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评估远程医疗保健网络“安全分娩”对高危孕妇、早产儿和患病新生儿及其家庭的影响:一项群组随机对照阶梯式试验研究方案。

Evaluation of the telemedical health care network "SAFE BIRTH" for pregnant women at risk, premature and sick newborns and their families: study protocol of a cluster-randomized controlled stepped-wedge trial.

机构信息

Center for Evidence-Based Healthcare, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

Saxony Center for Feto/Neonatal Health, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

出版信息

BMC Health Serv Res. 2024 Feb 14;24(1):200. doi: 10.1186/s12913-024-10667-z.

Abstract

BACKGROUND

The Perinatal Center of the University Hospital Carl Gustav Carus Dresden has initiated the telemedical healthcare network "SAFE BIRTH" to coordinate and improve specialized care in non-metropolitan regions for pregnant women and newborns. The network incorporates five intervention bundles (IB): (1) Multi-professional, inter-disciplinary prenatal care plan; (2) Neonatal resuscitation; (3) Neonatal antibiotic stewardship; (4) Inter-facility transfer of premature and sick newborns; (5) Psycho-social support for parents. We evaluate if the network improves care close to home for pregnant women, premature and sick newborns.

METHODS

To evaluate the complex healthcare intervention "SAFE BIRTH" we will conduct a cluster-randomized controlled stepped-wedge trial in five prenatal medical outpatient offices and eight non-metropolitan hospitals in Saxony, Germany. The offices and hospitals will be randomly allocated to five respectively eight sequential steps over a 30-month period to implement the telemedical IB. We define one specific primary process outcome for each IB (for instance IB#1: "Proportion of patients with inclusion criterion IB#1 who have a prenatal care plan and psychosocial counseling within one week"). We estimated a separate multilevel logistic regression model for each primary process outcome using the intervention status as a regressor (control or intervention group). Across all IB, a total of 1,541 and 1,417 pregnant women or newborns need to be included in the intervention and control group, respectively, for a power above 80% for small to medium intervention effects for all five hypothesis tests. Additionally, we will assess job satisfaction and sense of safety of health professionals caring for newborns (questionnaire survey) and we will assess families' satisfaction, resilience, quality of life and depressive, anxiety and stress symptoms (questionnaire surveys). We will also evaluate the cost-effectiveness of "SAFE BIRTH" (statutory health insurance routine data, process data) and barriers to its implementation (semi-structured interviews). We use multilevel regression models adjusting for relevant confounders (e.g. socioeconomic status, age, place of residence), as well as descriptive analyses and qualitative content analyses.

DISCUSSION

If the telemedical healthcare network "SAFE BIRTH" proves to be effective and cost-efficient, strategies for its translation into routine care should be developed.

TRIAL REGISTRATION

German clinical trials register.

DRKS-ID: DRKS00031482.

摘要

背景

德累斯顿大学卡尔·古斯塔夫·卡鲁斯医院的围产期中心发起了远程医疗保健网络“安全分娩”,以协调和改善非大都市地区孕妇和新生儿的专业护理。该网络包含五个干预包(IB):(1)多专业、跨学科的产前护理计划;(2)新生儿复苏;(3)新生儿抗生素管理;(4)早产儿和患病新生儿的机构间转移;(5)父母的心理社会支持。我们评估该网络是否改善了孕妇、早产儿和患病新生儿的家庭护理。

方法

为了评估复杂的医疗保健干预“安全分娩”,我们将在德国萨克森州的五个产前医疗门诊和八家非大都市医院进行一项集群随机对照分步楔形试验。这些办公室和医院将在 30 个月的时间内被随机分配到五个分别为八个连续的步骤,以实施远程医疗干预包。我们为每个干预包(例如 IB#1:“符合 IB#1 纳入标准的患者中,在一周内有产前护理计划和心理社会咨询的比例”)定义一个特定的主要过程结果。我们使用干预状态作为回归变量(对照组或干预组),为每个主要过程结果估计了一个单独的多层次逻辑回归模型。在所有 IB 中,分别需要将 1,541 名和 1,417 名孕妇或新生儿纳入干预组和对照组,以确保所有五个假设检验的小到中等干预效果的效力超过 80%。此外,我们将评估照顾新生儿的卫生专业人员的工作满意度和安全感(问卷调查),并评估家庭的满意度、适应力、生活质量以及抑郁、焦虑和压力症状(问卷调查)。我们还将评估“安全分娩”的成本效益(法定健康保险常规数据、流程数据)和实施障碍(半结构化访谈)。我们使用多水平回归模型调整相关混杂因素(例如社会经济地位、年龄、居住地),以及描述性分析和定性内容分析。

讨论

如果远程医疗保健网络“安全分娩”被证明是有效和具有成本效益的,就应该制定将其转化为常规护理的策略。

试验注册

德国临床试验注册处。

DRKS-ID:DRKS00031482。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f522/10865646/f32f87396f9b/12913_2024_10667_Fig1_HTML.jpg

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