<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Comparing the representation of medicinal products in RxNorm and SNOMED CT - Consequences on interoperability</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Jean Noel Nikiema</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Olivier Bodenreider</string-name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Bordeaux Population Health Research Center</institution>
          ,
          <addr-line>ERIAS, Univ. Bordeaux, Inserm UMR 1219, F-33000, Bordeaux</addr-line>
          ,
          <country country="FR">France</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>U.S. National Library of Medicine National Institutes of Health Bethesda</institution>
          ,
          <addr-line>Maryland</addr-line>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>Objectives: To compare the representation of medicinal products in RxNorm and SNOMED CT and assess the consequences on interoperability. Methods: To compare the two models, we manually establish equivalences between the types and definitional features of medicinal products entities in RxNorm and SNOMED CT. We highlight their similarities and differences. Results: Both models share major definitional features including ingredient (or substance), strength and dose form. SNOMED CT is more rigorous and better aligned with international standards. In contrast, RxNorm contains implicit knowledge, simplifications and ambiguities, but its model is simpler. Conclusions: Since their models are largely compatible, medicinal products from RxNorm and SNOMED CT are expected to be interoperable. However, specific aspects of the alignment between the two models require particular attention.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>RxNorm; SNOMED CT; medicinal products.</title>
      <sec id="sec-1-1">
        <title>Background</title>
        <p>
          Drug terminologies, such as RxNorm and the medicinal product
hierarchy of SNOMED CT (Systematized Nomenclature of
Medicine-Clinical Terms), support multiple use cases, including
electronic prescription, drug information exchange, medication
reconciliation, and analytics (including pharmacovigilance) (
          <xref ref-type="bibr" rid="ref1 ref2">1,2</xref>
          ). A
formal representation of medicinal products is needed for the
principled development and maintenance of such drug
terminologies, as well as for precisely aligning existing drug terminologies
(
          <xref ref-type="bibr" rid="ref3">3</xref>
          ).
        </p>
        <p>Many definitional characteristics of medicinal products are
similar among drug terminologies. For example, clinical drugs are
generally defined in terms of ingredient, strength and dose form.
However, the level of formality and the formalism used for
representing medicinal products may differ among terminologies.
Some attributes may also be specific to some terminologies
(especially for country-dependent attributes, such as packaging
information).</p>
        <p>
          In addition to existing drug terminologies, international standards
have been developed for the representation of medicinal products,
such as IDMP (Identification of Medicinal Products). IDMP (
          <xref ref-type="bibr" rid="ref4">4</xref>
          ),
a collection of recommendations from the International Standards
Organization (ISO).
        </p>
        <p>Interoperability among drug terminologies is especially important
for exchanging drug information internationally. For example, a
medication list established with RxNorm in the U.S. could be
made available to any electronic health record (EHR) system in
the world, in which drugs are represented using SNOMED CT.
To fully support this use case, however, the models of medicinal
products in RxNorm and SNOMED CT must be compatible, such
that one can be accurately translated into the other.</p>
        <p>
          We focus on RxNorm and SNOMED CT, because RxNorm is the
standard drug terminology in the U.S. and SNOMED CT is the
largest clinical terminology in the world, supported by a
consortium of over 40 countries. While the RxNorm model has been
analyzed (
          <xref ref-type="bibr" rid="ref5 ref6">5,6</xref>
          ), and reused to create others standards (
          <xref ref-type="bibr" rid="ref7 ref8">7,8</xref>
          ) and to
integrate drug terminologies worldwide (
          <xref ref-type="bibr" rid="ref8">8</xref>
          ), there has not been a
detailed comparison between RxNorm and SNOMED CT.
Moreover, the SNOMED CT model for medicinal products is
particularly interesting, because it was recently updated, in part to
comply with IDMP requirements (
          <xref ref-type="bibr" rid="ref9">9</xref>
          ).
