The Use of Evidence Synthesis in the Context of Healthcare: A Literature Review Primer

Abstract

Introduction: Evidence synthesis (ES) uses different systematic methodologies to compile a body of evidence on a given topic based on existing literature to help inform practice, policy, and future research decisions. There are multiple ways to categorize different types of ES. However, they differ in how they search, appraise, synthesize, and analyze data. Some of the most common ES forms include systematic, narrative, scoping, critical, environmental scans, and rapid reviews. Utility: ES offers a myriad of benefits. These methodologies provide an immediate response to a question using research that has already been approved, funded, and completed, highlighting potential applications in numerous non-research-focused disciplines. Additionally, when information is not readily available in the current literature, ES methodologies elucidate gaps in knowledge that otherwise would be masked. Finally, they enhance the development of novel strategies, studies, and theories by summarizing, appraising, and critiquing current literature despite not being a direct source of unknown information. Challenges: Several practical challenges inhibit the use of ES. To compile any form of ES, access to a wide range of databases and peer-reviewed journals is necessary, thus hindering feasibility for non-academic researchers and those in poorly funded research organizations. These challenges are often exacerbated in developing countries. Due to these barriers, ethical implications exist regarding the lack of inclusive evidence-building between scientists. Additionally, conducting a rigorous and rigid systematic review that analyzes every significant paper on a particular topic is highly time-consuming, thus hampering the effective utilization of ES in most labs. Limitations: ES techniques contain several inherent limitations. Firstly, research questions that are too specific such that existing literature is inadequate or questions that are too broad, such that existing literature is in excess, make ES methodologies weak in providing accurate answers. Additionally, to achieve objectivity, authors of ES studies need to create comprehensive inclusion/exclusion criteria. Unfortunately, this often fosters bias amongst different interpretations of the criteria, thus influencing what research gets included in the analysis. Subsequently, the validity of the entire ES method is jeopardized.