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

项目摘要/摘要 这一应用程序是一种新的数据科学和网络分析方法，用于量化促进者和 一个国际医疗中心联盟的低潮气量通风屏障，“是对 PAR-16-238，《卫生传播和实施研究》(R01)。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的最有效的治疗方法，可将死亡率降低20%-25%，而且 这是标准做法的一部分。然而，在ARDS患者中，LTVV的使用率低至19%。有一个穷人 了解采用LTVV的障碍：目前的方法是有缺陷的，因为它们将 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，并且不涉及特定于环境的变化。我们的研究团队此前发现了一些 患者和临床医生特定的LTVV采用的促进者和障碍。我们用了两种最先进的 数据驱动的方法-数据科学和网络分析-初步量化多样化的影响 一系列影响LTVV采用的潜在因素，包括基于网络和团队的因素。建议数 研究以实施研究综合框架(CFIR)和罗杰斯的扩散为指导 创新理论的一部分。这项拟议研究的总体目标是了解 在学术和社区环境中通过一个明确的、 在大型、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 为如何应用数据科学和网络分析来了解采用 复杂的干预。最重要的假设是有不同的患者、临床医生、网络和 基于团队的推动者和LTVV在学术和社区环境中采用的障碍。我们将决定 不同的患者和临床医生(目标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)，基于团队结构和动态(目标3团队 构建和建模)LTVV采用的推动者和障碍存在于学术和 社区医院设置。成功完成拟议的研究将提供全面的 了解学术界和学术界采用LTVV的促进者和障碍的差异 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施问题。实施战略，说明 因为所有这些因素可能更有可能导致重大的实践变化。

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.