Keywords Vocabulary, controlled - Logical Observation Identifiers Names and Codes, RxNorm -
Systematized Nomenclature of Medicine
Introduction
Over the past decades, biomedical ontologies and terminologies have increasingly been
recognized as key resources for knowledge management, data integration, and decision
support[1 ]. Among the dozens of ontologies and terminologies available, some have been identified
as having high impact on clinical practice and biomedical research[2 ] and their evolution has been investigated[3 ].
The recent acceleration in the deployment of electronic health record (EHR) systems
has precipitated the emergence of a few terminologies and their wide adoption in the
clinical community. Two of them, the Systematized Nomenclature of Medicine Clinical
Terms (SNOMED CT) and the Logical Observation Identifiers, Names, and Codes (LOINC®),
have become international standards. The last one, RxNorm, is used mostly in the U.S.,
but similar national drug terminologies exist in other countries (e.g., the NHS Dictionary
of medicines and devices (dm+d)[4 ] in the U.K., the Australian Medicines Terminology (AMT)[5 ] in Australia) and could have been substituted for RxNorm in this review. In addition
to being designated standards mandated for use in U.S. governmental programs, such
as the Meaningful Use incentive program[6 ], these three clinical terminologies have also been selected as the terminological
backbone of the Observational Medical Outcomes Partnership (OMOP) common data model
(CDM) used for clinical data warehouses internationally by OHDSI, the Observational
Health Data Sciences and Informatics collaborative[7 ].
While SNOMED CT, LOINC, and RxNorm are referenced in many articles investigating aspects
of their organization or use, no recent publication has characterized their similarities
and differences or analyzed how they can harmoniously contribute to an interoperable
health information ecosystem. The objective of this work is to provide a brief review
of their history, current state, and future development. This work is also an opportunity
to contrast them, and outline areas for greater interoperability among them. In addition
to being a review of the literature, this article also reflects the experience of
the three authors with the development of these three terminologies, respectively,
for at least a decade.
SNOMED CT
Brief History
Since the inception of the Structured Nomenclature of Pathology, SNOP, in 1965, the
various versions of SNOMED have developed both in terms of content and underlying
representation. Development of content is clearly illustrated by the number of elements
in the various systems. SNOP comprised “about 15,000 distinct medical objects, processes,
and concepts”[8 ]. It developed further into SNOMED-2, and later SNOMED International, which contained
150,000 concepts in the mid-1990s[9 ]. Its successor, SNOMED Reference Terminology (SNOMED RT), contained over 120,000
active concepts[10 ]. The initial version of SNOMED CT, the merger of SNOMED RT and the UK-based Clinical
Terms Version 3, released January 2002, consisted of 278,000 active concepts, a number
that has grown to 341,000 in the January 2018 release of SNOMED CT.
The representation has gone through phases of increasing formal rigor. The initial
SNOP and SNOMED versions were multi-axial systems that enabled post-coordination.
Whereas SNOP started as a 4-axis system, SNOMED International had expanded to using
12 axes: anatomy (topography), morphology (pathologic structure), normal and abnormal
functions, symptoms and signs of disease, chemicals, drugs, enzymes and other body
proteins, living organisms, physical agents, spatial relationships, occupations, social
contexts, diseases/diagnoses and procedures[9 ]. SNOMED RT abandoned the use of self-standing axes that could be combined into composite
codes in favor of a description logic formalism called Ontylog[11 ], based on the Knowledge Representation System Specification (KRSS) syntax and the
K-REP system[9 ]. Following contemporary naming conventions for description logics, the set of constructors
used corresponds to the EL ++ language[12 ]. SNOMED CT has continued to use this description logic as its underlying representation.
The evolution of the representation of “arthritis” through the history of SNOMED provides
an illustration of editorial changes over time, with examples from SNOMED-2, SNOMED
International, and the current version of SNOMED CT ([Table 1 ]).
Table 1
Evolution of the representation of “arthritis” through the history of SNOMED.
Source
Code
Name
Definition
SNOMED-2
D-3060
Inflammatory Athropathy
D-3050 | Disease of Joints | ; M-40000 | Inflammation |
SNOMED International
D1-20050
Arthritis
T-1 5001 | Joints | ; M-40000 | Inflammation |
SNOMED CT
3723001
Arthritis (disorder)
Formal definition, using concept identifiers:
≡ 64572001 ⊓ ∃ RoleGroup.(∃ 116676008.23583003 ⊓ ∃ 363698007.39352004)
Verbose definition, using fully specified names:
Equivalent to : Disease (disorder) AND RoleGroup SOME (Associated morphology SOME
Inflammation (morphologic abnormality) AND Finding site SOME Joint structure (body
structure))
Current State
SNOMED CT Content
Since the first release of SNOMED CT in January 2003, updated versions have been released
twice a year. [Figure 1 ] shows the evolution of the number of concepts, relationships, and English descriptions
over time. It shows that maintenance can lead to a decrease of the number of elements,
e.g., in January 2010 when, among others, the veterinary content was removed from
the International Release. The January 2018 release contains 341,000 active concepts,
1,062,000 active relationships and 1,156,000 active descriptions. The largest categories
of concepts in SNOMED CT are disorders (22%), procedures (17%), body structures (11%),
clinical findings other than disorders (10%), and organisms (10%). In SNOMED CT parlance,
descriptions are labels that describe the concepts, not textual definitions of the
concept, of which SNOMED CT contains only 4,000.
