Effective teaching of evidence-based medicine (EBM) to medical students is important for lifelong self-directed learning.

To evaluate educational experiences of internal medicine interns before and after maximum shift lengths were decreased from 30 hours to 16 hours.

Competence is essential for health care professionals. Current methods to assess competency, however, do not efficiently capture medical students' experience. In this preliminary study, we used machine learning and natural language processing (NLP) to identify geriatric competency exposures from students' clinical notes. The system applied NLP to generate the concepts and related features from notes. We extracted a refined list of concepts associated with corresponding competencies.

Traditional methods allowing medical students and residents to review their work and receive feedback are lacking. We developed a web-based portfolio system that collects all clinical documentation and allows teachers to give feedback electronically. In a randomized control trial, we found that this system significantly increased feedback to students, often exceeding clerkship expectations. Seventy-five percent of students found the system a "valuable teaching tool". Students in control and portfolio groups agreed that the system increased feedback.

Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and altered mental status (AMS).