1. Ontology and the future of Evidence-Based Medicine Dagstuhl 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: Consumer health:
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 tasks for 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: a system, whether automated or manual, that comprises people, machines, and/or methods organized to collect, process, transmit, and disseminate data that represent user information.

8. Communication & Interpretation
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> John Doe </subject>
<diagnosis> pyogenic granuloma of the left thumb </diagnosis>
</record>

11. Understanding content (3)
< >
< >John Doe</ >
< >
<finding site>
<laterality> </laterality>
</finding site>
</ >
</ >

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. Relevance for EHR & Semantic Interoperability
Ontology
EHR
The conceptualist approach

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 reality which
(1) reflects the properties of the objects within its domain in such a way that there obtains a systematic correlation between reality and 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: Ontology:
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. Training and awareness
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. Example: a person (in this room) ’s phenotypic gender
Reality:
Male
Female
Cognitive representation
[male]
[female]
In the EHR:
“male”
“female”
“unknown”
Other types of phenotypic gender ?

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:
the Web 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. Schematic representation
Internet
Referent Tracking (RT) Server
Web Server
Referent Tracking Web services
Referent Tracking Core System API
Security Module
·         RT Data
·         Imported Ontologies rules
Reasoner
Health Institution B registered WITH RT
EHR Client
Health Institution A hosting RT
Session Manager

31. Simple Graph Representation
Privacy issues

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: <
2 PREFIX 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