Moving beyond silos: How do we provide distributed personalized medicine to pregnant women everywhere at scale? Insights from PRE-EMPT

Peter von Dadelszen; Laura A Magee; Beth A Payne; Dustin T Dunsmuir; Sharla Drebit; Guy A Dumont; Suellen Miller; Jane Norman; Lee Pyne-Mercier; Andrew H Shennan; France Donnay; Zulfiqar A Bhutta; J Mark Ansermino

doi:10.1016/j.ijgo.2015.02.008

Moving beyond silos: How do we provide distributed personalized medicine to pregnant women everywhere at scale? Insights from PRE-EMPT

Int J Gynaecol Obstet. 2015 Oct:131 Suppl 1:S10-5. doi: 10.1016/j.ijgo.2015.02.008. Epub 2015 Feb 25.

Authors

Affiliations

¹ Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada; Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada. Electronic address: [email protected].
² Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada; Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada.
³ Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada; Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada.
⁴ Department of Anesthesia, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada; Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada.
⁵ Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada; Child and Family Research Institute, University of British Columbia, Vancouver, BC, Canada.
⁶ Department of Obstetrics, Gynecology and Reproductive Sciences and Bixby Center for Global Reproductive Health, University of California, San Francisco, CA, USA.
⁷ University of Edinburgh/MRC Centre for Reproductive Health, The Queen's Medical Research Institute, University of Edinburgh, UK.
⁸ Family Health Team, Bill & Melinda Gates Foundation, USA; Department of Global Health, University of Washington, Seattle, WA, USA.
⁹ Division of Women's Health, King's College London, London, UK.
¹⁰ Family Health Team, Bill & Melinda Gates Foundation, USA.
¹¹ Centre for Global Child Health, Hospital for Sick Children, University of Toronto, Toronto, Canada; Centre of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan.

PMID: 26433496
DOI: 10.1016/j.ijgo.2015.02.008

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.

Keywords: Maternal health; Mobile health; Newborn health; Outcome prediction; PRE-EMPT; Stillbirth.

MeSH terms

Adult
Algorithms
Decision Support Systems, Clinical*
Female
Health Facilities
Humans
Infant, Newborn
Maternal Health Services*
Pre-Eclampsia / mortality
Pre-Eclampsia / therapy*
Precision Medicine / methods*
Pregnancy
Pregnancy Complications / mortality
Pregnancy Complications / therapy*
Risk Assessment / methods
Telemedicine