JMIR MEDICAL INFORMATICS

Ohlsen et al

Original Paper

PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT
Expressions for Storage as HL7 FHIR Resources
Tessa Ohlsen1,2, MSc; Josef Ingenerf1,2, Prof Dr; Andrea Essenwanger3, MSc; Cora Drenkhahn2, MSc
1

IT Center for Clinical Research, University of Luebeck, Luebeck, Germany

2

Institute of Medical Informatics, University of Luebeck, Luebeck, Germany

3

mio42 GmbH, Berlin, Germany

Corresponding Author:
Tessa Ohlsen, MSc
IT Center for Clinical Research
University of Luebeck
Ratzeburger Allee 160
Luebeck, 23562
Germany
Phone: 49 45131015623
Email: t.ohlsen@uni-luebeck.de

Abstract
Background: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical
data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained
popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as asemantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinatedexpressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts.
However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, theboundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is
known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential
benefits, such as enabling flexible transformation of parts of PCEs.
Objective: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR
data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked
to the appropriate elements of the information model.
Methods: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. Byutilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner
and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified
as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as
the “Delta.” Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined forboth the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources.
Results: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600
randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified.
Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously
proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used toconfirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemedmeaningful by FHIR experts.
Conclusions: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure
mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT
Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which canthen be easily reused in a constrained manner by end users.
(JMIR Med Inform 2024;12:e57853) doi: 10.2196/57853

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KEYWORDS
SNOMED CT; HL7 FHIR; TermInfo; postcoordination; semantic interoperability; terminology; OWL; semantic similarity

Introduction
BackgroundThe growing digitization of medical records has led to anincrease in patient data available for health care analysis. These
data must be utilized to improve medical care and offer more
personalized treatments. However, to accomplish this, the ability
to automatically exchange and process data between different
systems is essential. This requires not only technicalcompatibility but also semantic interoperability, ensuring thatthe data’s meaning is preserved when transferred to anothersystem. The ability to exchange and utilize data across different
systems is crucial for fully leveraging digital medical records
and improving patient care [1].
To ensure semantic interoperability, it is essential to follow both
structural and semantic standards. Structural standards specify
the syntax for accessing data fields within information models.
In recent years, the newly developed HL7 (Health Level 7)
standard, FHIR (Fast Healthcare Interoperability Resources),
has gained international recognition due to its emphasis on
simplified implementation and the use of modern technologies[2]. Semantic standards, by contrast, involve terminologies thatuse language-independent codes to represent the meaning ofdata in an interoperable manner. SNOMED CT is widely
recognized as the most comprehensive medical terminology forenhancing semantic interoperability [3]. In 2021, Germany
acquired a national license for SNOMED CT, leading toincreased interest and usage of the terminology. SNOMED CT
concepts are now being integrated into data modeling efforts,such as the Medical Information Objects by the German National
Association of Statutory Health Insurance Physicians (NASHIP)[4] and the core data set of the Medical Informatics Initiative(MII) [5]. The use of SNOMED CT, which contains over
350,000 concepts, aims to provide a machine-readable
interlingua that minimizes coding issues specific to different
countries and medical fields. However, due to the complexityof natural language, not all medical situations can be accurately
coded using SNOMED CT’s extensive set of precoordinatedconcepts. To address this and avoid a rapid increase in thenumber of new concepts, SNOMED CT, unlike many othervocabularies, supports postcoordination. This feature allows
the combination of precoordinated concepts into new
expressions using a formal grammar.
Therefore, postcoordination is a unique feature that significantly
enhances the precision of medical documentation and greatly
increases SNOMED CT’s expressive power. However, theadoption of postcoordination has been slow due to various
challenges. While some of these obstacles have already beenaddressed [6-9], integrating postcoordinated expressions (PCEs)
into the electronic health records of legacy hospital information
systems remains difficult for several reasons (Textbox 1).

Textbox 1. Obstacles to integrating postcoordinated SNOMED Clinical Terms expressions into electronic health records.
•

Adherence to familiar data structures

Medical circumstances are usually documented using individual codes, and there are established methods for storing and processing these codes.
While restrictions such as length-limited data types can be managed with simple codes, they pose challenges when dealing with arbitrarily large formal
expressions, such as postcoordinated expressions (PCEs). This has led to concerns about the practicality of using PCEs [10].
•Lack of technical support

The technical handling of PCEs is inherently complex, requiring at a minimum a description logic reasoner and the implementation of several formal
specifications, such as the Concept Model, Compositional Grammar, and Expression Constraint Language, as defined by SNOMED International [3].
While specialized terminology servers can help alleviate the implementation burden, currently only the CSIRO (Commonwealth Scientific andIndustrial Research Organization) Ontoserver [11] supports postcoordination [12].
•Difficulties with FHIR (Fast Healthcare Interoperability Resources) search

When searching for information in FHIR resources, postcoordination is supported only if the exact same PCE has been explicitly and previously
defined in a FHIR CodeSystem supplement. These supplements enable the extension of the standard FHIR CodeSystem for SNOMED CT with acollection of PCEs (eg, for value set definitions), but they do not support the recording of PCEs in a patient’s electronic health record.

Given that these challenges are unlikely to be resolved in the
short term, a different approach is needed to ensureinteroperability between systems that support postcoordinationand those that do not. Consequently, this paper will present analternative representation of PCEs.
It has long been recognized that there is significant overlap
between the scopes of structural and semantic standards, leading
to unclear responsibilities and potentially ambiguous
representations of medical facts, as well as inconsistent
redundancies. This issue, known as the TermInfo problem[3,13-15], largely arose from the independent development ofhttps://medinform.jmir.org/2024/1/e57853

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these standards, which resulted in mutual coverage of the samedata elements (Figure 1). SNOMED CT’s postcoordination
capability further blurs the distinction between structure andsemantics, potentially exacerbating the existing problem.
However, PCEs are fully interpretable, allowing the information
components they contain to be identified and flexibly
disassembled. Building on extensive knowledge from previous
projects [6,16-18] and the current integration of SNOMED CT
with FHIR, the authors propose PCEtoFHIR—an application
designed to decompose PCEs for storage as FHIR resources ina manner that preserves their meaning.
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Figure 1. TermInfo: The same medical fact can be represented using either the terminology (left side) or the information model (right side) moreheavily.

