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Detect patients that will have increase rates of mortality and negative obstetric outcomes using clinical variables
The 2020 U.S. maternal mortality rate ranked last among all industrialized countries, and more than 60,000 women suffer from severe maternal morbidity annually. Maternal health data also evinces immense racial and ethnic disparities. Black women are three to four times more likely to die a pregnancy-related death compared to White women. Reducing these disparities is key to improving obstetric outcomes.
The 2020 U.S. maternal mortality rate ranked last among all industrialized countries, with 17.4 deaths per 100,000 pregnancies (2).For every maternal death measured, 100 women also experience severe obstetric morbidity (1). This is a potentially life-threatening outcome of labor and delivery, often resulting in significant health consequences. More than 60,000 women suffer from severe maternal morbidity annually, and the rate of maternal morbidity increased by 36% from 2008 to 2018 (1,3). Adverse obstetric outcomes also present a substantial financial burden. Severe maternal morbidity has been associated with an increase in maternity-related costs of 111% in commercially-insured populations and 175% in populations covered by Medicaid (4). Such increases are impactful; there were 3.75 million births in 2019 and Medicaid paid for approximately 50% of them (5,6).
Maternal health data evinces immense racial and ethnic disparities. On average, Black women are three to four times more likely to die a pregnancy-related death compared to White women, and they are up to 12 times more likely in some cities (1). In addition to a dramatically increased mortality rate, Non-Hispanic Black women have the highest rates for the majority of CDC severe morbidity indicators. Black women also have elevated rates of pregnancy-induced and chronic hypertension, asthma, placental disorders, preexisting diabetes, and blood disorders. Minority women are also less likely to have chronic conditions adequately managed prior to pregnancy, and they are more likely to experience complications due to these conditions (1).
Reducing potentially preventable maternal morbidity and mortality hinges on reducing the racial and ethnic disparities. In a study of maternal deaths 46% of black deaths were considered potentially preventable compared to 33% of white deaths (1). In addition to minorities having higher likelihood of chronic conditions during pregnancy, health outcome disparities are correlated with a plethora of socioeconomic-related factors, such as hospital quality. 75% of Black deliveries occur in a quarter of U.S. hospitals, but just 18% of White deliveries occur in the same hospitals (1). On average, these hospitals have higher risk-adjusted maternal morbidity rates. Similarly, a national survey of women’s childbearing experiences found that roughly 25% of respondents experienced discrimination during hospitalization, and Black and Hispanic women were nearly three times as likely to indicate concerns with their treatment due to race, ethnicity, and cultural background (7).
AI application designed to predict maternal mortality and obstetric outcomes in females using a comprehensive analysis of clinical variables. This tool harnesses advanced algorithms to evaluate a wide range of data points, such as medical history, current health indicators, and potential risk factors. It aims to identify early warning signs and high-risk cases, enabling healthcare providers to proactively manage and mitigate potential complications. The application's predictive capabilities extend from preconception through postpartum, offering valuable insights for personalized care plans. By effectively assessing and anticipating the needs of expecting mothers, this AI solution plays a crucial role in improving maternal and fetal health outcomes, reducing the incidence of complications, and ultimately, saving lives.
