Month: September 2022

In our last two posts on the topic of ‘Data Standards in Health Informatics’ we provided an overview of data standards in health informatics, and we covered SNOMED CT and ICD data standard in detail.

Before you read this article further, we highly recommend you read Part I and II of the series:

• Data Standards in Health Informatics | Part I
• Data Standards in Health Informatics | Part II

In this penultimate post (of data standards series) we will provide an in-depth view on ‘National Drug Codes (NDC)’ and ‘RxNorm’ data standards . Though both are used – in a loose sense – to provide data standards for drugs, however there are some subtle (and not so subtle) differences that one should be mindful of.

At a high level, NDC is FDA’s identifier for drugs and primarily used for drug reimbursement; whereas RxNorm is maintained by National Library of Medicine and primarily used in personal health records applications.

Let’s dive into details…

RxNorm

Hospitals, pharmacies, and other organizations use computer systems to record and process drug information. Because these systems use many different sets of drug names, it can be difficult for one system to communicate with another. To address this challenge, RxNorm provides normalized names and unique identifiers for medicines and drugs. The goal of RxNorm is to allow computer systems to communicate drug-related information efficiently and unambiguously through a semantic interoperation between drug terminologies.

RxNorm’s normalized names for clinical drugs and links to many of the drug vocabularies are commonly used in pharmacy management and drug interaction software, including those of First Databank, Micromedex, and Gold Standard Drug Database.

RxNorm includes the United States Pharmacopeia (USP) Compendial Nomenclature from the United States Pharmacopeial Convention. USP is a cumulative data set of all Active Pharmaceutical Ingredients (API). RxNorm contains the names of prescription and many over-the-counter drugs available in the United States.

It’s important to note that non-therapeutic radiopharmaceuticals, bulk powders, contrast media, food, dietary supplements, and medical devices are all out-of-scope for RxNorm. Medical devices include but are not limited to bandages and crutches.

RxNorm provides a unique RXCUI number (of up to eight digits) for each medication. RXCUI can be used to retrieve a great deal of information. Some FHIR Resources reference RxNorm codes. The NLM provides RxNav, a free RxNorm browser. It also provides a free API, for accessing the codes.

Example: if we search for drug trastuzumab on RxNav, we can see all related Brand Names.

National Drug Codes (NDC)

National Drug Codes (NDC) The National Drug Code (NDC) is a US-specific standard for medications maintained by the US Food and Drug Administration (FDA). It consists of a simple 10 digit, 3-segment number. The first segment indicates the manufacturer/labeler/vendor. The second indicates the product name. The third part indicates the packaging. The same medication can have many NDC codes particularly if its patent has expired so it can be produced as a generic drug by many manufacturers.

In the example below ‘50242’ is identifier for Genentech, ‘132’ corresponds to Herceptin and the last two digits correspond to packaging.

It’s noteworthy that you will not find trastuzumab (which is the normalized drug name for the brand Herceptin) in the NDC directory, however, it’s present RxNorm.

Sources:

[1] This article has been inspired by Mark L. Braunstein’s book: “Health Informatics on FHIR: How HL7’s API is Transforming Healthcare (Second Edition)”

[2] NIH: https://www.nlm.nih.gov/

[3] NDC: https://www.accessdata.fda.gov/scripts/cder/ndc/dsp_searchresult.cfm

In our last post on the topic of ‘Data Standards in Health Informatics – Part I’ we provided an overview of data standards in health informatics, and we covered SNOMED CT data standard in detail. In this post we will provide an in-depth view on ‘International Classification of Diseases (ICD) .

The International Classification of Diseases (ICD) is the oldest data standard, dating directly back to the 1800s and more indirectly to earlier centuries when researchers became interested in the causes of human mortality. Traditionally, ICD is a list or classification of medical diagnoses maintained by the World Health Organization (WHO) since 1948 and updated every 10 years. The current version, ICD-10, was adopted in 1994 but was not adopted in the US until 2015. [ICD-11 was rolled out starting Jan 2022 ].

If you look at ICD data then whether it’s ICD-9 or ICD-10, depends on the date of service. If the date of service was before Oct 1, 2014, then ICD-9 was used to code the diagnosis. And for date of service on or after Oct 1, 2014, ICD-10 was used . The switch from ICD-9 to ICD-10 adds complexity in analyzing longitudinal data that spans both ICD-9 and ICD-10.

The switch from ICD-9 to ICD-10 was a substantial effort because ICD-10 is a major quantitative and qualitative expansion over ICD-9. While ICD-9 had 13,000 codes the ICD-10 has some 68,000 codes to represent very specific clinical details.

One of the major differences between ICD-9 and 10 is presence of ‘laterality’ in ICD-10, something ICD-9 did not have. In technical terms, laterality is localization of function or activity on one side of the body in preference to the other, e.g., “Malignant neoplasm of right female breast, left-outer quadrant”. There is almost 50% expansion of the number of codes in ICD-10 (over ICD-9) because of laterality.

The table below shows how one ICD-9 code can map to three potential ICD-10 codes due to presence of laterality in ICD-10:

Beyond size, ICD-10 is an ontology capable of representing clinical relationships not represented in ICD-9. For example, ICD-10 can encode the fact that a patient has gout affecting their right ankle and foot and they have developed a uric acid deposit, called a tophus, in that area. ICD-9 cannot specifically code for gout located in the ankle and foot, much less on the right side.

The figure below shows the various components of the ICD-10 code:

Here is one more example to understand ICD-10 code better:

S52         Fracture of forearm
S52.5      Fracture of lower end of radius
S52.52    Torus fracture of lower end of radius
S52.521   Torus fracture of lower end of right radius
S52.521A Torus fracture of lower end of right radius, initial encounter for closed fracture

In the above example, S52 is the category. The fourth and fifth characters of “5” and “2” provide additional clinical detail and anatomic site. The sixth character “1” in this example indicates laterality, i.e., right radius. The seventh character, “A”, is an extension that provides additional information, which means “initial encounter” in this example. It also demonstrates the use of the full code titles, which was not the format in the ICD-9 diagnosis code set.

The ICD-10 code sets include greater detail, changes in terminology, and expanded concepts for injuries, laterality, and other related factors. The complexity of ICD-10 provides many benefits because of the increased level of detail conveyed in the codes. The complexity also underscores the need to be adequately trained on ICD-10 in order to fully understand reporting changes that have come with the new code sets.

Sources:
[1] This article has been inspired by Mark L. Braunstein’s book: “Health Informatics on FHIR: How HL7’s API is Transforming Healthcare (Second Edition)”