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In the news, the industry considers technology advancements for maternal healthcare, specifically AI, remote patient monitoring, and machine learning to improve prenatal care and predict hypertensive disorders. With the increase in tech, some call for greater billing transparency and enhanced care models that integrate physical and mental health services. Key organizations like the AHA and HHS are actively working on policy updates and resources to support these improvements.

10/03, HIT Consultant: The Evolution of Prenatal Monitoring: How AI and Remote Monitoring Can Improve the Provision of Care

The evolution of prenatal monitoring, from outdated in-clinic systems to advanced remote capabilities, could be the intro to a new era of more accessible and effective patient-centric care. 

10/3, AHA: AHA unveils revamped maternal and child health webpages

The AHA's redesigned platform offers three distinct subpages focused on Better Health for Mothers and Babies, child and adolescent health, and advocacy and policy.

9/26, Health Affairs: Pointing Toward Hope: Next Steps For Perinatal Mental Health

Research has found promise in collaborative care models for perinatal health, in which obstetricians collaborate with mental health clinicians within their own practices to provide care for patients directly within prenatal care settings. 

9/24, Modern Healthcare: HHS calls for remote patient monitoring billing transparency

The surge of remote patient monitoring since the COVID-19 pandemic has led to billing issues and other operational challenges and a lack of of transparency in billing for remote patient monitoring is creating challenges for regulators.

9/24, Medpage Today: An Action Plan for Hypertension in Pregnancy

The prevalence of hypertension in Black populations demands actionable strategies. The Preeclampsia Foundation's Racial Disparities Task Force aims to address racial inequities in maternal healthcare through community engagement, improved healthcare practices, and advanced research. 

Research

AJOG, November Issue: Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort

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