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  • 1. CHSS Data Center Work Weekend Ontology, Terminology, and Cardiovascular Surgery Nov 21, 2008 – Toronto, Canada Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences, and National Center for Biomedical Ontology, University at Buffalo, NY, USA
  • 2. Short personal history 1977 1989 1992 1998 2002 2004 2006 1993 1995 1959 - 2008
  • 3. Structure of this presentation
    • Data and where they (should) come from
    • Realism-based ontology
    • Referent Tracking
    • How to build ontologies from terminologies
    • How to link to patient data
    • How can disparate views been accommodated
  • 4. The central hypothesis
    • For disease registries to facilitate meaningful multi-institutional outcomes analysis, there must be:
        • Common language = nomenclature,
        • Mechanism of data collection (database or registry) with an established uniform core data set,
        • Mechanism of evaluating case complexity,
        • Mechanism to ensure and verify data completeness and accuracy,
        • Collaboration between medical subspecialties.
    JP Jacobs et.al. Nomenclature and Databases — The Past, the Present, and the Future: A Primer for the Congenital Heart Surgeon. Pediatr Cardiol (2007)
  • 5. Would this do ?
    • For disease registries to facilitate meaningful multi-institutional outcomes analysis, there must be:
        • Whatever sort of Common language = nomenclature,
        • Whatever sort of Mechanism of data collection (database or registry) with an established uniform core data set,
        • Whatever sort of Mechanism of evaluating case complexity,
        • Whatever sort of Mechanism to ensure and verify data completeness and accuracy,
        • Whatever sort of Collaboration between medical subspecialties.
    ?
  • 6. The answer is clearly …
    • … No !
    • There are
      • many such animals
      • of various sorts,
      • which all have shortcomings,
      • and therefore lead to the creation of even more such animals,
      • which finally end up suffering – more or less - from the same flaws.
  • 7. Mesh 2008: congenital heart defects
    • All MeSH Categories
    • Diseases Category
    • Cardiovascular Diseases
    • Cardiovascular Abnormalities
    • Heart Defects, Congenital
    All MeSH Categories Diseases Category Congenital, Hereditary, and Neonatal Diseases and Abnormalities Congenital Abnormalities Cardiovascular Abnormalities Heart Defects, Congenital All MeSH Categories Diseases Category Cardiovascular Diseases Heart Diseases Heart Defects, Congenital Alagille Syndrome Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum ... Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum ... Alagille Syndrome Aortic Coarctation Arrhythmogenic RV Dysplasia Cor Triatriatum ... ?
  • 8. SNOMED-CT version 2008.01.7AC
  • 9. SNOMED-CT’s ‘ Fallot’s trilogy ’ versus ‘ Fallot’s triad ’
  • 10. Trilogy of Fallot
    • Definition:
      • Combination of pulmonary valve stenosis and atrial septal defect with right ventricular hypertrophy .
    • Typical representational mistake:
      • From (correctly, if the definition is right) :
        • ‘ a patient which has Fallot’s triad
          • has a pulmonary valve stenosis,
          • has an atrial septal defect,
          • has a right ventricular hypertrophy.’
      • To (wrong, even if the definition is right) :
        • ‘ a Fallot’s triad
          • is a pulmonary valve stenosis,
          • is an atrial septal defect,
          • is a right ventricular hypertrophy.’
  • 11. In general: some alarming publications
    • Why most published research findings are false .
    • Ioannidis JPA ( 2005 ). PLoS Med 2(8): e124.
      • Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts.
    • Why Current Publication Practices May Distort Science .
    • Young NS, Ioannidis JPA, Al-Ubaydli O ( 2008, October 7 ) PLoS Med 5(10): e201. doi:10.1371/journal.pmed.0050201.
      • Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland,
  • 12. Key question:
    • Why is this ?
  • 13. ‘ The spectrum of the Health Sciences ’ http://www.uvm.edu/~ccts Turning data in knowledge
  • 14. What is missing here ? http://www.uvm.edu/~ccts Turning data in knowledge ?
  • 15. Source of all data Reality !