Related Work
Although there is a growing body of literature on the
postcoordination of SNOMED CT concepts [6,16,19-21], aliterature search revealed no existing publications specificallyaddressing the storage of PCEs in FHIR resources. However,a Health Level Seven International (HL7) working group isaddressing this topic and provides several resources. For selected
FHIR resources, such as Condition and Observation, mappingsof some SNOMED CT attributes to FHIR are currently available(see [22] and [23]). Additionally, the Confluence pages“SNOMED on FHIR” (“Bindings to FHIR Clinical Resources”)[24] outline various options for mapping SNOMED CT
attributes to FHIR while avoiding semantic overlaps. The
information from these documents is considered in our work.
Additionally, some papers discuss the use of SNOMED CT in
combination with standardized information models based on
the HL7 Reference Information Model (RIM), HL7 ClinicalDocument Architecture, and HL7 FHIR resources. For instance,a project by Perez-Rey et al [25] focused on linking the normal
form of precoordinated SNOMED CT concept definitions,normalizing SNOMED CT concepts, and binding them to HL7
RIM classes. This approach could potentially be extended to
PCEs. However, the HL7 version 3 standard has not been widelyadopted due to its complexity [1]. A project by
Arguello-Casteleiro et al [26] addressed mapping precoordinated
SNOMED CT concepts or PCEs from Consolidated ClinicalDocument Architecture to FHIR resources. While the objectivesof this approach are similar to those of our work,
Arguello-Casteleiro et al focused heavily on ontology. By
contrast, our work primarily utilizes the widely adopted FHIR
and SNOMED CT native specifications. Additionally, thespecific ocular diseases examined by Arguello-Casteleiro et al
pertain to a very narrow subset of SNOMED CT expressions.
Further, the publication by Arguello-Casteleiro et al does notprovide information on the extent to which all the data includedin the PCEs can be transferred into the FHIR representation.

Methods
Overview
This work aims to develop and implement an approach thatenables the storage of a SNOMED CT PCE within FHIR
resources using only precoordinated codes. In this alternative
representation, the PCE will be decomposed into precoordinatedconcepts, which can then be stored in appropriate elements ofcorresponding FHIR resources. An overview of the proposed
approach is shown in Figure 2.

Figure 2. The decomposition of a PCE into elements of (profiled) FHIR resources consists of 4 steps. FHIR: Fast Healthcare Interoperability Resources;
OWL: Web Ontology Language; PCE: postcoordinated expression.

A PCE, once verified for syntactic and semantic correctness,serves as the input. This PCE can be classified within SNOMED
CT using the Web Ontology Language (OWL) and a reasoner,
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allowing for the identification of its direct supertype ancestors.
Among these concepts, the most similar one to the PCE isselected as the Superconcept. The Delta is then calculated
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between the Superconcept and the PCE, encompassing all theinformation in the PCE that is not represented by theSuperconcept. In the final step, suitable elements ofcorresponding FHIR resources must be identified to store theinformation of the Superconcept and the Delta. To facilitate
this, FHIR StructureMaps that define these associations on a
general level need to be created in advance.

Validation of PCE
To ensure that flexible PCEs can be accurately interpreted andevaluated, they must adhere to the syntactic requirements of theCompositional Grammar and the semantic rules of the Concept
Model defined by SNOMED International. In the initial step,
the input PCE is checked for syntactic and semantic correctness
using the HL7 FHIR service $validate-code and Ontoserver.
Ontoserver, a FHIR-based terminology server provided by theAustralian Commonwealth Scientific and Industrial Research
Organization (CSIRO), supports and facilitates working withkey coding systems such as SNOMED CT and LOINC (LogicalObservation Identifiers Names and Codes) [11]. Additionally,
Ontoserver provides an integrated description logic reasoner
and full support for SNOMED CT postcoordination, enabling
PCE validation directly on the Ontoserver [11]. Only if a PCE
is both syntactically and semantically correct will the process
automatically proceed to the next steps.

Determining Superconcept Candidates
OWL serves as an exchange format for ontologies and is usedhere to determine the ancestors of a PCE. OWL allows for the

Ohlsen et al
representation of complex knowledge about concepts and theirinterrelationships [27,28]. SNOMED CT can also be represented
as an OWL ontology, with the definitions of individual
SNOMED CT concepts provided as OWL expressions in themonthly release packages of SNOMED CT, alongside otherformats [29].
To obtain SNOMED CT as an OWL ontology, the SNOMEDOWL Toolkit [30] was used in the Release Format 2 of theInternational Edition, dated 2023-04-30 [31]. The generated
ontology includes, among other components, precoordinated
SNOMED CT concepts and their definitions in functional
syntax.
PCEs, by contrast, are based on the syntax of the Compositional
Grammar. Therefore, a PCE must be transformed into an OWLClassExpression for further processing. For each component of
the PCE, the corresponding OWL counterpart is determinedbased on the existing concept definitions, as shown in Table 1.
An algorithm utilizing the Java (Oracle Corporation) libraryOWL API [32] is developed to automate this transformation.
The result of the algorithm is an OWL ClassExpression
structured similarly to the OWL ClassExpressions created by
SNOMED International for SNOMED CT concept definitions.
An exemplary OWL expression for a PCE is shown in Figure
3, with Fully Specified Names added for improved readability.
The symbol “:” serves as a placeholder for the definednamespace, which in this case is “http://snomed.info/id/” [29].

