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

一种新颖的数据科学和网络分析方法，用于量化国际医疗中心联盟中低潮气量通气的促进因素和障碍

• 批准号: 10178076
• 负责人: Curtis H. Weiss
• 金额: $ 68.09万
• 依托单位: NORTHSHORE UNIVERSITY HEALTHSYSTEM
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-09 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10178076
• 关键词: Acute; Admission activity; Adoption; Adult Respiratory Distress Syndrome; Affect; Attitude; Caring; Characteristics; Cohort Studies; Community Hospitals; Complex; Consolidated Framework for Implementation Research; Critical Illness; Data; Data Science; Diffusion of Innovation; Environment; Evidence based practice; Goals; Health; Healthcare Systems; Height; High Prevalence; Hypoxemia; Individual; Inflammatory; Intensive Care Units; International; Intervention; Investigation; Knowledge; Lead; Machine Learning; Measurement; Medical center; Methods; Modeling; National Heart, Lung, and Blood Institute; Nurses; Pathway Analysis; Patient-Focused Outcomes; Patients; Performance; Physicians; Professional Organizations; Pulmonary Edema; Research; Severities; Speed; Structure; Surveys; Syndrome; System; Testing; Tidal Volume; Variant; base; community setting; complex data; computer science; dissemination research; effective therapy; experimental study; health care settings; implementation research; implementation science; implementation strategy; lung injury; machine learning method; mortality; multidisciplinary; novel; respiratory; response; theories; ventilation

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.

项目摘要/摘要 该应用程序是“一种新颖的数据科学和网络分析方法，用于量化促进者和 国际医疗中心财团中潮汐量低的障碍是为了回应 PAR-16-238，《卫生中的传播与实施研究》（R01）。急性呼吸窘迫 综合征（ARDS）患病率很高（重症监护病房的10％），死亡率高达46％。低的 潮汐量通风（LTVV）是ARDS最有效的疗法，将死亡率降低20-25％，IS 标准实践的一部分。但是，LTVV的使用低至19％的ARDS患者。有一个贫穷 了解LTVV采用障碍的理解：当前的方法是不足的，因为它们纳入了 偏见，缺乏一致性和全面性，忽略人际网络或团队的影响 基于因素，并且不解决特定于设置的变化。我们的研究团队以前已经确定了一些 LTVV采用的患者和临床特异性促进因子和障碍。我们使用了两个最先进的 数据驱动的方法（数据科学和网络分析）初步量化潜水员的影响 影响LTVV采用的一系列潜在因素，包括基于网络和基于团队的因素。提议 研究以实施研究的合并框架（CFIR）和罗杰斯的扩散为指导 创新理论。拟议研究的总体目标是了解 通过确定的，一个确定的， 在大型的医疗中心的大型潜水联盟中进行系统研究，并通过 提供一个模型，以了解如何应用数据科学和网络分析以了解采用 复杂的干预。总体假设是患者，临床，网络和网络和 在学术和社区环境中采用LTVV的基于团队的促进者和障碍。我们将确定 是否不同的患者和临床 - （AIM 1研究，临床调查和数据科学分析）， 临床人际网络（AIM 2网络分析）以及基于团队结构和动态（AIM 3团队 构建和建模）学术与LTVV采用障碍的促进者和学术障碍存在 社区医院的环境。成功完成拟议的研究将提供全面的 了解学术和LTVV采用障碍的差异和学术障碍的差异 社区设置，并将通过作为数据科学和如何的模型来提高实施科学 网络分析可以应用于复杂的实施问题。说明的实施策略 因为所有这些因素可能更有可能导致实践重大变化。