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Nursing Terminology System Comparison

Paper Type: Free Essay Subject: Information Systems
Wordcount: 1859 words Published: 18th May 2020

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Nursing Terminology System Comparison

 As healthcare becomes centered around technology, there is a push for different terminologies to be utilized more and understood better. While the need for these terminologies is present, there is also a need for these terminologies and systems to become interoperable (Hammami, Bellaaj, & Kacem, 2014). By utilizing nursing terminologies and increasing interoperability nurses from all over the world will be better able to communicate with one another, charting will become more streamline, and patient will receive higher quality comprehensive care. Two of the main interdisciplinary terminology systems this paper will explore are Logical Observation Identifies Names and Codes (LOINC) and Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT).


 SNOMED CT was developed by the College of American Pathologists and England’s National Health Services (Rouse, 2010). SNOMED was originally developed in 1975, but throughout the years updates have been made and SNOMED CT was created in 2002 (The Office of the National Coordinator for Health Information Technology, 2017). It is used by healthcare providers of all types, in over eighty countries, to deliver quality care throughout multiple disciplinaries (Rouse, 2010). SNOMED CT works with other standardized organizations, such as the International Council of Nurses, to ensure all different healthcare professional can best utilize SNOMED CT (Millar, 2016).

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Currently, SNOMED CT includes 349,548 concepts (SNOMED International, 2019). On SNOMED CT the clinical concepts are divided into different categories such as clinical findings, body structure, and pharmaceutical products (Rouse, 2010). Due to the comprehensive scope of the concepts in SNOMED CT, there is a reduced need overlapping code systems (SNOMED International, 2019). SNOMED CT helps distribute information to and from healthcare providers worldwide and eliminates any confusion based on language barriers.

SNOMED CT is made up of three core components (SNOMED International, 2019). The first component are the concepts. Each concept signifies a different clinical meaning (SNOMED International, 2019). The next component are descriptions. There are two types of descriptions, fully specified names (FSN) and synonym (SNOMED International, 2019). FSN is a unique representation of a specific concepts meaning and a synonym is a term that can be used to display or select a concept (SNOMED International, 2019). There are currently 1,156,000 active descriptions within SNOMED CT (Bodenreider, Cornet, & Vreeman, 2018). The final component is relationships. Relationships are the associations between two different concepts (SNOMED International, 2019). There are 1,062,000 relationships within SNOMED CT (Bodenreider et al., 2018).

SNOMED CT is a singular aspect of what makes electronic medical health records effective. In order for SNOMED CT to be effective at supplying clinical documentation, uses need to be motivated to understand and properly implement it (SNOMED International, 2019). If used properly, SNOMED CT benefits healthcare providers, as well as the general population and researchers who are trying to implement evidenced based research into care (SNOMED International, 2019).


LOINC was developed in 1994 as a clinical terminology that identifies health measurements, observation, and documents (Bodenreider et al., 2018). In 1994 labs and clinical systems were using local names for coding which resulted in delayed data exchange between different systems (Bodenreider et al., 2018). To help fix this issue, LOINC created common terminology for laboratory findings and clinical observations (Bodenreider et al., 2018). Today, LOINC is made up of 86,000 different terms (Bodenreider et al., 2018).

LOINC uses a semantic model that consists of six major and four minor attributes (Bodenreider et al., 2018). The six major attributes are component, kind of property, time aspect, system type, type of scale, and type of method (Bodenreider et al., 2018). The method component is the only one that is optional. The main goals for LOINC is to help in the exchange of clinical care, outcomes management, and research (LOINC, 2019).

In 2002, LOINC was recognized by the American Nurses Association. A specific committee was developed to provide specific LOINC codes for nursing. The codes that were created for nursing were designed for key stages in the nursing process, such as assessment, goals and outcomes (U.S. National Library of Medicine, n.d.). These were created with nurses in mind to help streamline care.

Comparing and Contrasting

 SNOMED CT and LOINC are two of the most widely used multidisciplinary terminologies that help streamline coding for different electronic medical records (EMR). Both SNOMED CT and LOINC use different codes to term for different aspects of healthcare. Another similarity is that they both are conscientious about how important nursing terminologies are for healthcare. One of the main similarities between the two terminologies is the post-coordinated expressions (Bietenbeck, Boeker, & Schulz, 2018). SNOMED CT arrives at these measurements with a small number of entries but formal composition mechanisms and LOINC has a large amount of codes that allows for different mapping of the same analysis (Bietenbeck et al., 2019).

