PROJECT ABSTRACT Postpartum depression (PPD) is a strikingly common and potentially life-threatening mental health condition, affecting 1 in 5 mothers in the US. PPD poses serious health concerns, not only to mothers but also their children and the family. PPD has been associated with increased infant mortality, higher rates of hospitalizations, impaired mother-child attachment, developmental problems in children, and increased stress within families. However, in reality, women who are at high risk for depression during the childbearing years are usually neither identified nor treated. In addition, even when depression is detected by health professionals, women rarely obtain assistance, despite the wide availability of treatment choices. In this project, we plan to leverage large-scale, integrated electronic health record and claims from the New York City Clinical Data Research Network to develop automated, scalable risk prediction models for PPD. We will also develop a digital shared decision-making (SDM) tool for PPD treatment by working collaboratively with healthcare providers and patients. The PPD risk model and the digital SDM tool for PPD treatment developed in this STTR Phase I project will be used to conduct a future large, prospective, multi-site clinical effectiveness trial to test their feasibility and utility in routine clinical care of women during the postpartum period.

PROJECT NARRATIVE Postpartum depression (PPD) is a strikingly common and potentially life-threatening mental health condition, affecting 1 in 5 mothers in the US. Successful identification of patients who are at the risk of PPD can lead potential clinical interventions for improving patient outcomes. This proposal will use big clinical data and shared decision-making techniques to develop a digital health platform for identifying and treating patients with PPD.