        </p>
        <p>In this investigation, we compare the representation of medicinal
products in RxNorm and SNOMED CT. The objective of our
work is to analyze their similarities and differences and the
consequences of these differences on interoperability between the
two terminologies.</p>
      </sec>
      <sec id="sec-1-2">
        <title>Methods and results</title>
        <p>In this section, we describe the models of RxNorm and SNOMED
CT with focus on their definitional characteristics. Then we
identify similarities and differences between the two models.</p>
        <sec id="sec-1-2-1">
          <title>The SNOMED CT model for medicinal products</title>
          <p>
            The SNOMED CT, the largest clinical terminology in the world,
is an international clinical terminology based on a formal concept
model (
            <xref ref-type="bibr" rid="ref10">10</xref>
            ). SNOMED CT recently published a new model for the
representation of medicinal products integrating requirements
from IDMP (
            <xref ref-type="bibr" rid="ref9">9</xref>
            ). The model was developed to support
international usage. Therefore, it is restricted to generic drugs and does
not represent packaging information or branded drugs, which tend
to be country-specific.
          </p>
          <p>In accordance with requirements from IDMP, clinical drugs are
represented in a closed worldview. This means that characteristics
used to define clinical drugs must be sufficient and what is not
stated is false. In contrast, in the open worldview, what is not</p>
          <p>Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
stated is potentially true. For example, the representation of a
clinical drug containing Atorvastatin must clearly state that this
product only contains the substance Atorvastatin as its active
ingredient (i.e., without any other active ingredient). In the open
worldview, products containing Atorvastatin could also contain
other active ingredients, e.g., Amlodipine.</p>
          <p>
            As shown in Figure 1, the representation of medicinal products in
SNOMED CT is based on a model with six (
            <xref ref-type="bibr" rid="ref6">6</xref>
            ) entities, arranged
in a subclass hierarchy:
          </p>
          <p>Two medicinal product entities, in open and closed
worldview (e.g., open worldview: 108655000 | Product
containing cetirizine (medicinal product) and closed
worldview: 775140005 | Product containing only
cetirizine (medicinal product)).</p>
          <p>Two medicinal product form entities, in open and
closed worldview, (e.g., open worldview: 768065006 |
Product containing cetirizine in oral dose form
(medicinal product form) and closed worldview: 778701007 |
Product containing only cetirizine in oral dose form
(medicinal product form)).</p>
          <p>One medicinal product precisely entity in closed
worldview only (optional entity, currently not
represented in SNOMED CT – hypothetical example:
Product containing only cetirizine hydrochloride
(medicinal product)).</p>
          <p>One clinical drug entity, in closed worldview only
(e.g., 320818006 | Product containing precisely
cetirizine hydrochloride 10 milligram/1 each conventional
release oral tablet (clinical drug)).</p>
          <p>The representation of SNOMED CT entities is based on
"definitional roles" and related "types of values" in SNOMED CT
(Figure 1):</p>
          <p>Substance is the type of values for the active
ingredient, precise active ingredient and basis of strength
roles, for example 372523007 | Cetirizine (substance)
and 108656004 | Cetirizine hydrochloride (substance).
(The basis of strength is the substance in reference to
which strength is defined.)
Unit of measure is the type of values for the strength
unit roles, for example, 258684004 | milligram
(qualifier value).</p>
          <p>Number is the type of values for the strength value
roles, for example, 3445001 | 10 (qualifier value).</p>
          <p>Pharmaceutical dose form is the type of values for the
manufactured dose form role, for example, 421026006 |
Conventional release oral tablet (dose form).</p>
          <p>Unit of presentation is the type of values for the unit
of presentation role, for example, 732936001 | Tablet
(unit of presentation).
