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OpenEHR and IHE Ecosystem

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A fully archetype-based openEHR clinical system covering a number of hospital departments in the Ljubljana Medical Centre. The experience in construction of this system and its ongoing development shows that the openEHR archetype, template and querying approach can change the rules of software engineering economics.

Married to standard IHE services, the overall system is proving extremely flexible and adaptable to the continuous stream of new requirements.

This presentation covers the experience and lessons from this system over its 2 year development lifecycle, and discusses how it can inform strategic thinking for EHR / CDR development in three key areas:

1. the use of openEHR archetypes and templates to flexibly and efficiently store and retrieve all clinical content

2. the power of the Archetype Query Language (AQL), it's use in clinical applications and decision support systems

3. a new approach to enhance the IHE ecosystem with content querying capabilities based on archetypes enabling answers to population queries

Published in: Health & Medicine, Technology
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OpenEHR and IHE Ecosystem

  1. 1. Changing  the  Rules:  the  openEHR  and  IHE  ecosystem   Tomaž  Gornik,  Vice  President,  Marand   Borut  Fabjan,  Senior  Architect,  Marand   www.marand.com/thinkmed     thinkmed@marand.si    
  2. 2. Agenda  •  Developing  software  with  openEHR  •  Querying  the  EHR  •  Using  openEHR  and  IHE  •  Summary  T.  Gornik,  B.  Fabjan,  2012   2  
  3. 3. DEVELOPING  SOFTWARE  WITH   OPENEHR  T.  Gornik,  B.  Fabjan,  2012   3  
  4. 4. University  Children’s  Hospital   Ljubljana,  Slovenia  •  210  bed  teaching/research  hospital  •  All  pediatric  specialties  including  oncology,  surgery   and  PICU  •  New,  state-­‐of-­‐the-­‐art  facilities  and  equipment  •  No  legacy  IT  system  •  Motivated  staff  •  2  year  timeframe  T.  Gornik,  B.  Fabjan,  2012   4  
  5. 5. Software  development  challenges  •  Constant  change  in   –  Information   –  Care  Process   –  Technology   –  Patient  needs   –  Legislation     Requires  a  new  approach  to  managing  clinical  data    T.  Gornik,  B.  Fabjan,  2012   5  
  6. 6. The  Clinical  Process  T.  Gornik,  B.  Fabjan,  2012   6  
  7. 7. openEHR  •  Separation  of  content  and  technology  •  Archetypes  -­‐  Detailed  Clinical  Models  •  Existing  archetypes  for  many  clinical  terms  •  Templates  customize  data  set  for  each  use   case  T.  Gornik,  B.  Fabjan,  2012   7  
  8. 8. Archetypes  T.  Gornik,  B.  Fabjan,  2012   8  
  9. 9. Templates  T.  Gornik,  B.  Fabjan,  2012   9  
  10. 10. The  architecture   java   Int’l   Nat’l  /  local   Nat’l  /  local   archetypes   archetypes   templates   Template-­‐   C#   based   artefacts   etc   re   f   s   e   ts   terminology   canonical   Querying   openEHR   data   All  data  =  same  information  model  T.  Gornik,  B.  Fabjan,  2012   10  
  11. 11. Structured  data:  Nursing  T.  Gornik,  B.  Fabjan,  2012   11  
  12. 12. Structured  data:  Nursing  T.  Gornik,  B.  Fabjan,  2012   12  
  13. 13. Lessons  learned  •  Clinician  involvement   –  Using  CKM  to  develop  archetypes/templates   –  Produce  their  own  local  archetypes   –  Much  easier  than  HL7  v3      T.  Gornik,  B.  Fabjan,  2012   13  
  14. 14. Lessons  learned  •  Faster  development  cycle   –  Data  model,  GUI  •  Flexibility   –  Archetype  reuse,  versioning  •  Generation  of  downstream  artefacts  •  EHR  independent  of  application  (vendor)  T.  Gornik,  B.  Fabjan,  2012   14  
  15. 15. Example  T.  Gornik,  B.  Fabjan,  2012   15  
  16. 16. QUERYING  THE  EHR  T.  Gornik,  B.  Fabjan,  2012   16  
  17. 17. How  will  we  read  EHRs  50  yrs   from  now?  T.  Gornik,  B.  Fabjan,  2012   17  
  18. 18. Query  example  •  Get  all  the   patient’s  body   height  and  weight   readings  from   birth  to  present  
  19. 19. Archetype  Query  Language  •  Portable,  application  independent  (CDS  apps!)  •  AQL  based  interceptors  for  CDS,  alerts  •  URI  -­‐  addressable  data    
  20. 20. Graphs  and  Alerts  
  21. 21. AQL  Editor  
  22. 22. AQL  QBE  
  23. 23. USING  IHE  AND  OPENEHR  T.  Gornik,  B.  Fabjan,  2012   23  
  24. 24. IHE  –  Core  IT  Infrastructure   PIX/PDQ   Query   Pa*ent  Iden*ty  XRef  Mgr   A87631   M8354673993   PMS M8354673993   14355   Physician Office L-­‐716   14355   L-­‐716   A87631   ED Application Document   Document Registry   PACS Repository Document Repository EHR System Query  Document   Register  Document   (using  Pa*ent  ID) (using  Pa*ent  ID)   Provide & RegisterPACS Retrieve Document Maintain Lab Info. Time System Maintain Teaching Hospital Community Clinic Time Maintain Record Audit Time Event Audit record repository Time server ATNA CT Record Audit EventT.  Gornik,  B.  Fabjan,  2012   24  
