Health Terminologies
Last updated
Observational Research with EHR Data
Last updated
Health terminologies are used to capture, classify, and analyze data to
Support the conduct of health care
Manage the payments associated with healthcare, and
Facilitate research to improve the health of individuals and populations
A health terminology consists of a vocabulary of terms for precise, unambiguous communication, associated codes to facilitate computer processing of those terms, and an organization of those terms and codes to enable search and retrieval.
Let’s illustrate a terminology with LEGO® bricks. Each brick represents a vocabulary term. In the below example terminology, the terms are classified or organized by shape or size. And each term is assigned a code.
In another terminology, we may have some of the same or similar terms, but they are classified differently, in this case, by color, leading to the use of different codes.
The following coding systems are the vocabulary standards specified for data elements in the United States Core Data for Interoperability (USCDI), a standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange. Learn more about USCDI here.
"[The International Classification of Diseases Clinical Modification (ICD-CM)] is the official system of assigning codes to diagnoses and procedures associated with hospital utilization in the United States." [1] For more information about ICD-CM, visit https://www.cdc.gov/nchs/icd/icd9cm.htm.
"The Systematized Medical Nomenclature for Medicine - Clinical Terminology (SNOMED CT) is a clinical terminology sustem that provides a standardized and scientiically validated way of representing clinical inormation captured by clinicians." [2] For more information about SNOMED CT, visit https://www.snomed.org/five-step-briefing.
"[The Healthcare Common Procedure Coding System (HCPCS)] is a collection of standardized codes that represent medical procedures, supplies, products and services. The codes are used to facilitate the processing of health insurance claims by Medicare and other insurers." [3] For more information about HCPCS, visit https://www.cms.gov/medicare/coding-billing/healthcare-common-procedure-system.
"The Current Procedural Terminology (CPT) codes offer [clinicians] a uniform language for coding medical services and procedures..." [4] For more information about CPT, visit https://www.ama-assn.org/practice-management/cpt/cpt-overview-and-code-approval.
"[Logical Observation Identifiers Names and Codes (LOINC)] is a code system [] for clinical and laboratory observations, health care screening/survey instruments, and document type identifiers." [5] For more information about LOINC, visit https://loinc.org/.
"RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software..." [6]. For more information about RxNorm, visit https://www.nlm.nih.gov/research/umls/rxnorm/index.html.
The FDA requires all clinical drugs to be identified using a National Drug Code (NDC), "which is a universal product identifier for human drugs." [7] For more information about NDC, visit https://www.fda.gov/drugs/development-approval-process-drugs/national-drug-code-database-background-information.
The following groupers gather and categorize health terminology codes into searchable groupings. Researchers can efficiently gather health terminology codes through the use of these groupers, utilizing pre-existing phenotypes rather than creating their own.
"Phecodes are widely used and easily adapted phenotypes based on [ICD] codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults. [PhecodeX] is an expanded version of phecodes, [creating a] more robust representation of the medical phenome for global use in discovery research." [8]
Researchers can use PhecodeX to find pre-gathered and categorized groups of ICD-9-CM and ICD-10-CM codes.
For step-by-step PhecodeX instructions, refer to Health Data Standards / Health Terminologies / Finding Health Terminology Codes.
The Clinical Classifications Software (CCS) for ICD-9-CM (CCSR for ICD-10-CM) is a diagnosis and procedure categorization scheme that collapses ICD codes into a smaller number of clinically meaningful categories that are sometimes more useful for presenting descriptive statistics than individual ICD codes. [9]
For more information about CCS and CCSR, refer to the following resources:
For step-by-step CSS/CCSR instructions, refer to Health Data Standards / Health Terminologies / Finding Health Terminology Codes.
The Anatomical Therapeutic Chemical (ATC) classification system divides medications into different groups according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties. [10]
For more information about ATC, visit https://www.who.int/tools/atc-ddd-toolkit/atc-classification.
For step-by-step ATC instructions, refer to Health Data Standards / Health Terminologies / Finding Health Terminology Codes.
