Knowledge and Recommendations of Stakeholders regarding Ethical Oversight of Data Science Health Research: A qualitative study protocol

Document Type

Journal Article

Publication Date

10-20-2025

Journal

JMIR research protocols

DOI

10.2196/78557

Abstract

BACKGROUND: Data science health research (DSHR) employs novel computational methods and high-performance computing to analyze big data from conventional and non-conventional health and related sources to generate novel insights and communications. DSHR creates valuable assets but generates substantial ethical, legal, and social (ELS) challenges. Key gaps in current ethical oversight of DSHR include blurred boundaries between research and non-research data use, inadequate protection of data donors, power imbalances that risk extractive research practices, algorithmic biases, and regulatory inadequacies. Nigeria, a typical LMIC with rapidly expanding DSHR exemplifies this environment and concerns. OBJECTIVE: This study will elicit the knowledge and recommendations of Nigerian DSHR stakeholders and contribute to improved knowledge of the ELSI of DSHR and the development of novel ethical oversight frameworks. METHODS: Between October 2024 and January 2025, we conducted Key Informant Interviews (KII) with 65 stakeholders out of 87 individuals who were approached. The KII guide comprised 12 construct-based question domains addressing awareness of policies and laws, ethical oversight processes, ELSI considerations in policy development, experiences addressing DSHR challenges, organizational and procedural frameworks, ideal oversight components, stakeholder roles, research impact on ethics and policy, regulatory influences on research practices, equity-enhancing policies, and balanced regulations. The interviews lasted 60-90 minutes and were transcribed. We analyzed the transcripts using a hybrid deductive-inductive approach. A priori codes derived from research objectives provided the analytical framework while allowing for identification of emergent concepts. The iterative three-level coding process involved initial code generation, evaluation, and refinement, with codes grouped into thematic families and semantic networks representing hierarchical concept relationships. Query tools and Boolean operators were used to interrogate the codes to extract findings. RESULTS: Of 87 invited individuals, 22 (25%) were unable to participate. The 65 participants (mean (SD) age = 47.9 (7.9) years; 77% male) included data science health researchers (39%), biomedical researchers (26%), HREC members (19%), and policy makers (17%). Most held doctoral degrees (57%), were affiliated with academic institutions (69%) and government organizations (40%) and had received general research ethics training (77%). However, only 12% had received predominantly short-duration ethics-specific DSHR training, while 92% acknowledged the need for specialized DSHR ethics education. As of January 2025, the interview transcripts have been generated and checking completed, with qualitative analysis scheduled for completion by March 2025 and completion of primary manuscripts by the end of 2025. CONCLUSIONS: This study will generate stakeholder-informed recommendations for ethical oversight of DSHR that addresses issues relating to broad consent, ELSI, data ownership, benefit-sharing, and donor protection in resource-limited settings. Our findings will inform the global DSHR and research ethics communities on the development of contextually appropriate oversight mechanisms that promote equitable partnerships, co-ownership, and tiered data governance.

Department

Clinical Research and Leadership

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