Table 1. Various OWLa constructs are used for the representation of the components of a PCEb. While the components in the first 2 rows are exclusively
used in the native OWL ontology of SNOMED CTc, the OWL constructs below are also used for the transformation of PCEs.
PCE component

OWL construct

SNOMED CT concept

OWL Class

SNOMED CT attribute, attribute value: SNOMED CT concept

OWL ObjectProperty

Linking of ungrouped attribute relationships

OWL ObjectIntersectionOf

Linking between individual attribute relationships in a Role GroupOWL ObjectIntersectionOf

Linking between focus concept and all attribute relationships of ungrouped OWL ObjectIntersectionOf
attributes and Role Groups
Attribute relationship of attribute and SNOMED CT Identifier as attribute OWL ObjectSomeValuesFrom
value
All grouped attribute relationships and Role Group Identifiera

OWL ObjectSomeValuesFrom

OWL: Web Ontology Language.

b

PCE: postcoordinated expression.

c

SNOMED CT: SNOMED Clinical Terms.

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Figure 3. PCE on the left side and the associated OWL ClassExpression based on functional syntax on the right side. OWL: Web Ontology Language;
PCE: postcoordinated expression.

In the next step, the previously formed OWL ontology of
SNOMED CT, the created PCE-specific OWL ClassExpression,and a description logic reasoner are used to classify the PCE
within the existing SNOMED CT hierarchy. A reasoner
generates new knowledge through logical inferences from theexisting content of an ontology, such as determining
superrelationships between concepts [27]. In this work, the ELK
reasoner is used because it is one of the few reasoners capableof handling the extensive SNOMED CT ontology [33].

information contained within the PCE compared with any otherprecoordinated SNOMED CT concept.

The direct ancestors of PCEs are determined using the ELK
reasoner with the Java library elk-reasoner [33]. This process
identifies at least one OWL Class corresponding to a
precoordinated SNOMED CT concept. All identified
precoordinated SNOMED CT concepts are considered potential
Superconcept candidates.

While several semantic similarity measures have been proposedin the literature, this work uses a path-based approach developedby Sánchez and Batet [34]. This method is specifically designed
for large knowledge bases with a subtype-relationship–based
polyhierarchy, such as SNOMED CT [34]. The equation forthe calculation is as follows:

Semantic Similarity Measure
To identify the most similar concept, the semantic similarity
between concepts is assessed. This measure calculates thetaxonomic proximity between two elements within a knowledge
base, such as SNOMED CT. Higher semantic similarity
indicates that the two elements are more closely related [34].

Choosing Superconcept
Overview
One of the previously identified Superconcept candidates must
be selected as the Superconcept. The Superconcept is theprecoordinated SNOMED CT concept that most closely
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where T(ci) is the set of all ancestors of a concept ci and theconcept ci itself. To calculate the semantic similarity between
two concepts c1 and c2, the ratio between the set of nonshared
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Ohlsen et alancestors (nominator) and the union of all ancestors of bothconcepts (denominator) is considered [34].
In our work, the semantic similarity between the classified PCE
and each of its Superconcept candidates needs to be calculated.
Therefore, the equation is modified as follows:with ci being one of the Superconcepts. The calculated
similarities for the exemplary PCE are shown in Table 2.

Table 2. To choose the most fitting Superconcept, the semantic similarity between the PCEa and each of its Superconcept candidates is calculated using
the measure proposed by Sánchez and Batet [34]. The previously introduced exemplary PCE shares a larger ratio of ancestors with Allergic reaction
caused by tree nut than with Swelling. Thus, the former leads to a higher semantic similarity and is determined as Superconcept.

aSuperconcept candidates

Nonshared ancestors

Union of all ancestors

Semantic similarity

Allergic reaction caused by tree nut

8

30

2.10

Swelling

29

30

0.10

PCE: postcoordinated expression.

Implementation
An algorithm was developed to select the most suitable (ie, themost semantically similar) SNOMED CT concept from theSuperconcept candidates. The calculation of semantic similarity
is based on the graph-based approach by Sánchez and Batet[34], as described above. To achieve this, SNOMED CT mustfirst be transformed into a directed acyclic graph (DAG). The
DAG was constructed by algorithmically processing the Release
Format 2, version 2023-04-30 [31], of SNOMED CT using the
Python (Python Foundation) library NetworkX [35]. In the

resulting graph, SNOMED CT concepts are represented as
nodes, while their relations, including relation types, form theconnecting edges.
To enable semantic similarity calculation, the PCE must be
temporarily inserted into the DAG. For this purpose, a nodecalled “pce” is introduced into the graph as a subnode of its
focus concept and the previously determined Superconcept
candidates (Figure 4). The algorithm iterates over the individual
attribute values of the PCE and inserts edges between these
attributes and the “pce” node.

Figure 4. The PCE is represented as a subconcept of its focus concept and the Superconcept candidates. It also has edges to its attribute values. PCE:
postcoordinated expression; SNOMED CT: SNOMED Clinical Terms.

Next, the semantic similarity between the PCE and all potential
Superconcept candidates is calculated based on the DAG. The
Superconcept candidate with the highest semantic similarity
describes the PCE most accurately among all precoordinated
SNOMED CT concepts. This concept is then defined as theSuperconcept.