Fig. 1 Evolution of the number of concepts, relationships, and English descriptions in SNOMED
CT over time.
A significant design criterion for SNOMED is to keep concept expressions simple enough
to be broadly usable by clinicians, while maintaining faithful representation of concept
meaning[11 ]. Validity of concept expressions is determined by adherence to the concept model
underlying SNOMED CT. This concept model specifies which types of relationship are
allowed for which concepts, and what the allowed values are. For example, the concept
model specifies that “Method” is an allowed attribute relationship for “Procedure”
concepts, for which the value should be a type of “Action”. The January 2018 release
uses 82 relationship types, almost twice the number of the initial 42 relationship
types. Since July 2017, the concept model is available in machine-readable form, and
distributed as separate tables which are part of the semi-annual releases. Other tables
provide mappings to several versions of the International Classification of Diseases
(ICD-9-CM, ICD-10 and ICD-O). The current release format, called Release Format 2
(RF2), supports versioning, providing access to any previous release of SNOMED CT.
SNOMED CT Adoption
When ownership of SNOMED CT was transferred to the newly formed International Health
Terminology Standards Development Organization (IHTSDO, recently renamed SNOMED International)
in 2007, this organization consisted of nine member countries, with a joint population
approaching 500 million. In the majority of the inaugural member countries (USA, UK,
Canada, Australia, and New Zealand), the official language was English, and a minority
(Netherlands, Sweden, Denmark, Lithuania) had other official languages.
Over time, the organization has expanded, now covering 32 countries as of May 2018[13 ], with a total population of over 2 billion, and including a broad range of languages.
Each year, the SNOMED CT Expo[14 ] provides a forum for EHR vendors, health terminology specialists, and the community
of practice to exchange best practices and measure progress towards the implementation
of SNOMED CT across the world. The primary role of SNOMED CT as a reference terminology
does not require localization for the official language. Still, to be understood by
developers and clinicians, a complete or partial translation is an asset. Alternatively,
interface terminologies in the everyday language can be developed and mapped to SNOMED
CT[15 ]. Partial or full translations of SNOMED CT have been developed in Danish, Dutch,
French, Spanish, and Swedish.
Membership is a prerequisite for adoption, but it is not sufficient. While translations
and interface terminologies may facilitate adoption, regulations have had a strong
impact on adoption at a national level. Such regulations are in place in the USA,
where EHR systems are required to use SNOMED CT for documenting problem lists, procedures,
and some clinical findings, such as smoking status[16 ], and in the UK, where many health information systems “must use SNOMED CT as the
clinical terminology standard within all electronic patient level recording and communications
before 1 April 2020”[17 ]. Adoption of SNOMED CT was described in[18 ] and is monitored by SNOMED International[19 ].
SNOMED CT Collaborations
SNOMED CT is not developing in isolation, but increasingly collaborating and harmonizing
with other relevant standards in the area of structured and standardized storage and
exchange of biomedical data. As described in[3 ], this includes mapping, as well as binding of information model and terminology.
Mappings are maintained between SNOMED CT and a number of terminology systems. These
include the World Health Organization (WHO) classifications (e.g., versions ICD-10
and ICD-O of the International Classification of Diseases), as well as the International
Classification of Primary Care (ICPC-2), the International Classification for Nursing
Practice (ICNP), and LOINC. The latter is especially important in the context of the
specifications of the U.S. Meaningful Use incentive program, in which LOINC is the
primary choice for specifying attributes, and SNOMED CT the system of use for the
relevant attribute values. In other words, LOINC is used to specify the question (e.g.,
29308-4: “what is the diagnosis?”), and SNOMED CT to specify the answer (e.g., 3723001:
“Arthritis”). Adoption of this principle by both the Regenstrief Institute and SNOMED
International has formed the basis for a cooperation agreement in 2013[1 ].
SNOMED CT also has collaborations for specific domains. In the context of rare diseases,
collaboration with Orphanet leads to harmonization of content between SNOMED CT and
ORDO, the Orphanet ontology of rare diseases[20 ]. Medical device terminology is addressed in collaboration with the Global Medical
Device Nomenclature Agency (GMDNA). Finally, Kaiser Permanente's Convergent Medical
Terminology (CMT) provides concepts and descriptions to be considered for inclusion.
Moreover, SNOMED CT forms the backbone for the development of national extensions
by member countries of SNOMED International. National extensions typically contain
concepts that are important in a given country, but not in scope for the international
release of SNOMED CT. Other extensions, such as the veterinary extension, contain
content specific to a given community of practice.
