Implementing Findable, Accessible, Interoperable, Reusable (FAIR) Principles in Child and Adolescent Mental Health Research: Mixed Methods Approach

Abstract

Background: The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers.

objectives: To further the field of FAIR data, this study aimed to systematically identify barriers regarding implementing the FAIR principles in the area of child and adolescent mental health research, define the most challenging barriers, and provide recommendations for these barriers.

Methods: Three sources were used as input to identify barriers: (1) evaluation of the implementation process of the Observational Medical Outcomes Partnership Common Data Model by 3 data managers; (2) interviews with experts on mental health research, reusable health data, and data quality; and (3) a rapid literature review. All barriers were categorized according to type as described previously, the affected FAIR principle, a category to add detail about the origin of the barrier, and whether a barrier was mental health specific. The barriers were assessed and ranked on impact with the data managers using the Delphi method.

Results: Thirteen barriers were identified by the data managers, 7 were identified by the experts, and 30 barriers were extracted from the literature. This resulted in 45 unique barriers. The characteristics that were most assigned to the barriers were, respectively, external type (n=32/45; eg, organizational policy preventing the use of required software), tooling category (n=19/45; ie, software and databases), all FAIR principles (n=15/45), and not mental health specific (n=43/45). Consensus on ranking the scores of the barriers was reached after 2 rounds of the Delphi method. The most important recommendations to overcome the barriers are adding a FAIR data steward to the research team, accessible step-by-step guides, and ensuring sustainable funding for the implementation and long-term use of FAIR data.

Conclusions: By systematically listing these barriers and providing recommendations, we intend to enhance the awareness of researchers and grant providers that making data FAIR demands specific expertise, available tooling, and proper investments.

Keywords: FAIR data; FAIR principles; OMOP CDM; data interoperability; data quality; data standardization; health data; implementation; research data management.

Figure 1.. Overview of the process of the study. The entire process was coordinated by the data steward (RdG). Tooling: ATLAS is a tool to create cohorts in OMOP CDM; Cantrill Ladder score is used to measure well-being; Data Quality Dashboard is used to assess the quality of the data in OMOP CDM; Rabbit-in-a-Hat is a tool to design the conversion to OMOP CDM; Usagi is a tool to annotate local terms to standard concepts in OMOP CDM; White Rabbit is a tool to scan data. DREAMS: Dutch REsearch in child and Adolescent Mental health; ETL: Extract Transform Load; LDJ: Learning Database Youth; OMOP CDM: Observational Medical Outcomes Partnership Common Data Model; PROMIS: patient-reported outcomes measurement information system; PSQ: Parenting Stress Questionnaire.