Mapping Terminologies (Healthcare Data Management and Governance)

Systematized Nomenclature of Medicine: Clinical Terminology (SNOMED CT) refers to a set of clinical terminologies used in recording patient data to facilitate decision-making and clinical analytics. With over 30 contributing countries, SNOMED CT is specific and comprehensive and currently comprises of 300,000 concepts, 790,000 descriptions, 19 hierarchies, and 920,000 relationships (De Silva, MacDonald, Paterson, Sikdar, & Cochrane, 2011). Each of these concepts is represented by an individual number used to describe a complex condition. This presentation helps to provide a standard by which different medical conditions and symptoms can be referred to intentionally. 

SNOMED CT is crucial for several reasons. Firstly, it enables consistent recording of clinical information, as well as an accurate and comprehensive analysis of data, making it possible to identify patients requiring follow-up or change of treatment. Also, SNOMED CT enables support systems to check data and provide real-time advice to patients. It also eliminates the issues of the language barrier by enabling multilingual use and sharing of aptly structured information with others involved in delivering health care.

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SNOMED CT maps to other international standards such as ICD-10 CM.  Mapping to ICD-10 CM is essential in providing a common language for reporting and monitoring of diseases. As a result, different people can compare and share consistent data between hospitals, and regions in a standard way over a while (Fenton, Low, Abrams, & Butler-Henderson, 2017). Mapping to ICD-10 CM also helps in expediting the collection and storage of data for analysis and evidence-based decision making

While SNOMED CT is used for recording patient data, including socioeconomic, medication use, lifestyle behaviors, and family history, ICD-10 is aimed at recording diagnoses and procedures. It comprises of the American Medical Association’s CPT codes, used for billing (Wing, 2016). In essence, SNOMED CT encompasses of clinical terminologies, while ICD-10-CM comprises of medical billing lingoes. Both SNOMED CT and ICD-10-CM terminologies are used in an electronic health record system to provide a standard way of capturing and billing diagnoses in a common medical language.

The data included in SNOMED CT is based on description logic (DL) and functions under the open-world assumption that enables data queries incorporating inferences and explicitly stated relationships between different data elements (Campbell, Pedersen, McClay, Rao, Bastola, & Campbell, 2015). Owing to ontological and the hierarchical nature of the data, SNOMED CT systems are implemented in object-oriented or relational database architecture. The types of data presented in SNOMED CT and ICD-10-CM, its format, structure and how different data elements are used is described using a data dictionary.  The data dictionary also describes the rules in which the data stored in the system must abide by, and therefore helping to achieve semantic interoperability. 

If either SNOMED Ct or ICD-10-CM is not implemented in the electronic health records, an online mapping tool called I-Magic can be used. I-magic is designed to take inputs of problems, encounter diagnosis, procedure, health history, and smoking status. For instance, in this case, the search terms entered included Urinary incontinence, Heartburn, Postoperative abdominal pain, H/O: diabetes mellitus (history of diabetes), and Postpartum headache as shown in the table below:

Mapping Problems to ICD-10-CM

SNOMED CT CodeICD-10-CM CodeICD-10-CM Code Name
Urinary incontinence (165232002)R32Unspecified urinary incontinence
Heartburn (16331000)R12Heartburn
Postoperative abdominal pain (14440001000004101)R10.9Unspecified abdominal pain
H/O: diabetes mellitus (161445009)Z86.39Personal history of other endocrine, nutritional and metabolic disease
Postpartum headache (103008008)O99.89Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium


Campbell, W. S., Pedersen, J., McClay, J. C., Rao, P., Bastola, D., & Campbell, J. R. (2015). An alternative database approach for management of SNOMED CT and improved patient data queries. Journal of biomedical informatics57, 350-357.

De Silva, T. S., MacDonald, D., Paterson, G., Sikdar, K. C., & Cochrane, B. (2011). Systematized nomenclature of medicine clinical terms (SNOMED CT) to represent computed tomography procedures. Computer methods and programs in biomedicine101(3), 324-329.

Fenton, S. H., Low, S., Abrams, K. J., & Butler-Henderson, K. (2017). Health information management: changing with time. Yearbook of medical informatics26(01), 72-77.

Watzlaf, V. J., Garvin, J. H., Moeini, S., & Anania-Firouzan, P. (2007). The effectiveness of ICD-10-CM in capturing public health diseases. Perspectives in Health Information Management/AHIMA, American Health Information Management Association4.Wing, T. L. (2016). ICD-10 medical coding: the role of perioperative services in addressing implementation challenges. AORN journal103(2), 177-188.

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