Jane HunterHafsa Qureshi

Jane HunterM.Sc. eHealth candidate (part-time)Undergrad in Computer Engineering at

McMasterHave also done an MBA at McMasterWorking as a software developer at NCR

where we are currently creating a Java development infrastructure for next generation banking products

Married for 10 years as of June, no kids

Hafsa QureshiM.Sc. eHealth candidate (full-time, thesis

student)B.Sc. Health Informatics from University of

WaterlooTrying to look for internship employmentRecently married

Systematized Nomenclature of Medicine Clinical Terms

controlled medical vocabulary licensed and supported by the International Health Terminology SDO

provides a common language that enables a consistent way of indexing, storing, retrieving and aggregating clinical data across specialties and sites of care

comprehensive, multi-lingual clinical terminology that provides clinical content and expressivity for clinical documentation and reporting

Medical Subject Headings Comprehensive controlled vocabulary

for the purpose of indexing journal articles and books in the life sciences

Created and updated by the United States National Library of Medicine (NLM)

serves as a thesaurus that facilitates searching

used by the MEDLINE/PubMed article database and by NLM's catalog of book holdings

Unified Medical Language System compendium of many controlled

vocabularies in the biomedical sciences provides a mapping structure among these

vocabularies and allows one to translate among the various terminology systems

Comprehensive thesaurus and ontology of biomedical concepts

provides facilities for natural language processing

intended to be used mainly by developers of systems in medical informatics

Logical Observation Identifiers Names and Codes

universal standard for identifying medical laboratory observations

developed and is maintained by the Regenstrief Institute, a US non-profit medical research organization

publicly available at no cost. include medical and laboratory code

names, nursing diagnosis, nursing interventions, outcomes classification, and patient care data set.

International Statistical Classification of Diseases and Related Health Problems

provides codes to classify diseases and a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or disease

every health condition can be assigned to a unique category and given a code, up to six characters long

Why are there so many vocabularies?

Conclusion: No major terminology source can lay claim to being

the ideal resource for a computer-based patient record.

SNOMED is considerably more complete, has a compositional nature and a richer taxonomy. It suffers from less clarity, resulting from a lack of syntax and evolutionary changes in its coding scheme.

READ has greater clarity and better mapping to administrative schemes, is rapidly changing and is less complete.

UMLS is a rich lexical resource, with mappings to many source vocabularies. It provides definitions for many of its terms. However, due to the varying granularities and purposes of its source schemes, it has limitations for representation of clinical concepts within a computer-based patient record.

SNOMED CT is a clinical healthcare terminologya resource with comprehensive,

scientifically-validated contentessential for electronic health recordsa terminology that can cross-map to other

international standardsalready used in more than fifty countries

Each year, avoidable deaths and injuries occur because of poor communication between healthcare practitioners.

The delivery of a standard clinical language for use across the world's health information systems can therefore be a significant step towards improving the quality and safety of healthcare.

SNOMED CT aims to improve patient care through the development of systems to accurately record health care encounters.

Ultimately, patients will benefit from the use of SNOMED CT, for building and facilitating communication and interoperability in electronic health data exchange.

concepts are organized in hierarchies, from the general to the specific

allows very detailed (“granular”) clinical data to be recorded and later accessed or aggregated at a more general level

Concepts may have multiple parents, for example Infectious pneumonia is also an Infectious disease.

The Concept graph must be acyclic — a parent cannot be its own child.

From abscess to zygote, SNOMED CT includes more than 311,000 unique concepts.

There are almost 800,000 descriptions in SNOMED CT, including synonyms that can be used to refer to a concept.

a “concept” is a clinical meaning identified by a unique numeric identifier (ConceptID) that never changes. formally defined in terms of their relationships

with other concepts. give explicit meaning which a computer can

process and query on. Every concept also has a set of terms that

name the concept in a human-readable way.

ConceptIDs do not contain hierarchical or implicit meaning. The numeric identifier does not reveal any

information about the nature of the concept.

Concept descriptions are the terms or names assigned to a SNOMED CT concept

“Term” in this context means a phrase used to name a concept

A unique DescriptionID identifies a description

Multiple descriptions might be associated with a concept identified by its ConceptID

Some of the descriptions associated with ConceptID 22298006: Fully Specified Name: Myocardial

infarction (disorder) DescriptionID 751689013

Preferred term: Myocardial infarction DescriptionID 37436014

Synonym: Cardiac infarction DescriptionID 37442013

Synonym: Heart attack DescriptionID 37443015

Synonym: Infarction of heart DescriptionID 37441018

Each of the above descriptions has a unique DescriptionID

Concepts are represented by a unique human-readable Fully Specified Name (FSN).

Each concept has one unique FSN intended to provide an unambiguous way to name a concept. not necessarily the most commonly used for that

concept Each FSN ends with a “semantic tag” in

parentheses at the end of the concept to indicate the semantic category to which the concept belongs

For example, Hematoma (morphologic abnormality) is a FSN that

represents the description of what the pathologist sees at the tissue level

Hematoma (disorder) is a FSN which indicates the concept that would be used to code the clinical diagnosis of a hematoma by a general practitioner.

