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openEHR + SNOMED CT a perfect combination for clinical data querying


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This presentation is about using tools that implement two well known standards in the eHealth world, openEHR for EHR data management (generic store and query), and SNOMED CT terminology with the powerful expression mechanism.

This was presented in the "2nd Arctic Conference on openEHR and Archetype-based Clinical Information Systems"

Here is the talk:

Here is the full demo video:

And here an article about the implementation steps:

Published in: Healthcare
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openEHR + SNOMED CT a perfect combination for clinical data querying

  1. 1. openEHR + SNOMED CT: a perfect combination for clinical data querying
  2. 2. 2 @ppazos Pablo Pazos Gutierrez Computer Engineer from Uruguay Working in eHealth since 2006 Specialized on EHRs, Standards and Interoperability Member of the openEHR Spec, Localization, and Software Programs Working as consultant, trainer, coach, architect, dev
  3. 3. 3 Agenda • EHRServer: foss openEHR CDR • SNOMED CT Expressions • Uses cases • Empowering openEHR queries with SNOMED CT Expressions • Demo • Conclusion
  4. 4. EHRServer The first open source clinical data repository compliant with the openEHR standard
  5. 5. 5 EHRServer • Generic Clinical Data Store – store and query any kind of data structure – defined by openEHR Operational Templates – compliant with the openEHR Information Model – no need to change the source code to add support for new data structures • High Level Querying – based on openEHR Operational Templates – defined from a GUI – executed via REST API – doesn't require to write code or SQL
  6. 6. SNOMED CT Expressions A great mechanism to select specific subsets of the SNOMED concept graph
  7. 7. 7 SNOMED CT Expressions • SNOMED CT – is a graph of linked concepts – specialization / generalization hierarchies (is_a) – concepts have attributes (associated_with) • Expressions – allow to select part of the graph – expression:: operator focal_concept : refinement – refinement:: attribute_name = operator attribute_value – All types of diabetes: "<<" = all descendants • << 73211009 |diabetes mellitus| – All respiratory infections caused by a virus • << 275498002 |respiratory tract infection (disorder)| : 246075003 |causative agent| = 49872002 |virus|
  8. 8. Use Cases What we want/can do combining openEHR queries with SNOMED CT Expressions
  9. 9. 9 Use Cases • Clinical Decision Support – implement complex rules to launch alerts, reminders, recommendations – based on health problems (diabetes), conditions (obese), patient status (age > 50, sex = Male), taking some medication (oxymetazoline / vasoconstrictor), ... • Patient Selection for Clinical Trials – complex matching criteria to automate the selection process usually done by manually reviewing health records – selection based on health problems, risk factors, patient status, etc. • Patient Selection for Health Care Plans – for instance weight control plan – selection based on risk factors in combination with over weight • Data analysis, reporting, research, population health, ...
  10. 10. openEHR Queries + SNOMED CT Expressions Generic data querying + detailed content filters = complex queries done simple
  11. 11. Demo Let's check how this looks on the EHRServer (watch the full demo at
  12. 12. 12 Conclusions • openEHR – enables storing any kind of clinical data structure and provides generic data querying based on clinical information models (archetypes and templates) not on a specific database technology • SNOMED CT – has a lot of detailed clinical knowledge in it, and expression are a powerful mechanism to use that knowledge in specific scenarios • The combination – allows to create a powerful querying mechanism, mixing clinical content with coded data that can be filtered by expressions – enables quick implementation, ready to be used, of complex clinical data queries in minutes without writing code or recompiling the system
  13. 13. 13 References • EHRServer Clinical Data Management – • SNQuery SNOMED Expression Evaluation – • SNOMED CT Search – • loadEHR Loads Clinical Data into EHRServer – • Loading data into the EHRServer –
  14. 14. Thanks! @ppazos