  • 16. Today’s data generation and use observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome
  • 17. Key components data information knowledge hypotheses
    • Players
    • HIT
    • Outcomes
    generates generates generates influences about representation reality
  • 18. Current deficiencies
    • At the level of reality :
      • Desired outcomes different for distinct players
        • Competing interests
      • Multitude of HIT applications and paradigms
    • At the level of representations:
      • Variety of formats
      • Silo formation
      • Doubtful semantics
    • In their interplay:
      • Very poor provenance or history keeping
      • No formal link with that what the data are about
      • Low quality
  • 19. Where should we go?
  • 20. Ultimate goal (at least mine) A digital copy of the world
  • 21. Requirements for this digital copy
    • R1: A faithful representation of reality
    • R2 … of everything that is digitally registered,
            • what is generic  scientific theories
            • what is specific  what individual entities exist and how they relate
    • R3: … throughout reality’s entire history,
    • R4 … which is computable in order to …
            • … allow queries over the world’s past and present,
            • … make predictions,
            • … fill in gaps,
            • … identify mistakes,
            • ...
  • 22. In fact … the ultimate crystal ball
  • 23. The ‘binding’ wall How to do it right ? A cartoon of the world
  • 24. Major problems
    • A mismatch between what is - and has been - the case in reality, and representations thereof in:
        • (generic) Knowledge repositories, and
        • (specific) Data and Information repositories.
    • An inadequate integration of a) and b).
    Solutions Philosophical realism Referent Tracking P h i l o s o p h y H I T Realism-based Ontology
  • 25. Realism-based Ontology
  • 26. ‘ Ontology ’: one word, two meanings
    • In philosophy:
      • Ontology (no plural) is the study of what entities exist and how they relate to each other;
    • In computer science and (biomedical informatics) applications:
      • An ontology (plural: ontologies ) is a shared and agreed upon conceptualization of a domain;
    • Our ‘ realist ’ view within the Ontology Research Group combines the two:
      • We use realism , a specific theory of ontology , as the basis for building high quality ontologies , using reality as benchmark.
  • 27. Realism-based ontology
    • Basic assumptions:
      • reality exists objectively in itself, i.e. independent of the perceptions or beliefs of cognitive beings;
      • reality, including its structure, is accessible to us, and can be discovered through (scientific) research;
      • the quality of an ontology is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality.
  • 28. However: the dominant view in Comp Sc is conceptualism Semantic Triangle concept object term Embedded in Terminology
  • 29. The concept-based view P P P P P P P P P P P P isa class
  • 30. The realism-based view e.g. human e.g. all humans e.g. all humans in this room P P P P P P P P P P P P universal instance-of extension-of member-of class Defined class
  • 31. Ontology Terminology
  • 32. The ‘terminology / ontology divide’
    • Terminology:
      • solves certain issues related to language use , i.e. with respect to how we talk about entities in reality (if any);
        • Relations between terms / concepts
      • does not provide an adequate means to represent independent of use what we talk about, i.e. how reality is structured;
        • Women, Fire and Dangerous Things (Lakoff).
    • Ontology (of the right sort) :
      • Language and perception neutral view on reality.
        • Relations between entities in first-order reality
  • 33. Terminological versus Ontological approach
    • The terminologist defines:
      • ‘ a clinical drug is a pharmaceutical product given to (or taken by) a patient with a therapeutic or diagnostic intent ’. ( RxNorm)
    • The ontologist thinks:
      • Does ‘ given ’ includes ‘ prescribed ’?
      • Is manufactured with the intent to … not sufficient?
        • Are newly marketed products – available in the pharmacy, but not yet prescribed – not clinical drugs?
        • Are products stolen from a pharmacy not clinical drugs?
        • What about such products taken by persons that are not patients?
          • e.g. children mistaking tablets for candies.
