Nursing and Allied Health: Controlled Medical Vocabularies

CDE

Specialized Control Vocabularies

• Use controlled vocabularies to disambiguate and facilitate clear communication
• Assign an identifier to describe or refer to a specific concept
• Controlled vocabularies may be very broad, or cover a specific domain
• Mesh - Medical Subject Headings - is a controlled vocabulary that has both broad and narrower subject headings.

What is medical terminology?

• Medical terminology is a set of specialized terms that facilitate precise communication by minimizing or eliminating ambiguity.
• Features of a terminology
  • ​Unique Identifier
  • Official name
  • Synonyms (in many cases)
  • Examples:
    • ​CPT, ICD-10, SNOMED - CT, LOINC,  RXNorm

Medical Subject Headings

Medical subject headings are a controlled vocabulary used to index scholarly journals in nursing and allied health. They are very easy to use. Knowing and using the MESH terms increase the likelihood that you will find the articles you are seeking.  

There are several  ways to check your search terms to see if they are in the MESH index.  One method is called the MeSH on Demand.  Enter your term in the field designated.  For instance, enter the term Diabetes Mellitus (Type 2). Review terms above and below the term you entered in the MeSH tree.

CINAHLSubject Headings

The CINAHL subject headings are built onto the MeSH Headings Tree, Additional specific nursing and allied health headings are added as appropriate. Additionally, new terms from MeSH may be added as well.

CINAHL subject headings are updated and revised on annual basis toward the end of the calendar year. When new scientific and medical terms are introduced, new headings may be added and applied retroactively to records in the CINAHL databases.

International Classification of Diseases -- ICD-10 & ICD-11

International Classification of Diseases (ICD) - ICD-10-CM  has very specific diagnostic codes, a skill that both coders and physicians must master to code successfully. Moving beyond the transition to ICD-10, the new edition focuses on the key role proper documentation plays in supporting medical necessity.

The Diagnostic and Statistical Manual of Mental Disorders (DSM–5)

The Diagnostic and Statistical Manual of Mental Disorders (DSM–5) is the product of more than 10 years of effort by hundreds of international experts in all aspects of mental health. gnostic and Statistical Manual of Mental Disorders (DSM–5). see:  Changes to ICD-10-CM Codes for DSM–5 Diagnoses

Related articles in Academic Search Complete: Interesting Articles...

Greco, M. (2016). What is the DSM? Diagnostic manual, cultural icon, political battleground: an overview with suggestions for a critical research agenda. Psychology & Sexuality, 7(1), 6–22. https://doi.org/10.1080/19419899.2015.1024470

Kogan, C. S., Stein, D. J., Maj, M., First, M. B., Emmelkamp, P. M. G., & Reed, G. M. (2016). The Classification of Anxiety and Fear-Related Disorders in the ICD-11. Depression & Anxiety (1091-4269), 33(12), 1141–1154. https://doi.org/10.1002/da.22530

DSM eBooks in the EBSCO e-Book Full-Text Library

Electronic clinical quality measures (eCQMs) use data electronically extracted from electronic health records (EHRs) and/or health information technology systems to measure the quality of health care provided. The Centers for Medicare & Medicaid Services (CMS) use eCQMs in a variety of quality reporting and value-based purchasing programs.

Hospitals and providers use eCQMs to provide feedback on their care systems and to help them identify opportunities for clinical quality improvement. eCQMs are also used in reporting to CMS, The Joint Commission, and commercial insurance payers in programs that reimburse providers based on quality reporting.

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Ast, K., Kamal, A. H., Lindley, L. C., Matzo, M., & Rotella, J. D. (2018). Maintaining the Momentum of Measuring What Matters: Overcoming Hurdles To Develop Electronic Clinical Quality Measures. Journal of Palliative Medicine, 21(2), 123–124. https://doi.org/10.1089/jpm.2017.0515

Colin, N. V., Cholan, R. A., Sachdeva, B., Nealy, B. E., Parchman, M. L., & Dorr, D. A. (2018). Understanding the Impact of Variations in Measurement Period Reporting for Electronic Clinical Quality Measures. EGEMS (Generating Evidence & Methods to Improve Patient Outcomes), 6(1), 1–8. https://doi.org/10.5334/egems.235

Heisey-Grove, D., Wall, H. K., Helwig, A., Wright, J. S., & Centers for Disease Control and Prevention (CDC). (2015). Using electronic clinical quality measure reporting for public health surveillance. MMWR: Morbidity & Mortality Weekly Report, 64(16), 439–442.

