THis presentation gives the background of WHO's work on health information including the compilation of data from different sources using ICD; as well as revision of ICD with modern ontological methods.

1. Dr. T. Bedirhan Üstün
World Health Organization
Classifications , Terminologies, Standards
WHO on
Health Information

2. • Views expressed in this presentation are those of B.
Üstün
• They do not necessarily represent the policies of
conflict of interests declared:
• Presenter believes in: Scientific Methods, Ontologies,
Caveat

3. Tower of Babel
Tower of Babble

4. History of Disease & Health
in the World
• 243 BC: plague in China
• 800 s : smallpox in Japan
• 1090s: dysentery in Palestine
• 1340s: "Black Death" in Europe
• 1830s: cholera worldwide
• 1917–19: influenza worldwide
• …
• …
• 1976-2015 Ebola

5. William Farr to
• Farr developed the first national vital statistics system as a
instrument for epidemiologic studies.
• to crafted a disease nosology usable by vital statisticians and
epidemiologists led to the creation of the ICD
• The structure of the ICD derives from Farr's 1860 proposal.
150 year later WHO and the FARR Institute
share the vision of Farr to implement it further
in the digital health space

6. Genealogy of ICD  1664

7. ICD Revisions
139
161
179
189
205
214
200
954
965
1,040
1,164
8,173
1,967
14,473
1
10
100
1000
10000
100000
Farr/d'Espine
Bertillon
ICD1
ICD2
ICD3
ICD4
ICD5
ICD6
ICD7
ICD8
ICD9
ICD-9-M
ICD10
ICD-10-M
1853 1893 1900 1909 1920 1929 1938 1948 1955 1968 1975 1979 1990 1993

8. Shepherding
simple requirements
1. Count your sheep
• How many born ?
• How many dead ?
2. Don’t cry wolf !

9. Reporting of Mortality in the World

10. Information Paradox
0
100000000
200000000
300000000
400000000
500000000
600000000
700000000
800000000
1 2 3 4
YLLs
VR countries vs No VR
Burden of Mortality

13. Millenium Development Goals

15. the information YOU -
₋ have is not what you want
₋ want is not what you need
₋ need is not what you can have
Finagle's Law of Information
have
want
need
In other words there is always a gap
between what you have, need or want

16. Health Information needs Health Informatics
Computational
Processing
Knowledge
INPUTS
Analytical process OUTPUT
• Mechanisms
• Interventions
• Policies
• Statistics
• Aggregation
• Ontologies
• Data
• Information

17. GIGO:
Garbage In
Out ?

18. Can we build the Big Intelligence ?

19. Why is this Sooooo important ?

20. Sharing Meaning
YOU
• Think
• wish to express
• think you have just
expressed
• you expressed
• …
OTHER ONE
• wants to hear
• Actually hears
• wishes to understand
• understands
• …

21. How do we
optimize our
health
services
E-he@lth
Health Information Systems: Analog to Digital

22. Placing WHO Classifications in HIS & IT
Population Health
• Births
• Deaths
• Diseases
• Disability
• Risk factors
e-Health Record
Systems
ICD
ICF
ICHI
Classifications
KRs
Terminologies
Clinical
• Decision Support
• Integration of care
• Outcome
Administration
• Scheduling
• Resources
• Billing
Reporting
• Cost
• Needs
• Outcome

23. ICD-11 Revision Goals
1. Evolve a multi-purpose and coherent classification
• Mortality, morbidity, primary care, clinical care, research, public health…
• Consistency & interoperability across different uses
2. Serve as an international and multilingual reference standard for
scientific comparability and communication purposes
3. Ensure that ICD-11 will function in an electronic environment.
• ICD-11 will be a digital product
• Support electronic health records and information systems
• Link ICD logically to underpinning terminologies and ontologies (e.g. SNOMED, GO, …)
• ICD Categories “defined” by "logical operational rules" on their associations and details

24. Ontology (philosophy)
the Organization of Reality  !!!
 Ontology (computer science)
– the explicit – operational description of
the conceptualization of a domain
• Entities
• Atributes
• Values
• An ontology defines:
– a common vocabulary
– a shared understanding/exchange:
• among people
• among software agents
• between people and software
– to reuse data - information
– to introduce standards to allow
interoperability
What is “NOntology” ?

25. Knowledge Representation
the triad of things, thoughts and words
(Ogden & Richards, 1923 )
APPLE
TERM

26. THE CONTENT MODEL
Any Category in ICD is represented by:
1. ICD Concept Title
1.1. Fully Specified Name
2. Classification Properties
2.1. Parents
2.2 Type
2.3. Use and Linearization(s)
3. Textual Definition(s)
4. Terms
4.1. Base Index Terms
4.2. Inclusion Terms
4.3. Exclusions
5. Body Structure Description
5.1. Body System(s)
5.2. Body Part(s) [Anatomical Site(s)]
5.3. Morphological Properties
6. Manifestation Properties
6.1. Signs & Symptoms
6.2. Investigation findings
7. Causal Properties
7.1. Etiology Type
7.2. Causal Properties - Agents
7.3. Causal Properties - Causal Mechanisms
7.4. Genomic Linkages
7.5. Risk Factors
8. Temporal Properties
8.1. Age of Occurrence & Occurrence Frequency
8.2. Development Course/Stage
9. Severity of Subtypes Properties
10. Functioning Properties
10.1. Impact on Activities and Participation
10.2. Contextual factors
10.3. Body functions
11. Specific Condition Properties
11.1 Biological Sex
11.2. Life-Cycle Properties
12. Treatment Properties
13. Diagnostic Criteria

