Maternal morbidity and mortality are disproportionately experienced by persons of color in the United States. The authors of this article present a socioecological model for understanding the individual, interpersonal, organizational, community, and societal factors contributing to Black maternal morbidity and mortality. The authors outline several recommendations for improving care, including workforce diversification, incorporating social determinants of health and health disparities into health professional education, and exploring the impact of structural racism on maternal health outcomes.  

Machine learning uses data and statistical methods to enhance risk prediction models and it has been promoted as a tool to improve healthcare safety. Using Medicaid claims data for a large cohort of White and Black pregnant females, this study evaluated approaches to reduce bias in clinical prediction algorithms for postpartum depression and mental health service utilization. The researchers found that a reweighing method in machine learning models was associated with a greater reduction in bias than excluding race from the prediction models. The authors suggest further examination of potentially biased data informing clinical prediction models and consideration of other methods to mitigate bias.

Communication is an essential component of safe patient care. This review of 71 studies found that communication training interventions in obstetrics can improve communication skills and behavior, particularly when combined with team training. The authors identified a lack of evidence regarding the effect of communication trainings on patient safety outcomes and suggest that future research should assess this relationship. Study findings underscore the need for adequate communication trainings to be provided to all staff and expectant mothers and their partners.