  25. 25. IHE   IHE  Profiles  are  NOT  an  architecture   •  It  is  a  collection  of  architectural  components   •  To  build  into  new  or  existing  systems   •  To  aid  in  integration  T.  Gornik,  B.  Fabjan,  2012   25  
  26. 26. IHE  Benefits   Shortcomings   •  Querying  limited  to  document  •  Integration  profiles   metadata  •  Strong  industry  support   •  Minimal  data-­‐set  content  •  Focused  on  document   Profiles  /  coarse  grained  data     sharing   •  Mostly  CDA  L1/L2  •  Aids  integration   •  Non-­‐computable  health  data      T.  Gornik,  B.  Fabjan,  2012   26    
  27. 27. Simple  population  questions?  •  How  many  patients  have  been  diagnosed   with  Sickle  Cell  disease  last  year?  •  How  many  diabetes  patients  are  controlling   their  sugar?  •  What  is  the  percentage  of  patients  with  high   BMI?  T.  Gornik,  B.  Fabjan,  2012   27  
  28. 28. Semantic  underpinning  •  What  to  use  as  standard  RM?  •  Tried  HL7  RIM    •  Decided  to  use  openEHR  –  template  generated  XML   –  Semantically  consistent/validated   –  Directly  transforms  into  archetypes   –  Enables  querying  T.  Gornik,  B.  Fabjan,  2012   28  
  29. 29. openEHR  &  IHE  can  coexist  •  Benefits   –  Archetypes  –  maximal  data  set  –  key  for   agreement  on  data  structures   –  Use  Templates  to  generate  XML  structures  to   embed  in  CDA  L2/L3*   –  Distributed  EHR  –  supports  the  federated  model  T.  Gornik,  B.  Fabjan,  2012   29  
  30. 30. Solution?  •  We  need  a  new  IHE  profile   –  Addressing  content  query  needs   •  Specifying  the  query  parameters   •  Based  on  reference  model   –  Quick-­‐win:  IHE  On-­‐Demand  extended  with  Query   parameters    •  This  will  take  years  (at  least  two)  •  What  can  we  do  immediately?  T.  Gornik,  B.  Fabjan,  2012   30  
  31. 31. Solution?  •  Define  CDA  template  based  on  OpenEHR  Template/ Archetype    •  Leverage  IHE  Infrastructure  for  Document  Sharing  •  Use  IHE  DSUB  to  subscribe  /  retrieve  “archetypical”   topics  •  Use  IHE  On-­‐Demand  for  access  to  dynamic  information  T.  Gornik,  B.  Fabjan,  2012   31  
  32. 32. Example:  Child  health  screening  1.  HC  provider  publishes  Pediatric  Screening  Note  in  CDA  L2/L3*   format  2.  Public  Health  Authority  uses  DSUB  profile  to  be  notified  of  new   published  document  3.  PHA  stores  the  document  in  openEHR  registry  and  registers/ replaces  Growth  Chart  Document  CDA  L2/L3*    •  HC  provider  can  search  for  new  Growth  Chart  and  use  the  data  •  PHA  can  query  the  registry  for  reporting  and  CDS  T.  Gornik,  B.  Fabjan,  2012   32  
  33. 33. IHE  XDS  +  OpenEHR  +  DSUB   X.  Subscribe  to   document  metadata  of   Public  Health   DSUB.Broker   Authority   interest   DSUB.Publisher   6.  Search  for  “computed”   documents     3.  No*fica*on  on  new   XDS.Registry   document  availability   OpenEHR   2.  Registers  the   documents  metadata   and  pointer  with  the   4.  Retrieve  no*fied   Registry   document  from  HealthCare  Provider   Repository  (-­‐ies)   1.  Sources  post   document  to  the   5.  Post  “computed”   Repository   Source  of   document  to  the   Documents   Repository   XDS.Repository   T.  Gornik,  B.  Fabjan,  2012   33  
  34. 34. Next  step  •  A  system  with  openEHR  data  about  a  patient   could  register  this  in  an  IHE  registry  •  An  AQL  query  could  be  sent  out  to  those  sites   and  return  an  XML  result  set  •  Other  IHE  profiles  such  as  Consent  can  be   used  T.  Gornik,  B.  Fabjan,  2012   34  
  35. 35. Thinking  outside  the  box    Enhance  QED  with  support  for  openEHR?          T.  Gornik,  B.  Fabjan,  2012   35  
  36. 36. CIMI  Group  led  by  Stan  Huff,  IMH  •  Cambio  Healthcare  Systems     •  Mayo  Clinic  •  Canada  Health  Infoway/Inforoute  Santé   •  MOH  Holdings  Singapore     Canada     •  National  Institutes  of  Health  (USA)    •  CDISC     •  NHS  Connecting  for  Health    •  Electronic  Record  Services     •  Ocean  Informatics    •  EN  13606  Association     •  openEHR  Foundation    •  GE  Healthcare    •  HL7     •  Results4Care    •  IHTSDO     •  SMART    •  Intermountain  Healthcare     •  South  Korea  Yonsei  University    •  Kaiser  Permanente   •  Veterans  Health  Administration  T.  Gornik,  B.  Fabjan,  2012   36  
  37. 37. Summary  •  openEHR  changes  software  development   economics  •  AQL  offers  several  advantages  in  querying  EHR   data  •  openEHR  and  IHE  are  complementary   –  openEHR  provides  data,  context,  semantics,  querying   –  IHE  provides  interoperability/infrastructure  T.  Gornik,  B.  Fabjan,  2012   37  
  38. 38. Changing  the  Rules:  the  openEHR  and  IHE  ecosystem   Tomaž  Gornik,  Vice  President,  Marand   Borut  Fabjan,  Senior  Architect,  Marand   www.marand.com/thinkmed     thinkmed@marand.si      

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