1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Post-hoc interpretation of mutually incoherent information models: the role and benefits of realism-based ontology. September 1, 2009, Sarajevo Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is an Information Model? An information model is: –‘a representation of concepts, relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse that satisfy some industry need’. A ‘quality’ information model is: –‘an information model that is complete, sharable, stable, extensible, well-structured, precise, and unambiguous’. Y. Tina Lee. Information Modeling: From Design To Implementation. http://www.mel.nist.gov/msidlibrary/doc/tina99im.pdf

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why are there so many IM but no ‘quality’ IM? An information model is: –‘a representation of concepts, relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse that satisfy some industry need’. many domains, different needs within the same domain, selection of ‘concepts’, ‘relationships’, … relevant for the needs. can never be complete

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why are there so many? Blobel B, Pharow P: Analysis and Evaluation of EHR Approaches. MIE 2008, 26-28 May 2008, Göteborg, Sweden

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Why are so many incompatible? An information model is: –‘a representation of concepts, relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse that satisfy some industry need’. confusion about: –what ‘concepts’ and ‘relationships’ are, –whether a ‘domain of discourse’ is: »what is or can be said, versus, »that about what something is or can be said, –‘semantics’. can never be unambiguous and precise

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U No serious scholar should work with ‘concepts’

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Slow penetration of the idea …

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U More serious scholars become convinced … what is a concept description a description of?

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Crouching patient, Hidden data Dear Mr. President, a Data Model for my Electronic Health Records Nearly Killed Me Joe Bugajski Mr. President, your historic economic stimulus package (The American Recovery and Reinvestment Act of 2009), appropriated $19 billion for health information technology ("Technology Gets a Piece of Stimulus", New York Times, January 25, 2009). This week, your Director of the Office of Management and Budget (OMB), Peter Orszag, shockingly held that half of the US operating deficit can disappear with lower healthcare costs and these will obtain through electronic healthcare records (Daily Show, 6 April 2009). Today, the Wall Street Journal wrote that you proudly proclaimed that electronic healthcare records for the members of the US military, like my youngest son, and continuing through Veterans Affairs "will provide a 'seamless system' to facilitate information sharing and cut red tape, ending the need for veterans to transfer military records to receive benefits". Whereas Star Wars and Star Gate movie fantasies provide great fun, witnessing you, a world leader, spew delusional visions of a nation-covering, interoperable, secure, private, reliable, accurate, and instantaneous electronic healthcare data network is at best terrifying and at worst pernicious.

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two major problems in information modeling (1) Tyranny of the use case: –‘if most people wrongly believe that crocodiles are a kind of mammal, then most people would find it easier to locate information about crocodiles if it were located in a mammals grouping, rather than where it factually belonged’. (p89) Huhns MN, Stephens LM. Semantic Bridging of Independent Enterprise Ontologies. In: Kosanke K, ed. Enterprise Inter- and Intra- Organizational Integration: Building International Consensus. Boston, MA: Kluwer Academic Publishers; 2002:83 – 90.

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two major problems in information modeling (2) Assumption of inherent classification: –we identify every thing by a specific class to which it belongs; and –there exists a preferred set of classes to describe a domain. Sad consequences: –‘the complexity of problems in schema integration, schema evolution, and interoperability, –violates philosophical and cognitive guidelines on –classification and is, therefore, –inappropriate in view of the role of data modeling in representing knowledge about application domains’. Parsons, J. and Wand, Y. Emancipating instances from the tyranny of classes in information modeling. ACM Trans. Database Syst. 25, 2 (June 2000), 228–268.

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Both problems have a common ground Confusion brought about by the (dis)similarity between information and what the information is about: space time } } } } anamnesis clinical examination diagnosis therapeutic interventions

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U OpenEHR Information Model T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008 switching between data structures and what the data are about

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U HL7 RIM (core)

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U HL7 EHR structure For HL7, a document is an act !

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The right sort of ontology can help … In computer science: –a formal specification of a conceptualization

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Not the wrong sort: linking the wrong way… Martínez-Costa, Menárguez-Tortosa, Fernández-Breis, Maldonado. A model-driven approach for representing clinical archetypes for Semantic Web environments. Journal of Biomedical Informatics 42(1), February 2009, 150-164

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Not the wrong sort: not every term denotes (1) ‘A well-known problem in clinical information recording is the problem of assigning “status”, including variants like “actual value of P” (P stands for some phenomenon), “family history of P”, “risk of P”, “fear of P”, as well as negation of any of these, i.e. “not/no P”, “no history of P” etc. A proper analysis of these so called statuses shows that they are not “statuses” at all, …’ –this is so true ! ‘… but different categories of information as per the ontology. The common statement types mentioned here are mapped as follows: actual value of P ⇒ Observation (of P); no/not P ⇒ Observation (of excluded P or types of P, e.g. allergies). family history of P ⇒ Evaluation (that patient is at risk of P); no family history of P ⇒ Evaluation (that P is an excluded risk); risk of P ⇒ Evaluation (that patient is at risk of P); no risk of P ⇒ Evaluation (that patient is not at risk of P); fear of P ⇒ Observation (of FEAR, with P mentioned in the description);’ –some of these P’s do not exist at all ! T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Not the wrong sort: not every term denotes (2) ‘Another set of statement types that can be confused in systems that do not properly model information categories concern interventions, e.g. “hip replacement (5 years ago)”, “hip replacement(planned)”, “hip replacement (ordered for next tuesday 10 am)”.’ –this is so true ! ‘Ambiguity is removed here as well,with the use of the correct information categories, e.g. (I stands for an intervention): I (distant past/unmanaged/passively documented) – ⇒ Observation (of I present in patient); I (recent past) ⇒ Action (of I having been done to/for patient); I (proposed) ⇒ Evaluation, subtype Proposal (of I suggested/likely for patient); I (ordered) ⇒ Instruction (of I for patient for some date in the future).’ –some of these I’s do not exist at all ! T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Schemas like this need to be corrected T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An appropriate view on reality … T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An appropriate view on reality … K Bernstein, M Bruun-Rasmussen, S Vingtoft, SK Andersen, C Nøhr. Modelling and implementing electronic health records in Denmark. International Journal of Medical Informatics (2005) 74, 213—220.