Determining Delta
Although the Superconcept is the most similar precoordinatedconcept available in SNOMED CT, it does not cover all theinformation contained within the PCE. Therefore, the missing
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parts of information, referred to as the “Delta” between the PCE
and the Superconcept, need to be determined. This is achieved
using a graph-based approach, where both the attribute relationsof the PCE and those of the Superconcept are represented inseparate graphs. To facilitate merging later, a dummy root nodewith the same name is introduced into both graphs. Next, theattribute relations of the PCE and the Superconcept are added
as nodes and connected via edges, as shown on either side of
Figure 5. The Delta is then calculated by subtracting the edgesof the Superconcept graph from the PCE graph. This process
eliminates all equivalent components present in both graphs,
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leaving only the attribute relationships that are not or notprecisely represented in the Superconcept graph (see the Delta

Ohlsen et algraph in the middle of Figure 5).

Figure 5. When calculating the delta, the graph of the Superconcept is subtracted from that of the PCE. The determined edges contain attribute relations
that were not covered by the Superconcept but are necessary for the precise representation of the PCE. PCE: postcoordinated expression.

Mapping to FHIR Elements
In the final step, the Superconcept and the Delta must be stored
as precoordinated concepts in suitable FHIR elements. This canbe accomplished using either the base FHIR resources from
HL7 or more specific FHIR profiles. FHIR profiles arecustomized versions of FHIR resources tailored to national
characteristics, legislation, or specific use cases [35].
In our work, 2 sets of FHIR profiles were considered as target
representations for the mapping:••profiles of the NASHIP (version 1.4.0, based on FHIR R4)[4] andprofiles of the core data set of the German MII (version
1.0, based on FHIR R4) [5].

To accurately map a decomposed PCE to these profiled FHIR
resources, mapping rules are required to align its Superconcept
and Delta with the appropriate FHIR elements. As PCEs areoften highly individualized, they can be categorized according
to their focus concept, attribute relationships, and SNOMEDCT Expression Templates. Consequently, it is not necessary todefine a mapping rule for each PCE; instead, PCEs with similar
content can be mapped using a uniform set of rules.
To demonstrate the applicability of our approach, we consider
5 content categories of PCEs from the top-level hierarchies
Procedure and Clinical finding. These categories include
General procedures for Procedure and Allergies, Diseases due

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to allergy, Allergic reactions, and General clinical findings forClinical finding. To ensure the unambiguous assignment ofPCEs, SNOMED CT’s Expression Constraint Language (ECL)
is used to formally define the scope of each category, creating
pairwise disjoint partitions of the two hierarchies.
For each combination of 1 of the 5 content categories and eitherof the 2 sets of target representations, mapping rules aremanually defined based on the respective Expression Templates[36], concept definitions of related precoordinated concepts,
existing documents (as described in the “Related Work” section),and the author’s expertise. These rules are formally transcribed
into FHIR StructureMaps (R4) [37] using the Java library HAPIFHIR [38]. The resulting 10 FHIR StructureMaps are stored ona local HAPI FHIR server [39].
An excerpt of the FHIR StructureMap for the category “Allergic
reaction” and the NASHIP profiles is shown in Figure 6. Each
StructureMap contains exactly a single “rule” entry for theSuperconcept and each relevant attribute relationship for the
category. The mapping rule’s “source” is either the string“Superconcept” or the SNOMED CT identifier of the respective
attribute. For the “target” element, FHIRPath is used to traverse
the FHIR resources and explicitly identify the correct
subelement [2]. Illustrated in orange in Figure 6, the mappingof the attribute “Causative agent” (“246075003”) to the
FHIRPath AllergyIntolerance.code is exemplified. This specifies
that the attribute value associated with the PCE should be stored
in the code element of the AllergyIntolerance resource.

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Figure 6. Extract of the FHIR StructureMap for allergic reactions in JSON format. It contains an entry for each SNOMED CT attribute and theSuperconcept as well as references and names of the profiles. FHIR: Fast Healthcare Interoperability Resources; SNOMED CT: SNOMED ClinicalTerms.

Apart from the central mapping rules, some further information
is included in the FHIR StructureMaps.
If necessary, references between FHIR elements and resources
are indicated (Figure 6, in pink). Additionally, the FHIR
resources associated with the FHIRPath entries are mapped to
the names of the respective profiles (Figure 6, in green).

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With these general preparations completed, individual
decomposed PCEs can now be mapped to appropriate FHIR
resources. First, the PCE is assigned to 1 of the 5 content
categories by identifying the subsuming subset using the ECL
definitions and Ontoserver. Based on the category and desired
target representation, the correct FHIR StructureMap is selected.
The mapping rules in the StructureMap are then automatically
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Ohlsen et alapplied to the PCE’s Superconcept and Delta, determining the
combinations of FHIR elements and precoordinated SNOMED
CT concepts needed for the alternative representation.

Results

Ethics Approval

Bringing all the previously explained preliminary considerations
and processing steps together, a web application called“PCEtoFHIR” was developed (Figure 7). This single-page web
application was built using Angular (version 15.2.2; Google
LLC/Alphabet Inc.) and the Java framework Spring Boot(version 2.7.2; Java version 17).

This research neither involves human nor animal subjects so
ethics approval was not required.

Web Application

Figure 7. Excerpt of the web application PCEtoFHIR. FHIR: Fast Healthcare Interoperability Resources; PCE: postcoordinated expression.

An excerpt of the web application is shown in Figure 7. Afterthe entered PCE has been checked for syntactic and semantic
correctness, the Superconcept candidates, the Superconcept,
and the Delta are determined automatically. This information
is displayed to the user in the “Determine Super Concept andDelta” section. The Superconcept and Delta are then mapped
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to FHIR elements according to the category-specific FHIR
StructureMaps. The appropriate StructureMap is automaticallydetermined for the PCE based on its content category and thedesired mapping target is selected in a combo box. Figure 6
illustrates the target mapping category “KBV-Basisprofile”(KBV Base Profiles; Kassenärztliche Bundesvereinigung [KBV]JMIR Med Inform 2024 | vol. 12 | e57853 | p. 9
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is the German abbreviation for the NASHIP). Using theStructureMap’s mapping rules, the FHIRPaths, corresponding
SNOMED CT concepts, required references, and profile names
are displayed and can be copied to the clipboard or downloaded
as a text file. This information is shown in Figure 7 in the section“Mapping to elements of FHIR resources.”
The source code of PCEtoFHIR, the created FHIR
StructureMaps, and all validation results as described in thefollowing paragraphs are available on GitHub [40].

