the  end  of  about  three  to  six  months,  most  of  our  customers  have  achieved  what  we  call  the  achievement  level  1,  which  is  an  initial  statement  of  work  intended  to  help  set  a  really  solid  foundation  for  clinical  quality  improvement,  it  includes  many  different  analytic  applications  and  a  really  solid  platform  that  has  had  success  across  the  country  within  our  customer  base.      

And  then  testing  and  validation  is  happening  throughout  the  way  to  ensure  that  the  data  is  of  high  integrity  and  that  your  clinicians,  your  administrators,  your  caregivers  all  trust  the  data  that's  coming  to  them  through  these  applications.  

Next  slide  please…  

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Success  Stories  

As  a  result  a  result  of  that,  our  customers  have  been  very  successful.    We're  enormously  fortunate  to  work  with  some  organizations  that  have  really  embraced  the  utilization  of  data  and  analytics  to  help  providers  deliver  high  quality,  lower  cost  care.    Most  of  our  success  stories  incorporate  both  a  clinical  or  an  operational  improvement  component  and  a  cost  component.    So  you'll  see  for  example  MultiCare  reduced  sepsis  mortality  by  about  22%  in  one  year,  with  an  associated  $1.3  million  cost  savings.    And  North  Memorial  reduced  elective  early-­‐term  deliveries  by  75%  and  saw  a  modest  $200,000  bonus  payment  from  their  payer.    Texas  Children's  recently  focused  on  labor  cost  and  they  didn’t  want  to  reduce  their  workforce  in  order  to  help  reduce  their  salaries  and  benefits  expenses,  and  they  saw  a  2%  reduction  in  doing  that  using  our  labor  management  explorer.    And  that  2%  sounds  like  a  small  number  but  most  people  know  for  organization  the  size  of  Texas  Children's,  it's  probably  a  $10  to  $15  million  annual  savings  using  that  one  application.  

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Success  Stories  (continued)  

And  if  we  can  go  to  the  next  slide,  our  other  areas  of  success,  asthma  outcomes,  appendectomy  outcomes,  streamlining  operations,  and  even  helping  to  make  your  reporting  teams  more  efficient  at  delivering  data.    One  success  story  had  a  documented  average  time  to  create  a  report  and  deliver  it  to  an  average  user  from  about  97  hours  to  30  hours.    We  have  more  of  these  success  stories  out  on  our  website.    We  encourage  you  to  visit  www.HealthCatalyst.com  and  there's  a  'Success  Stories'  tab  there.    And  I'll  stop  now  after  this  sort  of  high  level  introduction  and  turn  it  over  to  Eric  Just,  a  very  good  friend,  a  dear  colleague,  who  will  help  you  learn  a  little  bit  more  about  the  way  we  do  this  and  then  we're  really  looking  forward  to  your  questions  afterwards.  

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Roadblocks  to  Success  

[Eric  Just]  Alright.    Thanks  Mike.    So  our  goal  is  to  really  have  one  of  those  success  stories  that  Mike  just  presented  for  every  single  one  of  our  clients.    And  one  of  the  first  things  that  we  do  with  a  client,  even  before  we  install  our  technology  and  doing  assessment  with  them,  we  try  to  understand  where  they  are  in  terms  of  their  use  of  analytics  and  the  ability  to  use  those  analytics  for  care  improvement.    And  we've  identified  some  common  roadblocks  to  success  that  we  see  with  many  clients  in  the  industry  today.  

One  of  the  first  roadblocks  to  success  is  we've  got  a  great  team  of  analysts  who  are  really  skilled  at  looking  at  data  and  interpreting  data  but  they  spend  so  much  time  collecting  data  that  they  really  cut  into  their  time  adding  value  by  interpreting  that  data.    So  we're  talking  about  Excel  documents  and  Access  databases  where  the  analyst  is  using  those  tools  to  collect  data  when  they  could  be  analyzing  data  more  effectively.  

Another  common  roadblock  we  see  is  that  we've  got  all  kinds  of  reports  and  dashboards  but  we're  not  using  them  to  help  improve  care.    We're  not  really  looking  at  relevant  data.      

And  lastly,  we  seem  to  see  a  problem  of  we  can  improve  care,  we  got  a  team  together  and  we  have  a  great  improvement  but  it's  not  sustainable.    As  soon  as  we  move  to  another  project,  the  previous  project  suffers.    So  we  have  trouble  sustaining  this  improvement.  

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Poll  Question  Which  common  roadblock  resonates  the  most  with  you?    98  respondents  

Before  we  move  on,  I  want  to  ask  a  quick  poll  question.    So  which  of  these  common  roadblocks  resonates  the  most  with  you?    Analysts  spend  too  much  time  gathering  data,  reports  and  dashboards  are  not  showing  relevant  data,  or  there's  difficulty  achieving  sustained  improvements.  

[Tyler  Morgan]  Alright.    We've  got  that  poll  question  up.    We'd  also  like  to  remind  you  while  you're  answering  this  poll  question  that  you  can  type  in  your  questions  to  the  presenters  in  the  questions  pane  of  your  control  panel.    We'll  go  ahead  and  close  this  poll  in  just  a  few  seconds.  

Alright.    We're  closing  the  poll  now  and  here  are  the  results.  

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Poll  Results  

[Eric  Just]  Oh  thank  you,  Tyler.    Okay.    So  it  looks  like  most  people  are  seeing  their  analysts  spend  too  much  time  gathering  data.  

Three  Critical  Elements  of  Success  

Alright.    So,  in  implementing  our  success  stories  with  our  clients,  we  have  identified  really  three  critical  elements  of  success  that  help  us  to  get  around  these  roadblocks.  

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Three  Critical  Elements  of  Success  -­‐  Analytics  

The  first  roadblock  of  analysts  spending  too  much  time  collecting  data  is  solved  by  having  a  solid  analytics  framework,  and  an  analytics  framework  really  creates  the  single  source  of  truth  for  the  organization  and  makes  it  really  easy  for  analysts  who  need  data  to  know  where  to  go  and  know  how  to  find  that  data.    It  also  provides  broad  distribution,  automated  broad  distribution  of  information  so  that  you  don’t  always  have  to  go  to  an  analyst  for  information.    So  there's  a  self-­‐service  component  here  as  well,  and  through  that  we  save  the  analyst  time  from  having  to  answer  more  routine  questions.    And  that  allows  enough  time  for  the  analysts  to  do  what  they  do  best  and  discover  patterns  in  data  and  decide  how  we  are  going  to  improve  based  on  this  data.  