Medicinal product
form only (MPFO)</p>
          <p>Closed-world entities</p>
          <p>Definitional features
MP/MPO:</p>
          <p>• Active ingredient (substance)
Medicinal product
precise only (MPP)</p>
          <p>MPP:</p>
          <p>• Precise active ingredient (substance)
(not represented) | Product
containing only cetirizine
hydrochloride (medicinal
product) |</p>
          <p>Clinical drug
320818006 | Product containing
precisely cetirizine hydrochloride
10 mil igram/1 each conventional
release oral tablet (clinical drug) |</p>
          <p>MPF/MPFO:
• Active ingredient (substance)
• Dose form (pharmaceutical dose form)
CD:
• Precise Active ingredient (substance)
• Dose form (pharmaceutical dose form)
• Basis of strength (substance)
• Strength units (unit of measure)
• Strength values (numbers)
• Units of presentation (units of presentation)
Examples</p>
          <p>Cetirizine
[RxCUI = 20610]</p>
          <p>RxNorm generic drug entities
has_ingredient</p>
          <p>Ingredient
Clinical Drug Component</p>
          <p>has_ingredient
consists_of</p>
          <p>Clinical Drug Form
Clinical Drug
isa</p>
          <p>IN:</p>
          <p>• Ingredient
SCDC:
• Ingredient
• Strength
SCDF:
• Ingredient
• Dose form
SCD:
• Ingredient
• Strength
• Dose form
• Quantity factor (optional)
• Qualitative distinction (optional)
Figure 2– Simplified RxNorm model for the representation of generic medicinal products showing the four types of entities defined in
the model, along with their definitional features and examples from the RxNorm terminology
Closed-worldview are “closed” with respect to their active
ingredient(s). More specifically, medicinal product and medicinal
product form entities are closed with respect to their active
ingredient(s), while medicinal product precisely and clinical drug
entities are closed with respect to their precise active ingredient(s).</p>
          <p>There are no hierarchical relations among substances. However,
there is a "modification of" relation between a modified substance
(e.g., ester or salt) and the corresponding base substance (e.g.,
between Atorvastatin calcium and Atorvastatin). Modified
substances can be further modified.</p>
          <p>IDMP requires that dose forms be defined in reference to a list of
dose forms from the European Directorate for Quality in
Medicines (EDQM). EDQM distinguishes between dose forms and
units of presentation. Units of presentation are used to express the
strength and quantity in countable entities, while dose forms
correspond to the physical structure of the medicinal product.</p>
          <p>In accordance with requirements from IDMP, strength units in
SNOMED CT are aligned with the international standard for units
of measure, UCUM (Unified Code for Units of Measure).</p>
          <p>Finally, depending on the unit of presentation, strength can be
represented as concentration strength, presentation strength or
both.</p>
        </sec>
        <sec id="sec-1-2-2">
          <title>The RxNorm model</title>
          <p>
            Created in 1992, RxNorm is a normalized terminology for clinical
drugs in the U.S. RxNorm represents both generic drugs and
branded drugs, as well as packs (
            <xref ref-type="bibr" rid="ref11">11</xref>
            ). The full model of RxNorm
contains ten entities, five for generic drug entities and five for
branded drugs entities. For comparison with SNOMED CT, we
only present RxNorm generic drug entities and also omit packs.
The simplified RxNorm model for generic drug entities includes
four entities (Figure 2):
          </p>
          <p>Ingredient, including base ingredient (IN), precise
ingredient (PIN), and multi-ingredient (MIN) (e.g., IN:
The representation of these entities relies on three mandatory and
two optional definitional features:</p>
          <p>Cetirizine [RxCUI = 20610], PIN: cetirizine
hydrochloride [RxCUI = 203150], MIN: Cetirizine /
Pseudoephedrine [RxCUI = 352367])
Clinical drugs component (SCDC), combining
ingredient and strength (e.g., cetirizine hydrochloride
10 MG [RxCUI = 1011480])
Clinical drugs form (SCDF), combining ingredient and
dose form (e.g., Cetirizine Oral Tablet [RxCUI =
371364])
Clinical drug (SCD), combining ingredient, strength
and dose form (e.g., cetirizine hydrochloride 10 MG
Oral Tablet [RxCUI = 1014678])</p>
        </sec>
      </sec>
    </sec>
    <sec id="sec-2">
      <title>Mandatory definitional features:</title>
      <p>ingredient (IN/PIN/MIN) (e.g., IN: Cetirizine
[RxCUI = 20610], PIN: cetirizine hydrochloride
[RxCUI = 203150], MIN: Cetirizine /
Pseudoephedrine [RxCUI = 352367])
dose form (DF) (e.g., Oral Tablet [RxCUI =
317541])
strength (e.g., 10 MG)
Optional definitional features (see below for examples):
quantity factor (QF)
qualitative distinction (QD)
Strength in RxNorm is normalized. In its units of measure (e.g.,
for volume, weight, surface), RxNorm uses one unit for each type
quantity (e.g., milligram for weight rather than gram or
microgram).</p>
      <p>
        The representation of dose forms in RxNorm is not based on a
specific standard (
        <xref ref-type="bibr" rid="ref12">12</xref>
        ). It is also important to note that the SCDs
Medicinal product
form only
Clinical drug
      </p>
      <p>Medicinal product
only
and SCDCs refer to the basis of strength substance (e.g., cetirizine
hydrochloride), while SCDFs refer to the base ingredient (e.g.,
cetirizine). Of note, ingredients in RxNorm can (purposely) be
understood as either the substance contained in a medicinal
product as active ingredient (e.g., “cetirizine the substance”) or the
class of all medicinal products containing this substance as active
ingredient. Precise ingredients (PINs) generally correspond to
modified forms of the corresponding base ingredients (INs). PINs
cannot be further modified.</p>
      <p>In addition, RxNorm does not explicitly have a notion of
"worldview" (i.e., open or closed worldview) for its entities.
