Workplace Harassment and Discrimination in Gynecology: Results of the AAGL Member Survey.

STUDY OBJECTIVE: To characterize workplace and sexual harassment and discrimination among physicians in gynecology. DESIGN: A beta-tested Internet survey was distributed by e-mail using the REDCap platform. All responses were anonymous. SETTING: The survey was distributed to the 7026 physician members of an international gynecologic society (AAGL), including faculty and trainees. PATIENTS: Not applicable. INTERVENTIONS: The survey was distributed on 3 occasions between July and September 2018. The survey contained questions on demographics, attitudes, experiences, and sequelae regarding harassment and discrimination in the workplace. Frequency distributions and nonparametric tests were performed to determine the percentages and types of harassment and discrimination among respondents. MEASUREMENTS AND MAIN RESULTS: A total of 907 physicians responded, including 603 US physicians and 304 non-US physicians; 59% identified as female and 40% as male, and 20% were trainees. Females were more likely than males to think the #MeToo movement was "justified and overdue" (p < .05), independent of age or trainee status. More females than males reported experiencing workplace discrimination (67% vs 39%; p < .001); gender-based discrimination was the most common basis for both. Females indicated decreased self-confidence and lower salary; males indicated fewer employment opportunities and lower patient volume. Harassment was reported by more females than males (53% vs 17%; p < .001), including sexual harassment (39% vs 11%, p <.05). Most experienced loss of self-confidence, felt the offender was in a position of power, and did not report the incident, often due to fear of reprisal. Multiple respondents experienced workplace-related sexual assault. CONCLUSION: Workplace harassment and discrimination are commonly experienced by female and male gynecologists and are usually related to a power differential. Improvements must be made in the workplace environment to achieve equity and a safe workplace free of harassment and discrimination.

Novel use of REDCap to develop an advanced platform to display predictive analytics and track compliance with Enhanced Recovery After Surgery for pancreaticoduodenectomy.

BACKGROUND: Prediction models are increasingly being used with clinical practice guidelines to inform decision making. Enhanced Recovery After Surgery (ERAS®) protocols are standardized care pathways that incorporate evidence-based practices to improve patient outcomes. Predictive analytics incorporated within a data management system, such as Research Electronic Data Capture (REDCap), may help clinicians estimate risk probabilities and track compliance with standardized care practices. METHODS: Predictive models were developed from retrospective data on 400 patients who underwent pancreaticoduodenectomy from 2008 through 2014. The REDCap was programmed to display predictive analytics and create a data tracking system that met ERAS guidelines. Based on predictive scores for serious complication, 30-day readmission, and 30-day mortality, we developed targeted interventions to decrease readmissions and postoperative laboratory tests. RESULTS: Predictive models demonstrated a receiver-operating characteristic area (ROC) ranges of 641-856. After implementing the REDCap platform, the readmission rate for high-risk patients decreased 15.8% during the initial three months following ERAS implementation. Based on predictive outputs, patients with a low-risk score received a limited set of postoperative laboratory tests. Targeted interventions to decrease hospital readmission for high-risk patients included home care orders and post-discharge instructions. CONCLUSIONS: The REDCap platform offers hospitals a practical option to display predictive analytics and create a data tracking program that meets ERAS guidelines. Prediction models programmed into REDCap offer clinicians a support tool to assess the probability of patient outcomes. Risk calculations based on predictive scores enabled clinicians to titrate postoperative laboratory tests and develop post-discharge home care orders.