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Three  Critical  Elements  of  Success  -­‐  Content  

To  get  up  the  issue  of  we're  not  looking  at  relevant  data,  there's  the  notion  of  content.    It's  not  just  about  the  analytics.    Content  is  how  we  define  clinically  driven  patient  populations.    Are  we  using  the  latest  evidence-­‐based  medicine  to  identify  wastes?    And  how  are  we  identifying  high  and  rising-­‐risk  patients.    All  of  the  algorithms,  all  of  the  criteria  used  to  define  cohorts  and  the  measures  that  we're  looking  at  using  evidence-­‐based  medicine  comprise  the  content  piece.    And  quality  content,  when  it's  combined  with  analytics,  results  in  reports  and  dashboards  that  are  showing  relevant  data  and  they're  making  out  to  a  broader  audience  

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Three  Critical  Elements  of  Success  –  Deployment  

And  finally  for  the  last  issue  of  not  being  able  to  sustain  improvement,  we  have  the  concept  of  deployment.    And  deployment  is  about  how  do  we  organize  around  these  analytics,  how  do  we  create  workgroups  that  are  focused  on  defining  content  that's  accurate  for  our  institution  and  creating  actionable  data,  and  how  do  we  skill  these  workers  so  that  we  can  organize  for  scalable  improvements  across  a  variety  of  clinical  and  operational  areas.    That's  really  what  the  deployment  system  is  concerned  with.    And  in  general,  going  back  to  my  platform  application  and  services  slide,  analytics  is  really  part  of  the  platform.    The  content  is  displayed  in  the  applications  and  our  services  arm  really  helps  with  the  deployment  aspect  of  this.    But  these  are  really  the  three  critical  elements  of  success  –  Analytics,  Content  and  Deployment  working  together.  

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Agenda  

So  for  the  rest  of  the  presentation,  I  just  wanted  to  walk  through  what  I'm  going  to  be  showing.    The  first  will  be  a  discussion  of  the  platform.    Then  we'll  do  a  demo  of  one  of  our  applications,  called  the  Key  Process  Analysis.    We'll  do  another  demo  that  was  used  to  create  one  of  those  success  stories  in  reducing  heart  failure  readmissions.    We'll  look  at  a  host  of  other  applications,  very  high  level.    And  finally  we'll  end  with  a  conclusion.    And  throughout  the  agenda,  we'll  be  tying  our  analytics  content  and  deployment  system  concepts  into  what  we  look  at.  

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Late-­‐Binding  ™  Data  Warehouse  Platform  Fast-­‐tracking  Analytics  and  Content  

So,  the  first  discussion  about  our  platform  is  really  about  fast-­‐tracking  analytics  and  content.    And  what  we  do  with  the  data  warehouse  platform  is  we  create  that  analytics  backbone  to  make  it  really  easy  for  an  analyst  who  needs  data  to  know  where  to  go  and  have  ways  for  them  to  get  that  data.    And  we  do  that  using  our  Late-­‐Binding  ™  Data  Warehouse  Architecture.    The  goal  of  the  architecture,  and  again  with  our  engagements  with  clients  in  general,  is  to  really  get  a  rapid  time  to  value.    And  to  do  that,  one  of  the  first  things  we  do  is  we  identify  the  key  data  sources  to  load  into  our  data  warehouse  platform.    And  when  we  do  this,  when  we  load  the  data  from  these  systems,  they  are  indicated  here,  these  (14:55)  at  the  bottom  represent  the  operational  data  collection  systems,  things  like  the  medical  record  system,  financial  system,  human  resources  claims,  all  sorts  of  data  sources  that  you  can  think  of.    When  we  map  those  data  sources  into  our  platform,  we're  not  making  a  lot  of  business  decisions  about  how  that  data  is  going  to  be  used.    We're  getting  that  data  in  to  our  platform  with  as  little  transformation  as  quickly  as  possible  and  that  allows  us  to  be  very  quick  and  have  more  flexibility  later  on  in  our  process  when  we  really  do  need  to  make  those  business  decisions.    And  because  we're  not  doing  large  scale  transformation,  it  makes  it  very  easy  for  us  to  create  tools  to  automate  this  process.      

So  we  have  a  tool  called  Source  Mart  Designer  which  accelerates  the  growth  of  this  analytics  repository  by  creating  an  automated  way  to  reaching  these  source  systems,  pull  out  

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information  about  what  data  elements  are  in  those  systems,  and  help  us  to  map  those  data  elements  into  our  source  mart  area  of  the  data  warehouse  and  you  can  think  of  a  source  mart  is  really  a  corner  of  the  data  warehouse  that  holds  data  from  a  particular  data  source.    A  Source  Mart  Designer  captures  all  that  we  call  that  metadata,  those  mappings,  it  captures  all  that  metadata  so  that  the  feed  these  systems  and  pulling  that  data  every  night  could  be  created  very  quickly  and  with  very  little  maintenance.  

We  have  another  tool  on  the  right-­‐hand  side,  and  the  tools  are  indicated  in  purple  because  they're  part  of  our  analytics  system,  where  they  facilitate  the  analytics  system,  is  Atlas.    And  Atlas  is  a  web-­‐based  tool  that  helps  us  to  look  into  all  of  that  metadata  that  we  collected  with  Source  Mart  Designer,  and  it's  a  key  tool  in  helping  analyst  to  locate  data  within  the  data  warehouse.    So  I  can  type  in  a  very  simple  query  in  Atlas  for  phone  number  and  I  could  find  all  the  tables  and  columns  that  deal  with  that  particular  data  element.    We  likened  it  to  Google  and  Wikipedia  because  we  can  also  edit  descriptive  information  about  the  fields  in  the  data  warehouse.    But  that's  a  critical  element  in  making  this  data  easy  to  access.  

The  next  layer  in  our  platform  is  about  linking,  standardization,  and  content.    So  by  linking,  we  mean  how  do  we  take  data  from  the  EMR  and  link  it  to  financial  data.    Well  there's  a  series  of  identifiers  that  are  available  to  us  to  create  those  links.    Those  are  called  common  linkable  identifiers.    We  also  have  standardized  data  structures  for  things  like  patients,  labs,  encounters  diagnoses,  and  medications.    This  (17:24)  confused  with  a  larger  scale  enterprise  data  model  but  it's  very  focused  on  these  key  elements  that  we  see  time  and  time  again  and  we  know  are  going  to  have  a  lot  of  value  in  creating  additional  data  structures  based  on  these  concepts.  

And  we  also  have  placeholders  and  provide  content  at  this  level.    So,  one  of  the  things  that  we  bring  with  the  Catalyst  platform  is  definitions  for  over  800  populations  out  of  the  box.    Now,  many  of  our  clients  want  to  get  their  hands  into  fine-­‐tuning  these  definitions  and  that's  allowable  by  our  platform,  but  starting  with  the  starter  set  really  accelerates  the  ability  to  look  across  a  variety  of  disease  conditions,  and  most  organizations  don't  have  time  to  define  800  definitions  themselves.    So  this  really  accelerates  that  content.      