While clinical drugs implicitly refer to a closed worldview,
ingredients, clinical drug components and clinical drug forms can be
understood in both open and closed worldview, leaving it to
queries to distinguish between the two.</p>
      <p>
        Finally, the Quantity Factor (QF) is a number followed by a unit
of measure corresponding to vial sizes or patch durations (e.g.,
"12H"). RxNorm does not explicitly state whether strength is
expressed as presentation strength or concentration strength.
Presentation strength can be derived from concentration strength by
multiplying the concentration strength by the quantity factor. (For
example, if the concentration strength is 1MG/ML and the QF is
2ML, the presentation strength is 2MG/2ML). The Qualitative
Distinction (QD) corresponds to some qualitative characteristic of
a drug outside the main definitional features (e.g., "sugar free"
and “abuse-deterrent”). QD and QF are optional modifiers used
in RxNorm to define medicinal products when it is clinically
relevant to identify such distinctions (
        <xref ref-type="bibr" rid="ref12">12</xref>
        ).
      </p>
      <sec id="sec-2-1">
        <title>Comparison of the RxNorm and SNOMED CT models</title>
        <p>To compare the two models, we manually establish equivalences
between their entities and between their definitional features,
based on our analysis of the two models.</p>
        <p>First, we need to disambiguate the notion of ingredient in
RxNorm (IN,PIN, MIN), because, as mentioned earlier, it can be
understood as either a substance or a class of medicinal products.
Therefore, as shown in Figure 3, ingredients in RxNorm
correspond to SNOMED CT medicinal products (in open and closed
worldview) or to SNOMED CT substances, which are active
ingredients of SNOMED CT medicinal products. In practice,
RxNorm ingredients are often associated with multiple SNOMED
CT entities, typically with one substance entity and one medicinal
product entity. Disambiguation consists in identifying which
SNOMED CT entity comes from the substance hierarchy (and
treating it as a value for the definitional feature “active
ingredient”), while the SNOMED CT entity corresponding to an entity
from the medicinal product hierarchy is marked as an asserted
equivalence for the RxNorm medicinal product entity.
RxNorm does not formally have the notion of "unit of
presentation". Units of presentation are implicitly represented through
dose forms in RxNorm, whereas the two notions are represented
separately in SNOMED CT. For example, in SNOMED CT,
tablet is the logical "unit of presentation" of the conventional release
oral tablet, while the two are conflated in the RxNorm dose form
“Oral Tablet”. Therefore, RxNorm dose forms generally
correspond to pairs of a pharmaceutical dose form and a unit of
presentation in SNOMED CT.</p>
        <p>In addition, there are no materialized entities for SCDCs in
SNOMED CT. Instead, strength and basis of strength substance
are associated as part of the definition of a clinical drug in
SNOMED CT. Therefore, SCDCs cannot be related to entities in
SNOMED CT, but their defining features are represented as part
of clinical drug entities.</p>
        <p>SCDs in RxNorm are equivalent to clinical drugs in SNOMED
CT as they essentially share the same definitional features. The
quantity factor in RxNorm has no direct equivalent in SNOMED
CT, but QF information is implicitly represented in the
presentation strength. In contrast, qualitative distinctions are absent from
the SNOMED CT model.</p>
        <p>While RxNorm only represents one level of modification
(between PIN and IN), SNOMED CT can represent arbitrary levels
of modification among substances.</p>
        <p>Both RxNorm and SNOMED CT have the notion of concentration
strength and presentation strength. However, RxNorm
emphasizes concentration strength (from which presentation strength
can be calculated using the quantity factor), whereas SNOMED
CT explicitly represent both presentation strength and
concentration strength when necessary.</p>
        <p>Finally, RxNorm normalizes all quantities to one unit (per type of
quantity), whereas SNOMED CT uses units that are most
clinically appropriate (following IDMP requirements). For example,
RxNorm uses 0.001 milligram and SNOMED CT 1 microgram.