  • 34. Cardiovascular surgery examples
    • Systemic venous anomaly, SVC, Bilateral SVC
    • Systemic venous anomaly, SVC, Bilateral SVC, Innominate absent
    • Systemic venous anomaly, SVC, Bilateral SVC, Innominate present
    • VA valve overriding
    • VA valve overriding, Aortic valve
    • VA valve overriding, Left sided VA Valve
    • VA valve overriding, Pulmonary valve
    • VA valve overriding, Right sided VA Valve
    • VA valve overriding-modifier for degree of override, Override of VA valve ,50%
    • VA valve overriding-modifier for degree of override, Override of VA valve .90%
    • VA valve overriding-modifier for degree of override, Override of VA valve 50–90%
    JP. Jacobs et.al. The nomenclature, definition and classification of cardiac structures in the setting of heterotaxy. Cardiol Young 2007; 17(Suppl. 2): 1–28
  • 35. The semantic triangle revisited concepts terms objects Representation and Reference First Order Reality about terms concepts
  • 36. Terminology Realist Ontology Representation and Reference First Order Reality about objects terms concepts representational units universals particulars
  • 37. Terminology Realist Ontology Representation and Reference First Order Reality about representational units universals particulars objects terms concepts
  • 38. Terminology Realist Ontology Representation and Reference First Order Reality about universals particulars objects terms concepts cognitive units communicative units representational units
  • 39. Terminology Realist Ontology Representation and Reference First Order Reality universals particulars cognitive units representational units communicative units Three levels of reality in Realist Ontology (1) Entities with objective existence which are not about anything (2) Cognitive entities which are our beliefs about (1)
    • Representational units in various
    • forms about (1), (2) or (3)
  • 40. The three levels in medical practice 1. First-order reality 2. Beliefs (knowledge) Generic Specific 3. Representation DIAGNOSIS INDICATION my doctor’s work plan my doctor’s diagnosis MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE BLOOD PRESSURE DRUG me my blood pressure my ASD my doctor my doctor’s computer ‘ atrial septal defect’ ‘ W. Ceusters’ ‘ my heart defect’
  • 41. Terminology is too reductionist What concepts do we need ? How do we name concepts properly?
  • 42. The power of realism in ontology design
    • Reality as benchmark !
    1. Is the scientific ‘state of the art’ consistent with biomedical reality ?
  • 43. The power of realism in ontology design
    • Reality as benchmark !
    2. Is my doctor’s knowledge up to date?
  • 44. The power of realism in ontology design
    • Reality as benchmark !
    3. Does my doctor have an accurate assessment of my health status?
  • 45. The power of realism in ontology design
    • Reality as benchmark !
    4. Is our terminology rich enough to communicate about all three levels?
  • 46. The power of realism in ontology design
    • Reality as benchmark !
    5. How can we use case studies better to advance the state of the art?
  • 47. Representations for portions of reality Level 1 Level 2 or 3 Level 3
  • 48. Referent Tracking
  • 49. Another problem to solve: how many disorders? 5572 04/07/1990 26442006 closed fracture of shaft of femur 5572 04/07/1990 81134009 Fracture, closed, spiral 5572 12/07/1990 26442006 closed fracture of shaft of femur 5572 12/07/1990 9001224 Accident in public building (supermarket) 5572 04/07/1990 79001 Essential hypertension 0939 24/12/1991 255174002 benign polyp of biliary tract 2309 21/03/1992 26442006 closed fracture of shaft of femur 2309 21/03/1992 9001224 Accident in public building (supermarket) 47804 03/04/1993 58298795 Other lesion on other specified region 5572 17/05/1993 79001 Essential hypertension 298 22/08/1993 2909872 Closed fracture of radial head 298 22/08/1993 9001224 Accident in public building (supermarket) 5572 01/04/1997 26442006 closed fracture of shaft of femur 5572 01/04/1997 79001 Essential hypertension PtID Date ObsCode Narrative 0939 20/12/1998 255087006 malignant polyp of biliary tract Three references of hypertension for the same patient denote three times the same disease. If two different fracture codes are used in relation to observations made on the same day for the same patient, they might refer to the same fracture The same type of location code used in relation to three different events might or might not refer to the same location. If the same fracture code is used for the same patient on different dates, then these codes might or might not refer to the same fracture. The same fracture code used in relation to two different patients can not refer to the same fracure. If two different tumor codes are used in relation to observations made on different dates for the same patient, they may still refer to the same tumor.
