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

Bridging the Chasm April 20, 2009. How Can NLM Help?. Betsy L. Humphreys, MLS National Library of Medicine National Institutes of Health U.S. Department of Health and Human Services blh@nlm.nih.gov. NLM Long Range Plan 2006-16.

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

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  1. Bridging the Chasm April 20, 2009 How Can NLM Help? Betsy L. Humphreys, MLS National Library of Medicine National Institutes of Health U.S. Department of Health and Human Services blh@nlm.nih.gov

  2. NLM Long Range Plan 2006-16 Continue/enhance standards work in response to U.S. government priorities and feedback from “real” use in electronic health records Continue support for R & D and policy studies to help define and develop “Next Generation” electronic health records 4

  3. Among types of content standards, NLM’s primary focus is terminology… Data elements, e.g., gender, presenting complaint) Descriptions of entities, e.g., birth certificate Messages,e.g., send test result Allowable values for data elements, which can be entire vocabularies Reference values, i.e., what is “normal”? Mapping/alignmentbetween different vocabularies and with message standards Information modelsthat define the context in which standards are used Survey questions and any coded responses Guideline, protocol, and algorithm formats 5

  4. 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

  5. NLM Board of RegentsWorking Group on Health Data Standards (Final report to be released in May 2009) Provide additional tools to help users incorporate standards where they will have a positive impact Establish tight feedback/improvement loop with a set of clinical system users Promote & enable collaboration in development of terminology value sets and useful clinical subsets

  6. NLM can help to: • Determine if concepts and terms are already present in standard terminologies • term look-up, batch lists, extraction from clinical texts • Map local vocabularies to standard terminologies • Add missing concepts and terms to standard vocabularies • Create, disseminate, and update standard terminology value sets for data elements • Provide clinically useful subsets of large terminologies

  7. UMLS Metathesaurus (Apr 2009) ~2,215,395 concepts (distinct meanings) ~8,006,171 unique concept names (some are minor variations) From 152 vocabulary sources (e.g., SNOMED CT, ICD-9-CM, Gene Ontology) In 19 different languages (all concepts have English names; some have names in other languages) Associated resources:UMLS Semantic Network, Browsers, Customization tool, Lexical tools, Mapping software, etc. 9

  8. Clinical Terminology Standardssupported, licensed, or developed by NLM • SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) • Broad clinical coverage: diseases, findings, anatomy, organisms, etc. • LOINC(Logical Observation Identifiers, Names, Codes) • Specific tests, measurements, assessment instruments • RxNorm • Clinical drugs (ingredient + strength + dose form) linked to ingredients, brand names, names used by VA, commercial drug knowledge bases, etc. (RxTerms – entry vocabulary tailored for rapid data entry by US users)

  9. RxNorm standard names for drugs containing Amoxicillin

  10. RxTerms: includes drugs in current US use & allows you to divide and conquer Type ‘amoxicillin’ Pick appropriate drug/route Pick appropriate form/strength 12

  11. 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 results data in ClinicalTrials.gov

  12. 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

  13. 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/services Period of NIH Budget Doubling – 1998-2003 • 2003- 2008 – Research ~ Workforce (US-wideSNOMED CT license) Development/Maintenance Dissemination ~ Policy ~ tools/services • 2009 – Goal: More tools/services to assist in implementation, feedback and enhancement

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