Automated Maternal Early Warning Systems:
Providing equitable care at the bedside

Maternal morbidity and mortality rates are at an all-time high in the United States. According to the Centers for Disease Control and Prevention (2019) 60% of pregnancy-related poor outcomes are preventable. Although root cause analysis data reveals several influencing factors, variation in clinical practice is recognized as a modifiable variable. Standardizing patient assessments utilizing advanced technology provides objective, quantifiable data that can lead to improved patient outcomes. Automated early warning systems powered by artificial intelligence is just one example of a clinical decision support tool that provides a consistent objective patient assessment. By utilizing science to reduce the impact of potential human fallibility, patient assessment can go beyond the traditional care model by reducing variance and subjectivity, providing the conduit to improved patient safety.

Please join PeriGen’s Chief Nursing Officer Dr. Alana McGolrick, DNP, RNC-OB, C-EFM and Clinical Engagement Specialist Karen Kolega, MSN-CNL, RNC-OB, C-EFM in an educational webinar on Automated Maternal Early Warning Systems: Providing equitable care at the bedside.