Assessing the Efficacy of an Online Preoperative Evaluation Course for PGY-1 Anesthesiology Residents

Abstract

The impact of an online postgraduate year (PGY-1) education program on anesthesiology resident knowledge base, anxiety, or preparedness has not been described previously. The literature shows resident knowledge of perioperative care is lower than expected. The Johns Hopkins Preoperative Evaluation and Anesthesia Course was designed as an 8 module, 8 month online academic curriculum for the program's PGY-1 class. Each module includes a pretest, topic synopsis, lecture video, moderated case discussion and a posttest. All PGY-1 residents entering the program in July 2012 were eligible to participate. Residents starting in July 2010 served as the control group. A survey was administered to measure self-assessed knowledge of and comfort with components of preoperative anesthesia care and perceived anxiety about starting the clinical anesthesia year. Additional outcome measures included performance on the pretest and postmodule tests and Anesthesia Knowledge Test scores from day 1 of Clinical Anesthesia year 1 (CA-1, PGY-2) orientation. Statistical analysis included independent t tests, the Mann-Whitney test, and sensitivity analyses. Residents in the intervention group showed an improvement of 16.25 to 39.60 percentage points between the pretest and posttest in each of the 8 subjects (P < .0001 in every subject). The intervention group median score was 24 percentile points higher on the Anesthesia Knowledge Test as compared with the control group (P = .0488; lower 95% CI, 9.92). Significant improvement was also seen across measures including comfort advising about medications (P < .0001), understanding of coexisting disease (P < .0001), comfort assessing patient airway (P = .0002), and anxiety about starting PGY-2 year (P = .0116). We have demonstrated significantly positive impact of a comprehensive, longitudinal online, asynchronous, multimodal educational intervention on PGY-1 residents using objective and subjective data.