Preliminary results of a clinical research and innovation scholarship to prepare medical students to lead innovations in health care.

There is consensus amongst national organizations to integrate health innovation and augmented intelligence (AI) into medical education. However, there is scant evidence to guide policymakers and medical educators working to revise curricula. This study presents academic, operational, and domain understanding outcomes for the first three cohorts of participants in a clinical research and innovation scholarship program.

Improving Well-Being Among Trainees: A Partnership to Reduce Barriers to Primary Care Services.

Background: Improved well-being is a focus for graduate medical education (GME) programs. Residents and fellows often express difficulty with visiting primary care physicians, and this issue has not been thoroughly investigated.

Objective: We reported implementation and utilization of a primary care concierge scheduling service and a primary care video visit service for GME trainees.

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

Background: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.

Methods And Findings: A curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs.