Team training in obstetric and neonatal emergencies using highly realistic simulation in Mexico: impact on process indicators

Background: Ineffective management of obstetric emergencies contributes significantly to maternal and neonatal morbidity and mortality in Mexico. PRONTO (Programa de Rescate Obstetrico y Neonatal: Tratamiento Optimo y Oportuno) is a highly-realistic, low-tech simulation-based obstetric and neonatal emergency training program. A pair-matched hospital-based controlled implementation trial was undertaken in three states in Mexico, with pre/post measurement of process indicators at intervention hospitals. This report assesses the impact of PRONTO simulation training on process indicators from the pre/post study design for process indicators. Methods: Data was collected in twelve intervention facilities on process indicators, including pre/post changes in knowledge and self-efficacy of obstetric emergencies and neonatal resuscitation, achievement of strategic planning goals established during training and changes in teamwork scores. Authors performed a longitudinal fixed-effects linear regression model to estimate changes in knowledge and self-efficacy and logistic regression to assess goal achievement. Results: A total of 450 professionals in interprofessional teams were trained. Significant increases in knowledge and self-efficacy were noted for both physicians and nurses (p < 0.001-0.009) in all domains. Teamwork scores improved and were maintained over a three month period. A mean of 58.8% strategic planning goals per team in each hospital were achieved. There was no association between high goal achievement and knowledge, self-efficacy, proportion of doctors or nurses in training, state, or teamwork score. Conclusions: These results suggest that PRONTO's highly realistic, locally appropriate simulation and team training in maternal and neonatal emergency care may be a promising avenue for optimizing emergency response and improving quality of facility-based obstetric and neonatal care in resource-limited settings.