Future Directions
The adherence to ℰℒ++ and, since 2009, the provision of a script to convert SNOMED
CT into OWL (Web Ontology Language) representation have enabled the use of SNOMED
CT in generic tools based on Semantic Web technologies, such as Protégé[21 ] and description logic reasoners, such as SnoRocket, Pellet, and FaCT++[22 ]. This has resulted in a broad range of interests and developments. On the one hand,
there are organizations that rely on the terminology as provided by SNOMED CT, mainly
using the hierarchical relationships. On the other hand, there is an interest in the
use of reasoners, which are essential for processing fully defined concepts in an
extension, and in expanding the language underlying SNOMED CT, for example to include
so-called concrete domains (e.g., dose strength of medication) or to support negation,
e.g., to explicitly express “non-viral disorders”[23 ]. The challenge is to provide a balance between these seemingly conflicting requirements,
for which SNOMED International has launched a “proposal to enhance SNOMED CT's logic
capabilities” late 2017[24 ]. This proposal will lead to the introduction of new tables, in which the OWL representation
of SNOMED CT content will be provided. In the longer run, this OWL representation
may allow for the use of a more expressive language, but the resulting relationships
table will still reflect the current expressivity. This accommodates concomitant use
of simpler tools for processing the SNOMED CT hierarchy and use of a more expressive
language that can be processed using default description logic reasoners.
LOINC
Brief History
Logical Observation Identifiers, Names, and Codes (LOINC®) is a clinical terminology
for identifying health measurements, observations, and documents. LOINC was initiated
in 1994 by the Regenstrief Institute, a non-profit medical research organization associated
with Indiana University. By 1994, many electronic systems were sending clinical information
as discrete results using messaging standards such as Health Level Seven (HL7) or
ASTM 1238 (American Society for Testing and Materials). Inside these messages, laboratories
and clinical systems used local, idiosyncratic names and codes to identify which test
was being reported. This was problematic for data exchange and aggregation because
of the large resources it takes to map codes between every participating system.
To solve this problem, Regenstrief organized the LOINC Committee to develop a common
terminology for laboratory and clinical observations[15 ]
[16 ]. Existing terminologies were not granular enough, focused on coding for billing
rather than clinical results delivery, or did not fit with the messaging models being
used. Because such a standard did not exist, the LOINC Committee embarked on creating
a terminology with the appropriate level of granularity for defining the names of
observations used in laboratory and clinical information systems. Since its creation,
LOINC has continued to be developed and published by the Regenstrief Institute as
a freely available global standard with a rich set of implementation tools. Today,
LOINC is used by a diverse global community who propel its continuous development.
LOINC's primary role is to provide identifiers and names for observations[25 ]. Here, we use observation as a generic term for health data represented in a particular
way. In different domains, these might be called tests, variables, or data elements.
Within and among health IT systems, observations are communicated with a structure
that has two key structural elements. The first element identifies what the observation
is, e.g., diastolic blood pressure, hematocrit, tobacco smoking status. The second
element carries the result value of the observation, e.g., 80 (mmHg), 40 (%), or “current
every day smoker”. When used together, these two elements carry the instance of a
specific test result for a given patient. A common pairing is to use LOINC as the
standard code for the observation, and SNOMED CT as the standard code for the observation
value when needed. This approach is endorsed by the developers of both terminologies
and fits their design purpose.
Some mistakenly believe that LOINC is only for laboratory tests. It is true that the
first release of LOINC in May 1995 contained only terms for laboratory testing, but
by December of 1996, LOINC had already added about 1,500 clinical measurement terms
(vital signs, ECG measures, etc). Now, more than 20 years and 60 releases later, LOINC
has grown significantly in other domains, including radiology[26 ], standardized survey instruments and patient-reported outcomes measures[27 ], clinical documents[28 ], nursing management data[29 ], and nursing assessments[22 ]. The LOINC Committee itself is now composed of three major composite committees:
Laboratory, Clinical, and Radiology.
Current State
LOINC Content
Regenstrief continues to update LOINC and publish twice-yearly releases. New concepts
are added to LOINC based on submissions from end users. The current version (December
2017, version 2.63) contains more than 86,000 terms covering the full scope of laboratory
testing (chemistry, microbiology, molecular pathology, …etc.) and a broad range of
clinical measurements (e.g., vital signs, ECG, patient-reported outcomes, …etc.).
In addition to distributing the terminology, Regenstrief makes available at no cost
a variety of supporting tools and resources, including the Regenstrief LOINC® Mapping
Assistant (RELMA®) and online search application[30 ].
LOINC uses a semantic data model containing six major and up to four minor attributes
to create fully-specified names for concepts.[16 ] The major attributes of the LOINC name are:
component (e.g., what is measured, evaluated, or observed)
kind of property (e.g., mass, substance, catalytic activity)
time aspect (e.g., 24-hour collection)
system type (e.g., context or specimen type within which the observation was made)
type of scale (e.g., ordinal, nominal, narrative)
type of method (e.g., procedure used to make the measurement or observation).
The atomic elements that make up each LOINC term name are called “Parts” and are also
assigned identifiers. The combination of attribute values produce term names that
are detailed enough to distinguish among similar observations. Of the six attributes,
only the method is optional and used only when necessary to distinguish among clinical
important differences.
