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.
