=Paper=
{{Paper
|id=Vol-3127/paper-2
|storemode=property
|title=The SPHN Ecosystem towards FAIR Data
|pdfUrl=https://ceur-ws.org/Vol-3127/paper-2.pdf
|volume=Vol-3127
|dblpUrl=https://dblp.org/rec/conf/swat4ls/OsterleTC22
}}
==The SPHN Ecosystem towards FAIR Data==
https://ceur-ws.org/Vol-3127/paper-2.pdf
The SPHN Ecosystem towards FAIR Data
Sabine Österle1[0000-0003-3248-7899],Vasundra Touré1[0000-0003-4639-4431] and
Katrin Crameri1[0000-0003-3656-3457]
1Personalized Health Informatics Group,
SIB Swiss Institute of Bioinformatics,
Basel, Switzerland
dcc@sib.swiss
Abstract. Health-related data originating from diverse sources are commonly
stored in manifold databases and formats, making it difficult to find, access and
gather data for research purposes. In addition, so-called secondary use scenarios
for health data are usually hindered by local data codes, missing dictionaries and
the lack of metadata and context descriptions. Following the FAIR principles
(Findable, Accessible, Interoperable and Reusable), we developed a decentral-
ized infrastructure to overcome these hurdles and enable collaborative research
by making the meaning of health-related data understandable to both, humans
and machines. This infrastructure is currently being implemented in the realm of
the Swiss Personalized Health Network (SPHN), a research infrastructure initia-
tive for enabling the use and exchange of health-related data for research in Swit-
zerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (se-
mantic definitions), the SPHN RDF Schema (linkage and transport of the seman-
tics in a machine-readable format), a project RDF template, extensive guidelines
and conventions on how to generate SPHN RDF schema, a Terminology Service
(converter of clinical terminologies in RDF), and a Quality Assurance Frame-
work (automated data validation with SHACLs and SPARQLs). The SPHN eco-
system has been built in a way that it can easily be adapted and extended by any
SPHN project to fit individual needs. By providing such a national ecosystem,
SPHN supports researchers in generating, processing and sharing FAIR data.
Keywords: Semantics, standards, clinical research infrastructure, terminology,
graph data, data-driven medicine
1 Starting position
To optimize the use of health-related data for Personalized Health Research (PHR),
both transdisciplinary scientific research and a broad range of infrastructural efforts are
required: Established structures and procedures are needed that enable rapid and wide-
ranging, controlled access to fit-for-purpose, interoperable and standardized
health(care) data, which are able to interact and be linked to state-of-the-art IT infra-
structures, research platforms and biobanks, while meeting data protection, privacy and
information security requirements [1]. In view of the federal structures in Switzerland
– with 26 cantons responsible for the provision of healthcare services – but also due to
Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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data protection and data security arguments, the Swiss Personalized Health Network
(SPHN) has opted for a decentralized approach in which data remain at their source and
are shared and combined solely in a project-specific manner [2]. The various data
sources that can potentially be linked for PHR range from health care facilities provid-
ing routine clinical data, to laboratory facilities providing bioanalytical and -omic data,
to patient-oriented clinical research registries and cohort studies, to citizen-controlled
health data. The heterogeneity of the data types and data sources poses a variety of
challenges, especially with regard to formats and standards, but also concerning the
level of granularity when it comes to the description of the data. Local data codes, miss-
ing dictionaries and the lack of metadata and contextual descriptions make it tremen-
dously difficult to link data derived from different sources and bring them together for
PHR purposes.
This federated approach with the above described characteristics calls for a compre-
hensive framework for data providers to generate and deliver data in an SPHN-
compliant and FAIR (Findable, Accessible, Interoperable and Reusable) way [3]. Local
production or collection and preparation of data must ensure that the data is understand-
able to both humans and machines. Moreover, since individual providers only have
insight into their own databases, harmonized and detailed guidelines as well as a solid
data quality framework are necessary to ensure that the data arriving at the researcher
is compatible and interoperable with that of other providers. The SPHN ecosystem to-
wards FAIR data addresses the various requirements described above and is intended
to support both data providers and researchers as a service infrastructure in their PHR
endeavors.