  • 50. Requirements for a digital copy of the world
    • R1: A faithful representation of reality
    • R2 … of everything that is digitally registered,
            • what is generic  scientific theories  realism-based ontologies
            • what is specific  what individual entities exist and how they relate
    • R3: … throughout reality’s entire history,
    • R4 … which is computable in order to …
            • … allow queries over the world’s past and present,
            • … make predictions,
            • … fill in gaps,
            • … identify mistakes,
            • ...
  • 51. The reality: a digital copy of part of the world Applying the grid should not give a distorted representation of reality, but only an incomplete representation !!!
  • 52. Key issue: keeping track of what the bits denote
  • 53.
    • explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes , ...
    Fundamental goal of Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.
  • 54. Method: numbers instead of words
      • Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity
    Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. 235 78 5678 321 322 666 427
  • 55. The essence of Referent Tracking
    • Keeping track of particulars
    • By means of singular and globally unique identifiers (#1, #2, #3, …)
    • That function as surrogates for these entities in information systems, documents, etc
    • And are managed IN a referent tracking system.
    Ceusters W. and Smith B. Tracking Referents in Electronic Health Records. In: Engelbrecht R. et al. (eds.) Medical Informatics Europe, IOS Press, Amsterdam, 2005;:71-76
  • 56.
    • ‘ John Doe’s ‘John Smith’s
    • liver liver
    • tumor tumor
    • was treated was treated
    • with with
    • RPCI’s RPCI’s
    • irradiation device’ irradiation device’
    The principle of Referent Tracking ‘ John Doe’s liver tumor was treated with RPCI’s irradiation device’ #1 #3 #2 #4 #5 #6 treating person liver tumor clinic device instance-of at t 1 instance-of at t 1 instance-of at t 1 instance-of instance-of at t 1 #10 #30 #20 #40 #5 #6 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of inst-of at t 2
  • 57. EHR – Ontology “collaboration”
  • 58. Reasoning over instances and universals instance-of at t #105 caused by
  • 59. Codes for types AND identifiers for instances 7 distinct disorders 5572 04/07/1990 26442006 closed fracture of shaft of femur 5572 04/07/1990 81134009 Fracture, closed, spiral 5572 12/07/1990 26442006 closed fracture of shaft of femur 5572 12/07/1990 9001224 Accident in public building (supermarket) 5572 04/07/1990 79001 Essential hypertension 0939 24/12/1991 255174002 benign polyp of biliary tract 2309 21/03/1992 26442006 closed fracture of shaft of femur 2309 21/03/1992 9001224 Accident in public building (supermarket) 47804 03/04/1993 58298795 Other lesion on other specified region 5572 17/05/1993 79001 Essential hypertension 298 22/08/1993 2909872 Closed fracture of radial head 298 22/08/1993 9001224 Accident in public building (supermarket) 5572 01/04/1997 26442006 closed fracture of shaft of femur 5572 01/04/1997 79001 Essential hypertension PtID Date ObsCode Narrative 0939 20/12/1998 255087006 malignant polyp of biliary tract IUI-001 IUI-001 IUI-001 IUI-003 IUI-004 IUI-004 IUI-005 IUI-005 IUI-005 IUI-007 IUI-007 IUI-007 IUI-002 IUI-012 IUI-006
  • 60. Requirements for a digital copy of the world
    • R1: A faithful representation of reality
    • R2 … of everything that is digitally registered,
            • what is generic  scientific theories
            • what is specific  what individual entities exist and how they relate
    • R3: … throughout reality’s entire history,
    • R4 … which is computable in order to …
            • … allow queries over the world’s past and present,
            • … make predictions,
            • … fill in gaps,
            • … identify mistakes,
            • ...
