| Literature DB >> 26433496 |
Peter von Dadelszen1, Laura A Magee2, Beth A Payne3, Dustin T Dunsmuir4, Sharla Drebit3, Guy A Dumont5, Suellen Miller6, Jane Norman7, Lee Pyne-Mercier8, Andrew H Shennan9, France Donnay10, Zulfiqar A Bhutta11, J Mark Ansermino4.
Abstract
While we believe that pre-eclampsia matters-because it remains a leading cause of maternal and perinatal morbidity and mortality worldwide-we are convinced that the time has come to look beyond single clinical entities (e.g. pre-eclampsia, postpartum hemorrhage, obstetric sepsis) and to look for an integrated approach that will provide evidence-based personalized care to women wherever they encounter the health system. Accurate outcome prediction models are a powerful way to identify individuals at incrementally increased (and decreased) risks associated with a given condition. Integrating models with decision algorithms into mobile health (mHealth) applications could support community and first level facility healthcare providers to identify those women, fetuses, and newborns most at need of facility-based care, and to initiate lifesaving interventions in their communities prior to transportation. In our opinion, this offers the greatest opportunity to provide distributed individualized care at scale, and soon.Entities:
Keywords: Maternal health; Mobile health; Newborn health; Outcome prediction; PRE-EMPT; Stillbirth
Mesh:
Year: 2015 PMID: 26433496 DOI: 10.1016/j.ijgo.2015.02.008
Source DB: PubMed Journal: Int J Gynaecol Obstet ISSN: 0020-7292 Impact factor: 3.561