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EnCoRe MOMS: Engaging Communities to Reduce Morbidity from Maternal Sepsis

EnCoRe MOMS: Engaging Communities to Reduce Morbidity from Maternal Sepsis
EnCoRe MOMS:让社区参与降低孕产妇败血症的发病率
批准号:
10611196
负责人:
Alexander M Friedman
金额:
$95.8万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-21 至 2023-09-20
关键词:
AccountabilityAccountingAddressAdvocacyAffectAlgorithmsBiometryBirthBlack raceCaringCessation of lifeClinicalCommunicationCommunitiesCommunity Health AidesCommunity HospitalsContinuity of Patient CareData SetDatabasesDisadvantagedDiscipline of NursingDiscipline of obstetricsEducationEffectivenessElectronic Health RecordEnsureEthnic OriginEvidence based interventionFocus GroupsHealth Services AccessibilityHealth Services ResearchHealth systemHospitalsIndividualInformaticsInpatientsInterventionInterviewJointsKnowledgeLeadLeadershipMaternal HealthMaternal MortalityMaternal-fetal medicineModelingMorbidity - disease rateNeighborhoodsOutcomeOutcome MeasurePatientsPatternPerinatalPerinatal EpidemiologyPersonsPhasePoliciesPostpartum PeriodPovertyPregnancyPreventionProcessProcess MeasureProtocols documentationProviderQuality of CareRaceReadinessReportingResearchResearch InfrastructureResourcesRiskSafetySamplingSepsisServicesSiteSocial EnvironmentSocial SciencesStandardizationStructural RacismSupportive careSystemTimeTrainingTranslatingUse EffectivenessVariantadverse maternal outcomesbaseclinical riskcommunity based participatory researchcommunity centercommunity engaged researchcommunity engagementcommunity partnershipdesigndisabilityethnic diversityevidence baseexperiencehealth care availabilityhealth equityhealth inequalitieshealth traininghigh riskimplementation frameworkimplementation processimplementation scienceimplicit biasindividual patientinpatient servicemachine learning modelmaternal morbiditymaternal outcomematernal riskmortalitymultidisciplinarynovelpatient orientedpostpartum morbiditypreventracial and ethnicracial diversityresponserisk predictionscreeningsevere maternal morbiditysocialsocial epidemiologysocial health determinantssocioeconomic disadvantagestandard of caresupport networkunderserved community

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中文摘要
翻译
产妇败血症是产妇死亡的第二大原因,是发病的主要原因, 大多数情况下。分娩、分娩和产后是脓毒症风险增加的时期,特别是对于种族和民族 少数人的生育。然而,几乎没有基于证据的干预措施。凭借我们广泛的社区 伙伴关系和社区组织领导咨询委员会(CoLAB),EnCoRe MoMS:让社区参与减少孕产妇败血症的发病率将解决三个高度相关的问题 具体目标:(目标1)制定、实施和评估社区知情的孕产妇败血症捆绑包, 不同的纽约市医院;(目标2)开发算法以优化分娩前后败血症的预测, (目标3)进行共同设计过程和定性研究,以探索经验,需求, 以及对产妇护理连续性、脓毒症预防和促进产后公平的认识。 在UG 3阶段,我们将建立强大的社区参与和研究基础设施,以:目标1a: 设计一个全面的产科脓毒症捆绑包,i)应用和优化标准循证医学 准备、承认、反应、报告和尊重护理的组成部分(二)纳入多学科 产科提供者隐性偏见培训,iii)整合健康的社会决定因素(SDOH)培训, 筛选目标2a.创建一个丰富的电子健康记录(EHR)数据库从围产期研究 联合体(PRC)。目标2b。整理表征健康的社会决定因素的社区级数据集 目标3a。(3a.1)完善我们的CoLAB和共同设计流程;(3a.2)深入个体患者 与来自同一地点的社区和医院利益相关者进行访谈(IDI)和焦点小组讨论(FGD) 探讨SDOH在护理获得/质量、结果差异方面的生活经验和观点, 护理连续性的解决方案。在UH 3阶段,我们将让社区参与实施我们的孕产妇败血症 护理模型,分析结果,并翻译结果。目标1b。实施我们的全面产科败血症 使用过程和结果指标捆绑并评估其有效性目标1c。定义EHR中的模式 供应商对疑似败血症的反应,捆绑包实施前后;分析 供应商响应变化和结果目标2c.协调患者级EHR和社区级SDOH 数据集,并使用机器学习模型来分析患者的个人和联合贡献, 邻近因素,以优化PRC样本内的脓毒症风险预测目标3b。(3b.1)完成 为另外三个医院站点提供定性患者IDI和利益相关者FGD;(3b.2)共同设计 综合支持性护理模式,与我们的社区合作伙伴共同领导,CoLAB,以及其他目标的结果, 需要孕产妇败血症社区参与,护理联系,教育,服务和政策努力。我们 由此产生的模型可以扩展到资源较少的医院和社区,并应用于其他 严重孕产妇发病率的可预防原因。
英文摘要
