JMIR Publications

ABSTRACT

Background:

In the European healthcare 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 catalogues (HMDC) assume a key function as they enable data allocation, sharing, and utilization practices. By their design, HMDC orchestrate health information for the purpose of findability, accessibility, interoperability, and reusability. However, despite a plethora of European initiatives pushing healthcare data ecosystems forward, actionable design knowledge about HMDC is scarce. This impedes both their effective development in practice and scientific exploration, causing idle innovation potential of health data.

Objective:

The research objective is to explore structural design elements of HMDC, classifying them alongside empirically reasonable dimensions and characteristics. In doing so, the development of HMDC in practice is facilitated, while also closing a crucial gap in theory (i.e., literature about actionable HMDC design knowledge).

Methods:

The study applies a rigorous methodology for taxonomy building following well-known and established guidelines from the domain of information systems (IS). Within this methodological framework, inductive and deductive research methods are applied (i.e., literature review, case study analyses, focus groups) to iteratively design and evaluate the evolving set of HMDC dimensions and characteristics.

Results:

The iteratively conceptualized and empirically grounded taxonomy proposes 20 dimensions encompassing 101 characteristics alongside which FAIR HMDC can be structured and classified. A particular focus is on the design of their metadata assets. Furthermore, those HMDC design elements are discussed against the background of five common use cases to show their relevance in real-world contexts.

Conclusions:

The findings contribute fundamental, yet actionable, design knowledge for building HMDC in European healthcare data ecosystems. They provide guidance for practitioners, while allowing both scientists and policymakers to navigate through this evolving research field and anchor their work. Hence, the study closes the research gap outlined above, which has prevailed in theory and practice.