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Clinical Vocabulary Standards

Multiple projects in this area continue to promote the development, enhancement, and adoption of clinical vocabulary standards. Inter-terminology mapping promotes the use of standard terminologies by creating maps to administrative terminologies, which allows re-use of encoded clinical data.

  • CORE Subset of SNOMED CT: The CORE Problem List Subset is derived from real clinical data from eight large-scale healthcare institutions. It identifies the most frequently used SNOMED CT terms in the problem list.
  • Newborn Screening, Coding, & Terminology Guide: The NLM Newborn Screening Coding and Terminology Guide and HRSA/NLM Guidance for Exchanging Electronic Newborn Screening Orders and Results Web site facilitates the use of electronic health data standards (LOINC, SNOMED CT, UCUM, and HL7) in recording and transmitting newborn screening test results. The Guide includes standard codes and terminology for newborn tests and conditions.
  • RxNav: RxNav is an interface to the RxNorm database, designed for displaying relations among drug entities. In addition to the browser, we created SOAP-based and RESTful application programming interfaces (APIs) enabling users to integrate RxNorm in their applications.
  • RxTerms: RxTerms is a drug interface terminology derived from RxNorm for prescription writing or medication history recording (e.g. in e-prescribing systems, PHRs). The advantages of RxTerms are: Free to use. Directly links to RxNorm, the U.S. drug terminology standard, and facilitates inclusion of RxNorm identifiers in electronic health records. Efficient data entry - RxTerms separates the full names in RxNorm into two parts: drug name + route.
Publications/Tools: 
Abhyankar S, Vreeman DJ, Westra BL, Delaney CW. Letter to the Editor-Comments on the Use of LOINC and SNOMED CT for Representing Nursing Data. Int J Nurs Knowl. 2017 Aug 30. doi: 10.1111/2047-3095.12183. [Epub ahead of print]
Vreeman DJ, Abhyankar S, McDonald CJ. Re: Unit conversions between LOINC codes Published June 19, 2017. J Am Med Inform Assoc. 2017 Aug 23. doi: 10.1093/jamia/ocx087. [Epub ahead of print]
Cui L, Zhu W, Tao S, Case JT, Bodenreider O, Zhang GQ. Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT. J Am Med Inform Assoc. 2017 Jul 1;24(4):788-798. doi: 10.1093/jamia/ocw175.
Fung K, Xu J, Amey F, Gutierrez AR, D'Have A. Leveraging Lexical Matching and Ontological Alignment to Map SNOMED CT Surgical Procedures to ICD-10-PCS. AMIA Annu Symp Proc. 2017 Feb 10;2016:570-579. eCollection 2016.
Topaz M, Goss F, Blumenthal K, Lai K, Seger DL, Slight SP, Wickner PG, Robinson GA, Fung KW, McClure RC, Spiro S, Acker WW, Bates DW, Zhou L. Towards improved drug allergy alerts: Multidisciplinary expert recommendations. Int J Med Inform. 2017 Jan;97:353-355. doi: 10.1016/j.ijmedinf.2016.10.006. Epub 2016 Oct 6.
Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ. Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM). J Biomed Inform. 2015 Oct;57:88-99. doi: 10.1016/j.jbi.2015.06.023. Epub 2015 Jul 15.
Deckard J, McDonald CJ, Vreeman DJ. Supporting interoperability of genetic data with LOINC. J Am Med Inform Assoc. 2015 May;22(3):621-7. doi: 10.1093/jamia/ocu012. Epub 2015 Feb 5.
Fung KW, Xu J. An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT. J Am Med Inform Assoc. 2015 May;22(3):649-58. doi: 10.1093/jamia/ocu022. Epub 2015 Feb 26.
Dhombres F, Winnenburg R, Case JT, Bodenreider O. Extending the coverage of phenotypes in SNOMED CT through post-coordination. Stud Health Technol Inform. 2015;216:795-9.
Fung KW, Jao CS, Demner-Fushman D. Extracting drug indication information from structured product labels using natural language processing. J Am Med Inform Assoc. 2013 May 1;20(3):482-8. doi: 10.1136/amiajnl-2012-001291. Epub 2013 Mar 9.

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