Centers for Disease Control and Prevention. (n.d.). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). Retrieved May 6, 2024, from https://www.cdc.gov/nchs/icd/icd9cm.htm
Vuokko, R. (2021). The Systematized Medical Nomenclature for Clinical Care (SNOMED CT): An Overview of a Clinical Terminology for Electronic Health Records. Healthcare Informatics Research, 27(1), 1–6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941898/
National Library of Medicine. (n.d.). HCPCS. U.S. National Library of Medicine. Retrieved May 6, 2024, from https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/HCPCS/index.html
American Medical Association. (n.d.). CPT overview and code approval. AMA Practice Management. Retrieved May 6, 2024, from https://www.ama-assn.org/practice-management/cpt/cpt-overview-and-code-approval
Centers for Medicare & Medicaid Services. (n.d.). LOINC. CMS Measures Management System. Retrieved May 6, 2024, from http://mmshub.cms.gov/measure-lifecycle/measure-specification/specify-code/LOINC
National Library of Medicine. (n.d.). RxNorm. U.S. National Library of Medicine. Retrieved May 6, 2024, from https://www.nlm.nih.gov/research/umls/rxnorm/index.html
U.S. Food and Drug Administration. (n.d.). National Drug Code Database: Background Information. FDA - U.S. Food and Drug Administration. Retrieved May 6, 2024, from https://www.fda.gov/drugs/development-approval-process-drugs/national-drug-code-database-background-information
Megan M Shuey, William W Stead, Ida Aka, April L Barnado, Julie A Bastarache, Elly Brokamp, Meredith Campbell, Robert J Carroll, Jeffrey A Goldstein, Adam Lewis, Beth A Malow, Jonathan D Mosley, Travis Osterman, Dolly A Padovani-Claudio, Andrea Ramirez, Dan M Roden, Bryce A Schuler, Edward Siew, Jennifer Sucre, Isaac Thomsen, Rory J Tinker, Sara Van Driest, Colin Walsh, Jeremy L Warner, Quinn S Wells, Lee Wheless, Lisa Bastarache, Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics, Bioinformatics, Volume 39, Issue 11, November 2023, btad655, https://doi.org/10.1093/bioinformatics/btad655
Agency for Healthcare Research and Quality. (n.d.). Clinical Classifications Software (CCS) - Healthcare Cost and Utilization Project (HCUP). Retrieved May 6, 2024, from https://hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp
World Health Organization. (n.d.). ATC Classification. Retrieved May 6, 2024, from https://www.who.int/tools/atc-ddd-toolkit/atc-classification
Bodenreider O, Cornet R, Vreeman DJ. Recent Developments in Clinical Terminologies - SNOMED CT, LOINC, and RxNorm. Yearb Med Inform. 2018 Aug;27(1):129-139. doi: 10.1055/s-0038-1667077. Epub 2018 Aug 29. PMID: 30157516; PMCID: PMC6115234.
Wei WQ, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ, Gamazon ER, Cox NJ, Roden DM, Denny JC. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS One. 2017 Jul 7;12(7):e0175508. doi: 10.1371/journal.pone.0175508. PMID: 28686612; PMCID: PMC5501393.
Megan M Shuey, William W Stead, Ida Aka, April L Barnado, Julie A Bastarache, Elly Brokamp, Meredith Campbell, Robert J Carroll, Jeffrey A Goldstein, Adam Lewis, Beth A Malow, Jonathan D Mosley, Travis Osterman, Dolly A Padovani-Claudio, Andrea Ramirez, Dan M Roden, Bryce A Schuler, Edward Siew, Jennifer Sucre, Isaac Thomsen, Rory J Tinker, Sara Van Driest, Colin Walsh, Jeremy L Warner, Quinn S Wells, Lee Wheless, Lisa Bastarache, Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics, Bioinformatics, Volume 39, Issue 11, November 2023, btad655, https://doi.org/10.1093/bioinformatics/btad655