Determining Superconcept Candidates
To ensure the correct functionality of the algorithm forgenerating OWL ClassExpressions, validation was performed
with 600 randomly selected precoordinated SNOMED CT
concept definitions. These definitions, from the monthly release2023-04-30 [31], were initially examined for diverse structures
and then transformed into OWL ClassExpressions using
PCEtoFHIR’s regular algorithm. The generated OWLClassExpressions was compared with the official OWLClassExpressions of the concepts, available as part of the same
SNOMED CT release, as reference data.

Ohlsen et aleach concept’s ancestors, these central features are prioritized.
While several path-based semantic similarity measures areavailable [34], they mostly rely on the shortest path between
concepts [41-44]. Sánchez and Batet [34] argue that in large
knowledge bases such as SNOMED CT, a concept inherits
information from several hierarchies and is connected to many
concepts simultaneously. Therefore, considering only theshortest path is insufficient.
In their proposed measure, Sánchez and Batet [34] consider theratio between the set of nonshared ancestors and the set of allancestors of both concepts. Adapted to our approach, this meansthat for the PCE and each of its Superconcept candidates, allancestors in the SNOMED CT hierarchy are determined, andthe ratio is calculated. Thus, this measure is applicable inprinciple.

In summary, the comparison revealed no discrepancies between
the OWL ClassExpressions generated by PCEtoFHIR and thosefrom the reference data.

To evaluate if the calculated values are reasonable beyond that,a practical validation by means of an exemplary sample wasdone in succession. For 33 PCEs taken from a publication by
Kate [45] (see detailed explanation in the “Overall Evaluation
With Existing PCEs” section), the most similar Superconcept
candidate was determined manually and compared with theSuperconcept calculated by PCEtoFHIR via Sánchez and Batet’s
semantic similarity measure. In 76% (25/33) of the cases, thesame Superconcept was chosen [34]. An analysis of theremaining 24% (8/33) revealed that different information
components within the PCE were prioritized during
Superconcept selection (eg, favoring localization over proceduretype), but the divergent choices made by the algorithm wereconsidered plausible. Consequently, the measure by Sánchez
and Batet [34] was found to be feasible for Superconcept
determination and is therefore used to calculate semantic
similarity in this work.

Choosing Superconcept

Mapping to FHIR Elements

As described previously, a path-based measure by Sánchez and
Batet [34] is applied to calculate semantic similarity in our work.
A preliminary analysis was conducted to ensure both thetheoretical and practical applicability of this measure.

As previously described, the Basisprofile of the NASHIP(version 1.4.0) [4] and the profiles of the Core Data Set of the
MII (version 1.0) [5] were used for mapping to the FHIR
profiles. To illustrate the mapping, 5 categories were considered.
For each category and profile type, a FHIR StructureMap was
created, resulting in a total of 10 FHIR StructureMaps. Table
3 shows the FHIR resources used for the mapping.

The comparison involved checking whether the OWLClassExpressions matched syntactically and semantically. Focus
concepts were excluded from the semantic validation because
the reference data’s OWL ClassExpressions are based on Stated
Concept Definitions, while our approach uses Inferred Concept
Definitions. Thus, the validation focused solely on whether thefocus concepts were syntactically in the correct position within
the OWL ClassExpression.

SNOMED CT’s most striking characteristics include its relianceon subtype relationships and the resulting, heavily interwoven
polyhierarchy. Using a path-based approach that incorporates

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Ohlsen et al

Table 3. Utilization of different FHIRa resources per profile type, organized by content category.
Category and profile type

FHIR resources

Allergies
NASHIPb

AllergyIntolerance

MIIc

Condition, Observation

Disease due to allergies
NASHIP

AllergyIntolerance, Condition

MII

Condition, Observation

Allergic reaction
NASHIP

AllergyIntolerance, Condition

MII

Condition, Observation

Clinical finding (general)
NASHIP

AllergyIntolerance, Condition

MII

Condition, Observation

Procedurea

NASHIP

Procedure

MII

Procedure

FHIR: Fast Healthcare Interoperability Resources.

b

NASHIP: National Association of Statutory Health Insurance Physicians.

c

MII: Medical Informatics Initiative.

For the category of general Clinical finding, the FHIR
StructureMaps include mappings for the Superconcept and thefollowing 5 SNOMED CT attributes: Causative agent, Finding
site, Associated morphology, Pathological process, and Clinicalcourse. In addition to the 5 SNOMED CT attributes, thefollowing attributes were considered for the remaining Clinicalfinding categories focusing on allergies: Has realization,
Occurrence, and Due to. Furthermore, for the hierarchy
Procedure, mapping rules for the Superconcept and thehttps://medinform.jmir.org/2024/1/e57853

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following 12 additional SNOMED CT attributes are established:
Method, Procedure site—Direct, Procedure site—Indirect,
Direct substance, Direct morphology, Using substance, Using
device, Using access device, Has intent, Access, Surgicalapproach, and Has focus.
Table 4 shows an example of the mapping of SNOMED CT
elements to FHIRPath for Allergic reactions. The complete setof mapping rules and the associated StructureMaps are available
online [46].

