Camillo Lamanna, MMathPhil, MBBS and Lauren Byrne, MBBS
Perhaps machine learning systems trained on patients’ electronic health records and social media footprints could be used as decision aids when patients lack capacity or face overwhelming decisions.
AMA J Ethics. 2018;20(9):E902-910. doi:
10.1001/amajethics.2018.902.
Rachel Koch, MD, John G. Meara, MD, DMD, MBA, and Anji E. Wall, MD, PhD
Single-procedure interventions with minimal follow-up and clear quality-of-life gain are well suited for surgical mission trips. But not all risks and benefits are easily assessed.
AMA J Ethics. 2019;21(9):E729-734. doi:
10.1001/amajethics.2019.729.
Elizabeth Hutchinson, MD, Vanessa Kerry, MD, MSc, and Sadath Sayeed, MD, JD
Guidelines are needed to help ensure that trainee, institutional, and faculty engagement in global health is ethically appropriate and mutually beneficial for all involved.
AMA J Ethics. 2019;21(9):E759-765. doi:
10.1001/amajethics.2019.759.
Emily L. Evans, PhD, MPH and Danielle Whicher, PhD, MHS
Clinical decision support systems leverage data generated in the course of standard clinical care to improve clinical practice. They need to ensure privacy and quality of patients’ data, but must also allow queries of electronic health records.
AMA J Ethics. 2018;20(9):E857-863. doi:
10.1001/amajethics.2018.857.
Social and behavioral data contained in electronic health records are essential for studying health disparities. Can researchers avoid bias when collecting, analyzing, and using such data?
AMA J Ethics. 2018;20(9):E873-880. doi:
10.1001/amajethics.2018.873.