Objective: To determine the amount of simulation training required for students to attain minimal competence and mastery of a vaginal delivery.Methods: An observational study was conducted at a US medical school between May 11, 2015, and May 8, 2016. Using a modified Angoff method, 10 members of the Obstetrics and Gynecology faculty evaluated a vaginal delivery procedural checklist and established cutoff scores for minimal competence and mastery. During a 5-week period, all third-year students received between two and five 45-minute vaginal delivery simulation sessions; performance was assessed during week 6. Performance according to the checklist was compared. Results:The cutoff score was 20 and 26 out of 30 for minimal competence and mastery, respectively. Among 115 students, mean checklist scores in final assessment rose with increasing number of simulations: 23.6, 25.1, 27.5, and 27.6 points for two, three, four, and five training sessions, respectively (P<0.001). The proportion of patients achieving mastery also increased with number of simulations: 34%, 59%, 73%, and 93% for two, three, four, and five training sessions, respectively (P<0.001). Two or three training sessions were sufficient to attain minimal competence in most students; however, no significant between-group difference was found. Conclusion:Simulation training exerts an increasing effect on performance with each additional session that students receive.

Highlights Barriers to travel included time, cost, companionship, navigation, and physical discomfort. Social support was an important facilitator of travel for care. A significant minority of patients preferred in-person visits to telemedicine.

Objectives To evaluate the efficacy of a urinary incontinence (UI) e-learning module (ELM) in undergraduate medical education. Methods An ELM was developed and validated to teach on UI learning objectives. A 21-item assessment was developed to test knowledge gained. A randomized-controlled trial and parallel nested-cohort study were performed to test the effectiveness of the validated UI-ELM compared with standard methods of UI learning. Students were recruited and enrolled at the onset of their obstetrics and gynecology clerkship. Assignments to either a week-long rotation of gynecologic (GYN) or urogynecologic (UroGyn) surgery were made independent of the study protocol. On the GYN rotation, students were randomly assigned to the UI-ELM intervention or no intervention (control group). The nested-cohort comprised students assigned to the UroGyn rotation. Parametric statistics were applied assessing score changes between the UI-ELM versus control/UroGyn groups. Results Eighty-three students rotated between June 2015 and February 2016. Fifty-five were assigned to GYN and randomized: 35 UI-ELM versus 20 no intervention; 28 were assigned to UroGyn. Students randomized to the UI-ELM had greater score improvement compared with controls (between group difference of +2.73; 95% confidence interval, 0.53–4.93; P = 0.02). Knowledge improvement was similar between students exposed to the UI-ELM compared with those with UroGyn exposure (between group difference, +0.91; 95% confidence interval, −1.05 to 2.88; P = 0.35). Conclusions The UI-ELM resulted in greater improvement in UI knowledge among third year medical students compared with traditional methods of learning and similar to those exposed to a UroGyn rotation.

Advancing the quality and safety of maternity care should be data-driven. Defining a standard set of clinical data elements, across electronic health record platforms and facilities, could accelerate performance measurement, benchmarking, and identification of better practices. In 2014, the American College of Obstetricians and Gynecologists and the American Society of Anesthesiologists launched the Maternal Quality Improvement Program, a data-driven national clinical registry for maternity care. Having an agreed-on set of discrete data elements related to labor and delivery will set the stage for analysis of this care. Through the use of clinical performance measures and data quality metrics, the Maternal Quality Improvement Program will provide an opportunity for health care providers to better understand the overall quality and safety of the maternity care provided within their institution.

No abstract