Preeclampsia is a disorder of pregnancy characterized by high blood pressure and high levels of protein in the urine. Hypertension is the leading cause of maternal morbidity and mortality in the United States and the leading cause of 7-day readmissions for obstetrics patients at the Hospital of the University of Pennsylvania (HUP).
While risk factors are known, no strategy exists for effective, reliable BP surveillance for at-risk patients. At the start of this project, blood pressure monitoring required patients to attend in-person clinic visits at the Helen O. Dickens Center for Women every two weeks, with the first visit occurring after the highest risk period for most women.
Our team worked closely with obstetrical services to develop and test a text-based intervention for hypertension.
We enrolled patients in a remote blood pressure monitoring program during the first seven days post-discharge from the Labor and Delivery floor at HUP.
Over the course of three pilots, thirty-two patients were discharged with electronic monitors and sent reminders via text message to check their blood pressure twice daily. Once submitted by the patient, blood pressure results were reviewed and responded to by an Ob/Gyn physician.
The hypertension intervention showed major improvement over the current standard of care and produced strong early evidence for a text-based strategy that enables effective, reliable blood pressure surveillance for at-risk patients.
There were no 7-day readmissions among enrolled patients, compared to 5% readmission rate among women monitored through in-person visits. The percent of patients to report at least one BP reading in the first week post-discharge jumped from 15% pre-intervention to 84% among all enrolled patients. The percent of patients to report BP readings on five of seven days in the first week post-discharge jumped from 0% pre-intervention to 69% among all enrolled patients.
After the conclusion of the pilot, the team received a Penn PCORI grant to refine their clinical algorithm for remote monitoring and develop and pilot a technology platform that automates the text-messaging intervention to enable the solution to scale.