From Data to Evidence: Evaluative Methods in Evidence-Based Medicine

Abstract

The amount of published information is increasing exponentially, and recent technologic advances have created systems whereby mass distribution of this information can occur at an infinite rate. This is particularly true in the broad field of medicine, as the absolute volume of data available to the practicing clinician is creating new challenges in the management of relevant information flow. Evidence-based medicine (EBM) is an information management and learning strategy that seeks to integrate clinical expertise with the best evidence available in order to make effective clinical decisions that will ultimately improve patient care. The systematic approach underlying EBM encourages the clinician to formulate specific and relevant questions, which are answered in an iterative manner through accessing the best available published evidence. The arguments against EBM stem from the idea that there are inherent weaknesses in research methodologies and that emphasis placed on published research may ignore clinical skills and individual patient needs. Despite these arguments, EBM is gaining momentum and is consistently used as a method of learning and improving health care delivery. However, if EBM is to be effective, the clinician needs to have a critical understanding of research methodology in order to judge the value and level of a particular data source. Without critical analysis of research methodology, there is an inherent risk of drawing incorrect conclusions that may affect clinical decision-making. Currently, there is a trend toward using secondary pre-appraised data rather than primary sources as best evidence. We review the qualitative and quantitative methodology commonly used in EBM and argue that it is necessary for the clinician to preferentially use primary rather than secondary sources in making clinically relevant decisions.