2 Design and Components of the Ecosystem
The SPHN Data Coordination Center (DCC) managed by the Personalized Health
Informatics Group of the SIB Swiss Institute of Bioinformatics is responsible for the
design and implementation of the Ecosystem for FAIR data in SPHN. The core com-
ponent of the ecosystem (see Figure 1 and Table 1) is the SPHN Resource Description
Framework (RDF) schema, which incorporates the semantic definitions of the SPHN
Dataset [4] in a machine processable way, following the W3C standard [5]. This RDF
schema integrates national and international standards such as the International Statis-
tical Classification of Diseases and Related Health Problems, 10th revision, German
modification [6] (ICD-10-GM), the Swiss classification for procedures [7]
(“Schweizerische Operationsklassifikation” CHOP), the Anatomical Therapeutic
Chemical Classification System [8] (ATC), the Systematized Nomenclature of Medi-
cine – Clinical Terms [9] (SNOMED CT), the Logical Observation Identifiers Names
and Codes [10] (LOINC) and, the Unified Code for Units of Measure [11] (UCUM).
All these standards can be used to express the data – be it through value set binding
and/or to precisely code data. In addition, SNOMED CT and LOINC are used as con-
trolled vocabularies to provide a meaning binding to some concepts defined in the
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SPHN Dataset. External terminologies are provided via the terminology service in an
SPHN-compliant RDF format. The terminology service is a built-in tool that converts
different versions of the standard terminologies and classifications into RDF and pro-
vides them to the data providers as well as data users in a secure and controlled envi-
ronment.
To allow a project to extend the SPHN RDF schema with its own concepts, the DCC
provides an RDF template that contains the basic metadata and imports needed for
building an SPHN-compliant RDF schema. A user guide describes how classes, prop-
erties, ranges, domains, etc. can be extended in an SPHN compliant way. The project
RDF schema and the external terminology files are used in the ETL (Extract, Trans-
form, Load) process, where data is extracted from the source, e.g. the clinical data ware-
house of a hospital, coded in the SPHN recommended standards and transformed into
RDF. The generated data are validated against a set of global SPHN Shapes Constraint
Language (SHACL) rules, or against a project specific SHACL set created by using the
SHACLer tool, both provided by the DCC. Valid RDF data is then sent from different
data providers to the place of analysis, where it can be combined, link, and analyzed.
Researchers are free to either use the RDF directly as input for their R or Python scripts,
transform it into a data model of choice or convert it into a flat file. To facilitate the
conversion, SPHN provides a set of SPARQL (SPARQL Protocol and RDF Query Lan-
guage) queries to extract each SPHN concept with their metadata in a flat format. To
allow automatic generation of such SPARQLs, the SPARQLer tool is provided, which
generates these SPARQL queries from any SPHN-compliant RDF schema.
Fig. 1. SPHN ecosystem for data interoperability.
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Table 1. Summarizes the main components of the SPHN ecosystem.
Component Description
SPHN Dataset The SPHN dataset contains the semantic description of
the SPHN concepts (definitions, properties, and recom-
mended standards).
SPHN RDF Schema The SPHN RDF schema is the technical exchange format
for SPHN data in a FAIR (Findable, Accessible, Interop-
erable, and Reusable) format. In addition, it provides
means to link other existing standard ontologies.
DCC Terminology Service The DCC terminology service provides SPHN compati-
ble, machine-readable versions of national (CHOP or
ICD-10 GM) and international (SNOMED CT, LOINC,
ATC, UCUM) terminologies and classifications in RDF-
compliant formats.
Template The template helps projects to develop their project-spe-
cific RDF schema. This template provides pre-filled basic
metadata annotations and imports the SPHN RDF schema
with its external terminologies and other general RDF li-
braries.