For example, the molar concentration of sodium measured in the plasma (or serum) with
quantitative result is represented in LOINC as shown in [Table 2 ], along with the correspondence between LOINC Parts and their equivalent concepts
in SNOMED CT.
Table 2
Representation of molar concentration of sodium measured in the plasma (or serum)
with its quantitative result in LOINC (2951-2) and correspondence between LOINC Parts
concepts and SNOMED CT.
Attribute
Value (Part)
LOINC code
SNOMED CT code
Component
Sodium
LP15099-2
304050002
Property
SCnc — Substance Concentration (per volume)
LP6860-3
118556004
Timing
Pt — Point in time (Random)
LP6960-1
123029007
System
Ser/Plas — Serum or Plasma
LP7576-4
122592007
Scale
Qn — Quantitative
LP7753-9
30766002
Method
--
Over time, LOINC has not only grown in size ([Figure 2 ]), but also developed additional data structures and content around its main codes
for individual observations. The LOINC release contains a basic hierarchy that organizes
LOINC codes into a set of is-a relationships. LOINC now has a detailed model for representing
enumerated collections of observations, such as laboratory panels (complete blood
count), assessment instruments (e.g., PHQ-9), data sets (National Trauma Data Standard),
and forms (e.g., U.S. Standard Birth Certificate). This content is published in a
special release artifact called the LOINC Panels and Forms File, with the current
version (December 2017) containing more than 3,000 panel terms. LOINC also has a detailed
model for connecting observation terms to structured answer lists. These answer lists
can be defined by extension or intension, and linked to observation terms with different
types (e.g., example, preferred, normative). This content is published in the LOINC
Answer File, with the current version (December 2017) containing links between 15,966
unique LOINC terms and 3,239 unique answer lists composed of coded LOINC Answers,
and including mappings to other terminologies such as SNOMED CT where they exist.
LOINC now also publishes the atomic elements (called Parts) that make up each LOINC
term name. The LOINC Part File includes the Part identifiers and names, links between
Parts and LOINC terms, and mappings from LOINC Parts to other terminologies such as
SNOMED CT and RxNorm where they exist.
Fig. 2 Evolution of the number of LOINC terms (all terms, and laboratory terms only) over
time.
In the last decade, Regenstrief has put special emphasis on crafting narrative summaries
for content within LOINC. These annotated summaries focus primarily on explaining
precisely what is being measured, how the observation is performed, what it is used
for, and its clinical relevance. Having such definitions within LOINC greatly enhances
the ability of LOINC users to make accurate mapping choices. Currently, there are
about 10,200 definitions for 10,000 unique parts as well as term-level definitions
for about 10,000 LOINC terms.
LOINC Adoption
LOINC has become widely adopted as the standard for laboratory and clinical observations
in the USA and internationally. Today, there are more than 60,000 registered users
from 170 countries and it has been translated into 18 variants of 12 languages[31 ]. More than 30 countries have adopted LOINC as a national standard. There are many
different kinds of LOINC users, including reference labs, radiology centers, health-related
federal agencies, care organizations, professional societies, health information exchange
networks, insurance companies, health IT vendors, in vitro diagnostic (IVD) testing
vendors, health app developers, and more.
Adoption of LOINC has enabled many kinds of large scale informatics applications.
Here we mention just a few examples. National health record systems in Austria (called
“ELGA”)[32 ] and Estonia (called “ENHIS”) use LOINC for standard coding of laboratory tests.
National health programs in Australia, Malaysia, the Netherlands, New Zealand, Qatar,
Rwanda, Saudi Arabia, Turkey, and many other countries use LOINC to enable interoperability.
These programs support initiatives in maternal health, disease surveillance, international
cross-border patient care, and more. In France, the public health code for practice
of laboratory medicine adopted the French specification of IHE XD-LAB profile for
laboratory reporting, which includes LOINC for lab test identification. Many countries,
such as Brazil, Italy, Spain, and Canada set national policies that enable interoperability
with regional health information exchanges. In Canada, for example, those exchanges
now include nearly all lab results across all provinces.
Within the USA, the Meaningful Use incentive program requires LOINC in messages reporting
laboratory test results, exchanging medical summaries, and sending data to cancer
registries and public health agencies. About 20 U.S. federal agencies have adopted
LOINC in various programs[33 ]. The U.S. Food and Drug Administration (FDA) will be requiring LOINC for lab test
data in regulated studies starting after March 2020, and they have articulated a broad
vision of using real-world evidence in post market surveillance that depends on standardized
data. The 2017 Interoperability Standards Advisory of the Office of the National Coordinator
for Health Information Technologies (ONC) lists LOINC for many interoperability needs,
including functional status, laboratory tests, imaging diagnostics, nursing observations,
vital signs, and social determinants of health. The Centers for Medicaid and Medicare
Services adopted LOINC for the patient assessment instruments required in post-acute
care settings. Large-scale research networks, such as PCORnet, the National Patient-Centered
Clinical Research Network[34 ], OHDSI, the Observational Health Data Sciences and Informatics research group[28 ], and the FDA's Mini-Sentinel[29 ], all use LOINC in their common data models.
