This research project aims to enhance the safety of childbirth by using advanced computer models to predict the risk of postpartum hemorrhage (PPH). PPH is a significant concern for mothers during and after delivery. Current risk assessment tools are basic and do not adapt to changing conditions. This study will investigate whether a new and recently validated model for predicting PPH, combined with a provider-facing Best Practice Advisory (BPA) regarding currently recommended strategies triggered by an increased predicted risk, can improve perinatal outcomes. This study will compare the current category based risk assessment tool with a new, enhanced prediction model which calculates risk based on 21 factors, automatically updates as new information becomes available during labor and, if elevated, provides a provider-facing Best Practice Advisory (BPA) recommending consideration of strategies that are institutionally agreed to represent high-quality practice. Investigators hypothesize that the enhanced care approach will result in improved perinatal outcomes. The goal of the study is to improve the wellbeing of mothers during childbirth by harnessing the power of modern technology and data analysis.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Numerical hierarchical composite score of postpartum morbidity and mortality at hospital discharge
Timeframe: Date of randomization to Postpartum hospital discharge (usually 2-4 days)
Numerical hierarchical composite score of postpartum morbidity and mortality at 30 days postpartum
Timeframe: 30 days postpartum