Ontology and the future of Evidence-Based Medicine Dagstuhl May 23th, 2006

1. Ontology and the future ofEvidence-Based MedicineDagstuhl May 23th, 2006 Werner Ceusters, MD Ontology Research Group Center of Excellence in Bioinformatics & Life Sciences SUNY at Buffalo, NY

2. Evidence Based Medicine • the integration of best research evidence with clinical expertise and patient values. • best research evidence:clinically relevant patient centered research into the accuracy and precision of diagnostic tests, the power of prognostic markers, and the efficacy and safety of care regimens. • clinical expertise:the ability to use clinical skills and past experience to rapidly identify each patient's unique health state. • patient values:the unique preferences, concerns and expectations each patient brings to a clinical encounter and which must be integrated into clinical decisions if they are to serve him.

3. Application of Evidence Based Medicine • Now: • Decisions based on (motivated/justified by) the outcomes of (reproducable) results of well-designed studies • Guidelines and protocols • Evidence is hard to get, takes time to accumulate. • Future: • Each discovered fact or expressed belief should instantly become available as contributing to ‘evidence’, wherever its description is generated.

4. Future scenarios • Data entered about a successful treatment of a case in X generates a suggestion for a similar case in Y; • Submission of a new paper to Pubmed on some ADR triggers an alert in EHR systems worldwide for those patients that might be at risk; • …  From reactive care to proactive care

5. Some problem areas • Pharmaceutical Industry: • Optimise drug discovery • Make “messy” databases more useful for everybody • Consumer health: • Opposing forces: • Quality of information • Make them consume • Malpractice suits • Public sector health: • Cost containment • Cost effectiviness of treatment, prevention • Bio-informatics world: • How to find out that a ‘discovery’ is a ‘new’ discovery ?

6. An action plan for a European eHealth Area. • By the start of 2005: • MS and EC should agree on an overall approach to benchmarking in order to assess the quantitative, including economic, and qualitative impacts of e-Health. • By end 2006: • in order to achieve a seamless exchange of health information across Europe through common structures and ontologies, MS, in collaboration with the EC, should identify and outline interoperability standards for health data messages and electronic health records, taking into account best practices and relevant standardisation efforts. • By end 2008: • the majority of all European health organisations and health regions (communities, counties, districts) should be able to provide online services such as teleconsultation (second medical opinion), e-prescription, e-referral, telemonitoring and telecare. Failed

7. One key issue: Semantic Interoperability • Working definition: • Two information systems are semantically interoperable if and only if each can carry out the tasksfor which it was designed using data and information taken from the other as seemlessly as using its own data and information. system: Any organized assembly of resources and procedures united and regulated by interaction or interdependence to accomplish a set of specific functions. information system: asystem, whether automated or manual, that comprises people, machines, and/or methods organized to collect, process, transmit, and disseminate data that represent user information.

8. Essential components Communication & Interpretation • People: physicians, nurses, patients, healthcare administrators, ... • Machines: • to make humans interact with the EHR, • to transmit data from one EHR to another • to enter data (lab analysers, EMR monitors, ...) • to interprete data (alerts, quality assessment, protocol selection, ...) • Data and information (data in context) • Procedures

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

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

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

12. Ontology based on Unqualified Realism • Accepts the existence of • a real world outside mind and language • a structure in that world prior to mind and language (universals / particulars) • Rejects nominalism, conceptualism, ontology as a matter of agreement on ‘conceptualizations’ • Uses reality as a benchmark for testing the quality of ontologies as artifacts by building appropriate logics with referential semantics (rather than model-theoretic)

13. The conceptualist approach R E A L I T Y B E L I E F Ontology EHR Relevance for EHR & Semantic Interoperability

14. Relevance for EHR & Semantic Interoperability The realist approach R E A L I T Y L O G O L K A I S N S G Ontology EHR

15. Terminology • A theory concerned with those aspects of the nature and the functions of language which permit the efficient representation and transmission of items of knowledge (J. Sager) • Precise and appropriate terminologies provide important facilities for human communication (J. Gamper)

16. Ontology • An ontology is a representation of some pre-existing domain of realitywhich • (1) reflects the properties of the objects within its domain in such a waythat there obtains a systematic correlation between realityand the representation itself, • (2) is intelligible to a domain expert • (3) is formalized in a way that allows it to support automatic information processing

17. A division of labour • Terminology: • Communication amongst humans • Communication between human and machine • Ontology: • Representation of “reality” inside a machine • Communication amongst machines • Interpretation by machines

18. Today’s biggest problem: a confusion between “terminology” and “ontology” • The conditions to be agreed upon when to use a certain term to denote an entity, are often different than the conditions which make an entity what it is. • Trees would still be different from rabbits if there were no humans to agree on how these things should be called. • “ontos” means “being”. The link with reality tends to be forgotten: one concentrates on the models instead of on the reality.