LOINC Collaborations
Regenstrief is committed to working with developers of health data standards that
are complementary to LOINC, including syntax standards for data exchange and other
terminology standards. Regenstrief and HL7 have a long-standing collaboration; a few
joint work highlights include clinical genomics guides, claims attachments specifications,
and approaches for representing vocabulary standards in Fast Healthcare Interoperability
Resources (FHIR) terminology services (Regenstrief Institute plans to make core LOINC
content available via a FHIR API as part of its normal release process beginning Summer
2018). Regenstrief and the IEEE Standards Association, developer of the 11073™ standards,
are collaborating to enhance the interoperability of traditional medical devices and
personal health devices. Regenstrief is an active member of the Health Standards Collaborative
(HSC) which provides an executive forum for senior leadership of the U.S. healthcare
standards development community to improve interoperability. Recently, Regenstrief
worked with the in vitro diagnostic (IVD) Industry Connectivity Consortium (IICC)
on a new specification for publishing vendor IVD tests associated with a set of LOINC
codes that identify the distinct observations produced by the test[35 ].
In 2013, Regenstrief and SNOMED International formed a landmark long-term collaborative
relationship to link the rich clinical semantics of SNOMED CT to LOINC, which provides
extensive coverage of laboratory tests and clinical measurements. Regenstrief and
the Radiological Society of North America (RSNA) have unified the RadLex™ Playbook
and LOINC radiology terms to produce a single, comprehensive standard for radiology
procedures with a shared governance.
Future Directions
The growth in user adoption and continued innovation in diagnostic testing continue
to fuel requests for new LOINC content. In particular, global initiatives in precision
health are expanding the interest in representing genomic data. LOINC has been actively
involved in the efforts to develop new models of reporting clinical genomic results,
and includes codes for cytogenetic or mutation analysis tests, specific chromosomal
alteration or mutation testing, and fully structured discrete genetic test reporting[31 ]. In addition, efforts such as a collaboration with the Clinical Pharmacogenetics
Implementation Consortium (CPIC) are facilitating the application of pharmacogenetics
to clinical practice by developing guidelines for clinicians[32 ]. Precision health initiatives are also driving interest in social, behavioral, and
environmental determinants of health, and LOINC is expected to keep adding content
for representing assessment instruments and community-level variables.
Significantly less effort is needed to achieve interoperability when data is standardized
upstream at the producing systems. In this regard, we are particularly excited about
efforts by IVD vendors to adopt and publish mappings from their internal codes to
LOINC codes. The LIVD standard[35 ] will greatly improve the efficiency and consistency with which laboratories can
deploy LOINC. We also anticipate that measurement devices and data collection apps
will increasingly incorporate standard terminologies into their emitted data, which
will ease their incorporation into downstream systems.
RxNorm
Brief History
At the dawn of the twenty-first century, there was no standard terminology for drugs
in the U.S.[36 ]. While many companies provide information about drugs for use in clinical information
systems (e.g., to support clinical decision), each drug knowledge base defines its
own codes and names for medications, making it difficult to exchange information across
clinical information systems and to retrieve information from different systems. For
example, the same transdermal patch delivering 0.583 milligrams of nicotine per hour
for 24 hours (e.g., to help with smoking cessation) is referred to with the following
codes and names in three of the major drug knowledge bases:
• 2707
nicotine 14 mg/24 hr transdermal film, extended release
• 102712
Nicotine 14 MG/24 HR Transdermal Patch, Extended Release
• 016426
NICOTINE 14 mg/24 hour TRANSDERM PATCH, TRANSDERMAL 24 HOURS
In addition to capitalization differences, there is a lack of standardization in naming
dose forms (transdermal film vs. transdermal patch) and units (24 hr vs. 24 hour),
making it difficult to parse names from multiple systems.
RxNorm was created to address the lack of standardization in drug names, and to make
drug terminologies interoperable by integrating them into a reference system[34 ]. Since the 1990s, the National Library of Medicine (NLM) has released the Unified
Medical Language System (UMLS), a terminology integration system in which names and
codes from all major biomedical terminologies are integrated, and equivalent terms
across vocabularies are identified. RxNorm can be thought of as a specialized version
of the UMLS. While both UMLS and RxNorm are built upon existing vocabularies, one
major difference between the two is that UMLS generally does not create names for
biomedical entities. In contrast, RxNorm creates a “normal form” for every drug entity
it integrates. In RxNorm parlance, normal forms are standardized terms for drug entities.
For example, the normal form for the nicotine patch discussed above is “24 HR Nicotine
0.583 MG/HR Transdermal System”, to which RxNorm permanently assigns the concept unique
identifier 198029. Unlike the UMLS, RxNorm also defines a rich network of named relationships
among the various types of drug entities it integrates (e.g., ingredient, brand name,
generic drug product, branded drug product).
The main use cases RxNorm was designed to support include electronic prescribing,
drug information exchange, and mapping across drug vocabularies (e.g., for medication
reconciliation purposes). Standard names and codes for drugs were also expected to
facilitate the development of standard clinical decision support rules involving medications.
