Jodyn Platt, PhD, MPH

A Michigan-based deliberative study found strong public support for patient-informed artificial intelligence (AI) labeling in health care, emphasizing transparency, privacy, equity, and safety to build trust.

There is widespread interest in understanding the role of health care in meeting social needs. This study examines community-wide activities, resources, and information technology used to manage social care.

This study identifies limited engagement with equity among academic medical centers as they develop governance processes for artificial intelligence (AI)/machine learning and predictive technologies.

Patients are less comfortable with predictive models used for health care administration compared with those used in clinical practice, signaling misalignment between patient comfort, policy, and practice.

As predictive models proliferate, providers and decision makers require accessible information to guide their use. Preventing and combating bias must also be priorities in model development and in communication with providers and decision makers.

Rapidly advancing health information technologies are changing the nature of team-based care; there is a critical need to examine how trust functions in contemporary team-based care.