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.
Concrete protocols for supporting trainees include convening team meetings, tracking bias incidents, collecting data, and initiating protective changes in culture.
AMA J Ethics. 2019;21(6):E513-520. doi:
10.1001/amajethics.2019.513.
Despite challenges of decision making for unrepresented patients, few laws or policy statements offer solutions. This article offers 5 key things to do.
AMA J Ethics. 2019;21(7):E582-586. doi:
10.1001/amajethics.2019.582.
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.