Best practices in sample management & pre-analytical quality control: overcoming challenges in resource-limited laboratory settings.

backgroundPre-analytical errors remain the leading source of laboratory diagnostic failures worldwide, accounting for approximately 60-75% of errors across the total testing process. These challenges are particularly pronounced in resource-limited settings (RLS), where inadequate infrastructure, unreliable transport systems, workforce shortages, and weak quality management systems compromise specimen integrity and diagnostic accuracy. Despite growing recognition of these barriers, evidence on practical, context-appropriate interventions remains disjointed.

objectivesThis study synthesizes the current evidence on strategies for strengthening pre-analytical sample management and quality control in RLS and to develop a practical framework for improving diagnostic reliability through adaptive protocols, human-centered quality interventions, and appropriate technologies.

methodA narrative review was conducted using a PRISMA-informed study selection process. Literature published between 2010 and 2025 was identified from PubMed, Scopus, Google Scholar, and grey literature. Eligible studies addressing pre-analytical quality in low- and middle-income countries (LMICs) were critically appraised for methodological quality, operational relevance, and contextual applicability. Evidence from 71 key sources was synthesized using a three-domain conceptual framework comprising adaptive protocols, human-centered quality, and appropriate technology.

resultThe review identified haemolysis, specimen misidentification, clotting, insufficient sample volume, and transport-related degradation as the predominant causes of pre-analytical failure, amplified by systemic weaknesses in resource-limited settings. Evidence demonstrated that context-adapted interventions including standardized phlebotomy practices, dried blood spot sampling, passive cooling systems, motorcycle and drone transport networks, continuous competency-based training, simplified visual standard operating procedures, non-punitive quality cultures, manual quality indicators, and affordable digital laboratory information systems, substantially improve specimen integrity, reduce rejection rates, strengthen traceability, and enhance laboratory efficiency and sustainability.

conclusionStrengthening pre-analytical quality in RLS requires integrated, locally-adaptable interventions rather than replication of high-resource laboratory models. Combining adaptive protocols, empowered healthcare workers, and scalable digital technologies within supportive national policies offers a sustainable pathway to improve diagnostic reliability, patient safety, and health system resilience while advancing equitable access to quality laboratory services.