Evidence-Based Laboratory Testing Algorithms: A Strategic Approach to Improving Diagnostic Accuracy

The increasing knowledge of evidence-based medicine (EBM) has reinforced the need for structured diagnostic approaches in laboratory testing. A laboratory testing algorithm - a stepwise, guideline-informed sequence of tests - can significantly improve diagnostic accuracy, reduce unnecessary testing, and boost clinical decision-making. These algorithms, combined with current evidence, clinical judgment, and test performance indices, offer clinicians a strategic framework for patient evaluation. In this review, we examine the concept, design, and application of evidence-based laboratory testing algorithms, with an example drawn from common clinical settings, such as the anemia testing algorithm. The presentation of these pathways focuses on calling attention to how diagnostic efficiency can be optimized in daily clinical practice. The integration of these algorithms into electronic health records (EHR), computerized physician order entry (CPOE) systems, and clinical decision-support systems (CDSS) is also recommended. Despite the potential of these algorithms, limitations to widespread adoption still exist, which include a lack of standardization, provider familiarity, interpretive huddles, and system-level limitations. However, as healthcare systems continue to evolve and prioritize cost-effectiveness and patient safety, laboratory professionals have a distinctive opportunity to lead the development and implementation of testing algorithms. This review also stresses the value of collaboration between laboratory professionals and clinical teams in shaping diagnostic strategies, and calls for expanded awareness, familiarization, education, and full integration of evidence-based approaches in laboratory medicine.