We  also  have  the  concept  of  hierarchies  which  are  used  for  stratification  in  our  application  and  we'll  look  at  one  of  those  today,  as  well  as  models  for  comorbidities,  risk  stratification,  and  patient  and  provider  attribution.  

The  set  of  tools  that  come  with  the  platform  to  help  us  manage  this  is,  one,  the  Subject  Area  Mart  Designer.    This  is  a  tool  that's  used  to  create  and  manage  the  content.    So  if  we  want  to  change  the  definition  of  one  of  our  populations  or  add  a  definition,  we  will  use  our  tool  called  Subject  Area  Mart  Designer.    And  that  tool,  the  data  that's  created  by  that  tool,  is  also  viewable  in  Atlas,  so  that  our  users  of  the  data  warehouse  can  view  content  definitions  and  a  lineage  in  Atlas  along  with  the  other  information  that  we  talked  about  from  the  Source  Mart  layer.  

Building  on  top  of  this  layer  and  using  the  same  tool  set,  we  have  the  concept  of  Subject  Area  Data  Marts.    And  Subject  Area  Data  Marts  are  really  focused  on  specific  areas  of  care.    So  one  

might  be  heart  failure,  it  might  be  a  clinical  area,  it  might  be  an  operational  area,  like  financial  management,  or  a  dashboard  that  looks  at  chronic  care  like  our  community  care  dashboard.    But  the  point  is  when  we're  creating  a  Subject  Area  Mart,  here  is  what  we're  building  in  those  more  complex  business  definitions  and  we're  engaging  clinical  and  operational  people  to  help  us  to  build  these  data  structures  and  validate  them  as  we  build  them.    This  is  where  we're  starting  to  quote  "bind"  information  and  that  binding  is  best  on  at  this  later  stage  when  we  have  engagement  from  the  people  who  will  really  be  using  this  to  improve  care.  

And  then  once  those  data  marts  are  built  actually  in  parallel  to  those  data  marts  being  built,  we  lay  on  visualizations.    And  the  visualizations  represent  the  applications  that  are  used  by  the  deployment  teams  to  actually  improve  care.    They  are  the  main  vehicle  for  giving  this  data  out  to  non-­‐technical  users.  

Where  Do  We  Start?  Key  Process  Analysis  

So  as  we're  building  the  analytic  repository  and  deciding  where  we  want  to  create  our  first  success  story,  this  is  where  our  key  process  analysis  comes  into  place.    And  a  key  process  analysis  is  an  application  that  sits  in  our  platform  and  is  used  to  help  prioritize  areas  where  we  think  we  can  have  the  biggest  effect  on  improvement.    I've  got  a  couple  of  background  slides  that  I'll  show  on  the  key  process  analysis  and  then  we'll  jump  out  to  a  demonstration  of  the  tool.  

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Clinical  Hierarchy  Organize  codes  into  a  clinically  meaningful  hierarchy  

One  of  the  things  a  want  to  point  out  that  you'll  see  in  the  tool  is  this  critical  piece  of  content  called  the  Clinical  Hierarchy.    And  the  Clinical  Hierarchy  was  actually  developed  by  our  clinical  leadership  led  by  Dr.  Burton  and  it  serves  to  organize  codes  into  clinically  meaningful  hierarchies  that  really  align  with  the  way  that  care  is  delivered.    So  a  lot  of  hierarchies  that  we  see  at  our  clients  are  based  on  more  administrative  or  financial  functions.    This  is  really  getting  our  Clinical  Hierarchy  that  fades  around  the  way  that  care  is  delivered  and  you'll  see  how  it  appears  in  the  tool  but  the  important  concepts.    So  we  map  a  variety  of  codes,  tens  of  thousands  of  codes,  into  groups  that  we  call  care  processes.    And  there's  about  455  distinct  care  processes.    We  roll  those  care  processes  up  into  care  process  families  and  there's  about  92  care  process  families.    And  those  92  care  process  families  roll  out  into  about  12  clinical  programs.  

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KPA:    Measuring  Opportunity  

Another  concept  I  want  to  introduce  before  the  demo  is  how  we  use  the  KPA  to  measure  opportunity.    And  as  Mike  discussed  in  the  very  first  slide  about  our  beginnings  at  Intermountain  Healthcare,  one  of  the  roadblocks  was  understanding  variation  and  the  KPA  is  really  designed  to  understand  that  variation  and  this  slide  is  meant  to  illustrate  why  we  look  for  variation,  how  we  use  it  to  measure  opportunity.  

So  if  you  imagine  that  this  blue  dot  in  the  middle  of  the  screen  represents  an  individual  provider  who  is  performing  vascular  procedures  and  this  provider  does  15  cases  per  year  and  does  those  cases  at  an  average  cost  of  $15,000  per  case.    Meanwhile,  it  appears  as  institutions  are  performing  the  same  procedures  for  about  $10,000  on  average  per  case.    What  if  we  could  take  what  Dr.  J  is  doing  and  standardize  his  care  closer  to  what  his  peers  are  doing?    In  this  case,  he  is  providing  care  at  about  $5000  above  the  mean  multiplied  by  15  cases.      

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If  we  could  take  what  he's  doing  and  move  him  to  the  mean,  that's  about  a  $75,000  cost  savings  opportunity.  

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KPA: Measuring Opportunity Using provider variation to calculate the potential financial impact of

improving and standardizing care processes

Mean Cost per Case = $10,000

• S4,000 x 25 cases = S100,000 opportunity

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Total Opportunity = $75,000

Cost Per Case, Vascular Procedures

KPA: Measuring Opportunity Using provider variation to calculate the potential financial impact of

improving and standardizing care processes

Mean Cost per Case = 10,000

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Total Opportunity= S1,200,000

Cost Per Case, Vascular Procedures

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And  then  if  we  layer  on  all  the  other  physicians  in  his  organization  and  see  there  may  be  another  one  who's  doing  25  cases  a  year  at  $4000,  that  equates  to  a  $100,000  opportunity,  and  we  keep  adding  these  numbers  up,  we  get  a  total  opportunity  for  the  ability  to  take  all  of  the  providers  who  are  performing  care  above  the  mean  cost  and  moving  them  to  the  mean.    Now,  this  is  not  an  ROI  calculator  but  it's  a  very  good  relative  measure  of  how  much  opportunity  there  is  in  different  clinical  areas  for  reducing  this  variation  and  this  forms  the  basis  of  the  key  process  analysis  tool.  

Demo:    Key  Process  Analysis  

So  I'm  going  to  jump  over  to  a  demo  here.  

