New instructional technologies have been increasingly incorporated into the medical school learning environment, including lecture video recordings as a substitute for live lecture attendance. The literature presents varying conclusions regarding how this alternative experience impacts students' academic success. Previously, a multi-year study of the first-year medical histology component at the University of Michigan found that live lecture attendance was positively correlated with learning success, while lecture video use was negatively correlated. Here, three cohorts of first-year medical students (N = 439 respondents, 86.6% response rate) were surveyed in greater detail regarding lecture attendance and video usage, focusing on study behaviors that may influence histology learning outcomes. Students who reported always attending lectures or viewing lecture videos had higher average histology scores than students who employed an inconsistent strategy (i.e., mixing live attendance and video lectures). Several behaviors were negatively associated with histology performance. Students who engaged in "non-lecture activities" (e.g., social media use), students who reported being interrupted while watching the lecture video, or feeling sleepy/losing focus had lower scores than their counterparts not engaging in these behaviors. This study suggests that interruptions and distractions during medical learning activities-whether live or recorded-can have an important impact on learning outcomes. Anat Sci Educ 11: 366-376. © 2017 American Association of Anatomists.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

Medical students at one public institution engaged in a self-directed, "parallel" Step 1 curriculum using third-party study resources. Several study behaviors were associated with improved USMLE Step 1 performance, informing both institutional- and student-directed preparation for this high-stakes exam.