Background:Surgical site infections (SSIs) portend high patient morbidity and mortality. Although evidence-based clinical interventions can reduce SSIs, they are not reliably delivered in practice, and data are limited on the best approach to improve adherence.Objective:To summarize implementation strategies aimed at improving adherence to evidence-based interventions that reduce SSIs.Design:Systematic reviewMethods:We searched PubMed, Embase, CINAHL, the Cochrane Library, the WHO Regional databases, AFROLIB, and Africa-Wide for studies published between January 1990 and December 2015. The Effective Practice and Organization Care (EPOC) criteria were used to identify an acceptable-quality study design. We used structured forms to extract data on implementation strategies and grouped them into an implementation model called the “Four Es” framework (ie, engage, educate, execute, and evaluate).Results:In total, 125 studies met our inclusion criteria, but only 8 studies met the EPOC criteria, which limited our ability to identify best practices. Most studies used multifaceted strategies to improve adherence with evidence-based interventions. Engagement strategies included multidisciplinary work and strong leadership involvement. Education strategies included various approaches to introduce evidence-based practices to clinicians and patients. Execution strategies standardized the interventions into simple tasks to facilitate uptake. Evaluation strategies assessed adherence with evidence-based interventions and patient outcomes, providing feedback of performance to providers.Conclusions:Multifaceted implementation strategies represent the most common approach to facilitating the adoption of evidence-based practices. We believe that this summary of implementation strategies complements existing clinical guidelines and may accelerate efforts to reduce SSIs.
When clinical and laboratory parameters are included, a model predicting intraoperative MT in patients undergoing liver transplantation is sufficiently accurate that its predictions could guide the blood order schedule for individual patients based on institutional data, thereby reducing the impact on blood bank resources. Ongoing evaluation of model accuracy and transfusion practices is required to ensure continuing performance of the predictive model.
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