Each concept has one Preferred Term meant to capture the common word or phrase used by clinicians to name that concept.

For example, the concept 54987000 Repair of common bile duct

(procedure) has the Preferred term “Choledochoplasty” to represent a common name clinicians use to describe the procedure.

Unlike FSNs, Preferred Terms are not necessarily unique

Occasionally, the Preferred Term for one concept may also be a Synonym or the Preferred Term for a different concept.

For example Cold sensation quality (qualifier value) has a preferred

term of “Cold.” Common cold (disorder) also has a synonym of “Cold.” In both cases, “cold” represents a common clinical phrase

used to capture the meaning of the FSN.

Synonyms represent any additional terms that represent the same concept as the FSN.

are not required to be unique across concepts.

Example: Some of the Synonyms associated

with ConceptID 22298006 which has the Fully Specified Name: Myocardial infarction (disorder) are: Synonym: Cardiac infarction

DescriptionID: 37442013 Synonym: Heart attack DescriptionID:

37443015 Synonym: Infarction of heart

DescriptionID: 37441018

four types of relationships Defining characteristics are IS_A relationships and

defining attributes. Qualifying characteristics are non-defining,

qualifying attributes. constrains the possible values an implementer can

select in assigning a qualifying characteristic to a concept

Historical relationships relate inactive concepts to active concepts. For example, a concept may be inactivated because it

is a duplicate. A “same-as” relationship would be created between the 2 concepts.

Additional relationships are other non-defining characteristics For example, PART OF which is retained for backward

compatibility with SNOMED RT.

“Supertype-Subtype relationships” or “Parent- Child relationships.”

IS_A relationships are the basis of the SNOMED CT’s hierarchies.

Attributes relate two concepts and establish the type of relationship between them.

Example: Lumbar discitis (disorder) (a concept in the Clinical

finding hierarchy) is related to concepts in the Body structure hierarchy through two attributes: FINDING SITE and ASSOCIATED MORPHOLOGY. Lumbar discitis (disorder) FINDING SITE Structure of lumbar intervertebral disc

(body structure) ASSOCIATED MORPHOLOGY Inflammation

(morphologic abnormality) The two attributes FINDING SITE and ASSOCIATED

MORPHOLOGY and their assigned values provide definition for the concept Lumbar discitis (disorder).

Example: the concept Pneumonia (disorder) is characterized with the

attribute FINDING SITE. Since pneumonia is a disorder of the lung, FINDING SITE has the value Lung structure (body structure). Pneumonia (disorder) FINDING SITE Lung structure (body structure)

Concepts in this hierarchy represent the result of a clinical observation, assessment or judgment, and include both normal and abnormal clinical states

contains the sub-hierarchy of Disease. Examples of Clinical finding concepts:

Clear sputum (finding) Normal breath sounds (finding) Poor posture (finding)

Examples of Disease concepts: Tuberculosis (disorder) Non-Hodgkin's lymphoma (disorder)

The Event hierarchy includes concepts that represent occurrences (excluding procedures and interventions)

Examples of Event concepts: Flood (event)Bioterrorisk attach (event)Earthquake (event)

This hierarchy contains such sub-hierarchies as Assessment scales and Tumor staging

Examples of Assessment scales concepts: Glasgow coma scale (assessment scale)Stanford Binet intelligence scale (assessment

scale) Examples of Tumor staging concepts:

International Federation of Gynecology and Obstetrics (FIGO) staging

Dukes staging system (tumor staging)

provides explicit links (cross maps) to health-related classifications and coding schemes in use around the worlde.g. diagnosis classifications such as ICD-9-

CM, ICD-O3, and ICD-10, as well as the OPCS-4 classification of interventions.

Additional cross-maps are also under development or consideration

Cross-maps facilitate reuse of SNOMED CT-encoded data for other purposes, such as reimbursement or statistical reporting

SNOMED CT is a multinational, multilingual terminology

has a built-in framework to manage different languages and dialects

The International Release includes a set of language-independent concepts and relationships available in US English, UK English, Spanish and

Danish Currently translations into French, Swedish,

Lithuanian, and several other languages planning to translate the standard into other

languages

This 85 year (258707000 year) old (70753007 old) (397659008 age) female (248152002 female) was admitted via the emergency room (50849002 emergency room admission) from the nursing home (42665001 nursing home) with shortness of breath (267036007 dyspnea), confusion (225440008 onset of confusion), and congestion (418092006 respiratory tract congestion). There was no history of (14732006 no history of) fever (386661006 fever) or cough (49727002 cough) noted. Patient also has a history of (392521001 history of) senile dementia (15662003 senile dementia) and COPD (13645005 chronic obstructive lung disease).

Prior to (288556008 before) admission (129273005 admission – action), the patient was taking the following medications:

Prednisone (116602009 prednisone), Lasix (81609008 furosemide), Haldol (349874003 oral haloperidol), and Colace (418528006 docusate). Patient has also been taking Lorazepam 0.5-mg tablet (349865000 oral form lorazepam) 2x a day (229799001 twice a day) as needed for anxiety (48694002 anxiety). Patient is also noted to have a vitamin C deficiency (76169001 ascorbic acid deficiency).

