The availability and quality of health data and health related data has increased exponentially over the past couple of years, due to technological advancements and increased infrastructure to capture and use this data. These advancements also mark a significant shift in the role health databanks and biobanks now play in healthcare research, clinical trials, treatment and care. Such a shift entails that databanks or large data repositories are a crucial infrastructure in the health and medicine ecosystem. Health databanks are data repositories, typically, containing largescale or population-level datasets on health information, which may range from biosamples or electronic health records to data from wearables or social media platforms.  While health databanks are critical for the scientific enterprise, they also play a pivotal role in the human experience of healthcare at large and are intricately related to the existing social, economic, and political structures influencing health outcomes and healthcare.

Consequentially, these technological advancements are fraught with systemic and structural issues, such as bias and exclusion, discrimination, power imbalances, and extractive research practices, which result in further marginalisation and disenfranchisement of vulnerable populations. Moreover, the collection and use of data at such a scale has a direct and long term impact on the legal, social and economic rights of participants and raises pertinent ethical questions that need urgent attention to ensure just, fair and equitable health outcomes. While the ethical discourse around medical research deals with some of these questions, there is a need to reconsider and reimagine the ethical framework for health research in order to meaningfully address the concerns that come with the use of data and other technologies in healthcare.

Parallel to these developments, there is a growing body of work on ethical data governance which recognises the individual and collective harms arising from the use of cogenerated data, such as privacy harms, discrimination and exclusion, profiling and surveillance. Among a gamut of data governance approaches is the data justice approach, which refers to frameworks that study prioritising the needs of structurally marginalised and vulnerable communities in order to redress structural and institutional injustices afflicting these groups. The health data justice approach primarily focuses on three aspects, first, equitable participation in health care and public health; second, building just and fair institution level norms and practices that promote social justice in health care and public health; and third, use of health data to benefit the communities and redress the existing systemic fault lines. By prioritising the needs and interests of marginalised communities, data justice approach is suitable for not only mitigating harms arising from existing systemic inequalities but also curbing the perpetuation of inequalities. 

To this end, we are exploring pragmatic pathways to embed a health data justice approach in the governance of databanks. One of the most significant pathways for integrating a data justice approach within health databanks is through adopting participatory mechanisms for data governance, which entail integrating patients or impacted stakeholders in the decision making process at each stage of the data and research lifecycle by creating an enabling environment for engagement. Participation holds a central role in achieving data justice because it has always been considered integral to social justice and is inextricably linked to democratisation of governance and decision making. Furthermore, participation is a powerful tool to tilt the power imbalance and mitigate risks arising from systemic inequalities and marginalisation of communities. Additionally, there is a growing adoption of participatory research methodologies for medical and health research, where participants are not mere subjects of study but are at the heart of research decisions and outputs. At the same time, there is a growing impetus for democratic and solidarity based mechanisms for governance of data. Thus, the future of data governance for health databanks lies at the intersection of these two approaches.

To this end, we at Aapti are filling in the gaps in health databank governance through our work in the domain of ethical health databank governance. Some of the questions that we have contended with in the past and continue to work on are:

• Pathways for embedding and operationalising meaningful participation in the governance of databanks and removing barriers to representation and participation.
• Developing other governance pillars that complement the participatory governance approach toward health data justice.
• Fiduciary duty of health databanks in protecting the interests of participants in cogenerated data and returning the value generated from the use of cogenerated data to the participants. 
• Ensuring sustainability of participation in governance while balancing it with participants’ interests and well-being.
• Building trust and legitimacy for large/population scale biobanks to ensure sustainable participation.
• Unpacking consent mechanisms in the context of long term research to preserve individual autonomy and agency.