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KPA  Pareto  Analysis  

And  the  first  thing  we'll  see  in  the  key  process  analysis  is  this  Pareto  Analysis.    And  in  the  Pareto  Analysis  we're  looking  at  data  that  is  being  stratified  by  the  clinical  hierarchy  that  I  introduced  in  the  slides.    So  you  can  see  the  clinical  programs,  the  care  process  family  and  the  care  processes  listed  on  the  left-­‐hand  side  here.    The  grain  of  this  middle  chart  is  the  care  process  family  level.    So  in  this  chart  we're  looking  at  things  like  heart  failure,  pregnancy,  ischemic  heart  disease.    That's  the  grain  of  the  care  process  family.    And  we're  looking  at  variable  direct  cost  by  default  and  we  have  variable  direct  cost  stratified  by  care  process  family  so  that  the  most  expensive  in  terms  of  variable  direct  cost  is  heart  failure  and  at  $9.9  million  that's  about  7.5%  of  all  of  the  variable  direct  costs  of  all  of  the  care  process  families  added  together.    And  pregnancy  is  next,  followed  by  ischemic  heart  disease,  and  so  on,  all  the  way  down  our  list  of  care  process  families.  

And  these  blue  dots  indicate,  each  blue  dot  indicates  a  care  process  family  and  the  position  on  the  Y  axis  is  the  percentage  of  the  total  variable  direct  cost  that  that  care  process  family  made  in  the  whole.    The  red  dots  indicate  the  cumulative  running  total  of  the  blue  dots  and  this  is  the  pattern  that  we  see  at  every  single  one  of  our  clients  where  there's  about  10  care  process  families  that  account  for  over  50%  of  the  variable  direct  cost  of  the  institution.    So  for  prioritizing  where  we  think  we  can  have  the  biggest  effect,  just  based  on  this  information  alone,  this  would  lead  us  to  believe  that  there's  about  10  care  process  families  that  should  probably  be  high  on  our  list.      

We're  looking  at  variable  direct  cost  by  default.    We  can  look  at  other  metrics  listed  here,  length  of  stay,  same  analysis  based  on  length  of  stay,  based  on  charges,  or  case  count.  

And  then  these  metrics  over  here  on  the  right,  labeled  opportunity,  equate  to  the  slide  that  I  showed  you  where  the  bubbles  were  moving  from  above  the  mean  to  the  mean.    This  is  how  we  calculate  where  that  biggest  opportunity  is.    And  according  to  this  analysis,  heart  failure  shows  up  with  the  most  opportunity  based  on  that  provider  opportunity  analysis  that  we  reviewed  in  the  slide.  

KPA  Bubble  Chart  

So  the  other  thing  that  I  mentioned  that  we  look  at  in  this  KPA  tool  is  variability.    And  in  this  chart,  we're  looking  at  variability  and  we've  moved  one  level  down  in  the  hierarchy.    So  we  see  a  lot  more  data  points  on  this  chart  because  we're  looking  at  care  processes  now,  not  care  process  families.  The  position  on  the  X  axis  is  the  total  variable  direct  cost  for  that  particular  care  process.    The  bubble  size  is  the  case  count  for  that  particular  care  process.    And  the  position  on  the  Y  axis  now  is  the  measure  of  variation.    So  we  do  a  calculation  of  a  coefficient  of  variation  to  allow  us  to  look  at  the  most  variable  processes.    So  we  really  not  focus  our  attention  on  the  upper  right-­‐hand  quadrant  of  this  chart.    These  are  the  highly  variable,  most  expensive  processes  and  these  are  the  areas  where  we  think  there's  great  opportunity  for  further  investigation  and  we  can  mouse  over  any  one  of  these  and  get  information  about  which  care  process  is  behind  it.  

So  if  I  mouse  over  this  red  bubble,  I  see  that  that  is  actually  heart  failure,  which  showed  up  the  highest  in  our  opportunity  analysis.  

So  let's  click  on  the  heart  failure  bubble  and  learn  a  little  bit  more  about  the  variability  behind  heart  failure.    This  chart  really  is  very  similar  to  the  chart  that  I  showed  you  in  the  slide.    Each  bubble  here  now  represents  an  individual  provider.    The  bubble  size  is  that  provider's  case  count  for  heart  failure.    The  position  on  this  X  axis  is  that  provider's  average  variable  direct  cost  for  heart  failure.    We've  added  a  Y  axis  here  and  that  Y  axis  is  the  severity  scale.    So  the  sickest  patients  are  up  at  the  top  of  the  chart  here  and  as  we  move  down,  and  we  expect  to  see  this  large  variation  for  the  sickest  patients.    There's  a  lot  of  complicating  conditions.    But  as  we  move  down  the  chart,  we  should  really  start  to  see  these  bubbles  start  to  stack  up  on  top  of  each  other.    

And  that's  not  necessarily  what  we  see  here.    We  still  see  significant  variation  even  at  these  lower  levels  of  severity.    So  this  is  how  the  tool  really  helps  to  focus  in  on  where  are  the  biggest  areas  of  opportunity  for  us.    Certainly  in  the  demonstration  it  appears  that  heart  failure  is  a  very  interesting  opportunity  for  focusing  on  care  improvement.    Now,  we  know  that  there's  a  lot  of  subjective  criteria  as  well  that's  just  bringing  the  voice  of  the  data  to  that  conversation  but  there's  a  lot  of  subjective  criteria  that  have  to  be  applied.    So  what  we  usually  do  with  the  KPA  is  we  narrow  it  down  through  about  4  or  5  focus  areas  and  then  we  apply  some  organizational  subjective  criteria,  such  as  quality  of  leadership  in  a  particular  area  or  variability  to  take  on  a  new  project  and  we  combine  the  subjective  with  this,  with  the  data  here,  with  the  KPA.  

Key  Process  Analysis  

So  just  in  summary,  the  KPA  is  a  tool  as  part  of  our  analytic  solution  that  uses  the  data  warehouse  and  relevant  content  to  determine  the  greatest  opportunity  for  quality  improvement  and  cost  reduction.    Key  content  pieces  are  the  clinical  hierarchy  that  we  use  to  stratify  according  to  classifications  that  match  care  delivery,  again  in  contrast  to  classifications  that  might  match  more  administrative  or  financial  groupings.    And  then  the  calculations  that  we  use  to  identify  the  variability  are  also  a  key  part  of  the  content.    And  this  tool  is  used  by  deployment  teams  to  prioritize  improvement  efforts.      

Heart  Failure  Readmissions  Introduction  

So  we're  going  to  jump  to  another  demonstration  here  around  heart  failure  readmissions.    Heart  failure  readmission  is  an  example  of  what  we  call  an  advanced  application.    And  advanced  applications  are  applications  that  are  typically  used  for  the  actual  improvement  of  care.    So  the  KPA  was  giving  us  kind  of  a  guide  post  to  helping  us  create  the  roadmap.    Now,  an  advanced  application  would  be  deployed  to  help  us  understand  where  we  actually  can  improve  those  processes  and  how  do  we  actually  achieve  that  improvement.    