RxNorm started in 2002. It was first released through the UMLS[37 ] and has been published as an independent terminology with monthly releases since
November 2004, and weekly updates since 2008 to reflect drugs recently marketed in
the U.S. market. The number of sources integrated in RxNorm has grown from 5 to 13.
Current State
RxNorm Content
Sources.
RxNorm currently integrates terminology information from most drug knowledge base
vendors (e.g., First DataBank, Multum, Micromedex, Gold Standard), as well as the
drug component of standard terminologies (e.g., SNOMED CT, MeSH). RxNorm also integrates
sources from several U.S. federal agencies, including the Food and Drug Administration
(FDA) Structured Product Labels, the Veterans Health Administration (VHA) National
Drug File, the Centers for Medicare & Medicaid Services (CMS) Formulary Reference
File, as well as the list of vaccines administered (CVX) maintained by the National
Center of Immunization and Respiratory Diseases at the Centers for Disease Control
and Prevention (CDC). More recently, RxNorm has also integrated international drug
resources, such as the Anatomical Therapeutic Chemical (ATC) Classification System
and DrugBank, a drug resource used in many research projects[36 ].
Organization.
The RxNorm drug model distinguishes between generic and branded drug entities and
identifies three major definitional elements for drug products, namely ingredient,
strength, and dose form, along with two additional elements, quantity factor and qualitative
distinction[38 ]. The major types of drug entities include ingredient (e.g., Azithromycin), brand
name (e.g., Zithromax), clinical drug (e.g., Azithromycin 250 MG Oral Tablet), and
branded drug (e.g., Zithromax 250 MG Oral Tablet). Generic and branded packs are collections
of clinical and branded drugs, respectively (e.g., Z-PAK, a branded pack of 6 tablets
of 250 milligrams of azithromycin). While drugs are sold mostly pre-packaged in some
countries (e.g., individual tablets in blister packs), packs are available for a minority
of drugs in the U.S. In addition to the six major drug entities, RxNorm provides entities
for navigational purposes. Clinical and branded drug components associate ingredient
(or brand name) and strength information, and clinical and branded dose forms associate
ingredient (or brand name) and dose form information. As shown in [Figure 3 ], the various types of drug entities in RxNorm are organized into a graph that can
be easily traversed, enabling users to navigate among types of entities (e.g., to
find the branded drugs associated with a given ingredient).
Fig. 3 Organizational structure of RxNorm, with its different types of drug entities (ingredient,
brand name, clinical drug component, branded drug component, clinical drug, branded
drug, generic pack, branded pack), using Azithromycin products as an example. Generic
entities are on the left-hand side and branded entities are on the right hand-side.
The definitional elements for each type of drug entity are indicated on the left.
The lines between types of drug entities represent named relationships in RxNorm.
(Relationship names are omitted for simplicity).
While its main organization principles have remained centered on the ingredient-strength-dose
form triad, RxNorm has added definitional features to accommodate distinctions, such
as duration for extended release forms and transdermal systems (e.g., the quantity
factor “24 HR” in “24 HR Nicotine 0.583 MG/HR Transdermal System”) and size of unit
of presentation for injectable medications (e.g., the quantity factor “40 ML” in “40
ML Ciprofloxacin 10 MG/ML Injection”), as well as qualitative elements for specific
drugs (e.g., the qualitative distinction “Sugar-Free” in “Sugar-Free Cholestyramine
Resin 4000 MG Powder for Oral Suspension”). A drug product in RxNorm is fully defined
by its set of ingredient, strength, dose form, quantity factor, and qualitative distinction
values. Types of ingredients include multi-ingredients (e.g., Sulfamethoxazole / Trimethoprim)
and “precise ingredients” (e.g., Atorvastatin calcium, Morphine Sulfate), generally
denoting, salts, esters, and complexes of base substances.
As illustrated in the examples above, Rx-Norm normal forms reflect the definitional
features of drug entities. Additionally, Rx-Norm explicitly links drug entities to
these features, which supports efficient processing. For example, the generic nicotine
patch “24 HR Nicotine 0.583 MG/HR Transdermal System” is linked to its ingredient
(Nicotine), strength (0.583 MG/HR), dose form (Transdermal System), and quantity factor
(“24 HR”).
Coverage.
The scope of RxNorm is different from that of drug knowledge bases. RxNorm focuses
on drug names and codes. In other words, clinical information (e.g., indications,
drug classes, and drug-drug interactions) and administrative information (e.g., drug
pricing) are out of scope for RxNorm. Although it integrates international sources
(e.g., ATC, DrugBank), RxNorm focuses on drug products marketed in the U.S. Finally,
non-therapeutic radiopharmaceuticals, bulk powders, contrast media, food, dietary
supplements, and medical devices, such as bandages and crutches, are all out of scope
for RxNorm[38 ].
In addition to the codes from drug terminologies and knowledge bases, RxNorm integrates
codes from the FDA's National Drug Code (NDC) Directory, which serve as product identifiers
for drugs in billing transactions, and contains links to Structured Product Labels
(i.e., package inserts) submitted by drug manufacturers to the FDA.
