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.  Hybridmodels  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.