Maternal sepsis is the second leading cause of maternal death, major cause of morbidity, and preventable in most cases. Labor, birth, and postpartum are periods of increased sepsis risk, particularly for racial and ethnic minoritized birthing people. Yet few evidence-based interventions exist. With our extensive community partnerships and community organized leadership advisory board (CoLAB), EnCoRe MoMS: Engaging Communities to Reduce Morbidity from Maternal Sepsis will address three highly related specific aims: (Aim 1) Develop, implement, and evaluate a community-informed maternal sepsis bundle in 4 diverse NYC hospitals; (Aim 2) Develop algorithms to optimize prediction of sepsis around delivery and postpartum; and (Aim 3) Conduct a co-design process and qualitative study to explore the experiences, needs, and perceived solutions for maternal care continuity, sepsis prevention, and promotion of equity in postpartum. In the UG3 phase we will establish robust community engagement and research infrastructures to: Aim 1a: Design a comprehensive obstetric sepsis bundle that i) applies and optimizes standard evidence-based components of readiness, recognition, response, reporting, and respectful care ii) incorporates multidisciplinary obstetric provider implicit bias training, and iii) integrates social determinants of health (SDOH) training and screening Aim 2a. Create a rich electronic health records (EHR) database from the Perinatal Research Consortium (PRC). Aim 2b. Collate neighborhood-level datasets characterizing social determinants of health (SDOH) Aim 3a. (3a.1) Refine our CoLAB and co-design process; (3a.2) Conduct in-depth individual patient interviews (IDIs) and focus group discussions (FGDs) with community and hospital stakeholders from one site to explore the lived experiences and perspectives of SDOH on care access/quality, outcome disparities, and solutions for care continuity. In the UH3 phase, we will engage community to implement our maternal sepsis care model, analyze results, and translate findings. Aim 1b. Implement our comprehensive obstetric sepsis bundle and evaluate its effectiveness using process and outcome measures Aim 1c. Define patterns in EHR of provider response to suspected sepsis, pre- vs post-bundle implementation; analyze associations between provider response variation and outcomes Aim 2c. Harmonize patient-level EHR and neighborhood-level SDOH datasets and use machine learning models to analyze the individual and joint contributions of patient and neighborhood factors to optimize sepsis risk prediction within the PRC sample Aim 3b. (3b.1) Complete qualitative patient IDIs and stakeholder FGDs for the three additional hospital sites; (3b.2) Co-design an integrative supportive care model, with our community partner co-lead, CoLAB, and results from other aims, that entails maternal sepsis community engagement, care linkages, education, services, and policy efforts. Our resulting model can be scaled to hospitals and communities with lesser resources and applied to other preventable causes of severe maternal morbidity.
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Modeling informatics data to track maternal risk and care quality
Modeling informatics data to track maternal risk and care quality
EnCoRe MOMS: Engaging Communities to Reduce Morbidity from Maternal Sepsis
SCH: Prediction of Preterm Birth in Nulliparous Women
  • 批准号:
    9928205
  • 项目类别:
  • 资助金额:
    $25.91万
  • 财政年份:
    2019
  • 负责人:
    Alexander M Friedman
  • 依托单位:
海外基金