The February 2018 edition of RxNorm includes 11,697 (base) ingredients, 6,053 brand
names, 18,486 clinical drugs, 10,425 branded drugs, 380 generic packs, and 456 branded
backs. [Figure 4 ] shows the evolution of the number of active RxNorm concepts over time[2 ].
Fig. 4 Evolution of the number of active RxNorm concepts over time.
Permanent identifiers.
With each monthly update, the RxNorm content is kept current and in sync with drugs
available on the U.S. market, i.e., new drug products are added and drug products
no longer available are retired. RxNorm identifiers are never reused and can be safely
used as permanent identifiers for drugs in clinical data warehouses and prescription
datasets. However, any given release of RxNorm only contains detailed information
about active drug products in that release.
RxNorm Adoption
Publication and usage.
RxNorm is published on a fixed schedule, with monthly releases on the first Monday
of each month. RxNorm is published as a set of relational files, with a schema similar
to that of the UMLS Metathesaurus. As with the UMLS, users need to complete a license
agreement to access to the RxNorm files. There is no cost for accessing RxNorm, but
some of the RxNorm content is subject to intellectual property restrictions, namely
the names and codes from proprietary sources. The “prescribable subset” of RxNorm
is a subset restricted to sources from Federal Agencies. Free of proprietary information,
these files are publicly available[38 ].
Also publicly available are graphic and programming interfaces developed to expose
some of the content of RxNorm. The RxNav browser[39 ] allows users to explore RxNorm from a variety of names and codes (including proprietary
names and codes), but only returns the information that is publicly available in RxNorm.
Similarly, the RxNorm application programming interface (API)[40 ] allows users to integrate RxNorm in their applications. RxNav and the RxNorm API
are also closely integrated with companion resources, facilitating access to additional
information, such as drug classes and drug-drug interaction information.
The RxNorm files are downloaded about 1,000 times each month. RxNav has over 2,000
unique users and serves some 500,000 queries annually. The RxNorm API has over 20,000
unique users and serves some 800 million queries annually. Usage of RxNorm has increased
markedly after the 2014 EHR certification criteria designated RxNorm as the vocabulary
for medications as part of the Meaningful Use incentive program.
Use cases.
The main use cases for RxNorm are presented below:
Electronic prescribing. The National Council for Prescription Drug Programs (NCPDP) is a standards development
organization. Its SCRIPT standard for e-prescribing[41 ] requires RxNorm as its standardized medication nomenclature.
Information exchange. RxNorm is often used as the drug vocabulary for exchanging drug medication information
across clinical or administrative systems. For example, the U.S. Department of Defense
(DoD) and Department of Veterans Affairs (VA) have relied on RxNorm to mediate drug
information across their respective electronic medical record systems[40 ].
Formulary development. The Centers for Medicare & Medicaid Services (CMS) use RxNorm in their Formulary
Reference File, as part of the guideline for Medicare drug benefits[42 ].
Reference value sets. The drug value sets used in electronic clinical quality measures for the Meaningful
Use incentive program are defined in reference to RxNorm[43 ].
Analytics. Increasingly, RxNorm is used as the standard for drugs in clinical data warehouses.
For example, OHDSI, the Observational Health Data Sciences and Informatics research
group, uses RxNorm for representing drugs as part of its Observational Medical Outcomes
Partnership (OMOP) common data model (CDM)[44 ]. PCORnet, the National Patient-Centered Clinical Research Network makes similar
use of RxNorm in its common data model[45 ].
Of note, analytics was not among the use cases RxNorm was initially designed to support.
One specific issue here is that many drug identifiers recorded in clinical data warehouses
may no longer be valid in the current release of RxNorm and detailed information about
the corresponding drugs may be missing. To address this issue, the RxNorm API has
developed functions to support a history mechanism for RxNorm entities and codes from
the National Drug Code (NDC) Directory[46 ]. Moreover, RxNorm is purposely biased towards drugs marketed in the U.S. To support
their international analytics efforts, the OHDSI research group has extended Rx-Norm
to drug terminologies used in other countries[47 ].
RxNorm Collaborations
In the development of RxNorm content, NLM has worked in close collaboration with the
vendors of drug knowledge bases, with federal partners, and with representatives of
the pharmacy services industry represented by NCPDP. Similarly, the development of
RxNav and the RxNorm API have greatly benefited from the feedback provided by their
user community. For the past five years, NLM has held an annual DailyMed/RxNorm Jamboree
Workshop to bring together the RxNorm stakeholders.
To extend the usefulness of RxNorm despite its limited scope, NLM has also initiated
partnerships with providers of clinical information that can be linked to RxNorm.
For example, NLM has developed companion APIs to link RxNorm drugs to various drug
classification systems and to publicly available sources of drug-drug interaction
information[48 ]. Drug classes for RxNorm drugs can also be explored through the Rx-Class application[49 ].
Future Directions
The new Medication Reference Terminology (MED-RT) was recently announced as the evolutionary
successor to the Veterans Health Administration's (VHA) National Drug File-Reference
Terminology (NDF-RT). While both NDF-RT and MED-RT have the purpose of linking drugs
to clinical information (e.g., indications, mechanism of action, physiologic effect),
MED-RT differs from NDF-RT in that it uses RxNorm identifiers for the drugs, instead
of the proprietary identifiers NDF-RT was using. The use of RxNorm identifiers in
MED-RT will greatly facilitate the interoperability between MED-RT and clinical data
warehouses that use RxNorm identifiers for drugs.
