Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.

Objective: To determine whether graph neural network based models of electronic health records can predict specialty consultation care needs for endocrinology and hematology more accurately than the standard of care checklists and other conventional medical recommendation algorithms in the literature.

Methods: Demand for medical expertise far outstrips supply, with tens of millions in the US alone with deficient access to specialty care. Rather than potentially months long delays to initiate diagnostic workup and medical treatment with a specialist, referring primary care supported by an automated recommender algorithm could anticipate and directly initiate patient evaluation that would otherwise be needed at subsequent a specialist appointment.

Transforming Interprofessional Roles During Virtual Health Care: The Evolving Role of the Medical Assistant, in Relationship to National Health Profession Competency Standards.

Introduction: Medical assistants (MAs) were once limited to obtaining vital signs and office work. Now, MAs are foundational to team-based care, interacting with patients, systems, and teams in many ways. The transition to Virtual Health during the COVID-19 pandemic resulted in a further rapid and unique shift of MA roles and responsibilities.

Qualitative Assessment of Rapid System Transformation to Primary Care Video Visits at an Academic Medical Center.

Background: The coronavirus disease 2019 pandemic spurred health systems across the world to quickly shift from in-person visits to safer video visits.

Objective: To seek stakeholder perspectives on video visits' acceptability and effect 3 weeks after near-total transition to video visits.

Design: Semistructured qualitative interviews.