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JMIR MEDICAL INFORMATICS

Ohlsen et al

Table 4. The SNOMED CT elements and the associated FHIRPath for the category “Allergic reaction” based on the profiles of the NASHIPa.

aSNOMED CT element

FHIRPath in NASHIP profiles

Super concept•

Condition.code

Causative agent•

AllergyIntolerance.code

Finding site•

Condition.bodySite

Associated morphology••

AllergyIntolerance.reaction.manifestation.coding:snomed
Condition.evidence.code

Pathological process••

AllergyIntolerance.reaction.manifestation.coding:snomed
Condition.evidence.code

Has realization••

AllergyIntolerance.reaction.manifestation.coding:snomed
Condition.evidence.code

Occurrence•

AllergyIntolerance.extension: abatement-lebensphase-von [47]Clinical course•

Extension of HL7: Condition.condition-diseaseCourse [48]Due to•Extension of HL7: Condition.condition-dueTo [49]

NASHIP: National Association of Statutory Health Insurance Physicians.

To ensure their correctness, the created FHIR StructureMaps
were validated by author AE, who had not been involved in the
PCEtoFHIR project up to that point. AE possesses in-depth
knowledge of FHIR and SNOMED CT and is profoundlyfamiliar with the MII and NASHIP profiles due to her former
and current work. She validated the FHIR StructureMaps basedon the definitions of the profiles used, ensuring the correct
choice of profiles, the accurate mapping of SNOMED CT
elements to FHIRPaths, and appropriate references. The
validation yielded the following results: the choice of profiles
and references was found to be entirely correct. The mapping
rules between SNOMED CT elements and FHIR paths werelargely correct as well; however, 8 suggestions for improvement
were provided. These suggestions were reviewed by the otherauthors, and the FHIR StructureMaps were updated accordingly
based on their agreement.

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Lastly, the finalized FHIR StructureMaps were analyzed fortheir coverage of the SNOMED CT attributes listed above.
Table 5 illustrates the number of attributes per category that
could be successfully mapped in the respective profile(mappable). Depending on the category, up to 4 attributes could
not be mapped to the native profiles (unmappable). For someof these attributes, existing FHIR extensions, such as those from
HL7, can be introduced into the profiles to achieve a more
complete mapping (with extension). Overall, between 56% (5/9)
and 92% (12/13) of attributes (76.1% on average) can be mapped
without modifications, depending on the category and profile.
By introducing these extensions, coverage could be increased
to an average of 93.5% (ranging from 67% [6/9] to 100% [eg,9/9, 6/6, 13/13, for individual combinations]).

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JMIR MEDICAL INFORMATICS

Ohlsen et al

Table 5. The number of SNOMED CT elements that can be mapped directly to the profiles are represented by “mappable,” whereas elements thatcannot be mapped are shown as "ummappable". FHIRa offers extensions to map items that are not mappable by default, which could reduce the numberof unmappable elements. The number of unmappable elements to represent by extension is shown in the last column.
Category: number of elements (total)

Profile type

Number of mappable elements (total)

Number of unmappable elements
Total

Could be mapped using
extensions

NASHIPb

5

4

1

MIIc

5

3

2

NASHIP

7

2

2

MII

7

2

2

NASHIP

7

2

2

MII

7

2

1

NASHIP

4

2

2

MII

4

2

2

NASHIP

12

1

1

MII

12

1

1

Allergies: 9 elements

Disease due to allergies: 9 elements

Allergic reaction: 9 elements

Clinical finding (general): 6 elements

Procedure: 13 elementsa

FHIR: Fast Healthcare Interoperability Resources.

b

NASHIP: National Association of Statutory Health Insurance Physicians.

c

MII: Medical Informatics Initiative.

Overall Evaluation With Existing PCEs
After validating several steps individually, the entire processof PCEtoFHIR was evaluated. To achieve a realistic scenario,
35 existing PCEs from the publication “Automatic Full
Conversion of Clinical Terms into SNOMED CT Concepts” by
Kate et al [45] were used, which are available online [50]. Thispublication presents a method for converting clinical texts into
SNOMED CT PCEs, with the 35 PCEs manually created fromclinical terms for a small-scale evaluation (see the “4.3.
Evaluation Methodology” section in [45]).
To use this data set as input for PCEtoFHIR, the 35 provided
PCEs were manually reviewed and automatically checked forsyntactic and semantic correctness, as described previously. As
a result, 2 PCEs were excluded from further processing: 1 forviolating cardinality restrictions of the Concept Model and 1
for being equivalent to another. The remaining 33 PCEs include
23 from the top-level hierarchy Clinical finding and 10 fromthe top-level hierarchy Procedure. The following SNOMED
CT attributes are used:••

Clinical finding: Associated morphology, Finding site,
Causative agent, Clinical course, Finding method, andPathological process
Procedure: Method, Procedure site—Direct, Using device,and Using substancehttps://medinform.jmir.org/2024/1/e57853

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These 33 PCEs were imported into PCEtoFHIR in bulk,
bypassing the web front end but using the developed algorithm
as usual. Each PCE was decomposed into a Superconcept andDelta, with FHIRPaths determined using the appropriate FHIR
StructureMap. Based on these FHIRPaths, the corresponding
attribute values were populated into FHIR resources, which had
been prefilled with additional required data elements (such as
references to a FHIR resource “Patient”). The resulting FHIR
resources for each PCE were then stored on the local HAPIFHIR server [39].
To determine whether all information contained within theoriginal PCE has been successfully translated into the FHIR
resources, the process shall now be reversed as shown in Figure
8. Based on the separate precoordinated SNOMED CT concepts
spread across multiple FHIR elements, a PCE will berecomposed and compared with the original. For this, the valuesof the stored FHIR resources were extracted, and the respective
StructureMap was applied in the reverse direction. The
recomposed PCE uses the Superconcept as the focus concept,
while its refinement consists of attribute relationships with theextracted SNOMED CT concepts as attribute values. For eachof the extracted concepts, the corresponding SNOMED CT
attribute must be determined according to the StructureMap’s
mapping rules. As most mappings are inherently unidirectional[3], using them in the reverse direction can lead to several
possible SNOMED CT attributes for some FHIRPaths. In these

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JMIR MEDICAL INFORMATICS
cases, the 3 rules listed in Textbox 2 are applied successively

Ohlsen et aluntil a clear distinction is achieved.

