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1 Betsy L. Humphreys, MLS Betsy L. Humphreys, MLS National Library of Medicine National Library of Medicine National Institutes of Health National Institutes of Health U.S. Department of Health and Human Services U.S. Department of Health and Human Services [email protected] [email protected] How Can NLM Help? How Can NLM Help? Bridging the Chasm Bridging the Chasm April 20, 2009 April 20, 2009
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Page 1: 1 Betsy L. Humphreys, MLS Betsy L. Humphreys, MLS National Library of Medicine National Library of Medicine National Institutes of Health National Institutes.

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Betsy L. Humphreys, MLSBetsy L. Humphreys, MLS National Library of MedicineNational Library of Medicine National Institutes of HealthNational Institutes of Health

U.S. Department of Health and Human ServicesU.S. Department of Health and Human Services

[email protected]@nlm.nih.gov

How Can NLM Help?How Can NLM Help?

Bridging the ChasmBridging the ChasmApril 20, 2009April 20, 2009

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NLM Long Range Plan 2006-16NLM Long Range Plan 2006-16

• Continue/enhance standards work in response to Continue/enhance standards work in response to U.S. government priorities and feedback from U.S. government priorities and feedback from “real” use in electronic health records“real” use in electronic health records

• Continue support for R & D and policy studies to Continue support for R & D and policy studies to help define and develop “Next Generation” help define and develop “Next Generation” electronic health recordselectronic health records

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Among types of content standards, Among types of content standards, NLM’s primary focus is NLM’s primary focus is terminologyterminology……

• Data elementsData elements, , e.g., gender, presenting complaint)e.g., gender, presenting complaint)

• Descriptions of entitiesDescriptions of entities, , e.g., birth certificatee.g., birth certificate

• Messages,Messages, e.g., send test resulte.g., send test result

• Allowable values for data elementsAllowable values for data elements, , which can be which can be entire entire vocabulariesvocabularies

• Reference values, Reference values, i.e., what is “normal”?i.e., what is “normal”?

• Mapping/alignmentMapping/alignment between different vocabularies and with between different vocabularies and with message standardsmessage standards

• Information modelsInformation models that define the context in which standards are that define the context in which standards are usedused

• Survey questionsSurvey questions and any coded responses and any coded responses

• Guideline, protocol, and algorithm formatsGuideline, protocol, and algorithm formats

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Some NLM Assumptions

• Standardizing some data elements during initial data capture will be cost-effective

• Standardization can occur “under the hood” in clinical information systems

• You don’t need to choose between standard vocabulary and accurate patient data

• We need to use and perfect the standard terminologies we already have – not create new ones

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NLM Board of RegentsNLM Board of RegentsWorking Group on Health Data StandardsWorking Group on Health Data Standards

(Final report to be released in May 2009)(Final report to be released in May 2009)• Provide additional tools to help users incorporate Provide additional tools to help users incorporate

standards where they will have a positive impact standards where they will have a positive impact • Establish tight feedback/improvement loop with a Establish tight feedback/improvement loop with a

set of clinical system users set of clinical system users • Promote & enable collaboration in development of Promote & enable collaboration in development of

terminology value sets and useful clinical subsetsterminology value sets and useful clinical subsets

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NLM can help to:NLM can help to:

• Determine if concepts and terms are Determine if concepts and terms are alreadyalready present in standard terminologiespresent in standard terminologies– term look-up, batch lists, extraction from clinical textsterm look-up, batch lists, extraction from clinical texts

• Map local vocabularies to standard terminologiesMap local vocabularies to standard terminologies• Add missing concepts and terms to standard Add missing concepts and terms to standard

vocabulariesvocabularies• Create, disseminate, and update standard Create, disseminate, and update standard

terminology value sets for data elementsterminology value sets for data elements• Provide clinically useful subsets of large Provide clinically useful subsets of large

terminologiesterminologies

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UMLS Metathesaurus (Apr 2009)UMLS Metathesaurus (Apr 2009)

~2,215,395 concepts (~2,215,395 concepts (distinct meaningsdistinct meanings))

~8,006,171 unique concept names (~8,006,171 unique concept names (some are minor variations)some are minor variations)

From From 152 vocabulary sources (152 vocabulary sources (e.g., SNOMED CT, ICD-9-CM, e.g., SNOMED CT, ICD-9-CM,

Gene OntologyGene Ontology))

In In 19 different languages (19 different languages (all concepts have English names; all concepts have English names;

some have names in other languagessome have names in other languages))

Associated resources:Associated resources: UMLS Semantic Network, Browsers, UMLS Semantic Network, Browsers,

Customization tool, Lexical tools, Mapping software, etcCustomization tool, Lexical tools, Mapping software, etc..

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Clinical Terminology StandardsClinical Terminology Standardssupported, licensed, or developed by NLM supported, licensed, or developed by NLM

• SNOMED CTSNOMED CT (Systematized Nomenclature of Medicine – (Systematized Nomenclature of Medicine – Clinical Terms)Clinical Terms)– Broad clinical coverage: diseases, findings, anatomy, organisms, etc.Broad clinical coverage: diseases, findings, anatomy, organisms, etc.

• LOINCLOINC (Logical Observation Identifiers, Names, Codes)(Logical Observation Identifiers, Names, Codes)– Specific tests, measurements, assessment instrumentsSpecific tests, measurements, assessment instruments

• RxNormRxNorm– Clinical drugs (ingredient + strength + dose form) linked to ingredients, Clinical drugs (ingredient + strength + dose form) linked to ingredients,

brand names, names used by VA, commercial drug knowledge bases, etc.brand names, names used by VA, commercial drug knowledge bases, etc.

((RxTerms RxTerms – entry vocabulary tailored for rapid data – entry vocabulary tailored for rapid data entry by US users)entry by US users)

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RxNorm standard names for drugs containing Amoxicillin

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RxTerms: RxTerms: includes drugs in current US use includes drugs in current US use & allows you to divide and conquer& allows you to divide and conquer

• Type ‘amoxicillin’Type ‘amoxicillin’

• Pick appropriate drug/routePick appropriate drug/route

• Pick appropriate form/strengthPick appropriate form/strength 12

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NLM also provides :

• Public access to: – Detailed Research Protocols and data elements (with

value sets) in dbGaP – database of Genomes and Phenomes

– Structured Product Labels for medications as released by the FDA (linked to standard RxNorm names and identifiers) in the DailyMed

– Reference values to identify the locations of clinically significant genetic variation in RefSeqGene

– “Standard” clinical trial registry and summary summary results results data in data in ClinicalTrials.govClinicalTrials.gov

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Marital status

• Among the variations found in dbGaP…– 1=Single 2=Married 3=Widowed 4=Divorced 5=Separated

– 1=Married 2=Single (never married) 3=Divorced 4=Widowed 5=Separated

– 1=Married 2=Widowed 3=Divorced/Separated 4=Never married 5=Unknown/refused

– 1=Never been married 2=Married 3=Officially separated 4=Divorced by law 5=Widow/Widower

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NLM and Health Data Standards

• 1971 – Research ~ Workforce (Informatics Training Grants)• 1986 – Research ~ Workforce (UMLS Project)

• 1991 – Research ~ Workforce (High Performance Computing) Dissemination ~ Policy

• 1996 - Research ~ Workforce (HIPAA)Dissemination ~ Policy ~ tools/servicesPeriod of NIH Budget Doubling – 1998-2003

• 2003- 2008 – Research ~ Workforce (US-wide SNOMED CT license)

Development/Maintenance Dissemination ~ Policy ~ tools/services

• 2009 – Goal: More tools/services to assist in implementation, feedback and enhancement


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