Quality Assurance The SPHN data quality assurance framework consists of
Framework a set of SHACL rules and statistical SPARQL queries to
validate the compliance of the RDF data produced to the
SPHN schema.
SHACLer The SHACLer tool allows the adaption of this quality as-
surance framework in a project specific manner. It is a
tool which extracts SHACL rules from an SPHN-
compliant project-specific RDF schema.
SPARQLer The SPARQLer tool automatically extracts one SPARQL
queries per concept of an SPHN-compliant RDF schema.
3 Evaluation
The SPHN semantic interoperability strategy and the corresponding ecosystem helps
researcher in Switzerland to address the FAIR data principles. While F1, F2, F3, A1,
I1, I2, I3 are covered in the SPHN strategy (see Table 2), the duty to fulfill F4 and R1
are in the responsibility of a project (e.g. choice of a license or of the repository).
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Table 2. List of FAIR criteria addressed in the SPHN semantic interoperability strategy.
FAIR criteria Addresses in the SPHN strategy
F1. (meta)data are assigned In the SPHN RDF schema, data are assigned to a Unique
a globally unique and per- Resource Identifier (URI) with the following namespace
sistent identifier. https://biomedit.ch/rdf/sphn-resource/.
F2. data are described with Administrative metadata is provided in the SPHN RDF
rich metadata (defined by file header (e.g. SPHN RDF schema version used, the ex-
R1 below) traction date of the data and the identifier of the data pro-
vider).
Descriptive metadata for all data elements, including
which data elements to include, their definition, standards
and/or value set to be used are included in the SPHN RDF
schema.
Additionally, properties of a concept provide additional
(meta)data of a data element such as the “method of a
measurement”.
F3. Metadata clearly and Metadata mentioned in F2 is part of the schema and there-
explicitly include the iden- fore linked with the data.
tifier of the data A project can include references to additional metadata
in the RDF schema, this needs to be addressed on the in-
dividual project level.
F4. (meta)data are regis- This FAIR criterion needs to be addressed on the individ-
tered or indexed in a ual project level.
searchable resource
A1. (meta)data are retriev- Since data is represented using the RDF standard, the
able by their identifier us- W3C query language SPARQL can be used to query the
ing a standardized commu- data.
nications protocol
A2. metadata are accessi- This FAIR criterion needs to be addressed on the individ-
ble, even when the data are ual project level.
no longer available
I1. (meta)data use a for- SPHN is using the W3C RDF standard as language for
mal, accessible, shared and knowledge representation.
broadly applicable lan-
guage
I2. (meta)data use vocabu- SPHN is currently using LOINC and SNOMED CT as
laries that follow FAIR controlled vocabulary, the system is however flexible to
principles be expanded to other controlled vocabularies.
I3. (meta)data include SPHN requires the reference to existing URIs of the ex-
qualified references to ternal terminologies provided, when possible, for the an-
other (meta)data notation of meta(data).
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R1. meta(data) are richly This FAIR criterion needs to be addressed on the individ-
described with a plurality ual project level.
of accurate and relevant at-
tributes
4 Challenges
The development of such a data interoperability ecosystem has presented us with
several challenges:
The choice of RDF as an exchange format comes with a cost, as clinical data is gener-
ally not encoded in RDF-compliant formats. The data transformation imposes an addi-
tional burden on data providers to map raw clinical data to the format requested by the
SPHN framework. To support data providers, the DCC has developed comprehensive
documentation and guidelines, explaining the strategy and how to represent data fol-
lowing the SPHN framework. For instance, the specification of conventions for the
definition of URIs of common resources helps to improve data interoperability between
equivalent clinical data coming from different data providers. Furthermore, training
events and hackathons have been organized to help facilitate the learning and under-
standing of semantic web standards to the SPHN community and the interested audi-
ence outside our network. A fundamental problem in the clinical environment is that a
substantial part of the information is usually not available in a structured form. Besides,
the part that is available is largely standardized for billing purposes rather than with the
aim of reflecting patient reality. In addition, the use of internationally recognized stand-
ard terminologies in healthcare is only emerging. Therefore, data providers need to put
a lot of efforts in the structuring of data and the mapping of local codes to standard
terminologies, in order to provide understandable, valuable and fit-for-purpose data for
researchers. The mapping task is not trivial and requires domain knowledge as well as
understanding of the used standard terminology terms to make sure that the semantic
meaning is correctly translated from the local codes to the applied standards.