Is SNOMED too complicated?

The Concepts Table contains all the concepts in SNOMED CT

This table relates the various terms used to name a single SNOMED CT concept.

This table contains the relationships between SNOMED CT concepts.

The content of SNOMED CT evolves with each release.

Drivers of these changes include changes in understanding of health and disease processes; introduction of new drugs, investigations, therapies and procedures; and new threats to health, as well as proposals and work provided by SNOMED partners and licensees.

Changes designated as minor require only a history record to record the change.

The history mechanism involves the following tables: Component History Table Component History References Table

A Subset refers to a set of Concepts, Descriptions, or Relationships that are appropriate to a particular language, dialect, country, specialty, organization, user or context.

The Subset Mechanism may be used to derive tables that contain only part of SNOMED CT

Subsets are not necessarily mutually exclusive

Subset mechanism involves the following tables: Subsets Table Subset Members Table

Cross Mappings enable SNOMED CT to effectively reference other terminologies and classifications.

Each cross map matches SNOMED concepts with another coding scheme that is called the “target scheme.”

Cross Mapping mechanism involves the following tables:Cross Map Sets TableCross Maps TableCross Map Targets Table

The International Health Terminology Standards Development Organization provides technical documentation for developing SNOMED compliant applications

Technical Reference Guide – 166 pages

Technical Implementation Guide – 211 pages Includes an appendix for working with HL7

(version 3)

All Version 3 products derive their semantic content from the RIM.

Intermediary models are used to constrain the RIM for use in a particular specification. Domain Message Information Model (D-MIM),

which constrains the portion of the RIM used by a committee in the derivation of all their messages.

Refined Information Model (R-MIM), and a Hierarchical Message Definition (HMD) specify a set of Message Types.

there will be several D-MIMs derived from the RIM; there will be several R-MIMs derived from each D-MIM; and there will be several HMDs derived from each R-MIM.

SNOMED CT will typically apply these constructs to these intermediary models, rather than to RIM itself.

The RIM includes a new set of data types developed for use within the HL7 Version 3 family of standards.

Data types that can carry ConceptIDs include:Coded with Equivalents (CE), which

carries a code, the name of the coding scheme the code is drawn from, and a display name corresponding to the code; and allows synonyms to be transmitted – such as an HL7 code and its equivalent SNOMED code;

Concept Descriptor (CD), which builds on the CE by supporting the post-coordination of codes (or, stated in another way, the combining of codes from a terminology to create a new concept).

RIM attributes of type CE or CD can also have a specified vocabulary domain.

These domains can include HL7-defined concepts or can be drawn from HL7-recognized coding systems such as LOINC or SNOMED CT.

Vocabulary domains have a coding strength that can be “Coded, No Extensions” (CNE), in which case the

only allowable values for the field are those in the vocabulary domain;

“Coded, With Extensions” (CWE), in which case values other than those in the vocabulary domain (such as local codes) can be used if necessary.

The vocabulary domain specifications stated in the RIM always refer to a complete vocabulary domain.

at the RIM level there is no specialization based on realm of use or on the context and needs of a specific message.

As RIM attributes are specialized to suit a specific message context, the domain of the attribute can be reduced (constrained) to reflect the specialization.

A vocabulary domain that has been constrained to a particular realm and coding scheme (such as SNOMED CT) is called a “value set.”

Retrieve an HL7 V3 messageA receiver of one or more HL7 messages

will need to be able to extract the coded information in the message and aggregate it with concepts sent in other messages or with concepts stored in a data repository.

The entire discussion of aggregation in this section assumes that valid and conformant HL7 messages are received.

Syntactic transformationConvert the concepts in the message into

a “canonical form” (using a derived equivalence between HL7 RIM attributes and SNOMED CT relationship types).

The use of guidelines and templates can constrain the inherent flexibility of an HL7 message, and can decrease the number of sub-steps required to perform a canonical transformation. Tightly coupled systems can take this into account, and establish bilateral agreements that will minimize message variability.

AggregationAggregate the various representations, all

expressed in a common canonical form.Use techniques that query for the primary

code AND the semantic properties of the concepts of interest.

What are the implications to system designers when they try and hide the use of SNOMED from the end user?

http://www2.infoway-inforoute.ca/Documents/R2_ENGLISH%20SC%20Guide%20and%20Standards%20Catalogue.pdf

http://en.wikipedia.org (Medical_Subject_Headings, International_Statistical_Classification_of_Diseases_and_Related_Health_Problems, Unified_Medical_Language_System, LOINC, SNOMED_CT)

http://www.ihtsdo.org/snomed-ct http://eagl.unige.ch/SNOCat Campbell JR, Carpenter P, Sneiderman C, Cohn S. Chute

CG, Warren J. Phase II Evaluation of Clinical Coding Schemes: Completeness, Taxonomy, Mapping, Definitions, and Clarity. Journal of the American Medical Informatics Association. 1997;4:238-51.

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=61239&blobtype=pdf

SNOMED Clinical Terms User Guide