Login / Signup

Return of Value in the New Era of Biomedical Research-One Size Will Not Fit All.

Dmitry KhodyakovAlexandra Mendoza-GrafSandra BerryCamille NebekerElizabeth Bromley
Published in: AJOB empirical bioethics (2019)
Background: There is a growing interest in creating large-scale repositories that store genetic, behavioral, and environmental data for future, unspecified uses. The All of Us Research Program is one example of such a repository. Its participants will get access to their personal data and the results of the studies that used them. However, little is known about what researchers should return to participants and how they should do it in a way that is valuable and meaningful to participants. Methods: To better understand the concept of "return of value" and the practice of returning valuable study information, we conducted semi-structured telephone interviews with 44 stakeholders with diverse perspectives on this topic. All interviews have been transcribed and coded thematically to identify the most salient themes, to explore differences between returning different types of study results, and to describe differences and similarities in perspectives of different stakeholder groups. Results: We found that one size does not fit all when it comes to returning value to participants: the decisions about return of results are affected by participant preferences, researchers' concerns about feasibility, the types of data collected, their level of granularity, and available options for supporting result interpretation. Conclusions: Our findings suggest that the key to operationalizing return of value and to identifying ways to return valuable information to study participants may be to find a point of equilibrium between criteria that may affect usefulness and feasibility. The point of equilibrium may vary by study, by participants' backgrounds and preferences, by their health literacy and access to regular healthcare, and by the resources available to professionals controlling the data. Future studies should explore the factors that determine the point of equilibrium between feasibility and usefulness.
Keyphrases
  • healthcare
  • electronic health record
  • big data
  • molecular dynamics simulations
  • risk assessment
  • machine learning
  • current status
  • dna methylation
  • health insurance
  • artificial intelligence