Clinical Terminologies

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Clinical Terminologies S. Trent Rosenbloom, MD MPH Associate Professor Departments of Biomedical Informatics, Internal Medicine and Pediatrics Vanderbilt University Medical Center September 11, 2013 Department of Biomedical Informatics BMIF 300

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Clinical Terminologies. September 11, 2013 Department of Biomedical Informatics BMIF 300. S. Trent Rosenbloom, MD MPH Associate Professor Departments of Biomedical Informatics, Internal Medicine and Pediatrics Vanderbilt University Medical Center. - PowerPoint PPT Presentation

Transcript of Clinical Terminologies

Page 1: Clinical Terminologies

Clinical Terminologies

S. Trent Rosenbloom, MD MPH

Associate ProfessorDepartments of Biomedical Informatics, Internal Medicine and PediatricsVanderbilt University Medical Center

September 11, 2013Department of Biomedical InformaticsBMIF 300

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• Terminologies can provide formal and machine-computable representations of knowledge and data

• 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

• Linkage – – is_a Disease of the Heart–has_severity Severities

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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).

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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

Conceptual ExperienceRepresentative Terms

Venus

Adapted from Campbell, ‘Representing thoughts, words, and things in the UMLS’, 1998.

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Venus

Mercury

Earth

Jupiter

Saturn

Neptune

Planets of the Solar System

inside outside

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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

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

Adapted from the UMLS Metathesaurus.

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• 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”

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Terminologies

• 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

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• What about the UMLS?(Unified Medical Language System)

– The UMLS is a terminology collection– Concepts are unique– No formal relationships among concepts

present, per se

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• 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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Terminology History

• 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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Terminology History

• “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:

EtiologyManifestations 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.

Terminology Desiderata

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• 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)

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• 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

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Post-coordination versus Pre-coordinationPost-coordination versus Pre-coordination

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Select One FlavorSelect One Flavor

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Select One ToppingSelect One Topping

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Select One ConeSelect One Cone

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……or…Select One Favoriteor…Select One Favorite

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Ice Cream

• Post-Coordination• Flexible• Wide choice• Rules implied• Explicit relationships• Inefficient• Permits Inappropriate

combinations

• Pre-Coordination• No flexibility• Limited choice• Asserted knowledge• Implied relationships• Efficient• Only appropriate

combinations

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• Consequences of post-coordination:

• Inefficient post-coordination: “too cumbersome for complex problem entry”

• Nonsensical Concepts

• Concept duplication

D5-46210 01 Acute appendicitis, NOS G-A231 01 Acute D5-46100 01 Appendicitis, NOS

M-41000 01 Acute inflammation, NOSG-CO06 01 InT-59200 01 Appendix, NOS

G-A231 01 AcuteM-40000 01 inflammation, NOSG-CO06 01 InT-59200 01 Appendix, NOS

Table. Duplication due to compositionality: four ways to compose ‘Appendicitis’ in SNOMED, from the CANON Group

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• Rigorous development may produce terminologies unusable by healthcare providers for routine clinical tasks.

• 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.

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• Rector’s six tasks for terminologies: 1) support efficient data entry and query formulation2) record and archive clinical information3) support sharing and reuse of clinical information4) infer and suggest knowledge according to decision

support algorithms5) support terminology maintenance6) create a natural language output from manual structured

input