When  we  implement  an  advanced  application,  part  of  our  deployment  recommendations  are  that  implementation  workgroup  comprised  of  both  clinical  and  technical  people  is  assembled  and  that  implementation  workgroup  helps  define  the  improvement  Aim  statement.    So  what  are  we  trying  to  accomplish  with  this  data  mart?    In  this  case,  we're  trying  to  reduce  heart  failure  readmissions.    How  do  we  define  our  patient  population,  and  you  know,  there's  a  population  definition  that  comes  out  of  the  box  with  Catalyst,  but  through  this  process,  we  will  refine  that  and  make  it  more  clinically  accurate.    And  how  do  we  identify  what  interventions  we're  going  to  perform  as  a  group  to  support  the  Aim  statement  of  reducing  readmissions.    And  all  three  of  these  elements,  the  Aim  statement,  the  population  definition,  and  the  specific  interventions  to  support  that  Aim,  are  part  of  the  content  piece  of  this  implementation.    And  

our  analytic  tool  is  a  data  mart,  underlined  data  mart,  and  a  visualization  on  top  that  allows  the  visualization  of  these  particular  metrics  and  our  ability  to  stratify  by  risk.    So  that's  just  kind  of  an  introduction  to  the  tool.    Now,  we'll  go  and  look  at  the  tool.  

Heart  Failure  Readmissions  

So  this  is  the  front  page  of  our  heart  failure  readmissions  dashboard  and  the  deployment  workgroup  that  was  set  up  around  this  decided  that  they  really  want,  of  course  they  wanted  to  get  these  high  level  overviews  of  their  heart  failure  readmission  numbers,  so  the  30-­‐day  and  90-­‐day  readmissions,  but  they  also  wanted  to  balance  those  readmission  metrics  with  utilization  metrics  for  ER  and  observation  space.    Oftentimes  if  we  push  too  hard  and  really  try  reductions  in  those  readmissions  that  we  see  an  increase  in  ER  utilization  and  observation  space.    So  this  is  really  –  these  two  metrics  on  the  right  serve  to  balance  our  30-­‐days  and  90-­‐day  readmission  numbers.      

What  we  see  in  the  middle  of  the  screen  here  now  are  these  specific  interventions  that  have  been  proven  through  evidence-­‐based  medicine  to  reduce  heart  failure  readmissions.    What  metrics  show  up  here  is  the  key  content  decision  by  that  implementation  team.    Medication  reconciliation,  do  patients,  are  they  having  their  medication  reconciled  both  by  admit  and  discharge,  are  our  patients  receiving  a  follow-­‐up  phone  call  after  their  discharge,  when  they're  discharged  from  acute  care  hospital,  do  they  have  an  appointment  for  their  primary  care  provider,  and  finally,  do  we  have  all  interventions  done.    So  have  all  three  of  these  been  done  for  a  particular  client.    So  we  have  this  high  level  dashboard  view  on  the  front  screen.  

Heart  Failure  Readmissions  

We  also  provide  the  ability  to  stratify  by  a  number  of  different  filters  and  those  are  listed  on  the  left  here.    One  of  the  most  important  ones  is  the  population  filter.    So  I  mentioned  that  our  deployment  team  developed  a  clinically  validated  cohort  definition  and  that  was  based  on  iteration  and  with  the  analytic  team  and  coming  up  with  criteria,  that  very  solid  clinical  definitions  and  the  heart  failure  patient,  and  we  can  look  at  this  definition  alone  and  we  can  also  look  at  a  specific  population  that  was  defined  using  CMS  core  measure  criteria.    So  it's  important  to  note  here  that  we  provide  a  system  to  capture  the  content,  these  cohort  definitions.    We  also  provide  a  way  to  toggle  between  two  different  pieces  of  content  that  apply  here.  

We  can  also  mix  and  match  individual  rules  here,  some  of  them  are  based  on  ICD9  codes  and  some  of  them  are  based  on  medications  to  identify  specific  patient  population  on  the  fly.    

Heart  Failure  Readmissions  

The  other  part  of  this  application  that's  very  flexible  is  how  we  identify  high  risk  patients.    These  risk  filters  on  the  left  are  various  ways  to  identify  patients  who  might  be  at  risk.    The  Physician  Flag  comes  from  the  EMR  and  some  of  our  clients  have  a  feel  to  their  EMR  that  allows  them  to  flag  a  patient  that’s  high  risk.    The  Charleson  Index  is  based  on  a  comorbidity  analysis.    And  the  Catalyst  Heart  Failure  Risk  Index  is  based  on  a  predictive  tool  that  we've  developed  that  presents  the  percentage  likelihood  that  a  patient  will  be  readmitted.    So  if  we  want  to  find  high-­‐risk  patients,  we  see  them  on  the  upper  end  of  this  Catalyst  Heart  Failure  Risk  Index.    So  there's  various  ways  for  us  to  slice  and  dice  the  data,  so  we  can  look  at  admit,  we  can  filter  by  location  within  the  institution  as  well.  

Heart  Failure  Follow-­‐up  Phone  Call  

Now,  in  terms  of  providing  actionable  data,  this  is  really  high-­‐level  overview  data,  we  want  to  know  who  are  the  patients  that  we  should  intervene  with,  and  I'll  just  show  one  example  of  a  drilldown  tab  here  that  looks  at  our  metric  if  the  patient  received  a  followup  phone  call.    Remember,  this  is  a  key  piece  of  content  that  was  defined  in  evidence-­‐based  medicine  that  says  patients  who  receive  a  follow-­‐up  phone  call  typically  do  much  better  in  terms  of  readmissions.  

So  what  this  chart  at  the  top  shows  here,  these  gray  bars,  indicate  the  number  of  patients  who  are  discharged  in  a  particular  timeframe  and  the  colored  dots  here  indicate  various  metrics.    So  the  blue  dots  indicate  how  many  patients  or  what  percentage  of  those  patients  were  called.    The  yellow  bar  indicates  how  many  of  those  patients  were  called  within  a  certain  amount  of  time  and  the  green  dots  indicate  whether  the  patient  was  actually  reached  or  not.    So  we  really  want  to  understand  which  are  the  patients  who  have  not  been  reached  and  we  can  use  the  

filter  on  the  left  here  that  will  allow  us  to  zoom  in  on  the  number  of  days  since  the  patient  is  discharged.    So  we  find  patients  who  are  discharged  within  the  last  two  weeks  or  14  days.  

