=Paper=
{{Paper
|id=Vol-3890/paper-13
|storemode=property
|title=A metadata profile to promote Real-World Data discoverability
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-13.pdf
|volume=Vol-3890
}}
==A metadata profile to promote Real-World Data discoverability==
https://ceur-ws.org/Vol-3890/paper-13.pdf
A Metadata Profile to Promote Real-World Data
Discoverability
Emiliano Reynares1, Andrea Splendiani2 and Hanne van Ballegooijen3
1 IQVIA, Provença 392, Barcelona, Spain
2 IQVIA, Kirschgartenstrasse 14, Basel, Switzerland
3 IQVIA Solutions B.V., Herikerbergweg 314, Amsterdam, the Netherlands
Abstract
Real-World Data (RWD) are patient level data routinely collected from a variety of sources other than
traditional clinical trials. RWD is increasingly needed for purposes such as making drug discovery more
efficient and supporting healthcare systems to become more sustainable. However, the specific
characteristics of RWD pose integration and findability-related challenges that arise from the
heterogeneity, dynamism, and voluminosity of their sources. While previous work has addressed these
issues by defining common data models and frameworks to integrate RWD into specific processes, a
machine-actionable representation is needed to promote data discoverability and fit-for-purpose
assessment. This paper introduces a Data Catalog Vocabulary (DCAT)-based metadata profile for an
RWD-specific metadata vocabulary. The profile aims to tackle the findability-related challenges by
enabling querying and processing of metadata in a standardized fashion.
Keywords
Real World Data, DCAT, metadata, findability, machine-actionable, metadata profile1
Introduction
RWD are defined as routinely collected data relating to a patient’s health status or the delivery of
healthcare as opposed to data retrieved from traditional clinical trials. The current landscape of
RWD comprises a large variety of types: clinical observations, medication, claims, molecular
profiling, family history, mobile health, environmental, patient reported and social media [1, 2].
RWD can provide a holistic view of people’s health by capturing information from sources that
reflect the experience, behavior, and social and environmental context of patients collected by
health records, claims data, registries, patient-reported outcomes, consumer credit-card
spendings, geospatial data, and medical devices-harvested data [3, 4].
RWD can make drug discovery more efficient by better identifying unmet medical needs,
optimizing the design of clinical studies, informing benefit-risk assessments, and enabling large-
scale analytics. It can expand the on-label indications for existing products by showing their
effectiveness in different populations and/or conditions [4, 5].
RWD can be used to make the healthcare systems more sustainable by supporting evidence-
based decision making, comparative effectiveness research, health technology assessment, and
pharmacovigilance. It can support the approval and market access of new drugs by
demonstrating their safety, efficacy, and cost-effectiveness in real-world settings. [6].
However, RWD have specific characteristics, which poses challenges for its integration and
findability [4, 7]. These challenges arise from the heterogeneity of RWD since it is retrieved from
sources that differ by nature and quality, their dynamism and voluminosity as more data are
generated and collected over time. Integration of RWD requires harmonization of data formats,
standards, and terminologies across sources and domains. Findability of RWD requires effective
Proceedings of SWAT4HCLS 2024: The 15th International Conference on Semantic Web Applications and Tools for
Health Care and Life Sciences, February 26-29, 2024, Leiden, The Netherlands.
emiliano.reynares@iqvia.com (E. Reynares); andrea.splendiani@iqvia.com (A. Splendiani);
hanne.vanballegooijen@iqvia.com (H. van Ballegooijen)
0000-0002-5109-3716 (E. Reynares); 0000-0002-3201-9617 (A. Splendiani)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
metadata (i.e., descriptive data that characterize other data creating a clearer understanding of
their meaning and suitability for a specific purpose).