Lettvin, R. J., Wayal, A., McNutt, A., Miller, R. S., & Hauser, R. (2018). Assessment and Stratification of High-Impact Data Elements in Electronic Clinical Quality Measures: A Joint Data Quality Initiative Between CancerLinQ® and Cancer Treatment Centers of America. JCO Clinical Cancer Informatics, 2, 1–10. https://doi.org/10.1200/CCI.17.00139

Benefits

• eCQMs use detailed clinical data to assess the outcomes of treatment by healthcare providers and organizations
• eCQMs foster the goal of access to real-time data for bedside quality improvement and clinical decision support

LOINC is a system used by laboratories and clinical observers to send clinical data electronically from laboratories to hospitals, physician's offices, and payers who use the data for clinical care and management purposes.

Benefits

A consistent and clear understanding of laboratory and other clinically relevant information

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Abhyankar, S., Vreeman, D. J., Westra, B. L., & Delaney, C. W. (2018). Letter to the Editor—Comments on the Use of LOINC and SNOMED CT for Representing Nursing Data. International Journal of Nursing Knowledge, 29(2), 82–85. https://doi.org/10.1111/2047-3095.12183

Dixon, B. E., Hook, J., & Vreeman, D. J. (2015). Learning From the Crowd in Terminology Mapping: The LOINC Experience. Laboratory Medicine, 46(2), 168–174. https://doi.org/10.1309/LMWJ730SVKTUBAOJ

Keenan, G. M., Yao, Y., Lopez, K. D., Sousa, V. E. C., Stifter, J., Macieira, T. G. R., Boyd, A. D., Herdman, T. H., Moorhead, S., McDaniel, A., & Wilkie, D. J. (2018). Response To: Letter to The Editor – Comments on The Use of LOINC and SNOMED CT for Representing Nursing Data. International Journal of Nursing Knowledge, 29(2), 86–88. https://doi.org/10.1111/2047-3095.12182

Lougheed, M. D., Thomas, N. J., Wasilewski, N. V., Morra, A. H., & Minard, J. P. (2018). Use of SNOMED CT® and LOINC® to standardize terminology for primary care asthma electronic health records. Journal of Asthma, 55(6), 629–639. https://doi.org/10.1080/02770903.2017.1362424

Matney, S. A., Settergren, T. (Tess), Carrington, J. M., Richesson, R. L., Sheide, A., & Westra, B. L. (2017). Standardizing Physiologic Assessment Data to Enable Big Data Analytics. Western Journal of Nursing Research, 39(1), 63–77. https://doi.org/10.1177/0193945916659471

Metke-Jimenez, A., Steel, J., Hansen, D., & Lawley, M. (2018). Ontoserver: a syndicated terminology server. Journal of Biomedical Semantics, 9(1), N.PAG. https://doi.org/10.1186/s13326-018-0191-z

Qi Li, Deleger, L., Lingren, T., Zhai, H., Kaiser, M., Stoutenborough, L., Jegga, A. G., Cohen, K. B., & Solti, I. (2013). Mining FDA drug labels for medical conditions. BMC Medical Informatics & Decision Making, 13(1), 1–11. https://doi.org/10.1186/1472-6947-13-53

Regenstrief Institute updates LOINC database and RELMA. (2018). CAP Today, 32(1), 54.

Upcoming LOINC workshop and meeting. (2016). CAP Today, 30(10), 102.

Upcoming LOINC conference. (2018). CAP Today, 32(2), 63.

Vreeman, D. J., & Richoz, C. (2015). Possibilities and Implications of Using the ICF and Other Vocabulary Standards in Electronic Health Records. Physiotherapy Research International, 20(4), 210–219. https://doi.org/10.1002/pri.1559

Wilkerson, M. L., Henricks, W. H., Castellani, W. J., Whitsitt, M. S., & Sinard, J. H. (2015). Management of Laboratory Data and Information Exchange in the Electronic Health Record. Archives of Pathology & Laboratory Medicine, 139(3), 319–327. https://doi.org/10.5858/arpa.2013-0712-SO

• SNOMED CT based clinical information benefits individual patients and clinicians as well as populations while supporting evidence-based care.
• Enabling support systems to check the record and provide real-time advice
• Allowing accurate and comprehensive analysis that identifies patients who require follow-up or changes of treatment
• Removing language barriers – SNOMED CT enables multilingual use

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Seckman, C., Fisher, C., & Demner-Fushman, D. (2013). OUTSTANDING POSTER-RESEARCH...University of Maryland School of Nursing’s 23rd Annual Summer Institute in Nursing Informatics (SINI), July 17 to 19, 2013. CIN: Computers, Informatics, Nursing, 31(9), 410. https://doi.org/10.1097/01.NCN.0000435223.29760.89

SNOWMED CT to ICD-10-CM Cross Map: Preview Release. (2012). NLM Technical Bulletin, 385, 27.

