A novel data science and network analysis approach to quantifying facilitators and barriers of low tidal volume ventilation in an international consortium of medical centers

项目总结/摘要 这个应用程序，“一种新颖的数据科学和网络分析方法来量化促进者和 国际医学中心联盟中低潮气量通气的障碍，”是对 PAR-16-238，《健康领域的传播和实施研究》（R 01）。急性呼吸窘迫 急性呼吸窘迫综合征（ARDS）的患病率很高（占重症监护病房入院人数的10%），死亡率高达46%。低 潮气量通气（LTVV）是治疗ARDS最有效的方法，可使死亡率降低20- 25%， 标准做法的一部分。然而，LTVV的使用率低至19%的ARDS患者。有一所穷人 理解LTVV采用的障碍：目前的方法是有缺陷的，因为它们包含 偏见，缺乏一致性和全面性，忽视人际网络或团队的影响， 基于因素，不解决设置特定的变化。我们的研究团队之前已经发现了一些 采用LTVV的患者和临床医生特定促进因素和障碍。我们用了两个最先进的 数据驱动的方法-数据科学和网络分析-初步量化的影响， 影响LTVV采用的一系列潜在因素，包括基于网络和团队的因素。拟议 研究工作以实施研究的统一框架（CFIR）和罗杰斯的扩散理论为指导 创新理论。拟议研究的总体目标是了解 促进者和障碍，LTVV通过学术和社区环境之间的一个明确的， 在一个大型的、多样化的医疗中心联盟中进行系统研究，并通过以下方式推进实施科学： 提供了一个模型，说明如何应用数据科学和网络分析来理解 复杂的干预。总体假设是，存在不同的患者、临床医生、网络和 在学术和社区环境中采用LTVV的团队促进者和障碍。我们将确定 无论患者和临床医生是否不同（Aim 1队列研究、临床医生调查和数据科学分析）， 临床医生人际网络-（目标2网络分析），团队结构和动态为基础的（目标3团队 建设和建模）的促进者和障碍LTVV采用之间存在的学术和 社区医院设置。成功完成拟议的研究将提供全面的 理解学术和学术之间采用LTVV的促进因素和障碍的差异 社区环境，并将通过作为数据科学和 网络分析可以应用于复杂的实现问题。实施战略， 因为所有这些因素可能更有可能导致重大的做法改变。

PROJECT SUMMARY/ABSTRACT This application, “A novel data science and network analysis approach to quantifying facilitators and barriers of low tidal volume ventilation in an international consortium of medical centers,” is in response to PAR-16-238, Dissemination and Implementation Research in Health (R01). Acute respiratory distress syndrome (ARDS) has high prevalence (10% of intensive care unit admissions) and mortality up to 46%. Low tidal volume ventilation (LTVV) is the most effective therapy for ARDS, lowering mortality by 20-25%, and is part of standard practice. However, use of LTVV is as low as 19% of ARDS patients. There is a poor understanding of the barriers to LTVV adoption: current approaches are deficient because they incorporate biases, lack consistency and comprehensiveness, ignore the influence of interpersonal network- or team- based factors, and do not address setting-specific variation. Our research team has previously identified some patient- and clinician-specific facilitators of and barriers to LTVV adoption. We have used two state-of-the-art data driven methods—data science and network analysis—to preliminarily quantify the impact of a diverse array of potential factors affecting LTVV adoption, including network- and team-based factors. The proposed research is guided by the Consolidated Framework for Implementation Research (CFIR) and Rogers' Diffusion of Innovations theory. The overall goals of the proposed research are to understand the differences in facilitators and barriers to LTVV adoption between academic and community settings through a definitive, systematic study in a large, diverse consortium of medical centers, and to advance implementation science by providing a model for how data science and network analysis can be applied to understand the adoption of a complex intervention. The overarching hypothesis is that there are different patient-, clinician-, network-, and team-based facilitators and barriers to LTVV adoption in academic and community settings. We will determine whether different patient- and clinician- (Aim 1 cohort study, clinician survey, and data science analysis), clinician interpersonal network- (Aim 2 network analysis), and team structure and dynamics-based (Aim 3 team construction and modeling) facilitators of and barriers to LTVV adoption exist between academic and community hospital settings. Successful completion of the proposed research will provide a comprehensive understanding of the differences in the facilitators of and barriers to LTVV adoption between academic and community settings, and will advance implementation science by serving as a model of how data science and network analysis can be applied to complex implementation problems. Implementation strategies that account for all these factors may be more likely to lead to significant practice change.