When a child or family begins to stand out because of patterns in history or physical findings, physicians must determine whether to take a closer look at the situation.
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.
This narrative information graphic contextualizes the lack of current maternal morbidity and mortality data in the United States since the Dobbs v Jackson Women’s Health Organization decision in 2022.
AMA J Ethics. 2024;26(1):E92-93. doi:
10.1001/amajethics.2024.92.
Dr Gillian R. Schmitz joins Ethics Talk to discuss her article, coauthored with Dr Robert W. Strauss: “What Should Students and Trainees Be Taught About Turfing and Where Patients Belong?”
Makenzie Doubek joins Ethics Talk to discuss her article, coauthored with Scott J. Schweikart: “Why Should Physicians Care About What Law Says About Turfing and Dumping Patients?”
Treatment decisions in high-risk situations require a dynamic relationship between doctor and patient in which patient preferences and clinician recommendations contribute equally in shaping a final treatment decision.
Turfing is a colloquialism that refers to what clinicians do to patients whose needs do not fit neatly and tidily into typical clinical placement protocols.
AMA J Ethics. 2023;25(12):E885-891. doi:
10.1001/amajethics.2023.885.