%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 9
%N 
%P e63396
%T Exploring Metadata Catalogs in Health Care Data Ecosystems: Taxonomy Development Study
%A Scheider,Simon
%A Mallick,Mostafa Kamal
%+ Fraunhofer Institute for Software and Systems Engineering, Speicherstraße 6, Dortmund, 44147, Germany, 49 231976774, simon.scheider@isst.fraunhofer.de
%K data catalogs
%K data ecosystems
%K findability, accessibility, interoperability, and reusability
%K FAIR
%K health care
%K metadata
%K taxonomy
%D 2025
%7 18.2.2025
%9 Original Paper
%J JMIR Form Res
%G English
%X Background: In the European health care industry, recent years have seen increasing investments in data ecosystems to “FAIRify” and capitalize the ever-rising amount of health data. Within such networks, health metadata catalogs (HMDCs) assume a key function as they enable data allocation, sharing, and use practices. By design, HMDCs orchestrate health information for the purpose of findability, accessibility, interoperability, and reusability (FAIR). However, despite various European initiatives pushing health care data ecosystems forward, actionable design knowledge about HMDCs is scarce. This impedes both their effective development in practice and their scientific exploration, causing huge unused innovation potential of health data. Objective: This study aims to explore the structural design elements of HMDCs, classifying them alongside empirically reasonable dimensions and characteristics. In doing so, the development of HMDCs in practice is facilitated while also closing a crucial gap in theory (ie, the literature about actionable HMDC design knowledge). Methods: We applied a rigorous methodology for taxonomy building following well-known and established guidelines from the domain of information systems. Within this methodological framework, inductive and deductive research methods were applied to iteratively design and evaluate the evolving set of HMDC dimensions and characteristics. Specifically, a systematic literature review was conducted to identify and analyze 38 articles, while a multicase study was conducted to examine 17 HMDCs from practice. These findings were evaluated and refined in 2 extensive focus group sessions by 7 interdisciplinary experts with deep knowledge about HMDCs. Results: The artifact generated by the study is an iteratively conceptualized and empirically grounded taxonomy with elaborate explanations. It proposes 20 dimensions encompassing 101 characteristics alongside which FAIR HMDCs can be structured and classified. The taxonomy describes basic design characteristics that need to be considered to implement FAIR HMDCs effectively. A major finding was that a particular focus in developing HMDCs is on the design of their published dataset offerings (ie, their metadata assets) as well as on data security and governance. The taxonomy is evaluated against the background of 4 use cases, which were cocreated with experts. These illustrative scenarios add depth and context to the taxonomy as they underline its relevance and applicability in real-world settings. Conclusions: The findings contribute fundamental, yet actionable, design knowledge for building HMDCs in European health care data ecosystems. They provide guidance for health care practitioners, while allowing both scientists and policy makers to navigate through this evolving research field and anchor their work. Therefore, this study closes the research gap outlined earlier, which has prevailed in theory and practice. 
%M 39964739
%R 10.2196/63396
%U https://formative.jmir.org/2025/1/e63396
%U https://doi.org/10.2196/63396
%U http://www.ncbi.nlm.nih.gov/pubmed/39964739