The World of Health IT Ontology–based Research in e-Health Vienna, Austria - October 23, 2007

1. The World of Health ITOntology–based Researchin e-HealthVienna, Austria - October 23, 2007 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. Presentation overview • A bit about me • Some problems that can be solved using ‘ontology’ • What are ‘ontologies’ • Realism-based ontology • Referent Tracking • EHR Archetypes • Coping with change

3. 1959 - ... Short personal history 2006 1977 2004 1989 1992 2002 1998

4. NYC Buffalo Chicago

6. Center of Excellence in Bioinformatics & Life SciencesBuffalo, NY

8. Google, November 2004

9. October 2007

10. Realism- based Ontology What is generic Instance-of What is specific Referent Tracking My work: ontology-based research

11. Access this presentation http://www.org.buffalo.edu/RTU/papers/WHIT2007-Ceusters.ppt

12. Part 1:What does ontology try to solve ?

13. Better care Better information A general belief:

14. Being better informed Better care Better information ‘Information’ versus ‘informing’

15. A general belief: Being better informed • Concerns primarily the delivery ofinformation: Being better informed Better Better care information

16. A general belief: Being better informed • Concerns primarily the delivery of information: • Timely, • Where required (e.g. bed-side computing), • What is permitted, • What is needed. • Involves: • Connecting systems, • Making systems interoperable: • Syntactically, • Semantically.

17. HIMSS Integration and Interoperability Steering Committee • the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: • Uniform movement of healthcare data, • Uniform presentation of data, • Uniform user controls, • Uniform safeguarding data security and integrity, • Uniform protection of patient confidentiality, • Uniform assurance of a common degree of system service quality. Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

18. HIMSS Integration and Interoperability Steering Committee • the ability of health IS to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities, covering the following dimensions: • Uniform movement of healthcare data, • Uniform presentation of data, • Uniform user controls, • Uniform safeguarding data security and integrity, • Uniform protection of patient confidentiality, • Uniform assurance of a common degree of system service quality. No mention of information quality Interoperability Definition and Background. Approved by HIMSS Board of Directors., 06/09/05.

19. Ontolog-Discussion: Healthcare Informatics Landscape “The Business Value for Health IT Ontology Tools in Health Data and Information Systems: • Facilitates development of open-standards, interoperable networks of health information systems and EHRs, • Supports patient safety and goals to reduce medical errors in health care delivery, • Promotes data quality in the electronic exchange of health information.” Is about quality preservation Marc Wine, August 25, 2005

20. Patient-specific information Medical “knowledge” “Better Information” must cover … • EHR • PHR • Various modality related databases • Lab, imaging, … • Classification systems • Terminologies • Ontologies • Textbooks

21. Utility Coverage Authority Accuracy Objectivity Timeliness Understandability How to assess whether information is “better” ? Understandability Seems to have received most attention thus far

22. Terminologies Ontologies Coding & classification systems Effectiveness of ‘semantic’ technologies Utility Coverage Authority Accuracy Objectivity Understandability Timeliness Understandability

23. Caveat ! There are ontologies and “ontologies” !!!

24. Holds only for Realism-based ontologies Utility Coverage Authority Accuracy Objectivity Timeliness Understandability Realism-based Ontologies: reality as benchmark !

25. Part 2:The many faces of “ontologies”

26. The word ‘Ontology’ has two meanings • Ontology: the science of what entities exist and how they relate to each other. • An ontology:a representation of some domain which • (1) is intelligible to a domain expert, and • (2) is formalized in a way that allows it to support automatic information processing.

27. Within the context of ‘anontology’,the word ‘domain’ has two meanings • For most computer scientists: • A representation of an agreed upon conceptualization about which man and machine can communicate using an agreed upon vocabulary • For philosophical ontologists: • A representation of a portion of reality • Still allowing for a variety of entities to be recognised by one school and refuted by another one

28. Three types of ontologies • Upper level ontologies: • (should) describe the most generic structure of reality • Domain ontologies: • (should) describe the portion of reality that is dealt with in some domain • Special case: reference ontologies • Application ontologies: • To be used in a specific context and to support some specific application

29. The dispute between … • “Practical engineers”: • If it works for our purposes, it is ok • Good philosophers: • If it works always, it is ok, and • It can only always work if it represents the relevant portion of reality faithfully.

30. Most “ontologies” are concept-based • But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: • meaning shared in common by synonymous terms • idea shared in common in the minds of those who use these terms • unit of describing meanings knowledge • universal that what is shared by all and only all entities in reality of a similar sort Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

31. Most “ontologies” are concept-based • But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: • meaning shared in common by synonymous terms • idea shared in common in the minds of those who use these terms • unit of describing meanings knowledge • universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity:

32. Most “ontologies” are concept-based • But what the word ‘concept’ denotes, is never clarified and users of it often refer to different entities in a haphazard way: • meaning shared in common by synonymous terms • idea shared in common in the minds of those who use these terms • unit of describing meanings knowledge • universal that what is shared by all and only all entities in reality of a similar sort These views require the involvement of a cognitive entity: This view does not presuppose cognition at all

33. Concept-orientation in ontology has sad consequences • Too much effort goes into the specification business • OWL, DL-reasoners, translators and convertors, syntax checkers, ... • Too little effort into the faithfulness of the conceptualizations towards what they represent. • Pseudo-separation of language and entities • “absent nipple”, “planned act”, “prevented abortion” • Many ‘ontologies’ and ontology-like systems exhibit mistakes of various sorts.