Figure 8. Validation process: The original PCE is decomposed by PCEtoFHIR as usual, and accordingly stored in FHIR resources. Based on thisrepresentation, the singular concepts are recomposed into a second PCE. The original and the recomposed PCE are compared. FHIR: Fast Healthcare
Interoperability Resources; PCE: postcoordinated expression.

Textbox 2. Rules applied for case distinction.
•

By attribute value range

For each of the attributes in question, a value range is defined by the SNOMED Clinical Terms Concept Model. This allows checking if the extracted
concept falls within the respective value range and is thus a valid value for the corresponding attribute. If only a single value range matches, the correct
attribute is identified.
•

By attribute hierarchy

Some SNOMED CT attributes are organized hierarchically (eg, Procedure device—Using device—Using access devices). If multiple attributes fromthe same hierarchy are available, the most general attribute within this hierarchy is selected (in this case: Procedure device).
•

By occurrence heuristic

The concept definitions of precoordinated concepts are analyzed to identify which SNOMED CT attribute is most frequently used for the given concept.
The attribute with the highest statistical occurrence is selected.

Finally, each recomposed PCE was compared with thecorresponding original PCE as a reference by testing theirsubsumption relationship via Ontoserver and the FHIR service$subsumes. For 32 of the 33 comparisons, the PCEs were
evaluated as equivalent, indicating that no information was lost
during the process. In the remaining comparison, therecomposed PCE was found to be a subclass of the original.
Further analysis revealed that while some semantic precision
was lost due to the required case distinctions, the FHIR
representation achieved through PCEtoFHIR remained
semantically equivalent to the original PCE. The approach thus
proved successful, demonstrating the preservation of a PCE’scontent.

Discussion
Principal Findings
This work aimed to develop an algorithm for generating
alternative representations of SNOMED CT PCEs. The“Introduction” section discussed various reasons for the
reluctance to use PCEs, including the preference for editing andsaving individual codes. Precoordinated concepts offer theadvantage of a single code representing a medical circumstance,along with a human-readable description. These aspects areseen as advantages when using precoordinated concepts but are
not present in postcoordination. However, by identifying a
Superconcept using OWL and semantic similarity, aprecoordinated SNOMED CT concept that most accurately
describes the PCE can be determined. The PCE, as a subconceptof this Superconcept, can then be effectively displayed andanalyzed. In addition, the decomposition of the PCE results in
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individual codes that users are familiar with from practicalexperience. FHIR, recognized as a central interoperable data
standard in health care, is gaining increasing importance bothin Germany [51] and globally [51-53].
The calculation of semantic similarity is crucial for identifying
the concept most similar to a PCE. In this work, the measure
by Sánchez and Batet [34] was selected due to its suitability for
SNOMED CT’s polyhierarchies and multiple inheritances.
While this measure has been validated as effective, otherapproaches to semantic similarity calculation could also be
considered. Although choosing an appropriate Superconcept isimportant, any remaining information is represented throughthe Delta, so alternative semantic similarity measures would
have minimal impact on the overall functionality of PCEtoFHIR.
However, PCEtoFHIR could be enhanced by allowing users tomanually select or adjust the Superconcept.
As described above, the Superconcept and the Delta are stored
in the relevant FHIR profiles, within their corresponding
elements. For this purpose, the profiles of the NASHIP [4] andthe profiles of the core data set of the MII [5] were utilized.
However, the approach can also be adapted to other profiles.
As shown in Table 5, depending on the content category andprofile type, between 56% (5/9) and 92% (12/13) of attributes
could be mapped directly to the respective profile without
modifications, demonstrating a high coverage of content but
also highlighting some gaps.
An analysis of the unmappable attributes revealed that these
relations mostly concern very specific details that occur
infrequently in precoordinated concept definitions. As PCEs
are generally constructed based on existing concept definitions,
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JMIR MEDICAL INFORMATICS
they are unlikely to regularly utilize these highly specificattributes. SNOMED CT, however, provides the capability toinclude medical facts at such a granular level. By contrast, HL7
FHIR was designed with a pragmatic approach, focusing on themost prevalent information. This fundamental design
discrepancy accounts for the majority of mapping challenges.
Nevertheless, as intended by the FHIR standard, suitable
extensions from HL7 are available to appropriately represent
some of these attribute relations (see Tables 4 and 5) and could
be integrated into the profiles to extend their coverage of PCEcontent. Currently, the core data set lacks a profile for the FHIR
resource AllergyIntolerance, which limits the representabilityof this content category. However, because the core data set isan ongoing modeling initiative, such a profile may be added in
the future.
Apart from the unmappable attributes, the proposed mapping
rules cannot always ensure an exact translation. In some cases,
there is no precisely matching FHIR element for a specificattribute, or multiple attributes must be mapped to the sameelement (eg, Using access device and Using device can both
only be recorded via the FHIR element Device.type, which isthen referenced via Procedure.usedReference). As a result, somesemantic precision may be lost. Therefore, it is advisable tostore the original PCE in the metadata of the FHIR
representation to ensure the preservation of the original meaning.
Nevertheless, the outlined difficulties in achieving asemantically equivalent representation through FHIR elements
highlight the precision attainable through SNOMED CT’s
postcoordination, underscoring its importance for detailed
medical data description.
Several validations were conducted to ensure the correctnessof both the individual processing steps and the overall
functionality of PCEtoFHIR. Although a large data set from
SNOMED International’s releases could be used to firmly
validate the OWL expression generation, other evaluations
required preexisting real-world PCEs of specific content
categories, which are not readily available. The employed setof 35 PCEs meets these criteria and effectively facilitates ourvalidation approaches. However, the limited number andsemantic variance of these exemplary PCEs suggest thatincorporating additional reference data could enhance thesignificance of the results.
Another validation included the manual review of the FHIR
StructureMaps by a FHIR expert. This review revealed only
minor inaccuracies, which were corrected in the current versionof StructureMaps. Using these StructureMaps, the mapping forthe considered categories was successfully completed and canbe extended to other subhierarchies of SNOMED CT without
difficulty. Hence, further possibilities of application may beconsidered, such as addressing the TermInfo problem. As
mentioned, the choice between representing medical facts in aterminology or an information model often varies and depends
on the intended use. For example, “Fracture of the left femur”
can either be represented using a single PCE as the FHIR
element Condition.code like•