This difference merely reflects differences in editorial guidelines,
as conversion between the two is trivial.</p>
        <sec id="sec-2-1-1">
          <title>Discussion</title>
          <p>Findings. Not surprisingly, the models used by RxNorm and
SNOMED CT for representing medicinal products are fairly
similar and essentially compatible. Both models share major
definitional features including ingredient (or substance), strength and
dose form. Only the qualitative distinction feature of RxNorm has
no correspondence at all in SNOMED CT.</p>
          <p>SNOMED CT is more rigorous and better aligned with
international standards. In SNOMED CT, differences tend to be made
explicit, e.g., between a substance and the class of medicinal
products containing this substance as an ingredient, or between
the class of all medicinal products containing only a given active
ingredient and the class of all medicinal products containing at
least this active ingredient . SNOMED CT also offers more
flexibility with relations among substances, as opposed to a fixed
precise ingredient to base ingredient relationship in RxNorm. This
precision comes at the price of a more complex model, and
possibly a steeper learning curve. In contrast, RxNorm contains
implicit knowledge, simplifications and ambiguities, but its model
is simpler.</p>
          <p>With features, such as explicit closed worldview for clinical drug
entities, use of standard dose forms from EDQM, use of UCUM
units, and use of clinically appropriate strength values, SNOMED
CT shows better compliance with international standards (namely
IDMP) than RxNorm does.</p>
          <p>Consequences on alignment. Since their models are largely
compatible, medicinal products from RxNorm and SNOMED CT are
expected to be interoperable. However, specific aspects of the
alignment between the two models require particular attention.
The values of ingredient can be aligned rather trivially (after
disambiguation between the two meanings of RxNorm ingredients,
substance and class of medicinal products containing this
substance as an ingredient).</p>
          <p>Strength entities require minimal attention, specifically for
converting RxNorm “fixed unit” into the clinically appropriate unit
used in SNOMED CT. Simple arithmetic is also required to
convert concentration strength and quantity factor in RxNorm to
presentation strength in SNOMED CT wherever appropriate.
In contrast, aligning dose forms requires more analysis, as
RxNorm dose forms generally correspond to pairs of a
pharmaceutical dose form and a unit of presentation in SNOMED CT.
The absence of correspondence for qualitative distinction in
SNOMED CT may lead to multiple clinical drugs in RxNorm
mapping to a single clinical drug in SNOMED CT. For example,
the distinction between Cholestyramine Resin 4000 MG Powder
for Oral Suspension [RxCUI = 848943] and its sugar-free form
Sugar-Free Cholestyramine Resin 4000 MG Powder for Oral
Suspension [RxCUI = 1801279] in RxNorm is lost in SNOMED
CT. This issue is unlikely to result in clinically significant
alignment errors.</p>
          <p>The absence of materialization of the clinical drug component
(SCDC) entity in SNOMED CT does not create an alignment
issue, because SCDCs are essentially navigational entities in
RxNorm. They are not crucial to any of the main use cases for
RxNorm or SNOMED CT.</p>
          <p>Future work. In future work, we plan to translate RxNorm into
the SNOMED CT model for medicinal products. The resulting
alignment would make RxNorm entities directly compatible with
SNOMED CT’s. One benefit of this alignment would be to assess
interoperability between RxNorm and SNOMED CT, potentially
enriching SNOMED CT with clinical drugs currently specific to
RxNorm. Additionally, this alignment would offer an opportunity
for quality assurance by identifying cases where alignment is
expected, but cannot be inferred (e.g., because of a difference in
basis of strength substance for a given clinical drug between
RxNorm and SNOMED CT).</p>
        </sec>
        <sec id="sec-2-1-2">
          <title>Conclusion</title>
          <p>In this investigation, we examined the similarities and differences
between the representation of medicinal products in RxNorm and
SNOMED CT. We established that both models share major
definitional features including ingredient (or substance), strength and
dose form. Because of subtle differences between the two models,
specific aspects of their alignment require particular attention.</p>
        </sec>
        <sec id="sec-2-1-3">
          <title>Acknowledgment</title>
          <p>This work was supported by the Intramural Research Program of
the NIH, National Library of Medicine. The authors would like to
thank the developers of RxNorm and the contributors to the
SNOMED International Drug Model Working Group for useful
discussion We are particularly grateful to Tammy Powell and
Chris Hui (RxNorm), and Julie James, Jim Case, Yongsheng Gao,
Emma Melhuish, Toni Morrison, Guillermo Reynoso, Farzaneh
Ashrafi and Phuong Skovgaard (SNOMED CT). We also thank
NLM colleagues, Lee Peters, Robert Wynne and Phill Wolf, for
useful discussion.</p>
        </sec>
        <sec id="sec-2-1-4">
          <title>Address for correspondence</title>
        </sec>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>Jean.nikiema@u-bordeaux.fr Olivier.bodenreider@nih.gov</title>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          1.
