Promoting patient engagement in cancer genomics research programs: An environmental scan.

A national priority in the United States is to promote patient engagement in cancer genomics research, especially among diverse and understudied populations. Several cancer genomics research programs have emerged to accomplish this priority, yet questions remain about the meaning and methods of patient engagement. This study explored how cancer genomics research programs define engagement and what strategies they use to engage patients across stages in the conduct of research.

Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks.

Background: Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of consensus on its application and evaluation.

Objective: We sought to identify current frameworks guiding the application and evaluation of AI for predictive analytics in medicine and to describe the content of these frameworks. We also assessed what stages along the AI translational spectrum (ie, AI development, reporting, evaluation, implementation, and surveillance) the content of each framework has been discussed.

Best-Worst Scaling and the Prioritization of Objects in Health: A Systematic Review.

Background And Objective: Best-worst scaling is a theory-driven method that can be used to prioritize objects in health. We sought to characterize all studies of best-worst scaling to prioritize objects in health, to assess trends of using best-worst scaling in prioritization over time, and to assess the relationship between a legacy measure of quality (PREFS) and a novel assessment of subjective quality and policy relevance.

Methods: A systematic review identified studies published through to the end of 2021 that applied best-worst scaling to study priorities in health (PROSPERO CRD42020209745), updating a prior review published in 2016.