Society values both the appropriate use of new technological and management innovations and the maintenance of a strong personal and therapeutic relationship between patients and physicians. The medical-home model may be able to accomplish both.
As billable procedures, advance care planning (ACP) conversations need measurable outcomes and training support. Integrating ACP into standard practice is key to ensuring clinicians deliver care that matters to patients.
AMA J Ethics. 2018;20(8):E750-756. doi:
10.1001/amajethics.2018.750.
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.
Pathologists should work cooperatively with clinicians and provide guidance about appropriate testing to uphold the medical ethics principle of justice.
AMA J Ethics. 2016;18(8):793-799. doi:
10.1001/journalofethics.2016.18.8.ecas5-1608.
An emerging medical ethics issue is whether to delay posting pathology reports to electronic health records (EHR) to allow clinicians time to follow up.
AMA J Ethics. 2016;18(8):826-832. doi:
10.1001/journalofethics.2016.18.8.pfor1-1608.
Dr John Banja joins us to discuss the promises and perils of artificial intelligence in health care applications, including potential “megarisks” posed by AI tools themselves.