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While they do have many similarities they also have a few differences. In a study that looked at how helpful providers found different terminologies to be SNOMED CT was found to be 60 percent helpful and LOINC was found to be 62.5 percent helpful (Thede & Schwirian, 2014).Another main difference between the two terminologies is that LOINC focuses on codes for the questions and SNOMD CT focuses on codes for the answers (LOINC, 2019). SNOMED CT and LOINC each has individualized and specific terminology models and have specific tools that are used for development (Bodenreider et al., 2018). Codes for SNOMED CT are comprised of exclusively numbers and codes for LOINC are comprised of bother letters and numbers.

Due to the similarities between LOINC and SNOMED CT, the two institutions have entered into a collaboration. The collaboration between the two terminologies was made due to demand and to improve the collection of patient data (Mary, Soualmia, & Gansel, 2017). In order to bring LOINC and SNOMED CT together, new algorithms have been created to improve the efficiency (Mary et al., 2017).

Case Study Using SNOMED CT and LOINC

 Asthma is one of most commonly diagnosed and treated conditions seen in chronic disease management. By creating EMRs that can interact with one another it allows for improved quality of care for patients, especially asthma patients. One study looked at how SNOMED CT and LOINC help the interoperability of EMRs when dealing with asthma (Lougheed, Thomas, Wasilewski, Morra, & Minard, 2018). An asthma care map was created to see what concepts each terminology system contained related to pulmonary function tests (Lougheed et al., 2018). It was found that SNOMED CT has a complete or partial match 92 percent of the time and LOINC had a complete or partial match 83 percent of the time (Lougheed et al., 2018). While the majority of terms are incorporated into SNOMED CT and LOINC, more terms need to be added to help the interoperability of EMRs related to asthma and to increase quality of care asthma patients receive (Lougheed et al., 2018).


 SNOMED CT and LOINC are two of the most widely used multidisciplinary terminologies on the market. While they have many similarities, they also have a few differences. SNOMED CT and LOINC are used to help EMRs on the back end to code for different diagnoses, procedures, labs, and treatments. These two terminologies help ensure a universal voice is used for all healthcare professionals.


  • Bietenbeck, A., Boeker, M., & Schulz, S. (2018). NPU, LOINC, and SNOMED CT: a comparison of terminologies for laboratory results reveals individual advantages and a lack of possibilities to encode interpretive comments. LaboratoriumsMedizin42(6), 267-275.
  • Bodenreider, O., Cornet, R., & Vreeman, D. J. (2018). Recent developments in clinical terminologies—SNOMED CT, LOINC, and RxNorm. Yearbook of medical informatics27(01), 129-139.
  • Hammami, R., Bellaaj, H., & Kacem, A. H. (2014). Interoperability for medical information systems: an overview. Health and Technology4(3), 261-272.
  • LOINC. (2019). FAQ: LOINC basics. Retrieved from https://loinc.org/faq/basics/
  • LOINC. (2019). FAQ: LOINC and other standards. Retrieved from https://loinc.org/faq/loinc-and-other-standards/
  • 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 Asthma55(6), 629-639.
  • Mary, M., Soualmia, L., & Gansel, X. (2017). Usability and Improvement of Existing Alignments: The LOINC-SNOMED CT Case Study: LNAI 10180.
  • Millar, J. (2016). The Need for a Global Language-SNOMED CT Introduction. Studies in health technology and informatics225, 683-685.
  • The Office of the National Coordinator for Health Information Technology. (2017). Standard nursing terminologies; a landscape analysis. Retrieved from https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf
  • Rouse, M. (2010). SNOMED CT (Systematized Nomenclature of Medicine — Clinical Terms). SearchHealthIT. Retrieved from https://searchhealthit.techtarget.com/definition/SNOMED-CT
  • SNOMED International. (2019). 5-step briefing. Retrieved from https://www.snomed.org/snomed-ct/five-step-briefing
  • Thede, L., & Schwirian, P. (2014). Informatics: The Standardized Nursing Terminologies: A National Survey of Nurses’ Experience and Attitudes—SURVEY II: Participants’ Perception of the Helpfulness of Standardized Nursing Terminologies in Clinical Care. OJIN: The Online Journal of Issues in Nursing20(1).
  • U.S. National Library of Medicine. (2019). Nursing resources for standards and interoperability. Retrieved from https://www.nlm.nih.gov/research/umls/Snomed/nursing_terminology_resources.html



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