While some initiatives have addressed the data integration issue by providing common data
models (CDMs) and analytical tools for observational data such as the Observational Medical
Outcomes Partnership (OMOP) CDM [8], the Heads of Medicines Agencies (HMA) and the
European Medicines Agency (EMA) advance on the findability topic by developing a framework
to better integrate RWD into the regulatory decision-making processes [9]. This framework
includes a list of metadata for RWD catalogues, a guide of good practices for the use metadata [10,
11]. HMA/EMA have also published Data Quality Framework for EU medicines regulation that
provides actions, metrics, and a maturity model to support data-driven regulatory decision
making [12].
However, it is not enough to have a list of abstract metadata to promote data discoverability
and fit-for-purpose assessment. This should be accompanied by an RWD-specific metadata
vocabulary. A machine-actionable representation of metadata able to be queried and processed
in a standardized fashion would be ideal but requires a model for the metadata vocabulary. The
Data Catalog Vocabulary (DCAT) and schema.org can be used as the foundation of such a model
[13, 14]. For instance, the Google Dataset Search service [15] relies on the structured description
of datasets published on the web using both DCAT and schema.org for dataset discovery [16].
These models are domain-agnostic i.e.: they describe cross-domain entities in a high-level fashion
and provide mechanisms to extend them for applications in domain-specific settings.
An extension of these models would enable the definition of the semantics and syntactic
aspects of the domain-specific metadata elements, as well as the relationships and constraints
among them. It would also facilitate the validation and quality assessment of the metadata and
allows the mapping and alignment with other metadata standards. A RWD domain-specific
extensions is still missing. It would lead to a better description of the RWD assets through
standardized metadata models and vocabularies, which would overcome the findability issues.
Schema.org and DCAT
Schema.org is a structured markup vocabulary to describe resources published on the web by
defining types of information and their corresponding properties. It is increasingly being applied
to web pages as it improves its findability and enhances the search experience for end users.
Schema.org also provides the mechanisms to extend the vocabulary by defining additional types,
properties, and constraints [17]. Bioschemas is one of those extensions aimed at improving the
exposure of structured data in life sciences by adding data types related to biology and defines a
set of minimum metadata properties [18, 19].
DCAT is a structured vocabulary developed by W3C and designed to enable a publisher to
describe datasets and data services in a catalog using a standard model. It comprises a minimal
set of classes, properties, and constraints specified through the Resource Description Framework
(RDF) data model [20]. DCAT also reuses terms from other vocabularies as proposed by the
Dublin Core Metadata Terms Recommendation [21]. DCAT can be extended by adding constraints
as cardinality restrictions, classes and properties, controlled vocabularies, and requirements for
access mechanisms. A DCAT extension is known as a DCAT profile. There are profiles for data
portals in Europe and geospatial, statistical, and national variations of it [22].
Despite the similarities, schema.org and DCAT show differences in their technical design and
the use cases they address. Schema.org is aimed at enhancing discovery and indexing of online
resources via search engines while DCAT is meant to represent detailed information on datasets
and data catalogues. Therefore, schema.org has a broader scope since it covers a variety of
resources like web pages, articles, books, and events. On the technical side, schema.org has a
simpler and flatter structure as it defines fewer classes and properties, does not use nested or
complex objects, and allows multiple types for the same resource [23].
A metadata profile for RWD
This paper introduces a DCAT-based metadata profile for an RWD-specific metadata vocabulary.
It is aimed to describe the specific details of the variety of types of data and sources comprised in
RWD catalogs. It enables the standardized definition of the essential aspects of the RWD datasets
such as therapeutic area, patient data, healthcare setting, legal and regulatory, and governance
details.
The proposed profile described below extends the core six DCAT classes. A dataset is a
collection of published data available for access or download in one or more serializations. Each
serialization is represented by a distribution that can be provided via data services. The
description of the datasets, distributions, and data services are registered in a catalog. The
catalog records are used to describe such a registration by providing, for instance, provenance
details. The dataset series allows to represent a collection of datasets that are published
separately but share some characteristics that group them.
A catalog compliant with the profile must fulfill the following additional constraints. The
catalog must have at least one catalog record. A catalog record must (a) describe the registration
of exactly one dataset or data service, (b) have the date of formal recording of the asset, and (c)
have the most recent date the catalog entry was updated. A dataset must (a) have an identifier,
(b) have a title, (c) have a textual description, and (d) be serialized through at least one
distribution.
