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Paper short abstract

Using the case of a health diplomacy site, we discuss social considerations of autonomy and clinical experiences to inform the design of ethically grounded data-sharing frameworks across contexts, including systems transitioning to digital platforms and those merging datasets for AI applications.

Paper long abstract

U.S. Venture-capital investments in AI in healthcare are projected to reach $11 billion in 2025. Central to the return on investment are the biospecimens and health information within data systems such as biodata-tracking technologies connected to personal devices, biobanks, and Electronic Health Records. The more access, system integration, and the greater number of people and data feeding the system, the better the learning and innovation to fulfill the promise of AI in healthcare. Investors know the value of this data and premise returns on the assumption of increasing access to these sources. Globally, personal health information, once protected by pre-AI regulatory systems, is now accessed through new means and frontiers of regulatory landscapes, most of which the average public is unaware of. Many national systems haven’t yet connected social and sensitive health data in cross-communicating networks. However, stakeholders from the Global North have long experimented with interoperable systems in health diplomacy recipient sites where they’ve piloted technologies gathering, tracing, and digitizing health information. Because of the nature of funding and the scientific satellite-metropole collaborations, datasets are fundamentally designed for sharing across systems and borders. Drawing on qualitative data from 30 interviews with health providers in Lesotho experienced in sharing health-related information with international partners for care, research, and product development we present data informing the social considerations for designing transparent, and ethically grounded data-sharing frameworks across diverse contexts, including large-scale research collaborations, smaller systems transitioning to digital platforms, and the merging of independent databases for interoperability and artificial intelligence applications.