          <string-name>
            <surname>Lupse</surname>
            <given-names>O-S</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Chirila</surname>
            <given-names>C</given-names>
          </string-name>
          -B,
          <article-title>Stoicu-tivadar L. Harnessing Ontologies to Improve Prescription in Pediatric Medicine</article-title>
          .
          <source>Studies in Health Technology and Informatics</source>
          .
          <year>2018</year>
          ;
          <fpage>97</fpage>
          -
          <lpage>101</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          2.
          <string-name>
            <surname>Farrish</surname>
            <given-names>S</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Grando</surname>
            <given-names>A</given-names>
          </string-name>
          .
          <article-title>Ontological approach to reduce complexity in polypharmacy</article-title>
          .
          <source>AMIA Annu Symp Proc</source>
          .
          <year>2013</year>
          ;
          <year>2013</year>
          :
          <fpage>398</fpage>
          -
          <lpage>407</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          3.
          <string-name>
            <given-names>Lai</given-names>
            <surname>EC-C</surname>
            , Ryan
          </string-name>
          <string-name>
            <given-names>P</given-names>
            ,
            <surname>Zhang</surname>
          </string-name>
          <string-name>
            <given-names>Y</given-names>
            ,
            <surname>Schuemie</surname>
          </string-name>
          <string-name>
            <given-names>M</given-names>
            ,
            <surname>Hardy</surname>
          </string-name>
          <string-name>
            <given-names>NC</given-names>
            ,
            <surname>Kamijima</surname>
          </string-name>
          <string-name>
            <surname>Y</surname>
          </string-name>
          , et al.
          <article-title>Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges</article-title>
          .
          <source>Clinical Epidemiology</source>
          .
          <source>2018 Jul;Volume</source>
          <volume>10</volume>
          :
          <fpage>875</fpage>
          -
          <lpage>85</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          4.
          <string-name>
            <given-names>European</given-names>
            <surname>Medicines</surname>
          </string-name>
          <article-title>Agency</article-title>
          .
          <article-title>Introduction to ISO Identification of Medicinal Products</article-title>
          , SPOR programme [Internet].
          <year>2016</year>
          . Available from: https://www.ema.europa.eu/documents/other/introduction-iso
          <article-title>-identification-medicinal-products-spor-programme_en</article-title>
          .pdf
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          5.
          <string-name>
            <surname>Dhavle</surname>
            <given-names>AA</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Ward-Charlerie</surname>
            <given-names>S</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Rupp</surname>
            <given-names>MT</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kilbourne</surname>
            <given-names>J</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Amin</surname>
            <given-names>VP</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Ruiz</surname>
            <given-names>J</given-names>
          </string-name>
          .
          <article-title>Evaluating the implementation of RxNorm in ambulatory electronic prescriptions</article-title>
          .
          <source>Journal of the American Medical Informatics Association</source>
          . 2016 Apr;
          <volume>23</volume>
          (
          <issue>e1</issue>
          ):
          <fpage>e99</fpage>
          -
          <lpage>107</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          6.
