Transcript Slides - Vanderbilt University

September 21, 2015
Department of Biomedical Informatics
BMIF 6300
Standards and Terminologies
S. Trent Rosenbloom, MD MPH
Associate Professor and Vice Chair
Departments of Biomedical Informatics, Internal Medicine and Pediatrics
Vanderbilt University Medical Center
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Standards are principles and rules designed
to ensure that methods used and products
created reliably and consistently conform to
expectations
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Software Standards Detail:
 minimum set of functions provided
 methods used to achieve those functions
 formatting of the data structure
 minimum set of functions provided
“Content standards”
 methods used to achieve those functions
“Functional standards”
 formatting of the data structure
“Syntactic standards” ← Messaging, e.g., HL7 XML
“Semantic standards” ← Terminology, e.g., SNOMED CT
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Terminologies can provide formal and
machine-computable representations of
knowledge and data
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Such representation can facilitate
interoperability, dissemination, decision
support, research
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Terminologies are formal representations of
entities and their interrelationships.
 Embodied as concepts, terms, linkages
▪ Concepts are the cognitive representation of entities or
meanings
▪ Terms are evocative words or phrases
▪ Linkages are explicitly defined relationships
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Concept - ischemic injury and necrosis of heart
muscle cells resulting from absent or
diminished blood flow in a coronary artery
Terms –
▪ Myocardial Infarction
▪ Heart Attack
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Linkage –
▪ is_a Disease of the Heart
▪ has_severity Severities
Morning
Star
Evening Star
The second planet from the sun, having an average radius of 6,052 kilometers (3,761 miles), a mass
0.815 times that of Earth, and a sidereal period of revolution about the sun of 224.7 days at a mean
distance of approximately 108.2 million kilometers (67.2 million miles).
Physical Entity
The second planet from the sun, having an average radius of 6,052 kilometers (3,761
miles), a mass 0.815 times that of Earth, and a sidereal period of revolution about the
sun of 224.7 days at a mean distance of approximately 108.2 million kilometers (67.2
million miles).
Representative Terms
Morning
Star
Conceptual Experience
Evening Star
Venus
Adapted from Campbell, ‘Representing thoughts, words, and things in the UMLS’, 1998.
Planets of the Solar System
inside
outside
Mercury
Jupiter
Venus
Saturn
Earth
Neptune
Concept: Myocardial Infarction
CUI: C0027051
Semantic Type: Disease or Syndrome
Entity: Gross necrosis of the myocardium, as a result of interruption of the blood supply to the
area. (Dorland, 27th ed)
Representative Terms (synonyms):
 Myocardial Infarction
 Attack coronary
 Cardiac infarction
 Heart attack
 Infarction of heart
 MI
 MI - Myocardial infarction
 Myocardial Infarct
 Myocardial infarction (disorder)
 Myocardial infarction syndrome
 myocardium; infarction
Adapted from the UMLS Metathesaurus.
More Specific Concepts (children):
Acute myocardial infarction
Old myocardial infarction
Microinfarct of heart
True posterior wall infarction
Aborted myocardial infarction
Other specified anterior myocardial infarction
Silent myocardial infarction
Subsequent myocardial infarction
Postoperative myocardial infarction
First myocardial infarction
Myocardial infarction with complication
Non-Q wave myocardial infarction
There are a lot of terminologies
In 2003, the National Committee on Vital Health and
Statistics (NCVHS) recommended a subset of existing
terminologies as:
“uniform data standards for patient medical record
information (PMRI) and the electronic exchange of
such information”
PMRI standards:
 SNOMED CT (as licensed by the National Library of Medicine)
- for the exchange, aggregating, and analysis of patient
medical information.
 Logical observation Identifiers Names and Codes - for the
representation of individual laboratory tests
 Federal Drug Terminologies:
▪ RxNorm;
▪ The representations of the mechanism of action and physiologic effect
of drugs from NDF-RT;
▪ Ingredient name, manufactured dosage form and package type form
the FDA
UMLS (Unified Medical Language System)
 The UMLS is a terminology collection
 Concepts are unique
 No formal relationships among concepts
present, per se
Using the UMLS:
 Semantics and relationships from source
terminologies lost (or implied)
 May mix up different levels of detail from
different terminologies
 Can loose link with source terminology, which
can hinder maintenance
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Classification scheme for the London Bills of
Mortality - 16th century
John Gaunt’s refinement - middle of the 17th
century
International Classification of Diseases (ICD) first adopted in Paris in 1900
Multi-axial Standardized Nomenclature of
Diseases (SND) – 1928
Standardized Nomenclature of Diseases and
Operations (SNDO) - 1933
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“Modern era for clinical descriptions”
With SND and SNDO
▪ Multiaxial: users could model complex concepts by
constructing them from more primitive building blocks
▪ Designed to classify diseases based on:
Etiology
Manifestations
Relationships between them
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Statement of purpose, scope, and comprehensiveness
Complete coverage of domain specific content
Use of concepts rather than terms, phrases and words (concept
orientation)
Concepts do not change with time, view or use (concept consistency)
Concepts must evolve with change in knowledge
Concepts identified through nonsense identifiers (context-free identifier)
Representation of concept context consistently from multiple hierarchies
Concepts have single explicit formal definitions
Support for multiple levels of concept detail
Absence of or methods to identify duplication, ambiguity, and synonymy
Integration with other terminologies
Mapping to administrative terminologies
Adapted from Cimino, ‘Desiderata for controlled medical vocabularies in the twenty-first century’, 1998.