19. What to do about it ? (1) • Research: • Revision of the appropriatness of concept-based terminology for our purposes • Relationship between models and that part of reality that the models want to represent • Adequacy of current tools and languages for representation • Boundaries between terminology and ontology and the place of each in semantic interoperability in healthcare

20. What to do about it ? (2) • Training and awareness • Make people more critical wrt terminology and ontology promisses • What is needed must be based on needs, not on the popularity of a new concept • But in a system, it’s not just your own needs, it is each component’s needs ! • Towards “an ontology of ontologies” • First description • Then quality criteria

21. Ultimate goal Ontology continuant disorder person CAG repeat EHR Juvenile HD #IUI-1 ‘affects’ #IUI-2 #IUI-3 ‘affects’ #IUI-2 #IUI-1 ‘causes’ #IUI-3 ... Referent Tracking Database

22. 3 fundamentally different in levels • the reality on the side of the patient; • the cognitive representations of this reality embodied in observations and interpretations on the part of clinicians and others; • the publicly accessible concretizations of such cognitive representations in representational artifacts of various sorts, of which ontologies and terminologies are examples.

23. Other types of phenotypic gender ? Example: a person (in this room) ’s phenotypic gender • Reality: • Male • Female • Cognitive representation • [male] • [female] • In the EHR: • “male” • “female” • “unknown”

24. 4 fundamental reasons for making changes • changes in the underlying reality • does the appearance of an entry (in a new version of an ontology or in an EHR) relate to the appearance of an entity or a relationship among entities in reality?; • changes in our (scientific) understanding; • reassessments of what is considered to be relevant for inclusion (notion of purpose), or: • encoding mistakes introduced during data entry or ontology development.

25. Key requirement Any change in an ontology or data repository should be associated with the reason for that change !

26. Example: a person (in this room) ’s gender in the EHR • In John Smith’s EHR: • At t1: “male” at t2: “female” • What are the possibilities ? • Change in reality: transgender surgery • Change in understanding: it was female from the very beginning but interpreted wrongly • ( No change in relevance ) • Correction of data entry mistake • (was understood as male, but wrongly transcribed)

27. Possible combinations OE: objective existence; ORV: objective relevance; BE: belief in existence; BRV: belief in relevance; Int.: intended encoding; Ref.: manner in which the expression refers; G: typology which results when the factor of external reality is ignored. E: number of errors when measured against the benchmark of reality. P/A: presence/absence of term.

28. Possible evolutions

29. Towards an implementation • A client-server application in which the server is composed of four layers: • theWeb Server Layer (WSL) provides the interface to clients via web services; • the RT Core application programming interface (API) encapsulates the data services related to storage and retrieval. Its Security Module validates the access rights before any data service; • the database layer stores all the RT data, and; • the reasoner layer (RL) performs inferences upon specific requests, based on the information available in the database and, if available, the ontologies that have been used for the descriptions of the portions of reality. Shahid Manzoor

30. Health Institution A hostingRT Internet EHR Client Referent Tracking Core System API Security Module Session Manager ·         RT Data ·         Imported Ontologies rules Reasoner Referent Tracking (RT) Server Web Server Referent Tracking Web services Health Institution Bregistered WITH RT EHR Client Schematic representation

31. Privacy issues Simple Graph Representation

32. Complete structure

33. UML-diagram for the entities in the RDF-graph

34. Querying the RTDB using ontologies (SPARQL) Retrieve particulars that are related to the universal face 1 PREFIX rts: <http://ecor#> 2 PREFIX fma: <http://FMA#> 3 SELECT ?p ?u ?v 4 WHERE {?p rts:relation ?u . 5 ?u a rts:PtoU . 6 ?u rts:u ?t . 7 ?t a fma:Face . 8 }

35. Querying the RTDB using ontologies (SPARQL) 1 WHERE {?p rts:relation ?rf . 2 ?rf a rts:PtoP . 3 ?rf rts:p ?f . 4 ?f a fma:Head . 5 ?f rts:relation ?rd . 6 ?rd a rts:PtoP . 7 ?rd :p ?d . 8 ?d a dis:DISEASES AND INJURIES . } Retrieve patients with diseases in the head

36. Test interface