Implementation Science: Application of Evidence-Based Practice Models to Improve Healthcare Quality.
Worldviews Evid Based Nurs
; 18(2): 76-84, 2021 Apr.
Article
em En
| MEDLINE
| ID: mdl-33779042
ABSTRACT
BACKGROUND:
Translating research into practice is complex for clinicians, yet essential for high quality patient care. The field of implementation science evolved to address this gap by developing theoretical approaches to guide adoption and sustained implementation of practice changes. Clinicians commonly lack knowledge, time, and resources of how evidence-based practice (EBP) models can guide implementation, contributing to the knowledge-to-practice gap.AIM:
This paper aimed to equip clinicians and other healthcare professionals with implementation science knowledge, confidence, and models to facilitate EBP change in their local setting and ultimately improve healthcare quality, safety, and population health outcomes.METHODS:
The field of implementation science is introduced, followed by application of three select models. Models are applied to a clinical scenario to emphasize contextual factors, process, implementation strategies, and outcome evaluation. Key attributes, strengths, opportunities, and utilities of each model are presented, along with general resources for selecting and using published criteria to best fit clinical needs. Partnerships between implementation scientists and clinicians are highlighted to facilitate the uptake of evidence into practice. LINKING EVIDENCE TO ACTION Knowledge of implementation science can help clinicians adopt high-quality evidence into their practices. Application-oriented approaches can guide clinicians through the EBP processes. Clinicians can partner with researchers in advancing implementation science to continue to accelerate the adoption of evidence and reduce the knowledge-to-action gap.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Melhoria de Qualidade
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
/
Sysrev_observational_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article