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.

End-stage heart failure patients can experience depression along with their chronic illness. Multidisciplinary responses are critical.

This image aims to promote reflection about patients’ feelings of sadness, despair, helplessness, and uncertainty upon being diagnosed.

The DSM-5 Task Force’s handling of the ethical controversy over the bereavement exclusion demonstrates the need for more inclusive deliberative processes.

The communication training program at University of Washington Medical Center aims to develop best practices for effective pathology communication.

When a patient is incompetent and unrepresented, alternative strategies must be implemented to document and try to rectify an error.

Women who are pregnant might not treat their mental illnesses because they overestimate risks of medication and underestimate risks of leaving their illness untreated.

As machine learning becomes increasingly common in health care, these systems’ data, algorithms, and recommendations raise critical justice questions.

When errors happen, too often clinicians are at odds with each other about how to respond to a patient or a patient’s loved ones after that patient suffers harm.

John is one patient-sitter whose cancer and portraiture experiences illuminate what it means to witness, to express regard for another’s difficult health and health care experiences.