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READ Codes and SNOMED Coding Systems

Table of Contents

Read Codes
Relationship between READ Codes and SNOMED
SNOMED REFSETS
Reasons for Using SNOMED
References

Read Codes

READ Codes are an integral component in clinical research and practice history. READ Codes designing commenced in 1986 by Dr. James Read with colleague Dr. David Markwell (Benson & Grieve, 2016). The codes were known as Read Clinical Classifications but commonly referred to as READ Codes after Dr. James Read. Research by Morales et al. (2017) defines the READ Codes as a thesaurus of coded clinical terms used and integrated into general healthcare UK. The READ Codes successfully supported clinical coding systems used in general practice (Benson & Grieve, 2016). A program was developed to help data in computerized systems by general practitioners. Vision has been used as a standard program for computerizing the data (Nissen et al., 2017). Consequently, the coding system was used in general medical practice diagnosis and recording of information, effectively recording and storing data.

Understanding how the Read Codes are constructed is essential to healthcare practitioners. The coding system is constructed by assigning a unique text string representing certain diagnoses and symptoms (Nissen et al., 2017). A necessity for the codes design was the development of a pre-defined fixed length for the codes and terms to be used. The system utilized a set of alphanumeric codes and terms of up to 30 characters as the specified length of the READ Code. The coding scheme was anticipated to cover computerized records of patients comprehensively. The alphanumerical data used numbers between 0-9 and letters A-Z, excluding I and O to represent the hierarchical order of data within different classifications (Benson & Grieve, 2016).

The codes representation would depend on the type of data being recorded. As the study by Benson and Grieve (2016) reveals, the clinical recording system categorized data into a broad spectrum distinguished based on specific classifications. The classifications are based on the disease, procedures, occupations, History, examinations, prevention, and administration. Data recording was systematically arranged into these classes to represent a patients record specifying the type of information being recorded.

A relevant example would be the breakdown of B136, which represents four hierarchical levels. The first level, denoted by B, described neoplasm as the diagnosis. The code further specifies the diagnosis as malignant neoplasm denoted as B1 as the second hierarchy and the third level B13 as Carcinoma Stomach. The fourth level of the alphanumerical is B136, represented as Ca greater curvature-stomach as the last level. Additionally, an example that factors in the classification of History in recording data for a smoking-related diagnosis. Benson and Grieve (2016) presented that the first level denotes the category of history/symptoms as 1, which changes to 13 in the second level to indicate social/personal History. Further, the record in the third level, 137, specifies that the History relates to tobacco consumption. A variation in the fourth level of the hierarchy may vary depending on the extent or History of smoking.

Varying numbers in the last order represent the individual extents of the diagnosis. Some of the numbers represented include 1371 for a non-smoker, 1372 for a trivial smoker, 1373 for a light smoker, 1376 for a very heavy smoker, 1377 for an ex-smoker, and 1378 for a tobacco consumption unknown (Benson & Grieve, 2016). These variations can represent various possibilities in a diagnosis and adequately denote the data in a computerized system.

Relationship between READ Codes and SNOMED

Systemized Nomenclature of Medicine, abbreviated as SNOMED, is a concept subject of discussion in clinical research and practices. Extensive research by Kate (2020) describes the concept of SNOMED as a comprehensive framework for representing clinical descriptions based on distinct categorization and conceptualization. Globally, clinical practitioners are gradually transitioning to adopt SNOMED clinical terms from using READ codes in describing and recording clinical and healthcare-related phrases (Benson & Grieve, 2016). The transition is necessitated to develop a standardized system of recording information to increase efficiency. Bhattacharyya (2016) reported that SNOMED was an evolution based on merging two systems Systemized Nomenclature of Pathology (SNOP) developed in America and Read Codes developed and used in the UK. The information substantiates a close relation between the SNOMED terminology system and the READ Codes coding system. The similarity in the conceptualization is that the two systems aimed to develop a standard of representing data.

SNOMED REFSETS

SNOMED REFSETS are identified as integral parts of defining SNOMED clinical terms. The REFSETS are references used to determine the SNOMED components to associate a clinical term and provide added properties with members of the set (National Library of Medicine & National Center for Health Statistics, 2017). Subsequently, they support the identification of subsets within the contents of SNOMED CT, alternative hierarchical structures are represented, and classifications from cross maps are created. The added alternatives and subsets of reference influence the increased purpose and mechanism of extensibility (National Library of Medicine & National Center for Health Statistics, 2017). The different sets ensure that the SNOMED coding system is comprehensive.

Reasons for Using SNOMED

SNOMED CT is a resourceful coding system that can be used in various ways for recording data. The system is essential to mitigate the coding of terms in medical practice by developing a standardized mechanism of representing clinical terms used by general practitioners allowing efficient interpretation of the information described (Bhattacharyya, 2016). The coding system is essential in clinical practice to create a global standard of recording and storing data.

Consequently, the system is necessitated to achieve global unison in healthcare systems. According to Benson and Grieve (2016), the system was a vital evolution to the coding system that enhanced the operability of clinical data influencing the adoption of a common global coding system. The globally standardized system is an added advantage for clinical research, allowing effective data interoperability in the medical field of practice. SNOMED facilitates the reduction of language barriers, ensuring data can be used anywhere, regardless of the diversity of languages worldwide. As a result, healthcare practitioners can share and reuse clinical data to enhance and advance knowledge in medicine and science in general (Bhattacharyya, 2016).

Objectively a clinician should encode data and interpret using SNOMED code. The distinction of diseases may vary and is denoted by the SNOMED CT identifier that comprises the diagnosiss concept, description, and relationship (Benson & Grieve, 2016). In representing data on a computerized system, a general practitioner may describe a term either by specifying the term or using a related synonym used in medical practice. Using this information, SNOMED interprets the information and translates the description codes as 900000000000003001 to denote the specific term used to describe the subject and 900000000000013009 to denote that a synonym was used (NHS, 2016). The comprehensive codes are vital in health records to accurately register healthcare operations, improving data relevance, access, and adequacy. Conclusively, the SNODEM coding system has radicalized science and research and has made contributions to enhancing clinical and healthcare practice.

References

Benson, T., & Grieve, G. (2016). Coding and Classification Schemes. Principles of Health Interoperability, 135154. Web.

Bhattacharyya, S. B. (2016). Introduction to SNOMED CT (1st ed. 2016 ed.). Springer.

Kate, R. J. (2020). Automatic full conversion of clinical terms into SNOMED CT concepts. Journal of Biomedical Informatics, 111, 103585. Web.

Morales, D. R., Lipworth, B. J., Donnan, P. T., Jackson, C., & Guthrie, B. (2017). Respiratory effect of beta-blockers in people with asthma and cardiovascular disease: population-based nested case control study. BMC Medicine, 15(1). Web.

National Library of Medicine & National Center for Health Statistics. (2017). SNOMED CT To ICD-10-CM Map Release Notes. NIH.GOV. Web.

NHS. (2016). Technical Report UK Edition: NHS Realm Description Refsets. Web.

Nissen, F., Morales, D. R., Mullerova, H., Smeeth, L., Douglas, I. J., & Quint, J. K. (2017). Validation of asthma recording in the Clinical Practice Research Datalink (CPRD). BMJ Open, 7(8), e017474. Web.

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