Coverage achieved by one of two ways
▪ Post-coordination - complex concepts from different levels
of detail are composed as needed from fundamental
concepts
(e.g., ‘chest pain’ composed from the concepts ‘chest’ and ‘pain’
when needed)
▪ Pre-coordination - all levels of detail are modeled with
distinct concepts
(e.g., ‘chest pain’, ‘substernal chest pain’, and ‘crushing substernal
chest pain’ are all in the terminology)

Completeness measured by Coverage:
▪ coverage calculated as the proportion of concepts
covered by a terminology
▪ multiple studies: post-coordinated terminologies
generally have better coverage than pre-coordinated
terminologies
Post-coordination versus Pre-coordination
Select One Flavor
Select One Topping
Select One Cone
…or…Select One Favorite
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Post-Coordination
▪
▪
▪
▪
▪
▪
Flexible
Wide choice
Rules implied
Explicit relationships
Inefficient
Permits Inappropriate
combinations
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Pre-Coordination
▪
▪
▪
▪
▪
▪
No flexibility
Limited choice
Asserted knowledge
Implied relationships
Efficient
Only appropriate
combinations
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Consequences of post-coordination:
D5-46210 01 Acute appendicitis, NOS
G-A231 01 Acute
D5-46100 01 Appendicitis, NOS
T-59200 01 Appendix, NOS
G-CO06 01 In
T-59200 01 Appendix, NOS
▪ Inefficient post-coordination: “too cumbersome for
M-41000 01 Acute inflammation, NOS
01 Acute
complex
problem entry” G-A231
G-CO06
01 In
M-40000 01 inflammation, NOS
Table. Duplication due to compositionality: four ways to compose ‘Appendicitis’ in SNOMED, from the
CANON Group
▪ Nonsensical Concepts
▪ Concept duplication
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Rigorous development may produce terminologies
unusable by healthcare providers for routine clinical
tasks.
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Rector: tension between clinical usability and
meticulous knowledge representation mirrors the
conflict ▪ human users require flexible, expressive terminologies that model
common colloquial phrases
▪ computer programs are generally designed to process formally defined
concepts having rigidly defined interrelationships.

Rector’s six tasks for terminologies:
1) support efficient data entry and query formulation
2) record and archive clinical information
3) support sharing and reuse of clinical information
4) infer and suggest knowledge according to decision
support algorithms
5) support terminology maintenance
6) create a natural language output from manual structured
input
Generally a set of flexible, user friendly, colloquial
terms displayed in via computer programs.
Use assertional medical knowledge to support
efficiency, size and focus
Have been used for problem list entry, clinical
documentation, provider order entry
Rosenbloom ST, et al. Interface terminologies: facilitating direct entry of clinical data into electronic health
record systems. J Am Med Inform Assoc. 2006 May-Jun;13(3):277-88..
 SNOMED CT 2012AA in the UMLS contains:
▪ over 311,000 unique concepts, 10% are diagnoses
▪ almost 800,000 descriptions
▪ approximately 1,360,000 links
S-CT
 SNOMED CT CORE subset in the UMLS contains:
▪ about 5,000 unique concepts, primarily diagnoses
▪ covers 95% of diagnoses recorded from 7 healthcare sites
(Beth Israel Deaconess Medical Center, Intermountain Healthcare, Kaiser Permanente, Mayo Clinic,
Nebraska University Medical Center, Regenstrief, Hong Kong Hospital Authority)
CORE
S-CT
Fung KW, Rosenbloom ST, et al. Testing Three Problem List
Terminologies in a simulated data entry environment. AMIA
Annu Symp Proc .2011:445-54.
 SNOMED CT VA-KP subset in the UMLS contains:
▪ about 17,000 unique concepts
▪ primarily contains precoordinated concepts from the
“Clinical Finding” hierarchy
CORE
S-CT
VA-KP
 Institutional subsets / supersets
▪ can be created locally as SNOMED CT extensions
▪ can be created locally without regard to SNOMED CT
▪ may or may not follow standard formalisms
CORE
S-CT
local
VA-KP
CORE
1,449
2,437
1,756
S-CT
302,537
527
2475
CCPSS
986
12,675
9510
VA-KP
* Statistics thanks to Lina Sulieman