  • 61. Eternal memory
  • 62. Accept that everything may change:
    • changes in the underlying reality:
      • Particulars come, change and go
  • 63. Identity & instantiation child adult caterpillar butterfly t person animal Living creature
  • 64. Accept that everything may change:
    • changes in the underlying reality:
      • Particulars come, change and go
    • changes in our (scientific) understanding:
      • The plant Vulcan does not exist
  • 65. Reality and representation: both in evolution IUI-#3 O-#2: ‘cancer’ O-#1: ‘benign tumor’ Repr. O-#0: diabolic possession = “denotes” = what constitutes the meaning of representational units … . Therefore: O-#0 is meaningless t U1: benign tumor U2: malignant tumor p3 Reality
  • 66. Accept that everything may change:
    • changes in the underlying reality:
      • Particulars come, change and go
    • changes in our (scientific) understanding:
      • The plant Vulcan does not exist
    • reassessments of what is considered to be relevant for inclusion (notion of purpose).
    • encoding mistakes introduced during data entry or ontology development.
  • 67. Changes over time
    • In John Smith’s Electronic Health Record:
      • At t 1 : “male” at t 2 : “female”
    • What are the possibilities ?
    • Change in reality:
      • transgender surgery
      • change in legal self-identification
    • Change in understanding : it was female from the very beginning but interpreted wrongly
    • Correction of data entry mistake: it was understood as male, but wrongly transcribed
    • ( Change in word meaning )
  • 68. Requirements for a digital copy of the world
    • R1: A faithful representation of reality
    • R2 … of everything that is digitally registered,
            • what is generic  scientific theories
            • what is specific  what individual entities exist and how they relate
    • R3: … throughout reality’s entire history,
    • R4 … which is computable in order to …
            • … allow queries over the world’s past and present,
            • … make predictions,
            • … fill in gaps,
            • … identify mistakes,
            • ...
  • 69. Referent Tracking System Components
    • Referent Tracking Software
        • Manipulation of statements about facts and beliefs
    • Referent Tracking Datastore:
        • IUI repository
        • A collection of globally unique singular identifiers denoting particulars
        • Referent Tracking Database
        • A collection of facts and beliefs about the particulars denoted in the IUI repository
    Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.
  • 70. Place in the Health IT arena
  • 71. How to build an ontology from a terminology?
  • 72. Steps in ontology building
    • For all terms identified in the terminology, find the entities in reality that are directly denoted;
    • Determine the top categories these entities belong to;
    • Determine for any dependent entity:
      • If process: the continuants that participate in it
      • If dependent continuant: the continuant upon which it depends
    • For any entity determined in step 3, go to step 2.
    Rudnicki R, Ceusters W, Manzoor S, Smith B. What Particulars are Referred to in EHR Data? A Case Study in Integrating Referent Tracking into an Electronic Health Record Application. In Teich JM, Suermondt J, Hripcsak C. (eds.), American Medical Informatics Association 2007 Annual Symposium Proceedings, Biomedical and Health Informatics: From Foundations to Applications to Policy, Chicago IL, 2007;:630-634.
  • 73. Building the Ontology underlying a terminology (MDS) MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … MDS terms BFO Class-relations U 2 U 3 U 5 U 4 U 6 U 11 U 7 U 14 U 13 U 10 U 12 U 17 U 16 U 1 U 9 U 8
  • 74. Adding another terminology U 2 U 1 U 7 U 17 U 9 U 3 U 5 U 4 U 6 U 11 U 10 U 14 U 12 U 13 U … OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U 8 BFO
  • 75. Adding another terminology U 2 U 1 U 7 U 17 U 9 U 3 U 5 U 4 U 6 U 11 U 10 U 14 U 12 U 13 U … OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … … CARE 1 CARE 2 CARE 3 CARE 4 MDS terms CARE terms U 15 U 16 U 8 BFO
  • 76. How to link to patient data ?
  • 77. Semantic integration of data expressed in distinct terminologies
    • Purpose:
      • Better comparability
      • Statistical validation of the ontology
        • Explanation of observed correlations between assessment data elements
        • Finding patient subpopulations exhibiting correlations which are near-significant without the ontology, but significant with the ontology
    • Two level integration:
      • Type level : poor man’s linkage
      • Particular level: rich man’s linkage
  • 78. ‘ Poor man’s’ data linkage U 2 U 1 U 7 U 17 U 9 U 3 U 5 U 4 U 6 U 11 U 10 U 14 U 12 U 13 U … MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U 8 pt4 pt3 Patient data
  • 79. Data linkage using multiple instruments
  • 80. Problems with this level
    • Exclusive focus on universals , ignoring that in data collection (almost) everything is about particulars .
