The Future of Health Information

1. The Future of Health Information Barry Smith Ontology Research Group Center of Excellence in Bioinformatics and Life Sciences University at Buffalo ontology.buffalo.edu/smith

2. Collaborations • National Center for Biomedical Ontology (http://NCBO.us) • WHO Collaborating Center for Terminology • Cleveland Clinic Semantic Database • SNOMED CT – Disease Ontology • German national Electronic Health Record initiative [Health Version 11]

3. Overview of this talk • The role of ontology • The role of HL7 • The future of health information

4. Overview of this talk • The role of ontology • The role of HL7 • The future of health information

6. we need to know where in the body, where in the cell we need to know what kind of disease process we need semantic annotation of data = we need ontologies

9. Ontologies are systems of terms for annotating dataThey are controlled vocabularies designating the types of entities in realityData designate the instances of these types

10. The Gene Ontology: A set of standardized textual descriptions of • cellular locations • molecular functions • biological processes • used to annotate the entities represented in the major biochemical databases • thereby creating integration across these databases

11. what cellular component? what molecular function? what biological process?

12. The process of data annotation • yields a slowly growing computer-interpretable map of biological reality within which major databases are automatically integrated in semantically searchable form

13. need to extend the methodology to other domains, including clinical medicine  need disease, symptom (phenotype) ontologies But now

14. need for prospective standards to ensure mutual consistency and high quality of clinical counterparts of GO need to ensure consistency of the new clinical ontologies with the basic biomedical sciences if we do not start now, the problem will only get worse The Problem

15. The Solution • establish common rules governing best practices for creating ontologies and for using these in annotations • apply these rules to create a complete suite of orthogonal interoperable biomedical reference ontologies

16. First step (2003) • a shared portal for (so far) 58 ontologies • (low regimentation) • http://obo.sourceforge.net NCBO BioPortal

18. id: CL:0000062 name: osteoblast def: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." is_a: CL:0000055 relationship: develops_from CL:0000008 relationship: develops_from CL:0000375 Second step (2004):reform efforts initiated, e.g. linking GO to other OBO ontologies to ensure interoperability GO + Cell type = Osteoblast differentiation: Processes whereby an osteoprogenitor cell or a cranial neural crest cell acquires the specialized features of an osteoblast, a bone-forming cell which secretes extracellular matrix. New Definition

19. Third step (2006) The OBO Foundryhttp://obofoundry.org/

20. The OBO Foundry • a family of interoperable gold standard biomedical reference ontologies to serve the annotation of • scientific literature • model organism databases • clinical data • experimental results

21. Compare the UMLS Metathesaurus a system of post hoc mappings between independent source vocabularies built by trained experts massively useful for information retrieval and information integration creates out of literature a semantically searchable space

22. for UMLS local usage respected regimentation frowned upon cross-framework consistency not important no concern to establish consistency with basic science different grades of formal rigor, different degrees of completeness, different update policies no path towards improvement no path towards support for logical reasoning

23. The OBO Foundry is a prospectivestandard designed to guarantee interoperability of ontologies from the very start (contrast to: post hoc mapping) established March 2006 12 initial candidate OBO ontologies – focused primarily on basic science domains several being constructed ab initio now 16 ontologies

25. Building out from the original GO

26. The vision OBO low-regimentation ontology portal OBO Foundry high-regimentation collaborative initiative to create a gold standard suite of interoperable ontologies

27. Ontologies under construction • Common Anatomy Reference Ontology • Disease Ontology (DO) [SNOMED CT] • Biomedical Image Ontology (BIO) • Environment Ontology (EnvO) • Biobank Ontology (BrO) • Clinical Trial Ontology (CTO) [with WHO Global Trial Bank, Immune Tolerance Network, ACGT Advancing Genomics Clinical Trials in Cancer EU IP]

28. Clinical Trial Ontology • part of a larger project called the Ontology for Biomedical Investigations (OBI)

29. OBI controlled vocabulary for biomedical investigations including • protocols • instrumentation • material • data • types of analysis and statistical tools applied to the data http://obofoundry.org/

30. Clinical Trial Ontology • To serve merger of data schemas • To serve flexibility of collaborative clinical trial research • To serve design and management of clinical trials • To serve data access and reuse – send me all trials which ...

31. Ontology vs. Database Schema • Separate development of data schemas and ‘information models’ (HL7) and terminologies such as SNOMED CT • the two do not work together

32. Ontology vs. Database Schema • diabetes => disease • diabetes => string • temperature => quality • temperature => integer

33. CTO

34. CTO Continuant

35. CTO Occurrent

36. Clinical Trial Ontology Working Group • http://www.bioontology.org/wiki/ • Workshop on May 16-17, 2007

37. The role of ontology • The role of HL7 • The future of health information

38. HL7 V3 “the data standard for biomedical informatics” • http://aurora.regenstrief.org/~schadow/ HL7TheDataStandardForBiomedicalInformatics.ppt

39. HL7 V2 a workable messaging standard faced the problem of local dialects seeks to solve this problem by having all HL7 artifacts conform to a single ‘Reference Information Model’ (the RIM) HL7 V3

40. is there a single, successful RIM-implementation? After 10 years?And many attempts?And gigantic investments of energy and funding?

41. There are clear examples of failure of billion-dollar implementations resting on the RIM and of programmers involved in such failures who are tearing out their hair, and blaming HL7

42. Is it justified, in these circumstances, to promote HL7 V3 as an ISO Standard in the domain of patient care?

43. One indispensable foundation for a successful standard a correct and uniform interpretation of its basic terms • Act • Participation • Entity • Role • ActRelationship • RoleLink

44. Demonstrably, the HL7 community does not understand its own basic terms • Sometimes ‘Act’ means information about an act • Sometimes ‘Act’ means real-world action • Sometimes ‘Act’ means a mixture of the above • Sometimes in the very same sentence

45. Consequences of unclarity here • Different user groups have interpreted the same classes in different ways • Different message specifications used different interpretations • This recreates interoperability problems • Can we be sure that these problems will not lead to incidents relevant to patient safety?

46. Even with clarity – and clear documentation – the RIM would still be in bad shape http://hl7-watch.blogspot.com/

47. Where are diseases • Acts ? • Things, Persons, Organizations ? • Participations ? • Roles ? • ActRelationships ? • RoleLinks ?

48. The HL7 Clinical Genomic Standard • defines an allele as the observation of an allele • defines a phenotype as the observation of an observation

49. The $ 35 bn. NHS Program “Connecting for Health” • has applied the RIM rigorously, using all the normative elements, and it discovered that it needed to create dialects of its own to make the V3-based system work for its purposes (it still does not work)

50. The RIM has no coherent answer • Basic categories cannot be agreed upon even for common phenomena like snakebites. • HL7 V3 dialects are formed – and the RIM does not do its job.