RxNorm will continue to improve compliance with international standards, such as standards
developed by the International Organization for Standardization (ISO) for the identification
of medicinal products (IDMP)[50 ]. As part of this effort, RxNorm will explicitly represent information, such as the
basis of strength substance (BoSS), and will refine its representation of strength
to further normalize units and facilitate computation of medication doses. These changes
will enhance interoperability between RxNorm and the international drug model in SNOMED
CT, and facilitate the use of RxNorm as a national extension of SNOMED CT for medications.
RxNorm is also working in close collaboration with the developers of Fast Healthcare
Interoperability Resources (FHIR), in particular to expose the RxNorm content as medication
resources[51 ]. The availability of RxNorm medications in FHIR represents an important step, because
FHIR medication resources play a key role in other resources defined for adverse events,
medication dispensation, and medication orders.
Discussion
Similarities and Differences
SNOMED CT, LOINC, and RxNorm are all integrated in the UMLS Metathesaurus[37 ], which identifies equivalences among the concepts they share and provides a common
interface to access them. Additionally, drugs from SNOMED CT are integrated in RxNorm,
drugs from LOINC are mapped to RxNorm, and “parts” concepts from LOINC are mapped
to SNOMED CT ([Table 2 ]). These mappings create tight, curated integration among them, independent of the
UMLS. SNOMED CT, LOINC, and RxNorm have all been selected to support interoperability
not only from a regulatory perspective in the Meaningful Use incentive program, but
also from a practical perspective in the OMOP common data model developed by the OHDSI
research group. All three are also actively supporting FHIR.
Despite recent efforts to standardize terminology services, SNOMED CT, LOINC, and
RxNorm each have a specific terminology model, and rely on different formalisms and
specific tooling for their development. While the benefits of converting LOINC to
a description logic formalism have been demonstrated[52 ], it remains difficult to adapt legacy tooling and change development practices.
Although built natively with description logics, SNOMED CT can only move slowly to
a more expressive dialect.
Uniform Terminology Services
With growing use of clinical terminologies in health IT applications, so has the need
for enterprise terminology services that can make available code systems, value sets,
and mappings across different terminologies through well-defined interfaces. Early
work to define such terminology services[52 ]
[53 ] matured into specifications such as the joint HL7 and OMG standard Common Terminology
Services 2[53 ]. These efforts informed development of terminology services capabilities in the
rapidly spreading Fast Healthcare Interoperability Resources (FHIR) standard[54 ]. The core FHIR standard and reference servers now have native support for SNOMED
CT, LOINC, and RxNorm, which is opening many possibilities for dynamically accessing
these terminologies in developer - and implementer - friendly manner via common web
technologies and an open application programming interface (API). Key resources such
as the Value Set Authority Center developed by the NLM[55 ] and enterprise terminology servers (e.g., Ontoserver, Distributed Terminology System)
already support FHIR-based access to these terminologies.
Integrating SNOMED CT, LOINC and RxNorm
Over the past few years, collaboration among the developers of SNOMED CT, LOINC, and
RxNorm has increased. The Memorandum of Understanding signed between SNOMED CT and
LOINC developers has paved the way for leveraging SNOMED CT for the representation
of the building blocks of LOINC (e.g., substances, organisms) and for a more consistent
representation of clinical and laboratory observations in SNOMED CT. Similarly, the
publication of the new international drug model in SNOMED CT facilitates the development
of compatible national extensions, such as RxNorm, and better support for IDMP in
RxNorm also helps to make it more consistent with SNOMED CT.
However, while this evolution leads to greater compatibility and interoperability,
integration of SNOMED CT, LOINC, and RxNorm still requires mappings among the three
terminologies. Moreover, these three terminologies use different formalisms and tools
for their representation, have their own release cycles and versioning mechanisms,
which makes their seamless integration non trivial, if at all possible. A further
step towards effective integration is being pursued under the auspices of the Veterans
Health Administration (VHA) and the Healthcare Services Platform Consortium (HSPC),
under the codename SOLOR[56 ]. The proposal calls for using description logics for the representation of the three
terminologies, while only SNOMED CT currently uses this formalism.
Conclusions
With different starting points, representation formalisms, funding sources, and evolutionary
paths, SNOMED CT, LOINC, and RxNorm have evolved over the past few decades into three
major clinical terminologies supporting key use cases in clinical practice. Despite
their differences, partnerships have been created among their development teams to
facilitate interoperability and minimize duplication of effort. Further integration
has been proposed, but will require additional resources to bring these terminologies
closer together. The benefits of the integrated terminologies in terms of homogenous
semantics and inherent interoperability should, however, outweigh the complexity added
to the system. Meanwhile, the availability of these three clinical terminologies through
the unified terminology services of FHIR will already facilitate their usage in support
of interoperability in healthcare.