    • Therefore Referent Tracking must be brought in the picture.
  • 81. Referent Tracking solves this problem:
    • It is true that:
      • (1) ‘ All Americans have one mother’
      • (2) ‘ All Americans have one president’
    • But:
      • (1) ‘ all Americans have a distinct mother ’
      • (2) ‘ all Americans have a (numerically) identical president ’
  • 82. From ‘poor man’s’ to ‘rich man’s’ data linkage U 2 U 1 U 7 U 17 U 9 U 3 U 5 U 4 U 6 U 11 U 10 U 14 U 12 U 13 MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … MDS terms U 16 U 8 pt4 pt3 Patient data formula
  • 83. Rich man’s data linkage: focus on particulars U 6 U 11 MDS 3 MDS 4 pt4 pt3 Instance-of Particular relations pt4 IUI-1 U 6 IUI-2 IUI-3 U 11 IUI-4 IUI-5 pt3
  • 84. Many more combinations possible
    • The terms used in MDS 4 denote distinct particulars related to both patients
    • One of the terms used in MDS 4 denotes the same particular for both patients
    pt4 IUI - 1 U 6 IUI - 2 IUI - 3 U 11 IUI - 4 IUI - 5 pt3 pt4 IUI - 1 U 6 pt4 IUI - 1 U 6 IUI - 1 U 6 IUI - 2 IUI - 3 U 11 IUI - 2 IUI - 3 U 11 IUI - 4 IUI - 5 pt3 IUI - 4 IUI - 5 pt3 pt4 IUI - 1 U 6 IUI - 2 IUI - 3 U 11 IUI - 5 pt3 pt4 IUI - 1 U 6 IUI - 1 U 6 IUI - 2 IUI - 3 U 11 IUI - 5 pt3
  • 85. What has worked ? How have disparate views been accommodated?
  • 86. Definitions for ‘Adverse Event’ QUIC an injury that was caused by medical management and that results in measurable disability. D9 JTC an untoward, undesirable, and usually unanticipated event, such as death of a patient, an employee, or a visitor in a health care organization. Incidents such as patient falls or improper administration of medications are also considered adverse events even if there is no permanent effect on the patient. D8 CDISC any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment D7 NCI any unfavorable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medical treatment or procedure that may or may not be considered related to the medical treatment or procedure D6 IOM an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient D5 BRIDG an observation of a change in the state of a subject assessed as being untoward by one or more interested parties within the context of a protocol-driven research or public health. D4
  • 87. At least one argument
    • There is no entity which would be such that, were it placed before these authors, they would each in turn be able to point to it and respectively say – faithfully and honestly –
      • “ that is an observation ” (definition D4),
      • “ that is an injury ” (definition D9),
      • “ that is a laboratory finding ” (definition D6).
    • Clearly,
      • nothing which is an injury can be a laboratory finding, although, of course, laboratory findings can aid in diagnosing an injury or in monitoring its evolution.
      • nothing which is a laboratory finding, can be an observation, although, of course, some observation must have been made if we are to arrive at a laboratory finding.
  • 88. Hypothesis
    • Because …
      • all the authors of the mentioned definitions use the term ‘ adverse event ’ in some context for a variety of distinct entities, and
      • these contexts look quite similar
        • in each of them, more or less the same sort of entities seem to be involved
    • … there is some common ground (some portion of reality) which is such that the entities within it can be used as referents for the various meanings of ‘ adverse event ’.
  • 89. Why does this matter ?
    • Be precise about what representational units in either an ontology or data repository stand for.
