No abstract

There is a paucity of research on the quality and quantity of clinical teaching in the emergency department (ED) setting. While many factors impact residents' perceptions of attending physicians' educational skill, the authors hypothesized that the amount of time residents spend with attending in direct teaching is a determinant of residents' perception of their shift's educational value. Researchers shadowed emergency medicine (EM) attendings during ED shifts, and recorded teaching time with each resident. Residents were surveyed on their assessment of the educational value (EV) of the shift and potential confounders, as well as the attending physician's teaching quality using the ER Scale. The study was performed in the EDs of two urban teaching hospitals affiliated with an EM residency program. Subjects were EM residents and rotators from other specialties. The main outcome measure was the regression of impact of teaching time on EV. Researchers observed 20 attendings supervising 47 residents (mean 2.35 residents per attending, range 2-3). The correlation between teaching time in minutes (mean 60.8, st.dev 25.6, range 7.6-128.1) and EV (mean 3.45 out of 5, st. dev 0.75, range 2-5) was significant (r = 0.302, r = 0.091, p< 0.05). No confounders had a significant effect. The study shows a moderate correlation between the total time attendings spend directly teaching residents and the residents' perception of educational value over a single ED shift. The authors suggest that mechanisms to increase the time attending physicians spend teaching during clinical shifts may result in improved resident education.

Background Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. Methods and findings Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system. Conclusions Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.