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Normative challenges in data governance: insights from global health research.

Mathew MercuriClaudia I Emerson
Published in: Advances in health sciences education : theory and practice (2024)
Many important questions in health professions education require datasets that are built from several sources, in some cases using data collected for a different purpose. In building and maintaining these datasets, project leaders will need to make decisions about the data. While such decisions are often construed as technical, there are several normative concerns, such as who should have access, how the data will be used, how products resulting from the data will be shared, and how to ensure privacy of the individuals the data is about is respected, etc. Establishing a framework for data governance can help project leaders in avoiding problems, related to such matters, that could limit what can be learned from the data or that might put the project (or future projects) at risk. In this paper, we highlight several normative challenges to be addressed when determining a data governance framework. Drawing from lessons in global health, we illustrate three kinds of normative challenges for projects that rely on data from multiple sources or involved partnerships across institutions or jurisdictions: (1) legal and regulatory requirements, (2) consent, and (3) equitable sharing and fair distribution.
Keyphrases
  • electronic health record
  • global health
  • big data
  • public health
  • quality improvement
  • healthcare
  • mental health
  • data analysis
  • climate change
  • health information
  • deep learning
  • single cell
  • current status