Assessing The Efficiency of Automated Vs Manual Blood Typing Methods.

Abstract

Accurate blood typing is critical in transfusion medicine to prevent life-threatening complications. This review systematically compares the efficiency of automated and manual blood typing methods, evaluating analytical accuracy, operational throughput, cost-effectiveness, and scalability. A literature search spanning 1990–2024 identified 75 studies, which were analyzed to synthesize evidence on both methodologies. Automated systems, leveraging gel microcolumns, solid-phase assays, and AI integration, demonstrated superior accuracy (99.8% concordance vs. 98.5% for manual methods) and throughput (150–300 samples/hour vs. 50–60 samples/hour), with 60–75% lower error rates due to reduced human intervention. However, high initial costs, technical complexity, and infrastructure dependencies limit their adoption in resource-constrained settings. Manual techniques, such as slide and tube agglutination, remain cost-effective and adaptable for low-volume or emergency testing but are prone to subjectivity, longer turnaround times, and higher misclassification rates (8%). Emerging advancements, including point-of-care devices and CRISPR-based typing, promise to bridge current gaps. The review concludes that while automation optimizes precision and scalability in high-volume laboratories, manual methods retain niche relevance in complex serological cases and low-resource environments. Hybrid models integrating automated workflows for routine testing and manual protocols for discrepancies are recommended to balance efficiency and accessibility. Future efforts should prioritize subsidizing automation in underserved regions, advancing AI equity, and strengthening technician training to enhance global transfusion safety.