Preeclampsia Risk Factor Screening Tool

Abstract

Preeclampsia is a serious condition in pregnancy that can lead to maternal and neonatal morbidity and mortality. This quality improvement project aimed to leverage machine learning in the electronic medical record (EMR) to enhance early identification of patient-specific preeclampsia risk factors. Prior to implementation, an extensive chart review revealed a significant gap in low dose aspirin (LDA) prescriptions. A significant number of patients met the criteria for LDA prophylaxis; however, they were never instructed to initiate it. Over 14 weeks, a paper-based preeclampsia risk factor screening checklist was initially administered to pregnant women across four office locations before the Preeclampsia Risk Factor Screening Tool was fully integrated into the EMR. This early risk identification triggered a best-practice alert (BPA) and an LDA electronic order in the EMR during new obstetric (OB) intake visits. Following implementation, LDA prescriptions rates increased by 15.6% from the previous year prior to the project implementation. Using The Preeclampsia Risk Factor Screening Tool as a first trimester screening algorithm can bridge the gap in clinical guideline adherence, ensuring timely initiation of LDA between 12 to 28 weeks of pregnancy.