Coaching through liminal phases: A qualitative study of graduating medical students' perceptions of the value of coaching experiences over the course of medical school.

Purpose: To explore graduating medical students' insights on the value of coaching experiences during each year of medical school while examining how coaching may support student development at various stages of training.

Methods: We invited all graduating students who participated in the coaching program from first through fourth year to participate in one 90-minute virtual focus group. We conducted a thematic analysis of all the focus group transcripts using inductive open coding to develop themes.

The Moderating Role of Ethnicity on Depressive and Anxiety Symptoms and Pain Catastrophizing in Hispanic/Latinx and Non-Hispanic/Latinx White Youth with Chronic Pain.

Objectives: Chronic pain (CP) significantly impacts emotional and physical well-being and overall quality of life across diverse populations in the United States (U.S.).

Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.

Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'rules-of-thumb'. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making.

Integration of pharmacogenetic data in epic genomic module drives clinical decision support alerts.

Introduction: The Precision Medicine Program (PMP) at the University of Florida (UF) focuses on advancing pharmacogenomics (PGx) to improve patient care.

Methods: The UF PMP, in collaboration with the UF Health Pathology Laboratory (UFHPL), utilized Health Level Seven (HL7) standards to integrate PGx data into Epic's Genomic Module to enhance the management and utilization of PGx data in clinical practice.

Results: A key feature of the Genomic Module is the introduction of genomic indicators-innovative tools that flag actionable genetic information directly within the electronic health record (EHR).