34. Some examples • Gene Ontology • menopause part_of death * • SNOMED • both uterii is_a uterus * • UMLS • blood pressure is_a lab result • GALEN • vomitus contains carrot *corrected in most recent version

35. Snomed CT (July 2007):“fractured nasal bones”

36. bones nose fracture SNOMED-CT: abundance of false synonymy

37. A patient with a fractured nasal bone = A patient with a broken nose = A patient with a fracture of the nose Coding / Classification confusion

38. A patient with a fractured nasal bone A patient with a fractured nasal bone = = A patient with a broken nose A patient with a broken nose = = A patient with a fracture of the nose A patient with a fracture of the nose Coding / Classification confusion

39. Snomed CT (July 2007):“fractured nasal bones”

40. Snomed CT (July 2007): “fractured nasal bones” • Problems of multiple inheritance: • (1) “… ISA fracture of skull and facial bones” • Which facial bones are not part of the skull ? • If there would be non-skull facial bones, how many fractures are then required ? • (2) “… ISA fracture of mid-facial bones” • Which mid-facials bones or not facial bones ? • If all, then (1) is redundant • (3) “… ISA injury of nasal bones” • Are not all fractures “injuries’ and if not, why would then all nasal fractures be injuries ?

41. Mistakes in “ontologies”: a plurality of reasons • Lack of ontology development skills: • Domain experts and computer scientists are not trained in ontological analysis • Many ontology building manuals and ‘example ontologies’ contain fundamental mistakes • Wine ontology, pizza ontology, … • Confusing terminology • Class, instance, concept, … • Unwarranted faith in: • Ontology authoring tools (Protégé) • Ontology languages (OWL, UML, …)

42. Compare: ‘Death by UML Fever’ • It is important to emphasize that UML itself is not the direct cause of any maladies described herein. • Instead, UML is largely an innocent victim caught in the midst of poor process, no process, or sheer incompetence of its users. • UML sometimes does amplify the symptoms of some fevers as the result of the often divine-like aura attached to it. • For example, it is not uncommon for people to believe that no matter what task they may be engaged in, mere usage of UML somehow legitimizes their efforts or guarantees the value of the artifacts produced. Alex E. Bell. Death by UML Fever. Queue 2(1), March 2004, ACM Press, 72 – 80, 2004

43. Who would not be impressed ? • Fig. 10: BRIDG Comprehensive Class and attribute diagram - (Logical diagram), p99

44. HL7-RIM Animal • Definition: A subtype of Living Subject representing any animal-of-interest to the Personnel Management domain. LivingSubject • Definition: A subtype of Entity representing an organism or complex animal, alive or not. Smith B, Ceusters W. HL7 RIM: An Incoherent Standard, Stud Health Technol Inform. 2006;124:133-138.

45. Fundamental mistake in HL7 RIM • Act as statements or speech-acts are the only representation of real world facts or processes in the HL7 RIM. The truth about the real world is constructed through a combination (and arbitration) of such attributed statements only, and there is no class in the RIM whose objects represent "objective state of affairs" or "real processes" independent from attributed statements. As such, there is no distinction between an activity and its documentation. HL7 Reference Information Model V 02-14n11/1/2006 - Basis for Normative Edition 2007 Retrieved Oct 20, 2007 from http://www.hl7.org/Library/data-model/RIM/C30214n/rim0214nc.zip

46. Thus: watching sports is as good as doing sports HL7 as causal factor in pandemic obesity

47. AdverseEvent (BRIDG logical model p168, HE!) • Type: Class Assessment • Status: Proposed. Version 1.0. Phase 1.0. • Package: Clinical Research Activities Keywords: • Detail: Created on 05/24/2006. Last modified on 01/26/2007. • GUID: {CD620136-3CB9-4382-802B-F6CA82F98C10} • An observation of a change in the state of a subject that is assessed as being untoward by one or more interested parties within the context of protocol-driven research or public health.

48. Being critical ≠ being negative RFQ-NCI-60001-NG: Review of NCI Thesaurus and Development of Plan to Achieve OBO-Compliance Grant to Apelon (H. Solbrig) to improve NCIT

49. Will HL7 RIM become better ? • cc August 31, 2007To: HL7 Co-chairsFR: Chuck Meyer, Chair, HL7 Board of DirectorsRE: Version 3 Editing projectDear Co-chairs:The HL7 Board of Directors has identified the need to review the existing V3 portfolio of standards for clarity, consistency, and ease of use. To that end, it has approved a renewal of contract to review existing documentation, identify issues, and propose tactics for making the V3 standard and supporting materials easier to understand and use.

50. Will ontologies in general become better ? • There is hope !