Abstract

BackgroundEvidence-based clinical algorithms (EBCA) are knowledge tools to promote evidence use by codifying evidence into action plans to facilitate appropriate care. However, their impact on process and outcomes of care varies considerably across practice settings and providers, highlighting the need for tailoring of both these knowledge tools and their implementation strategies to target end users and the setting in which EBCAs are to be employed.Leadership at the Tikur Anbessa Specialized Hospital emergency department (TASH-ED) in Addis Ababa, Ethiopia identified a need for context-appropriate EBCAs to improve evidence uptake to mitigate care gaps in this high volume, high acuity setting. We aimed to identify barriers and facilitators to utilization of EBCAs in the TASH-ED, to identify priority targets for development of EBCAs tailored for the TASH-ED context and to understand the process of care in the TASH-ED to inform implementation planning.MethodsWe employed a multi-component qualitative design including: semi-structured interviews with TASH-ED clinical, administrative and support services staff, and Toronto EM physicians who had worked in the TASH-ED; direct observation of the process of care in TASH-ED; document review.ResultsAlthough most TASH-ED participants reported an awareness of EBCAs, they noted little or no experience using them, primarily due to the poor fit of many EBCAs to their practice setting. All participants felt that context-appropriate EBCAs were needed to ensure standardized and evidence-based care and improve patient outcomes for common ED presentations. Trauma, sepsis, acute cardiac conditions, hypertensive emergencies, and diabetic keto-acidosis were most commonly identified as priorities for EBCA development. Lack of medication, equipment and human resources were identified as the primary barriers to use of EBCAs in the TASH-ED. Support from leadership and engagement of stakeholders outside the ED where EBCAs were believed to be less well accepted were identified as essential facilitators to implementation of EBCAs in the TASH-ED.ConclusionsThis study found a perceived need for EBCAs tailored to the TASH-ED setting to support uptake of evidence-based care into routine practice for common clinical presentations. Barriers and facilitators provide information essential to development of both context-appropriate EBCAs and plans for their implementation in the TASH-ED.

Highlights

  • Evidence-based clinical algorithms (EBCA) are knowledge tools to promote evidence use by codifying evidence into action plans to facilitate appropriate care

  • This study found a perceived need for EBCAs tailored to the Tikur Anbessa Specialized Hospital (TASH)-emergency department (ED) setting to support uptake of evidence-based care into routine practice for common clinical presentations

  • TASH staff participants included a range of clinical, support service and administrative roles, with 5 participants fulfilling combined clinical and administrative roles

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Summary

Introduction

Evidence-based clinical algorithms (EBCA) are knowledge tools to promote evidence use by codifying evidence into action plans to facilitate appropriate care Their impact on process and outcomes of care varies considerably across practice settings and providers, highlighting the need for tailoring of both these knowledge tools and their implementation strategies to target end users and the setting in which EBCAs are to be employed. They accomplish this by “codifying evidence into specific rules or action plans” that can facilitate the delivery of appropriate evidence-based care [2, p 1016] Their impact on process and outcomes of care varies considerably across practice settings and providers [2,3,4], highlighting the need for tailoring of both knowledge tools and their implementation strategies to target end users and the setting in which EBCAs are to be employed [4]. Engagement of stakeholders throughout the process is important to ensuring that issues of applicability, feasibility and sustainability are adequately addressed

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