Dr Margaret M. Sullivan joins Ethics Talk to discuss her article, coauthored with Emily E. Lazowy, Dr Jill S. Roncarati, Dr Howard K. Koh, and Dr James J. O’Connell: “Training Clinicians to Care for Patients Where They Are."
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.
Consideration of what constitutes sufficient information about how donation protocols can interfere with a patient’s dying process is a key feature of consent processes.
AMA J Ethics. 2018;20(8):E708-716. doi:
10.1001/amajethics.2018.708.
Nicole Martinez-Martin, JD, PhD, Laura B. Dunn, MD, and Laura Weiss Roberts, MD, MA
Calibrating a machine learning model with data from a local setting is key to predicting psychosis outcomes. Clinicians also need to understand an algorithm’s limitations and disclose clinically and ethically relevant information to patients.
AMA J Ethics. 2018;20(9):E804-811. doi:
10.1001/amajethics.2018.804.
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.