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Improving maternal safety is an ongoing patient safety priority. This systematic review concluded that maternal near miss events are negatively associated with various aspects of quality of life. Women exposed to maternal near miss events were more likely to have overall lower quality of life, poorer mental and social health, and suffer negative economic consequences.

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.