Heart  Failure  Follow-­‐up  Phone  Call  

What  this  table  here  shows  is  a  list  of  the  patients  who  were  not  reached  and  it  has  information  about  the  medical  record  number,  their  name,  their  age,  their  unit,  as  well  as  their  phone  number,  and  the  list  is  ordered  by  that  risk  indicator  that  we  talked  about  before.    So  the  highest  risk  patients  come  off  at  the  top  here.    So  if  we're  really  interested  in  using  analytics  to  provide  actionable  data,  we're  looking  at  the  highest  risk  patients  at  the  top.    So  if  we  start  at  the  top  of  the  list  and  move  down,  we're  going  to  have  this  effect.    And  I  should  point  out  of  course  that  all  of  the  data  is  completely  scrubbed  and  de-­‐identified.    These  are  make-­‐believe  names  and  phone  numbers  but  what  you  can  see  here  is  how  we  incorporate  all  of  this  content  to  really  get  us  to  the  point  where  we  can  take  action  based  on  the  data  and  this  is  a  tool  that  helps  at  least  one  of  our  clients  achieve  reduction  in  the  heart  failure  readmissions.  

Heart  Failure  Readmissions  Conclusion  

So  just  in  summary,  the  analytic  tools  provide  a  baseline  readmission  metrics,  also  showing  balance  metrics,  and  provide  that  drilldown  to  the  patient  level  reports  that  help  us  to  decide  how  we're  going  to  intervene  and  we  have  it  ranked  by  the  predictive  risk  score.  

The  content  system  allows  us  to  have  multiple  cohort  definitions  and  multiple  risk  stratification  models  and  toggle  between  various  versions  of  those,  as  well  as  making  sure  that  we  are  using  the  latest  in  evidence-­‐based  medicine  to  focus  on  those  interventions  that  we  know  are  going  to  improve  the  outcomes.    

And  finally  the  deployment  is  really  handled  by  these  care  improvement  teams,  these  workgroup  teams,  that  use  agile  improvement  methodologies  to  incorporate  this  content  and  use  this  application  to  identify  and  take  action  on  those  patients.  

Product  Portfolio  

So  in  our  demos  today,  we  saw  a  very  small  portion  of  the  Catalyst  Portfolio.    We  wanted  to  give  you  a  taste  of  what  we  can  do  and  how  it  relates  to  the  three  critical  elements  of  success  –  Analytics,  Content  and  Deployment.    So  I  put  a  star  next  to  the  tools  that  we  looked  at.    We  looked  at  the  Pareto  tool  and  we  also  looked  at  one  example  of  what  we  call  a  Population  Suite,  and  I  mentioned  that  that  falls  into  a  category  that  we  call  advanced  applications  where  we're  looking  at  metrics  that  specifically  deal  with  a  clinical  area  or  a  workflow  area  or  a  patient  injury  prevention  and  these  tools  really  (39:27)  the  tools  that  provide  actionable  information  for  us  to  improve  care  delivery.    We  have  lots  and  lots  of  these  advanced  applications.    We  just  looked  at  a  couple  of  examples  on  the  screen.  

On  the  left-­‐hand  side,  we  have  our  foundational  and  discovery  applications  and  foundational  and  discovery  applications  are  meant  to  provide  more  basic  data  to  broader  audiences  and  we  help  our  clients  get  set  up  very  quickly  with  these  so  that  basic  questions  like  "what  is  my  average  length  of  stay  for  heart  failure  patients,  how  many  diabetic  patients  do  I  have  who  haven't  been  in  for  primary  care  in  the  recent  months".    These  are  all  sorts  of  the  questions  that  should  be  answered  very  easily  and  that  are  answered  through  our  foundational  and  discovery  applications.    So  with  these,  we're  really  helping  to  reduce  the  burden  on  the  analyst  time  and  having  the  analyst  not  have  to  feel  all  of  these  more  basic  questions.    So  that's  what  we  see  in  the  foundational  and  discovery  space.    The  Pareto  tool  or  the  KPA  that  we  looked  at  is  one  of  our  discovery  applications.  

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Population  Explorer  

One  of  our  foundational  applications  is  Population  Explorer.    I'm  just  going  to  do  a  couple  of  screenshots  here  to  give  you  a  taste  of  some  other  tools  that  we  have.    And  there  are  demos  of  these  tools  on  our  website.    So  if  you  see  a  tool  here  that  you're  interested  in,  go  to  that  website  and  click  on  demos  and  you  will  see  a  recorded  demo  of  these  tools.      

So  Population  Explorer  is  really  the  window  into  those  800  pre-­‐defined  populations  that  we  talked  about  that  come  with  the  platform.    It  allows  us  to  look  at  metrics  across  the  continuum  of  care.    So,  on  this  front  screen  that  you  see  on  the  slide  here,  we're  looking  at  readmission  rates  over  time  and  trending,  we're  looking  at  cost  data  over  time,  as  well  as  length  of  stay  data.    Other  tabs  show  demographic  data,  risk  profiles,  and  information  about  when  the  last  time  these  patients  have  been  into  primary  care.    It  uses  a  way  to  quickly  get  high  level  information  across  any  of  those  pre-­‐defined  populations  that  come  with  the  platform.      

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Community  Care  Screening  and  chronic  disease  management  

Community  care  is  a  dashboard  that's  based  on  screening  and  chronic  disease  management  and  that  falls  into  our  advanced  application  category.    And  what  you  see  on  the  screen  here  is  that  we're  looking  at  several  different  populations,  we're  looking  at  A1  –  for  diabetics  in  the  middle  here  you  see  we're  looking  at  A1c  screening  and  LDL  screening.    We  have  a  variety  of  preventative  metrics  that  we're  looking  at  around  immunizations  and  screening.    And  on  this  screen,  we're  looking  at  how  the  system  is  doing  with  respect  to  these  various  metrics  as  a  whole.    The  tool  also  allows  to  drill  into  the  organizational  structure,  so  we  can  look  at  metrics  work  and  compare  across  the  different  clinics  within  our  system  and  get  laid  down  to  the  provider  level.    We  also  have  a  patient  centric  view  in  the  community  care  dashboard  that  allows  us  to  look  at  how  a  particular  patient  is  doing  with  respect  to  these  measures,  and  this  is  the  tool  that's  highly  customizable.    So  the  metrics  you  see  here  are  really  examples  of  what's  possible,  what  we've  done  with  one  of  our  clients,  and  it  really  provides  a  framework  for  that  organizational  drill  through  on  these  metrics  that  particularly  pertains  to  community  care.  

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Financial  Management  Explorer  Analyze  costing,  billing,  and  payment  information  alongside  financial  and  volume  metrics  

And  additionally  we  have  tools  that  are  not  clinical  in  nature.    So  we  have  a  tool  called  Financial  Management  Explorer  and  it's  based  largely  on  financial  data,  looking  at  costing,  billing  and  payment  information,  looking  at  financial  and  volume  metrics.    And  one  of  the  things  you  see  on  the  left-­‐hand  side  here  is  that  clinical  hierarchy.    So  this  tool  allows  us  to  slice  these  financial  metrics  by  our  clinical  hierarchy,  that  key  piece  of  content  that  we  saw  in  the  key  process  analysis.    So  there's  a  common  threat  here  I  think  you  can  see.  