A set of previously proposed Linked Data modeling patterns were applied to design the profile
[24]. The Custom Datatype pattern to further describe the structure of literal values, Label
Everything to ensure that every resource has a human-readable name, Link Not Label to ensure
the complex entities are modeled as resources instead of labels, Multi Lingual Literal to allow for
internationalized text, Qualified Relation to improve the modeling of complex
relationships, Repeated Property to allow for multi-valued properties, Topic Relation to states the
linkage between content-related resources, and Typed Literal to constrain the properties allowed
values.
The profile defines a design pattern to assert the presence or absence of the features on the
datasets [11]. This pattern comprises (1) the is described by property to link a dataset with
the Qualified Existential class, (2) the Qualified Existential class to asserts the occurrence of a
dataset feature through the is data available and is about dataset feature properties, and (3) the
Dataset Feature vocabulary of features.
Usage of this pattern provides a mechanism to extend the profile by defining new dataset
features on the Dataset Feature vocabulary. This allows us to avoid an explosion in the number of
properties that would hamper its maintainability.
The metadata profile also defines additional classes and a variety of properties. The Patient
Group class and its descendants – the Patient Gender Group and Patient Age Group classes – allow
to describe the population aspect of the datasets.
An overview of the profile core classes, and the additional properties is shown in Figure 1. The
diagram should be interpreted under the RDF open-world assumptions about the sub-class-of
semantics in contrast to the object-oriented generalization meaning.
Conclusions and future work
This paper introduced a DCAT-based metadata profile for an RWD-specific metadata vocabulary.
The model is aimed at tackling the findability-related challenges by enabling querying and
processing of metadata in a standardized fashion. It extends the core DCAT classes through the
definition of additional constraints, classes, and properties. A design pattern to specify the data
elements applicable to the description of the RWD sources is also proposed.
As a proposal in its early stages, it opens several lines of further development. This involves a
comprehensive analysis of the metadata vocabulary defined by the HMA/EMA and the
assessment of a potential alignment with the proposed model.
Figure 1: A DCAT metadata profile overview. The dcterms: prefix refers to Dublin Core terms,
dcat: to the DCAT terms, foaf: to the FOAF Ontology, prov: to the W3C PROV Ontology, and xsd: to
the XML Schemas data types. The rws: prefix and custom vocabs annotated elements refers to
items proposed by this profile.
The documentation of the data and metadata quality aspects could be enhanced by reusing
specific frameworks such as the Data Quality Framework for EU medicines regulation published
by the HMA/EMA.
The extension of the schema.org vocabulary by adding RWD-specific terms is a topic to further
explore. The experience of the Bioschemas community in the life sciences domain, and the set of
mappings to schema.org recommended by the DCAT specification can leverage this line of work.
This article describes work-in-progress for which the authors are looking for collaborations,
and kindly ask interested parties to get in touch for further details. Given this scenario, the
licensing issue is under discussion. This makes it unfeasible to openly release a fully-fledged RDF
serialization of the proposed DCAT profile.
Acknowledgements
Special thanks go to Michèle Arnoe and Marie Sophie Schwalm (IQVIA) for the useful comments
on the overall process of cataloguing and publishing real-world data, and for the feedback on this
manuscript.
References
[1] FDA, Real-World Evidence, 2023. URL: https://www.fda.gov/science-research/science-and-
research-special-topics/real-world-evidence.
[2] B. Swift, L. Jain, C. White, V. Chandrasekaran, A. Bhandari, D. A. Hughes, P. R. Jadhav,
Innovation at the Intersection of Clinical Trials and Real-World Data Science to Advance
Patient Care. Clinical And Translational Science, 11 (2018): 450-460. doi:10.1111/cts.12559.
[3] McKinsey & Company, Real-world data quality: What are the opportunities and challenges?,
2023. URL: https://www.mckinsey.com/industries/life-sciences/our-insights/real-world-
data-quality-what-are-the-opportunities-and-challenges.