          <string-name>
            <surname>Liu</surname>
            <given-names>S</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Wei</surname>
            <given-names>Ma</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Moore</surname>
            <given-names>R</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Ganesan</surname>
            <given-names>V</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Nelson</surname>
            <given-names>S.</given-names>
          </string-name>
          <article-title>RxNorm: prescription for electronic drug information exchange</article-title>
          .
          <source>IT Professional</source>
          .
          <year>2005</year>
          Sep;
          <volume>7</volume>
          (
          <issue>5</issue>
          ):
          <fpage>17</fpage>
          -
          <lpage>23</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          7.
          <string-name>
            <surname>Wang</surname>
            <given-names>L</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Zhang</surname>
            <given-names>Y</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Jiang</surname>
            <given-names>M</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Wang</surname>
            <given-names>J</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Dong</surname>
            <given-names>J</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Liu</surname>
            <given-names>Y</given-names>
          </string-name>
          , et al.
          <article-title>Toward a normalized clinical drug knowledge base in China-applying the RxNorm model to Chinese clinical drugs</article-title>
          .
          <source>Journal of the American Medical Informatics Association</source>
          .
          <source>2018 Jul</source>
          <volume>1</volume>
          ;
          <issue>25</issue>
          (
          <issue>7</issue>
          ):
          <fpage>809</fpage>
          -
          <lpage>18</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          8.
          <string-name>
            <surname>Hanna</surname>
            <given-names>J</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Joseph</surname>
            <given-names>E</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Brochhausen</surname>
            <given-names>M</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hogan</surname>
            <given-names>WR</given-names>
          </string-name>
          .
          <article-title>Building a drug ontology based on RxNorm and other sources</article-title>
          .
          <source>Journal of Biomedical Semantics</source>
          .
          <year>2013</year>
          ;
          <volume>4</volume>
          (
          <issue>1</issue>
          ):
          <fpage>44</fpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          9.
          <string-name>
            <surname>Bodenreider</surname>
            <given-names>O</given-names>
          </string-name>
          ,
          <string-name>
            <surname>James</surname>
            <given-names>J.</given-names>
          </string-name>
          <article-title>The New SNOMED CT International Medicinal Product Model</article-title>
          .
          <source>In: Proceedings of the International Conference on Biological Ontology (ICBO</source>
          <year>2018</year>
          ). Oregon, USA,;
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          10.
          <string-name>
            <surname>Héja</surname>
            <given-names>G</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Surján</surname>
            <given-names>G</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Varga</surname>
            <given-names>P</given-names>
          </string-name>
          .
          <article-title>Ontological analysis of SNOMED CT</article-title>
          .
          <article-title>BMC Medical Informatics</article-title>
          and
          <string-name>
            <given-names>Decision</given-names>
            <surname>Making</surname>
          </string-name>
          .
          <year>2008</year>
          ;
          <volume>8</volume>
          (
          <issue>Suppl 1</issue>
          ):
          <fpage>S8</fpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          11.
          <string-name>
            <surname>Nelson</surname>
            <given-names>SJ</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Zeng</surname>
            <given-names>K</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kilbourne</surname>
            <given-names>J</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Powell</surname>
            <given-names>T</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Moore</surname>
            <given-names>R</given-names>
          </string-name>
          .
          <article-title>Normalized names for clinical drugs: RxNorm at 6 years</article-title>
          .
          <source>Journal of the American Medical Informatics Association</source>
          . 2011 Jul;
          <volume>18</volume>
          (
          <issue>4</issue>
          ):
          <fpage>441</fpage>
          -
          <lpage>8</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          12.
          <string-name>
            <surname>Bodenreider</surname>
            <given-names>O</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Cornet</surname>
            <given-names>R</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Vreeman</surname>
            <given-names>D</given-names>
          </string-name>
          .
          <article-title>Recent Developments in Clinical Terminologies - SNOMED CT, LOINC, and RxNorm</article-title>
          .
          <source>Yearbook of Medical Informatics</source>
          .
          <year>2018</year>
          Aug;
          <volume>27</volume>
          (
          <issue>01</issue>
          ):
          <fpage>129</fpage>
          -
          <lpage>39</lpage>
          .
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>