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Implementation  

In  terms  of  implementation,  our  implementation  is  really  based  on  the  realization  that  we  can  provide  value  if  we  focus  on  specific  areas  and  get  you  up  and  running  without  tackling  the  entire  organization  first.    So  it's  an  agile  approach.    Our  first  achievement  level  is  really  designed  to  get  our  clients  up  and  running  with  the  platform,  with  our  metadata  tool,  with  three  very  important  source  marts,  and  five  foundational  apps  and  a  handful  of  discovery  applications  as  well.    This  achievement  level  1  is  designed  for  us  to  get  up  and  running  very  quickly  and  start  to  push  this  data  out  to  your  organization.      

Achievement  level  2  is  really  based  on  rounding  out  the  source  marts  that  flow  into  the  data  warehouse  and  getting  some  additional  foundational  and  discovery  applications  but  now  in  achievement  level  2  is  where  the  deployment  team  starts  to  take  shape  and  we  start  to  tackle  some  advanced  modules  around  populations  and  workflow.    And  typically  achievement  level  2  is  where  we  identify  those  first  success  stories,  like  Mike  presented  on  the  first  slide.      

Then  achievement  levels  3  and  4  are  designed  to  expand  that  relationship  and  bring  in  more  applications  and  help  to  scale  those  improvements  across  the  organization  and  across  clinical  areas.  

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…Back  to  the  Catalyst  Story  

So  going  back  to  the  Catalyst  story  that  Mike  opened  with,  our  approach  to  success  and  these  three  critical  elements,  it  starts  with  our  creation  story  and  that  creation  story  predates  our  company.    Our  company  was  founded  on  understanding  what  it  takes  to  be  successful  in  analytics  and  building  them  to  the  DNA  of  our  company.    When  we  first  started  out,  we  had  the  concept  of  this  three-­‐legged  stool.    This  was  in  some  of  our  very  early  slide  presentations  where  we're  presenting  these  three  critical  elements  to  success.    We  did  decide  that  the  stool  was  decidedly  low-­‐tech  for  a  technology  company  and  moved  to  a  slightly  more  abstract  version  of  the  then  diagram  here.    But  the  message  is  the  same  and  I  wanted  to  show  this  just  to  show  you  that  this  is  part  of  who  we  are  as  a  company    We  understand  what  it  takes  to  be  successful.    It's  not  just  technology.    Technology  is  a  huge  part  of  it  and  that's  really  a  very  large  portion  of  what  we  bring,  but  we  also  bring  the  concept  of  content  and  having  valid  content  and  the  deployment  systems  to  really  ensure  that  our  customers  are  successful.  

Our  goal  is  to  make  sure  that  all  of  our  customers  have  'success  stories'  and  this  is  the  model  that  we  used  to  help  ensure  that  success.  

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Poll  Question  Which  do  you  think  your  organization  needs  the  most  help  with?  

So  before  we  conclude  here,  we  wanted  to  ask  a  quick  poll  question.    Which  do  you  think  your  organization  needs  the  most  help  with?    The  analytics,  content,  or  deployment?  

[Tyler  Morgan]  Alright.    We  have  that  poll  question  up.    And  while  you  guys  are  answering  that  poll  question,  it  looks  like  we've  got  a  lot  of  great  questions  that  are  coming  in.    I  would  like  to  remind  you,  you  still  have  the  opportunity  to  ask  questions  by  typing  your  questions  into  the  questions  pane.    I  would  like  to  address  that  we  have  had  several  questions  about  if  the  slide  deck  will  be  available  afterwards.    This  webinar  is  being  recorded  and  we  will  provide  everyone  with  links  to  the  recorded  webinar,  as  well  as  the  presentation  slides  and  the  like.    

So  we're  going  to  go  ahead  and  close  the  poll  now  and  let's  share  the  results.  

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Poll  Results  

[Eric  Just]  Okay.    So  most  of  our  participants  feel  that  their  organization  needs  the  most  help  with  the  analytic  system.    Great.    And  of  course  there's  a  good  balance  between  the  content  and  the  deployment  system  as  well.    Thank  you,  Tyler.  

Thank  you  Upcoming  Educational  Opportunities  

So  at  this  point,  I'd  like  to  thank  the  audience.    We  really  appreciate  everyone's  attendance  and  attention  here.    I  just  want  to  highlight  some  upcoming  educational  opportunities  through  the  Catalyst  webinar  series.    

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Healthcare  Analytics  Summit  Transforming  Healthcare  Through  Analytics  

[Tyler  Morgan]  Thank  you,  Erick.    One  thing  we  would  like  to  highlight  as  well  is  our  summit  that's  coming  up.    And  as  a  matter  of  fact,  like  I  mentioned  at  the  beginning  in  the  introduction  of  the  webinar,  we  have  two  passes  to  give  away  for  the  Healthcare  Analytic  Summit  we'll  be  holding  on  September  24th  and  25th.    The  first  is  a  pass  for  single  registration.    The  second  is  a  pass  for  a  team  of  three.    This  drawing  is  very  very  simple.    But  before  we  do  this,  I  would  like  to  mention  that  the  registrations  for  the  summit  so  far  has  exceed  our  most  optimistic  expectations,  coming  in  at  over  three  times  our  best  case  scenario,  so  much  so  that  we're  now  working  to  find  extra  space  because  at  this  rate  we  would  run  out  of  passes  by  the  end  of  July  or  early  August.    That's  two  months  ahead  of  time.    We  still  have  these  free  passes  to  offer  you  but  we  simply  ask  that  if  you  enter  the  contest,  that  you  are  confident  that  you  could  travel  to  Salt  Lake  City  on  those  dates  to  maximize  the  chance  that  the  winner  can  come.  

Poll  Results  

And  it  looks  like  that  about  64%  of  you  are  interested  in  being  able  to  attend  with  your  teams.  

QUESTIONS  AND  ANSWERS  

So  let's  go  ahead  and  jump  in  to  the  questions  and  answers  here.    Now,  we've  got  a  lot  of  great  questions  here.  

QUESTIONS   ANSWERS  What  is  the  frequency  of  moving  the  data  from  the  sources  into  the  source  mart?    Is  it  real-­‐time,  daily,  etc.?  

That's  a  great  question.    We  typically  start  with  a  nightly  feed.    So  once  every  evening,  that's  really  the  default  configuration  for  our  ETL  tools.    In  certain  circumstances  of  frequency  if  higher,  then  every  night  is  required  and  our  tools  handle  that.    We  can  set  a  smaller  interval.    And  in  certain  cases  we  ask  for  a  longer  interval.    So  in  certain  cases  that  data  is  just  not  and  the  source  system  isn't  refreshed  every  night  and  we  can  go  longer  as  well.    So  typically  at  the  day  we  can  go  more  frequent  or  less  frequent  depending  on  the  business  requirements.  