[4] P. Naidoo, C. Bouharati, V. Rambiritch, N. Jose, S. Karamchand, R. Chilton, R. Leisegang, Real-
world evidence and product development: Opportunities, challenges and risk
mitigation. Wien Klin Wochenschr, 133 (2021): 840-846. doi:10.1007/s00508-021-01851-
w.
[5] BMC Series blog, Opportunities and Challenges for Evidence-based making in Health Sciences
using Real-World Data, 2022. URL:
https://blogs.biomedcentral.com/bmcseriesblog/2022/04/12/opportunities-and-
challenges-for-evidence-based-decision-making-in-health-sciences-using-real-world-data/.
[6] SDG group, From Real World Data to Real World Evidence, 2023. URL:
https://www.sdggroup.com/en-US/insights-room/real-world-data-real-world-evidence.
[7] ICMRA, ICMRA statement on international collaboration to enable real-world evidence
(RWE) for regulatory decision-making, 2022. URL:
https://www.icmra.info/drupal/sites/default/files/2022-07/icmra_statement_on_rwe.pdf.
[8] OHDSI, Standardized Data: The OMOP Common Data Model. Accessed in December 2023.
URL: https://www.ohdsi.org/data-standardization/.
[9] HMA/EMA, Real-world evidence framework to support EU regulatory decision-making,
2023. URL: https://www.ema.europa.eu/en/documents/report/real-world-evidence-
framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf.
[10] HMA/EMA, List of metadata for Real World Data catalogues, 2022. URL:
https://www.ema.europa.eu/en/documents/other/list-metadata-real-world-data-
catalogues_en.pdf.
[11] HMA/EMA, Good Practice Guide for the use of the Metadata Catalogue of Real-World Data
Sources, 2022. URL: https://www.ema.europa.eu/en/documents/regulatory-procedural-
guideline/good-practice-guide-use-metadata-catalogue-real-world-data-sources_en.pdf.
[12] EMA, Data Quality Framework for EU medicines regulation, 2022. URL:
https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/data-
quality-framework-eu-medicines-regulation_en.pdf.
[13] W3C Working Draft, Data Catalog Vocabulary (DCAT) - Version 3, 2023. URL:
https://www.w3.org/TR/vocab-dcat-3/.
[14] Schema.org. Accessed in December 2023. URL: https://schema.org.
[15] Google Dataset Search. Accessed in December 2023. URL:
https://datasetsearch.research.google.com.
[16] Google Dataset structured data. Accessed in December 2023. URL:
https://developers.google.com/search/docs/appearance/structured-data/dataset.
[17] R. V. Guha, D. Brickley, S. Macbeth, Schema.org: evolution of structured data on the web.
Communications of the ACM, 59 (2016): 44-51. doi:10.1145/2844544.
[18] Bioschemas. Accessed in December 2023. URL: https://bioschemas.org.
[19] A. J. G. Gray, C. A. Goble, R. Jimenez, Bioschemas: From Potato Salad to Protein Annotation. In
ISWC (Posters, Demos & Industry Tracks), 2017. URL: https://ceur-ws.org/Vol-
1963/paper579.pdf.
[20] W3C Working Group. RDF 1.1 Primer, 2014. URL: https://www.w3.org/TR/rdf11-primer/.
[21] Dublin Core, DCMI Metadata Terms Specification. Accessed in December 2023. URL:
https://www.dublincore.org/specifications/dublin-core/dcmi-terms/.
[22] SEMIC EU, DCAT-AP 3.0.0 Release, 2023. URL:
https://joinup.ec.europa.eu/collection/semic-support-centre/solution/dcat-application-
profile-data-portals-europe/release/300.
[23] W3C, ISO 19115 – DCAT – Schema.org mapping, 2018. URL:
https://www.w3.org/2015/spatial/wiki/ISO_19115_-_DCAT_-_Schema.org_mapping.
[24] L. D. I. Davis, Linked Data Patterns, 2022. URL: https://patterns.dataincubator.org.