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Abstract

Background: Laboratory errors can significantly compromise diagnostic accuracy and patient safety. Errors occur across pre-analytical, analytical, and postanalytical stages, often exacerbated in resource-limited settings by systemic and human factors. This study aimed to develop and evaluate a structured framework to minimize laboratory errors, improve operational efficiency, and enhance patient safety. Methods: A mixed-methods approach was employed, encompassing three phases: descriptive, interventional, and evaluative. Baseline error rates were identified and categorized through root cause analysis (RCA) and Failure Mode and Effects Analysis (FMEA). Interventions included barcoding systems, AI-based quality assurance tools, standardized operating procedures (SOPs), and competency-based training programs. The framework was piloted in rural, urban, public, and private laboratories over six months, with quantitative and qualitative assessments of its effectiveness. Results: Baseline errors were most prevalent in the pre-analytical stage (55%), followed by analytical (30%) and post-analytical stages (15%). Following implementation, error rates decreased significantly: pre-analytical errors by 80%, analytical errors by 67%, and post-analytical errors by 70%. Key performance indicators, such as turnaround time (TAT) and sample rejection rates, improved by 43% and 73%, respectively. Patient safety metrics, including treatment delays and adverse events,improvedby32%and25%,whilepatientsatisfactionincreasedto90%.Conclusions:The structured framework effectively reduced laboratory errors, improved operational metrics, and enhanced patient safety outcomes. This model is replicable across diverse healthcare settings and underscores the importance of integrating technology, training, and standardized processes in laboratory error management.