Realist Ontology for Electronic Healthcare Records

1. Realist Ontology forElectronic Healthcare Records Dr. Werner Ceusters, MD European Centre for Ontological Research Universität des Saarlandes Saarbrücken, Germany

2. Electronic Healthcare Record • A collection of electronic data about a single patient relevant for his health. • Exists in many forms with various degrees of automation: • Scanned documents • “Machine readable” documents (text, XML,...) • Personal (GP), departmental, hospital wide, ... • Multiple challenges: • Deontological: safety, security, confidentiality • Technical: structure and architecture, communication • Pragmatic: getting them used • Machine interpretable: triggering and allerts

3. Focus of this presentation • Role of ontology in maximizing the potential uses of the EHCR ... : • For the patient’s own benefit • For the advance of science • Hence, for the health of the population • ... by making the contents understandable both for humans and machines.

4. Understanding content (1) “John Doe has a pyogenic granuloma of the left thumb” John Doe has a pyogenic granuloma of the left thumb

5. Understanding content (2) • <record> • <patient>John Doe</patient> • <diagnosis>pyogenic granuloma of the left thumb</diagnosis> • </record> • <record> • <subject>John Doe </subject> • <diagnosis>pyogenic granuloma of the left thumb </diagnosis> • </record>

6. Understanding content (3) • <129465004> • <116154003>John Doe</116154003> • < 8319008 > 17372009 • <findingsite> 76505004 • <laterality>7771000</laterality> • </findingsite> • </ 8319008 > • </129465004>

7. Current “state of the art” onmeaning in healthcare informatics • A pervasive bias towards “concepts” • Content wise: • Work based on ISO/TC37 that advocates the Ogden-Richards theory of meaning • Corresponds with a linguistic reading of “concept” • Architecture wise: • In Europe: work based on CEN/TC251 WG1 & WG2 that follow ISO/TC37 • In the US: HL7, inspired by Speech Act Theory • “Concepts” used as elements of information models, hence mixing a linguistic and engineering reading.

8. Topics • Meaning and understanding • Biomedical terminologies and concept systems • EHCR architecture • Problems of the concept-based approach • Ontology as part of the solution

9. Dyadic models of “meaning” • Saussure (language philosopher): • signe / signifiant (sign/concept) • Ron Stamper (information scientist): • thing-A STANDS-FOR thing-B • Major drawback: • excludes the “referent” from the model, i.e. that what the sign/symbol/word/... denotes

10. Triadic models of meaning: The Semiotic/Semantic triangle Reference: Concept / Sense / Model / View / Partition Sign: Language/ Term/ Symbol Referent: Reality/ Object

11. Aristotle’s triadic meaning model Words spoken are signs or symbols (symbola) of affections or impressions (pathemata) of the soul (psyche); written words (graphomena) are the signs of words spoken (phoné). As writing (grammatta), so also is speech not the same for all races of men. But the mental affections themselves, of which these words are primarily signs (semeia), are the same for the whole of mankind, as are also the objects (pragmata) of which those affections are representations or likenesses, images, copies (homoiomata). Aristotle, 'On Interpretation', 1.16.a.4-9, Translated by Cooke & Tredennick, Loeb Classical Library, William Heinemann, London, UK, 1938. pathema semeia  gramma/ phoné pragma

12. my your understanding understanding Richards’ semantic triangle • Reference (“concept”): “indicates the realm of memory where recollections of past experiences and contexts occur”. • Hence: as with Aristotle, the reference is “mind-related”: thought. • But: not “the same for all”, rather individual mind-related reference symbol referent

13. R1 R2 R3 mole (skin lesion) mole (unit) mole (animal) Don’t confuse with homonymy ! “mole”

14. One concept understanding of x understanding of y referent symbol Different thoughts Homonymy R2 R3 R1 mole “skinlesion” mole “unit” “mole” mole “animal”

15. And by the way, synonymy... the Aristotelian view Richards’ view “sweat” “sweat” “perspiration” “perspiration”

16. Frege’s view • “sense” is an objective feature of how words are used and not a thought or concept in somebody’s head • 2 names with the same reference can have different senses • 2 names with the same sense have the same reference (synonyms) • a name with a sense does not need to have a reference (“Beethoven’s 10th symphony”) sense name reference (=referent)

17. conception concept actor definition representation referent term referent Tetrahedric extensions CEN/TC251 ENV 12264 FRISCO model (information science)

18. Nurse reaches for the clamp Haydom Lutheran Hospital, Tanzania The theory in practice He wants me to hold that kocher Oops, this is too slippery to hold any longer Take this, please !

19. = ? Issues in communication Take the kocher, please.

20. Concept-based Terminology kocher

21. Kocher clamp in SNOMED-CT

22. Living subject person nonPersonLS Place Organisation Material ManufacteredM Device Container Employee Patient LicensedEntity Access Managed Participation PatientEncounter ControlAct Supply Diet WorkingList Procedure Observation PublicHealthcare DiagnosticImage DeviceTask SubstanceAdministration FinancialContract Account FinancialTransaction InvoiceElement Entity Role Participation Role Link ActLink Act Language Communication Communication Function Context Structure HL7-RIM From Speech Acts to Information Model

23. CEN ENV 13606 CEN’s view on reality andthe healthcare record “The real world of health and health care is made up of individual clinical situations (of which the participants are called “associate topics”), that are described by an EHCR author as clinical statements. Within an EHCR system each clinical statement will be expressed as an elementary healthcare record entry.”