Please  define  the  term  Late-­‐Binding  ™.   Late-­‐Binding  ™  applies  to,  when  we're  talking  about  the  platform,  we  have  the  layers  of  the  platform,  many  data  warehouse  methodologies  attempt  to  bind  business  rules  and  definitions  to  data  as  it's  brought  in  to  the  data  warehouse.    So  before  we  even  do  anything,  we're  already  doing  a  heavy  transformation  in  creating  a  business  rule  as  part  of  the  scripts  that  we  create  to  load  that  data  into  the  data  warehouse.    That's  an  example  of  early  binding.    That  leads  to  really  long  time  to  value  in  our  experience.    

And  Late-­‐Binding  ™  is  really  when  I  show  those  data  marts  at  the  top  of  the  stack,  that's  where  we're  building  in  those  definitions  and  creating  those  bindings,  as  we  call  them,  to  the  business  rules.    And  the  later  we  do  that  binding,  the  more  flexibility  we  have  at  our  source  mart  layer  as  we're  not  building  those  definitions  through  that  source  mart  layer  and  the  more  flexible  we  are  if  one  of  those  definitions  changes.    And  that's  certainly  our  experience  in  implementing  our  advanced  analytics,  is  that  there's  often  changing  definitions  or  tweaks  that  we  want  to  make  and  by  doing  it  later  in  the  process,  it  leaves  us  that  ability.  

What  point  do  you  integrate  master  data  management?   That's  a  great  question  and  our  master  data  

management  strategy  is  really  very  institution-­‐specific.    So  some  organizations  are  already  doing  work  in  managing  their  master  data  and  they  have  tools  and  data  sources  that  we  can  bring  in  to  the  data  warehouse  as  master  data  sources  that  we  can  map  to  those  sources.    Other  institutions,  we  have  to  do  a  little  bit  more  work  in  our  platform  to  get  at  the  master  data  and  create  the  linkages  that  we  need  to  create  in  order  to  use  that  master  data.    But  it's  variable  and  it  really  does  depend  on  the  institution.    Our  strategy  is  –  we  finally  have  got  as  many  implementations  to  that  as  we  have  clients  just  because  different  clients  are  doing  master  data  at  different  levels.  

Do  users  have  access  to  the  source  marts  or  is  it  restricted  for  IT  developers?    I'm  trying  to  understand  if  source  marts  are  similar  to  staging  data  as  one  does  with  mainstream  data  warehousing  methodologies.  

That's  a  great  question.    So  yes,  users  have  access  to  the  source  mart  and  that's  why  we  make  all  that  information  available  in  Atlas.    We  make  it  usable  by  having  it  searchable  and  available  on  Atlas  and  really  there's  a  lot  of  data  in  those  source  marts.    We're  not  pulling  in  a  focused  data  set.    We're  really  going  broad  and  we  want  people  to  access  data  directly  from  those  source  marts  when  there's  a  data  element  that  may  not  be  built  in  the  data  mart  yet.    So  we  absolutely  encourage  access  beyond  just  developers  to  that  layer.  

[Mike  Doyle]  Would  you  mind  if  I  just  add  to  that  real  quickly.    So  part  of  my  background,  guys,  is  I  was  the  data  warehouse  manager  out  at  Allina  Health  for  about  four  years  and  we  had  a  lot  of  experience  with  folks  getting  in  and  querying  the  source  marts  directly.    It  also  tied  into  data  governance.    And  probably  one  of  the  things  users  after  seeing,  you  guys  after  seeing  this  presentation,  may  want  to  also  look  at  some  of  the  content  on  our  website  around  data  governance,  but  we  use  the  concept  of  a  data  steward  or  a  person  to  help  grant  access  to  those  source  marts  appropriately.  So  just  in  case  anybody  thinks  that  it's  a  broad  open  access  to  all  the  data  in  the  organization,  we  have  a  really  great  approach  to  work  with  you  to  help  you  to  find  the  right  people  to  provide  access  to  and  the  right  processes  to  integrate  with  your  own  access  granting  flow  and  workflow  within  your  organization  to  make  that  happen.    I  just  want  to  add  that  part  to  that  as  well.  

How  is  this  product  licensed?   So  I  could  probably  take  that.    This  is  Mike.    We  have  two  different  high  level  approaches  to  licensing.    There's  a  standard  perpetual  license  model  where  you  have  a  license  to  the  applications,  the  platform,  and  all  of  the  technology  that  goes  into  it,  as  well  as  support  and  maintenance  that  helps  to  ensure  that  you're  able  to  keep  current  with  our  software  and  professional  

services  that  we  talked  about  at  the  beginning  of  the  care  improvement  services  and  the  installation  services  that  help  you  to  get  that  installed  and  in  the  later  achievement  levels  to  support  those  clinical  improvement  teams.      

But  that  can  also  be  offered  as  a  subscription  model  which  is  basically  just  a  way  to  take  the  cost  of  the  perpetual  license  model  and  break  it  up  into  monthly  chunks.      

I  see  the  value  of  the  KPA,  etc.,  for  cost  containment.  How  are  patient  outcomes  brought  into  the  system  for  analysis?  

That's  a  great  question.    The  KPA  is  really  a  tool  that  –  as  you  mentioned,  a  lot  of  it  is  based  on  cost  data  and  that's  a  good  measure,  but  the  variability  is  oftentimes  tied  to  quality  outcomes.    So  the  more  variable  a  process  is,  the  more  we  notice  that  outcomes  are  not  optimal  and  that  quality  is  lower.    So  by  reducing  variation,  there's  almost  an  automatic  result  of  improving  outcomes  because  we're  standardizing  on  the  best  evidence-­‐based  care.    So  reducing  that  variation  is  really  how  the  KPA  is  measuring  outcomes.  Bad  outcomes  are  tied  to  higher  levels  of  variation.  

How  do  you  obtain  the  clinical  data  for  the  interventions?  

So  in  terms  of  the  clinical  data  that  we're  looking  at,  most  of  that  information  typically  comes  from  the  EMR  and  we're  using  documentation  in  the  medical  record  to  determine  whether  particular  interventions  are  required  or  have  been  done.    So  the  EMR  is  the  main  source  of  that  data.    There  are  some  other  instances  where  we're  pulling  from  other  systems  but  the  EMR  is  definitely  the  main  source.  

How  interested  are  you  in  a  demonstration  of  Health  Catalyst's  solutions?  

[Tyler  Morgan]  

And  we  just  like  to  say,  on  behalf  of  Eric  Just  and  Mike  Doyle,  as  well  as  all  the  folks  at  Health  Catalyst,  thank  you  so  much  for  joining  us  today.    This  webinar  is  now  concluded.  

Appendix  

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Three  Systems  of  Care  Delivery  

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Population  Explorer  Summary  

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Community  Care  Summary  

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