MyHealthyPregnancy mobile health app: Combining behavioral science and machine learning for risk communication during the peripartum period

项目总结/摘要 问题：怀孕期间的抑郁症影响大约10%的女性，与低血糖有关。 出生体重和早产。同样，高达9%的孕妇经历过亲密伴侣暴力。 (IPV)和虐待，超过41%的攻击导致身体伤害，近30%需要医疗 治疗如果不加以治疗，这些风险每年至少使卫生系统损失500亿美元。缓解措施已证明 困难，妇女不愿意在临床访问期间透露，临床医生不知道 资源没有综合的保健技术，能够及时披露风险， 然后，怀孕有助于做出减轻风险的决定。幸运的是，大多数育龄妇女 年龄拥有智能手机，用户报告舒适透露健康信息的智能手机下， 正确的条件。 Naima Health提出的解决方案：Naima Health正在开发一个数字健康平台， MyHealthyPregnancy（MHP）智能手机应用程序，带有集成EPIC的提供商门户，以（i） 在怀孕早期确定风险，（ii）将这些风险告知妇女及其提供者，以及（iii）协助 关于风险缓解的决策。MHP使用ACOG批准的筛查识别风险，然后帮助 患者和提供者做出关于缓解的实时决定。建议的解决方案符合 CDC优先开发基于移动的应用程序的心理健康和IPV决策支持系统 筛查评估和转诊 拟议的SBIR工作：在第一阶段，我们开发了专家和机器学习模型来识别风险 在怀孕期间，然后特点的问题面临西班牙语妇女。在第二阶段，我们将这些 I期结果使用与患者和提供者的半结构化访谈，以了解特定研究中心 心理社会风险筛查和转诊的要求，然后更新MHP平台以满足这些要求 （目标1）。然后，我们使用定性认知测试来验证更新后的平台的性能。 与患者和提供者一起进行测试，以确保平台符合特定的要求（目标2）。最后， 我们在两个合作诊所评估了该平台的可接受性和可行性，重点是 通过平台检测到的抑郁症和IPV与历史比率相比， 通过患者呼叫/点击率和提供者转介来衡量风险缓解措施（目标3）。

Project Summary/Abstract Problem: Depression during pregnancy affects approximately 10% of women and is related to low birthweight and preterm birth. Similarly, up to 9% of pregnant women experience intimate partner violence (IPV) and abuse, with over 41% of assaults resulting in physical injury, and almost 30% requiring medical treatment. When untreated, these risks cost health systems at least $50B/year. Mitigation has proven difficult, where women are reluctant to disclose during clinical visits, and clinicians are unaware of resources. There are no integrated health technologies that enable timely disclosure of risks during pregnancy then aid in making decisions about risk mitigation. Fortunately, most women of reproductive age own a smartphone, and users report comfort disclosing health information to smartphones under the right conditions. Naima Health’s Proposed Solution: Naima Health is developing a digital health platform that pairs our MyHealthyPregnancy (MHP) smartphone application with an EPIC-integrated provider portal to (i) identify risks early in pregnancy, (ii) communicate those risks to women and their providers, and (iii) assist decision-making about risk mitigation. MHP identifies risk using ACOG-approved screenings, then helps patients and providers make real-time decisions about mitigation. The proposed solution aligns with the CDC’s priority of developing mobile app-based decision support systems for mental health and IPV screening, assessment, and referral. Proposed SBIR Work: In Phase I we developed expert and machine learning models to identify risks during pregnancy, then characterized issues facing Spanish-speaking women. In Phase II we extend these Phase I results using semi-structured interviews with patients and providers to understand site-specific requirements for psychosocial risk screening and referral, then update the MHP platform to meet those requirements (Aim 1). We then validate the updated platform’s performance using qualitative cognitive testing with patients and providers to ensure the platform meets site-specific requirements (Aim 2). Finally, we evaluate the platform’s acceptability and feasibility at two collaborating clinic sites, focusing on the rate of depression and IPV detected through the platform compared to historical rates, and the prevalence of risk mitigation actions measured through patient calls/click-throughs and provider referrals (Aim 3).