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 decentralized 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 initiative for enabling the use and exchange of health-related data for research in Switzerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (semantic definitions), the SPHN RDF Schema (linkage and transport of the semantics 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 Framework (automated data validation with SHACLs and SPARQLs). The SPHN ecosystem 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.
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 wideranging, 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 infrastructures, 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 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 providing 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, missing dictionaries and the lack of metadata and contextual descriptions make it tremendously 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
comprehensive framework for data providers to generate and deliver data in an
SPHNcompliant and FAIR (Findable, Accessible, Interoperable and Reusable) way [
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
component 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
Statistical 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
Medicine – Clinical Terms [
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, properties, 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, Transform, Load) process, where data is extracted from the source, e.g. the clinical data warehouse 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 Language) 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.
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). 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/.
F4. (meta)data are registered or indexed in a searchable 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 exR1 below) traction date of the data and the identifier of the data provider).
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 thereexplicitly 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 individual project level.
This FAIR criterion needs to be addressed on the individual project level.
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 individble, even when the data are ual project level. no longer available I1. (meta)data use a formal, accessible, shared and broadly applicable language I2. (meta)data use vocabularies that follow FAIR principles I3. (meta)data include qualified references to other (meta)data
SPHN is using the W3C RDF standard as language for knowledge representation.
SPHN is currently using LOINC and SNOMED CT as controlled vocabulary, the system is however flexible to be expanded to other controlled vocabularies.
SPHN requires the reference to existing URIs of the external terminologies provided, when possible, for the annotation of meta(data).
R1. meta(data) are richly described with a plurality of accurate and relevant attributes
This FAIR criterion needs to be addressed on the individual project level.
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 generally not encoded in RDF-compliant formats. The data transformation imposes an additional 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 following 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 understanding of semantic web standards to the SPHN community and the interested audience 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 standard 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 external 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.
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 principles in the medical domain in Switzerland is however still a long way. SPHN is therefore not only building an infrastructure framework but is also investing in research support, 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