    • Each such unit in an ontology should come with additional information on whether it denotes:
      • an entity at level 1, level 2 or level 3
        • and
      • a universal, or a defined or composite class
  • 90. Examples from our adverse event domain ontology concretized (through text, diagram, …) piece of knowledge drawn from state of the art principles that can be used to support the appropriateness of (or correctness with which) processes are performed involving a subject of care information entity DC care reference C18 Level 3 cognitive representation, resulting from a harm assessment , and involving an assertion to the effect that a structure change is or is not a harm dependent continuant DC harm diagnosis C16 cognitive representation of a structure change resulting from an act of perception within a subject investigation dependent continuant DC observation C15 Level 2 looking for a structure change in the subject of care process DC subject investigation C12 aspect of an anatomical structure deviation from which would bring it about that the anatomical structure would either (1) itself become dysfunctional or (2) cause dysfunction in another anatomical structure dependent continuant U structure integrity C8 change in an anatomical structure of a person process U structure change C7 act of care that might have caused harm to the subject of care act of care DC act under scrutiny C2 person to whom harm might have been done through an act under scrutiny independent continuant DC subject of care C1 Level 1 Description (role in adverse event scenario) Particular Type Class Type Denotation
  • 91. Representing particular cases
    • Is the generic representation of the portion of reality adequate enough for the description of particular cases?
    • Example: a patient
      • born at time t0
      • undergoing anti-inflammatory treatment and physiotherapy since t2
      • for an arthrosis present since t1
      • develops a stomach ulcer at t3.
  • 92. Anti-inflammatory treatment with ulcer development #13 is_about #9 since t 3+x cognitive representation in #3 about #9 #13 #12 has_participant #9 at t 3+x #12 has_agent #3 at t 3+x noticing the presence of #9 #12 #11 has_agent #9 since t 3 #11 has_participant #8 since t 3 #11 instance_of C10 at t 3 change brought about by #9 #11 #10 has_participant #9 at t 3 coming into existence of #9 #10 #9 part_of #7 since t 3 #1’s stomach ulcer #9 #8 instance_of C8 since t 0 #8 inheres_in #7 since t 0 #7’s structure integrity #8 #7 member C6 since t 2 #1’s stomach #7 #6 part_of #2 #1’s physiotherapy #6 #5 part_of #2 #5 member C2 since t 3 #1’s anti-inflammatory treatment #5 #4 member C5 since t 1 #1’s arthrosis #4 #3 member C4 since t 2 the physician responsible for #2 #3 #2 instance_of C3 #2 has_participant #1 since t 2 #2 has_agent #3 since t 2 #1’s treatment #2 #1 member C1 since t 2 the patient who is treated #1 Properties Particular description IUI
  • 93. Advantage 1: reduce ambiguity in definitions
    • E.g. ‘ adverse drug reaction: an undesirable response associated with use of a drug that either compromises therapeutic efficacy, enhances toxicity, or both.’ (Joint Technical Committee)
      • May denote something on level 1 , e.g. a realizable entity which exists objectively as an increased health risk; in this sense any event ‘ that either compromises therapeutic efficacy, enhances toxicity, or both ’ is undesirable;
      • May denote something on level 2 , so that, amongst all of those events which influence therapeutic efficacy or toxicity, only some are considered undesirable (for whatever reason) by either the patient, the caregiver or both; or
      • May denote something relating to level 3 , so a particular event occurring on level 1 is undesirable only when it is an instance of a type of event that is listed in some guideline, good practice management handbook, i.e. in some published statement of the state of the art in relevant matters.
  • 94. Advantage 2: reveal hidden assumptions
    • E.g.: ‘ adverse event: an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient’ (IOM)
    • But:
      • An ‘act of omission’ is under the realist agenda not an entity that exist at level 1, but rather a level 3 entity denoting a configuration in which not was done what good practice requires to be done,
      • Something what not exist at level 1, cannot cause harm by itself,
      • Thus it must be the underlying disease.
  • 95. Conclusion
    • Health data management involves many actors and IT systems: semantic interoperability is thus a key issue.
    • Ontologies (of the right sort) provide a deep level of semantic interoperability between IT systems, thereby keeping track:
      • of what is the case;
      • of what is known by some actor(s);
      • of what has been and still needs to be done.
    • Realism-based ontology , as a discipline, helps in creating ontologies of the right sort.