24. Selected Component Complex Folder Architectural Component Composition Headed Section Original Component Complex Record Component Cluster Link Item Root Architectural Component Data Item Specialisation Data Item Specialisation Data Item Specialisation Data Item Data Item Specialisation CEN ENV 13606 EHR Extended Architecture Elementary healthcare record entries

25. OCC specialisation Description Examples of Component Names Folder High-level subdivisions of the entire EHCR for a patient, usually grouping entries over long time-spans within one organisation or department, or for a particular health problem GP Record Inpatient Stay Diabetes Care Record Composition A set of record entries relating to one time and place of care delivery; grouped contributions to an aspect of health care activity; composed reports and overviews of clinical progress Consultation Operation Notes Discharge Summary Vital Signs Chart Headed Section Sub-divisions used to group entries with a common theme or derived through a common healthcare process Past Medical History Presenting Symptoms Examination Findings Treatment Plan Cluster Low-level aggregations of elementary entries (Record Items) to represent a compound clinical concept Heart Sounds Differential White Cell Count Insulin Schedule CEN ENV 13606 Types of Original Component Complexes

26. Component unique identifier Language Related date and time 0..1 1 Component Statusinformation 0..n Related healthcare agent 1 Architectural Component 1 0..n Component name structure 1 Subject of care identifier Originating Healthcare agent 1 0..n 1 Originating date and time Distribution Rule Reference CEN ENV 13606 Architectural Component Attributes Refer to situations and statements and rely on terminology

27. CEN ENV 13606 “Archetypes” • clinical situation • pertains to body component, product, environment • has context facet subject of information, process status, role for dates • has information qualifier knowing mode • has information source actor • has qualifier communication modality • has qualifier relevance • has role role for clinical situation • Is stated by actor, healthcare organisation • has temporal marker timing marker To be used to build terminologies that may be used for the EHR

28. urine bladder gallbladder inflammation gall bladder urinary bladder gall biliary cystitis urine inflammation gall cystitis urinary bladder inflammation gallbladder gallbladder inflammation urinary bladder inflammation Structure of concept-based terminologies

29. “Axiom” • Concept-based terminology (and standardisation thereof) is there as a mechanism to improve understanding of messages, originally by humans, now also by machines. • It is NOT the right device to explain why reality is what it is, how it is organised, etc., (although it is needed to allow us to communicate on insights thereof).

30. Why not ? • Ad hoc readings of statements of the type C1-relationship-C2 • Human has-part head // Human has-part finger • California is-part-of United States // California isa name • labial vein isa vein of head // labial vein isa vulval vein • Concepts not necessarily correspond to something that (will) exist(ed) • Sorcerer, unicorn, leprechaun, ... • Definitions set the conditions under which terms may be used, and may not be abused as conditions an entity must satisfy to be what it is • Language can make strings of words look as if it were terms • “Middle lobe of left lung”

31. What is then the right way ? Realist Ontology !

32. If, later, you can remember just one thing of this presentation, then make sure it is this one: If somebody uses the word “ontology”, ALWAYS let him be specific about what he understands by it.

33. The O-word N. Guarino, P. Giaretta, "Ontologies and Knowledge Bases: Towards a Terminological Clarification". In Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, N. Mars (ed.), pp 25-32. IOS Press, Amsterdam, 1995.

34. “Ontology” • In Information Science: • “An ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents.” • In Philosophy: • “Ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality.”

35. The T-Box has no meaning without the A-Box to be used by software (agents) in a machine, and NOT by humans • does not rely on what people know or think, hence no “concepts”, not just epistemology • instance driven, although it accepts universals that are not instanciated • does not “create” or “constrain” reality My use of the word ontology a for a computer understandable representation of some pre-existing domain of REALITY, reflecting the properties of the objects within its domain in such a way that there obtain substantial and systematic correlations between reality and the ontology itself. modified from Barry Smith

36. This surgeon with some relations Part of This mask This amputation stump This hand Haydom Lutheran Hospital, Tanzania Back to to the operating theatre A lot of objects present

37. This wound being closed by holding ... with some relations That woundfluid drained Part of This kocher being held in that hand of that surgeon Back to to the operating theatre A lot of processes going on Haydom Lutheran Hospital, Tanzania

38. epistemology ontology “Axiom” • If the picture is not a fake, we (i.e., me and this audience) KNOW that that hand, that surgeon, ... EXIST(ed), i.e. ARE (were) REAL. • But importantly: that hand, surgeon, kocher, mask, ... EXIST(ed) independent of our knowledge about them and also the part-relationship between that hand and that surgeon, and the processes going on, are (were) equally real.

39. (Simplified) Logic of classes • primitive: • entities: particulars versus universals • relation inst such that: • all classes are universals; all instances are particulars • some universals are not classes, hence have no instances: pet, adult, physician • some particulars are not instances; e.g. some mereological sums • subsumption defined resorting to instances:

40. Basic Formal Ontology Basic Formal Ontology consists in a series of sub-ontologies (most properly conceived as a series of perspectives on reality), the most important of which are: • SnapBFO, a series of snapshot ontologies (Oti ), indexed by times • SpanBFO a single videoscopic ontology (Ov). Each Oti is an inventory of all entities existing at a time. Ov is an inventory (processory) of all processes unfolding through time.

42. Take home message:a need for a clean separation of knowledge AND ontology Pragmatic knowledge: what users usually say or think, what they consider important, how to integrate in software Alan Rector Knowledge of classification and coding systems: how an expression has been classified by such a system Knowledge of definitions and criteria: how to determine if a concept applies to a particular instance Surface linguistic knowledge: how to express the concepts in any given language Conceptual knowledge: the knowledge of sensible domain concepts Ontology: what exists and how what exists relates to each other