Finally, data validation is a critical step to ensure that the data generated is usable by
researchers. The SHACL rules validate the compliance of the data in respect to the RDF
schema and additional features such as cardinalities, but in combination with the exter-
nal terminology they also validate that only valid codes are used to encode the data.
Although this might sound trivial, such a control step brings an immense advantage
over other systems where codes are represented as strings and not as reference links to
external terminologies. The SPHN SHACLer tool allows the projects to easily expand
these validations to their schema extensions.
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5 Conclusion
The SPHN ecosystem for FAIR data supplies data providers and researchers with
the tools and services to make data (more) FAIR. The full adaption of the FAIR princi-
ples in the medical domain in Switzerland is however still a long way. SPHN is there-
fore not only building an infrastructure framework but is also investing in research sup-
port, education and training to foster the understanding and implementation of these
new technologies.
Acknowledgments. The authors would like to acknowledge the SPHN Working
groups: Clinical Data Semantic Interoperability, chaired by Christian Lovis and the
RDF Task force of the Hospital IT chaired by Katie Kalt as well as all representatives
of the Swiss University Hospitals (HUG, CHUV, USB, USZ and Inselspital) and the
SIB Swiss Institute of Bioinformatics for their contributions. Our special thanks goes
to Philip Krauss from Trivadis part of Accenture for this contribution to the design and
implementation of the ecosystem.
Correspondence. Dr. Sabine Österle (sabine.oesterle@sib.swiss) Personalized Health
Informatics Group, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
References
[1] SPHN, “Swiss Personalized Health Network. Report from the National
Steering Board 2016 – 2019.” doi: 10.5281/zenodo.404412.
[2] A. K. Lawrence, L. Selter, and U. Frey, “SPHN - The Swiss personalized health
network initiative,” Stud. Health Technol. Inform., vol. 270, pp. 1156–1160,
2020, doi: 10.3233/SHTI200344.
[3] M. D. Wilkinson et al., “Comment: The FAIR Guiding Principles for scientific
data management and stewardship,” Sci. Data, vol. 3, pp. 1–9, 2016, doi:
10.1038/sdata.2016.18.
[4] C. Gaudet-Blavignac, J. L. Raisaro, V. Touré, S. Österle, K. Crameri, and C.
Lovis, “A national, semantic-driven, three-pillar strategy to enable health data
secondary usage interoperability for research within the swiss personalized
health network: Methodological study,” JMIR Med. Informatics, vol. 9, no. 6,
pp. 1–10, 2021, doi: 10.2196/27591.
[5] “WC3 RDF.” https://www.w3.org/RDF/.
[6] “ICD-10 GM.” https://www.dimdi.de/dynamic/en/classifications/icd/icd-10-
gm.
[7] Bundesamt für Statistik, Medizinisches Kodierungshandbuch. Der offizielle
Leitfaden der Kodierrichtlinien in der Schweiz. 2020.
[8] “ATC.” https://www.whocc.no.
8
[9] “SNOMED CT.” https://www.snomed.org/.
[10] C. J. McDonald et al., “LOINC, a universal standard for identifying laboratory
observations: A 5-year update,” Clin. Chem., vol. 49, no. 4, pp. 624–633, 2003,
doi: 10.1373/49.4.624.
[11] “UCUM.” http://unitsofmeasure.org.