Multidimensional Tracing of Error Sources and Structured Corrective Strategies for Leukocyte Count and Differential on Automated Hematology Analyzers

Leukocyte count and differential are core indicators for clinical infection assessment, hematological disease diagnosis, and therapeutic monitoring. Currently, clinical testing heavily relies on automated hematology analyzers. Influenced by factors such as specimen characteristics, limited analyzer recognition capability, and improper manual operations, automated testing often produces unreliable results, interfering with clinical decision-making and threatening patient safety. Existing studies remain fragmented, lacking a multidimensional error-tracing framework and practical corrective strategies covering the entire testing workflow. A narrative review was conducted to summarize the causes, identification methods, and corrective strategies for unreliable leukocyte results. Relevant literature up to August 2026 was searched in PubMed/MEDLINE and CNKI. We categorized multidimensional error sources into specimen-related interferents, leukocyte morphological and developmental abnormalities, and preanalytical or systematic factors, and established a practical diagnostic workflow spanning from anomaly recognition to result reporting. By incorporating both legacy and current analytical systems, we compared the characteristic scattergram patterns of various interferents across analyzers with different detection principles, broadening the applicability to diverse clinical laboratories. To better ensure the reliability of corrective strategies and provide diverse alternative options, we not only summarized widely accepted routine practices but also included case-based experimental methods. The proposed multidimensional error-tracing system and practical diagnostic workflow significantly improve analytical accuracy and reduce clinical risks. However, current hematology analyzers cannot automatically identify or correct complex interferences. Future systems must integrate multi-platform advantages, optimize complex cell recognition capabilities, and enrich multidimensional scattergram information to overcome existing analytical limitations. Furthermore, integrating artificial intelligence technology holds immense potential to drive automated hematology analysis toward a more intelligent and precise direction.