Condition.code: 71620000:{363698007=722738000}

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Ohlsen et al(Fracture of femur : {Finding site = Structure of bone ofleft femur})or by splitting the semantic meaning up into two precoordinated
SNOMED
CT
conceptsusingfurtherelement-code-combinations of the FHIR resource Condition:••

Condition.code: 71620000 |Fracture of femur (disorder)|Condition.bodySite: 722738000 |Bone structure of left femur(body structure)|.

This variability in expressing medical facts was leveraged in
the presented approach, enabling flexible transformation
between terminology-focused and information model–focused
representations. This allows for an alternative when replacing
PCEs and may help address some challenges related to the
TermInfo problem. By enabling flexible switching between
different expression methods, semantic interoperability is
maintained regardless of the representation paradigm used inan electronic health record. Additionally, this approachfacilitates the plausibility check of recorded medical informationby allowing the integration of disjointed elements (eg, scatteredacross various FHIR elements) into a single interpretable
expression.
Despite the general ambiguity regarding the scope of semantic
versus structural standards, some specific recommendations do
exist, such as the suggestion that “contextual meaning should
rather be represented via the information model” [54].
Contrarily, concepts within the SNOMED CT hierarchy
Situation with explicit context encompass contextual informationthat extends beyond the typical scope of a terminology, such assuspected diagnoses, procedures not done, or family history
facts. This additional contextual information can affect logicalconclusions. An explanation of the reasons behind this,involving epistemological versus ontological components ofmeaning, is beyond the scope of this paper (see [3,13]). Basedon the logical definitions of concepts directly related to PCEs,
the approach presented in this paper enables the extraction ofproblematic pieces of information and their storage within
separate elements of the information model. As a result, a
concept like 165008002 |Allergy testing not done (situation)|
could be represented by separating the epistemological aspect“not done” into the suitable FHIR element Procedure.status:••

Procedure.code: 252512005 |Allergy test (procedure)|
Procedure.status: not-done (according to the required HL7
FHIR ValueSet).

In this way, the integrity of SNOMED CT’s hierarchies may
be preserved.

Conclusions
The use of PCEs greatly enhances SNOMED CT’s capacity tocapture medical details comprehensively. However, despite its
advantages, postcoordination has not yet been widely adopted
in routine data collection. To address this, PCEtoFHIR offersa solution to ensure semantic interoperability between systems
that are adept at postcoordination and those that are not, byleveraging the globally accepted HL7 FHIR standard to provide
an alternative representation. State-of-the-art techniques indescription logic and terminology services are integrated into
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JMIR MEDICAL INFORMATICSa largely automated web application that decomposes PCEs into
their core components. Validation of both individual steps andthe overall process confirms the approach’s functionality.
PCEtoFHIR is designed and implemented modularly, positioning
it for future adaptations in the evolving landscape of healthinformatics. In addition to straightforward extensions to other
SNOMED CT hierarchies or FHIR profiles by adding more

Ohlsen et al
StructureMaps, the algorithm can be adapted to work with otherinformation models, such as openEHR or relational databases.
The approach also holds the potential for addressing further
challenges in semantic and structural standards, such as the
TermInfo problem. By reversing the processing direction—from
FHIR elements back to PCE—meaningful SNOMED CT-based
analyses could be facilitated.

Acknowledgments
This work is funded by the German Federal Ministry of Education and Research (BMBF) as part of the Medical Informatics
Initiative Germany (grant number 01ZZ2312A).

Conflicts of Interest
None declared.

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Abbreviations
CSIRO: Commonwealth Scientific and Industrial Research Organization
DAG: directed acyclic graph
ECL: Expression Constraint Language
FHIR: Fast Healthcare Interoperability Resources
HL7: Health Level 7
KBV: Kassenärztliche Bundesvereinigung
LOINC: Logical Observation Identifiers Names and Codes
MII: Medical Informatics Initiative
NASHIP: National Association of Statutory Health Insurance Physicians
OWL: Web Ontology Language
PCE: postcoordinated expression
RIM: Reference Information Model
SNOMED CT: SNOMED Clinical Terms

Edited by C Lovis; submitted 28.02.24; peer-reviewed by C Lien, S Lee; comments to author 21.04.24; revised version received
14.06.24; accepted 22.07.24; published 17.09.24
Please cite as:
Ohlsen T, Ingenerf J, Essenwanger A, Drenkhahn C
PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources
JMIR Med Inform 2024;12:e57853
URL: https://medinform.jmir.org/2024/1/e57853
doi: 10.2196/57853
PMID:©Tessa Ohlsen, Josef Ingenerf, Andrea Essenwanger, Cora Drenkhahn. Originally published in JMIR Medical Informatics(https://medinform.jmir.org), 17.09.2024. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license
information must be included.

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