US Extension to SNOWMED CT Updated. (2012). NLM Technical Bulletin, 385, 24.

RX Norm and RX NAV are two different products for describing drugs:

CINAHL PLUS WITH FULL TEXT: Interesting Articles...

Müller, L., Gangadharaiah, R., Klein, S. C., Perry, J., Bernstein, G., Nurkse, D., Wailes, D., Graham, R., El-Kareh, R., Mehta, S., Vinterbo, S. A., & Aronoff-Spencer, E. (2019). An open access medical knowledge base for community driven diagnostic decision support system development. BMC Medical Informatics & Decision Making, 19(1), N.PAG. https://doi.org/10.1186/s12911-019-0804-1

The RxClass Browser is a web application for exploring and navigating through the class hierarchies to find the RxNorm drug members associated with each drug class.

The purpose of the National Library of Medicine Unified Medical Language System (UMLS) is to facilitate the development of computer systems that behave as if they "understand" the meaning of the language of biomedicine and health. The UMLS provides data for system developers as well as search and report functions for less technical users.

There are three UMLS Knowledge Sources:

• The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies
• The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain
• The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing

VSAC - Value Set Authority Center

The VSAC value sets are lists of codes and corresponding terms that define clinical concepts, from NLM-hosted standard clinical vocabularies (such as SNOMED CT®, RxNorm, LOINC® and others), Data and information from Centers for Medicare & Medicaid Services (CMS) electronic Clinical Quality Measures (eCQMs) utilize VSAC.

CDEs - Common data elements are used in clinical research, patient registries, and other human subject research in order to improve data quality and opportunities for comparison and combination of data from multiple studies and with electronic health records.

• electronic data inter change
  
  MLA (Modern Language Assoc.)
  Gerald Glandon. Information Systems for Healthcare Management, Eighth Edition. Vol. Eighth edition, Health Administration Press, 2014.
  
  APA (American Psychological Assoc.)
  Gerald Glandon. (2014). Information Systems for Healthcare Management, Eighth Edition: Vol. Eighth edition. Health Administration Press.
  EDI - Electronic Data Exchange
• EHR - Electronic Health Record
• EMR - Electronic Medical Record
• ENIAC - Electronic Numerical Integrator and Calculator
• FDDI - Fiber Distributed Data Exchange
• GAN - Global Area Network
• HIE - Health Information Exchange
• HIMSS - Healthcare Information Management Systems Society
• HIPAA - Healthcare Insurance Portability and Accountability Act
• HIT - Healthcare Inforamtion Technology

The Health Care Provider Taxonomy code set is an external, nonmedical data code set designed for use in an electronic environment, specifically within the ASC X12N Health Care transactions. This includes the transactions mandated under HIPAA.  Link to Version 21.0

Centers for Medicare & Medicaid Services (CMS.gov).  Use the National Uniform Claim Committee (NUCC) Code set list.  See:  Table 1: PROV-CLASSIFICATION-TYPE (PRV088) codes and descriptions (T-MSIS Data Dictionary)

• Level I, Provider Grouping
  A major grouping of service(s) or occupation(s) of health care providers. For example: Allopathic & Osteopathic Physicians, Dental Providers, Hospitals, etc.

• Level II, Classification
  A more specific service or occupation related to the Provider Grouping. For example, the Classification for Allopathic & Osteopathic Physicians is based upon the General Specialty Certificates as issued by the appropriate national boards. The following boards will however, have their general certificates appear as Level III areas of specialization strictly due to display limitations of the code set for Boards that have multiple general certificates: Medical Genetics, Preventive Medicine, Psychiatry & Neurology, Radiology, Surgery, Otolaryngology, Pathology.

• Level III, Area of Specialization
  A more specialized area of the Classification in which a provider chooses to practice or make services available. For example, the Area of Specialization for provider grouping Allopathic & Osteopathic Physicians is based upon the Subspecialty Certificates as issued by the appropriate national boards.