27. The ICD Foundation Component
• is a collection of ALL ICD
entities like diseases,
disorders...
• It represents the whole ICD
universe.
• In a simple way, the foundation component is similar to a “store” of
books or songs.
• From these elements we build a selection as a linearization.
• This analogy may however be misleading because there are many links
between the ICD entities (like parent-child relations and other).
• The ICD entities in the Foundation Component:
• are not necessarily mutually exclusive
• allow multiple parenting ( i. e. an entity may be in more
than one branch, for example tuberculosis meningitis is
both an infection and a brain disease)

28. The ICD Linearizations
• A linearization is a subset of the
foundation component, that is:
• Fit for a particular purpose: reporting mortality,
morbidity, or other uses
• Jointly Exhaustive of ICD Universe (Foundation
Component)
• Composed of entities that are Mutually Exclusive of
each other
• Each entity is given a single parent

29. 30
Foundation: ICD
categories with
- Definitions, synonyms
- Clinical descriptions
- Diagnostic criteria
- Causal mechanism
- Functional Properties
Find Term
SNOMED-CT,
International Classification of Functioning,
Disability and Health (ICF)…
Linearizations
Mortality
Morbidity
Primary Care

30. • Open and Collaborative Platform
• Web based
• Like WIKIPEDIA
• But
• by the Content Model
• with
• by the TAGs , and scientific peers

31. ICD11 βeta
• http://www.who.int/classifications/icd/revision
• Beta – Browser & Print
10 look & feel + descriptions – code structure !
• ICD-11 Beta draft is NOT FINAL
• updated on a daily basis
•NOT TO BE USED for CODING except
for agreed FIELD TRIALS
βeta

32. ICD-11 Features
Internet Based
Platform
Content Model
Multi Lingual Representations
Definitions
Input from
all Stakeholders
‫لعربية‬ Arabic
官话 Chinese
English English
Français French
Русский язык Russian
Español Spanish
Deutsch German
Português Portuguese
Field Trials for
Use Cases
Electronic Health Record
Ready

33. Beta
• Comments
• Proposals
• Field Trials
• Review Mechanism

34. Incentives for Participants

35. ICD-11 Timeline
• 2014 : Beta : Field Trials Version
• Systematic/scientific reviews
• Vigorous crowdsourcing
• Field Trials
• 2017 : Final version for WHA Approval
• 2018+ implementation
 Perpetual DIGITAL editing – review cycles

36. • Essential for EHR
• Enhance Care
• Decision Support
• Safety & Quality
• Better Collaboration
• Monitoring & Evaluation
• Better Health Information
• Less Administration

37. SNOMED : Old and Current
Former
SNOMED
Enterprise
College
American
Pathologists
Global
Network
Overall Health Care

38. Why work together?
– WHO & IHTSDO
– Coverage & Adequacy
– Quality – Reliability - Utility
– MultiLingual Applicability
– Interoperability
– Sustainability
– Member States:
Enable health care delivery and
compile health information
SNOMED & WHO Classifications
are synergistic and not antagonistic

39. The «Common Ontology»
Purpose
• To provide a common formal knowledge representation structure to
enable interoperability between:
• ICD-11 and SNOMED CT.
• a shared semantics

40. Ultimate “Turing-like” Test
If common ontology
achieved

41. Grade 3 hypertension
Grade 2 hypertension
Grade 1 hypertension
High normal
normal
optimal
120 130 140 150 160 170 180
Systolic pressure
Diastolicpressure
172
102
110
105
100
95
90
85
80
Knowledge Representation
43

42. Rewriting ICD Using SNOMED
example of Depressive Disorder F32.0
A. Low mood {41006004}
Loss of interest {417523004 }
Low energy {248274002}
1. Appetite (decrease, increase) {64379006, 72405004}
2. Body weight (decrease, increase) {89362005, 8943002}
3. Sleep (decrease, increase) {59050008, 77692006}
4. Psychomotor (decrease, increase) {398991009, 47295007}
5. Libido loss {8357008}
6. Low self esteem {286647002, 162220005}
7. Guilt, self blame {7571003}
8. Thoughts of death …
9. Suicide Ideation {102911000, 6471006}
B.

43. Beyond GoogleTM
Semantic Interoperability for HIS
• Search using Concepts above Words
• How many patients do have diabetes mellitus type II?
• Extraction of Concepts from Health Records
• Automated extraction of Hb1Ac results of selected patients with DM type II from lab
reports within last year
• Statistical Index on Community Collections
• Calculation of coverage gap for treatment need for diabetes mellitus
• Concept Navigation across Collections
• Comparison of region A with region B etc
45

44. Real Time Public Health
Rule-based Aggregation @ Individual, Facility, Population levels
Public Health,
Epi & Surveillance
Findings InterventionsEvents
Clinical Information
Reimbursement
Resource Management

45. Clinical Use Case:
Exploration of Cough
Fever
386661006
COUGH
49727002
WET COUGH
sputum
28743005
Hemoptisia
Blood in Sputum
207069003
• X-ray : Tbc?
• Culture
399208008
104184002
• Diagnosis: Tuberculosis 154283005
A 15.0
• Treatment: DOTs { 324453004 }

46. Interoperability

47. Future Steps
1. Linking individual data to public health indicators
2. Standards for public health indicators
• Entities – relations ( n-ontology?: scientific compilation)
• Architecture
• Flow
• Aggregation process

48. Uniform Resource Identifiers
URI: //id.who.int/….
• enable links to other established terminology,
ontologies
• allow impact analysis possible via W3C
• e.g. where on the world these are used or not used
• Useful for translations:
• the concepts will indicate a language-independent construct
and translations will refer to the unique source concept.

50. … BUILDING BLOCKS OF HEALTH INFORMATION …
Avoiding an e-tower of Babel

51. Questions & Answers
ustunb@who.int
@ustunb
bedirhan-ustun