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U … can still lead to an erroneous ‘ontology’ T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008 Clinical Investigator Recording (CIR) ontology

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U … and to leaving observed distinctions implicit ‘not knowing’ or ‘not specifying’ something is not a property of that what is not known or that about what a specification should be given, but a property of the agent involved. T Beale, S Heard, D Kalra, D Lloyd. The openEHR Architecture Support Terminology. Revision: 1.0.1; 04 Aug 2008

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The right sort of ontology can help … In computer science: –a formal specification of a conceptualization leads to bad ontologies In philosophy: –a representation of reality In the OBO Foundry: – a representational artifact which is intended to represent universals and some defined classes. foundation in philosophical realism

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1.There is an external reality which is ‘objectively’ the way it is; 2.That reality is accessible to us; 3.We build in our brains cognitive representations of reality; 4.We communicate with others about what is there, and what we believe there is there. Basic axioms 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

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Two sorts of representations L1 R L2L3 beliefs symbolizations ‘about’

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U So this is the right framework, though not well implemented T Beale, S Heard, D Kalra, D Lloyd. EHR Information Model. Revision: 5.1.1. 16 Aug 2008

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The three levels applied to diabetes management 1. First-order reality 2. Beliefs (knowledge) GenericSpecific DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG me my blood glucose level my NIDDM my doctor my doctor’s computer 3. Representation ‘person’‘drug’‘insulin’‘W. Ceusters’‘my sugar’

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Distinction between Ontologies and Information Models Ontologies should represent only what is always true about the entities of a domain (whether or not it is known to the person that reports), Information models (or data structures) should only represent the artifacts in which information is recorded. –Such information may be incomplete and error-laden which needs to be accounted for in the information model rather than in the ontology itself.

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U instanceOf Realism-based ontology basics (1) some continuant particular some continuant universal instanceOf at t some occurrent particular some occurrent universal

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Sorts of relations U1U2 P1 P2 UtoU: isa, partOf(UU), … PtoU: instanceOf, lacks, denotes(PU)… PtoP: partOf, denotes, …

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U instanceOf at t 2 instanceOf at t 1 instanceOf at t 2 The importance of temporal indexing this-1’s stomach benign tumor instanceOf at t 1 this-4 malignant tumor partOf at t 1 stomach partOf at t 2

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U tt t instanceOf The essential pieces material object spacetime region me some temporal region my life my 4D STR some spatial region history spatial region temporal region dependent continuant some quality located-in at t … at t participantOf at toccupies projectsOn projectsOn at t

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Diseases : L1  Diagnoses L2/L3 Diagnosis: A configuration of representational units; Believed to mirror the person’s disease; Believed to mirror the disease’s cause; Refers to the universal of which the disease is believed to be an instance. #56 John’s Pneumonia #78 John’s portion of pneumococs Pneumococcal pneumonia caused by Instance-of at t1 Disease isa

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some motivations and consequences (2) A diagnosis can be of level 2 or level 3, i.e. either in the mind of a cognitive agent, or in some physical form. Allows for a clean interpretation of assertions of the sort ‘these patients have the same diagnosis’:  The configuration of representational units is such that the parts which do not refer to the particulars related to the respective patients, refer to the same portion of reality.

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Perfect ‘semantic’ tools are useless … … if data captured at the source is not of high quality Prevailing EHR systems don’t allow data to be stored at acceptable quality level: –No formal distinction between disorders and diagnosis –Messy nature of the notions of ‘problem’ and ‘concern’ –No unique identification of the entities about which data is stored Unique IDs for data-elements cannot serve as unique IDs for the entities denoted by these data-elements

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Making existing EHR systems RT compatible

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations in Referent Tracking Portion of Reality Entity Particular Universal Defined class Representation Non-referring particular Denotator IUI RT-tuple corresponds-to Configuration represents CUIUUI denotes is about Representational unit denotes contains class Extension … … … Relation RUI denotes Information content ent.

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance: the way RT-compatible systems ought to interact with representations of generic portions of reality instance-of at t #105 caused by

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MedtuityEMR Patient’s Encounter Document

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusions Current information modeling practices hamper semantic interoperability; Prevailing approaches to ‘ontology’ aren’t much better; There is improvement however: –some acknowledge the problem, but either don’t find the solution, or don’t wish to use the solution. It takes courage indeed …