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. 2021 Mar 20;21(1):106.
doi: 10.1186/s12911-021-01468-z.

A principal components analysis of factors associated with successful implementation of an LVAD decision support tool

Kristin M Kostick  1 , Meredith Trejo  2 , Arvind Bhimaraj  3 , Andrew Civitello  4 , Jonathan Grinstein  5 , Douglas Horstmanshof  6 , Ulrich P Jorde  7 , Matthias Loebe  8 , Mandeep R Mehra  9 , Nasir Z Sulemanjee  10 , Vinay Thohan  11 , Barry H Trachtenberg  3 , Nir Uriel  12 , Robert J Volk  13 , Jerry D Estep  14 , J S Blumenthal-Barby  2
Affiliations

A principal components analysis of factors associated with successful implementation of an LVAD decision support tool

Kristin M Kostick et al. BMC Med Inform Decis Mak. .

Abstract

Background: A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts.

Methods: We examined associations between "reach", a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson's r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach.

Results: We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients.

Conclusions: Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether "core predictors" of success vary across different intervention types.

Keywords: Decision support intervention; Facilitators and barriers; Implementation success; Principal components analysis.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Changes in average reach levels across clinical sites over a period of 18 months (November 2019–April 2020) following initial implementation (two months consisting of orientation and startup) in September–October 2018
Fig. 2
Fig. 2
Multidimensional scale of site characteristics in relation to reach
See this image and copyright information in PMC

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