A Framework for Standardizing Quality Control Practices in Clinical Laboratory Testing

Thesis Abstract

Inadequate standardization of quality control practices in clinical laboratory testing poses significant challenges to diagnostic accuracy, reliability, and overall laboratory efficiency, thereby impacting patient safety and healthcare outcomes. This study aims to develop a comprehensive framework aimed at standardizing quality control (QC) procedures across diverse clinical laboratory settings. The specific objectives are to identify core gaps and inconsistencies in current QC practices, examine the influence of existing quality management systems, and construct an evidence-based, scalable framework that can be implemented effectively to enhance QC standardization nationwide. Employing a mixed-methods research design, the study integrates qualitative and quantitative approaches to achieve its aims. The qualitative component involves thematic analysis of in-depth interviews with laboratory managers, quality assurance officers, and senior technologists from 40 publicly funded and private laboratories, selected through purposive sampling to capture a diverse range of operational contexts. Thematic analysis is guided by grounded theory principles to explore prevailing challenges and contextual factors affecting QC practices. The quantitative component comprises a survey administered to 500 laboratory personnel across similar institutions, selected via stratified random sampling. The survey instrument incorporates Likert-scale items assessing current QC adherence, perceived barriers, and facilitators, complemented by items measuring laboratory characteristics such as accreditation status and staffing levels. Data validity and reliability are established through expert panel review and Cronbach’s alpha coefficient of 0.86. Data analysis involves descriptive statistics to profile current QC practices, followed by multiple regression analysis to examine relationships between laboratory features and QC adherence levels. The qualitative data are coded thematically using NVivo software, with coding reliability ensured through intercoder agreement exceeding 85%. Integration of findings facilitates the identification of critical intervention points, informing the development of a conceptual model for QC standardization. The model emphasizes capacity-building, implementation of standardized protocols, continuous training, and quality management systems aligned with ISO 15189 standards. It is subsequently validated via expert review panels and pilot testing in five laboratories representing different operational scales. Expected findings suggest significant variability in QC practices related to resource availability, staff training, and institutional policies. The study anticipates that the proposed framework will address identified gaps by providing clear, adaptable guidelines, fostering consistency, and promoting a culture of continuous quality improvement. The framework’s implementation is projected to enhance diagnostic accuracy, reduce error rates, and improve compliance with international accreditation standards. This research contributes to the growing body of knowledge on laboratory quality management by offering a theoretically grounded, empirically validated framework built upon the Theory of Planned Behavior and the Service Quality Model, tailored specifically for the context of clinical laboratory testing. The main conclusion underscores that effective standardization of QC practices requires a multifaceted, context-sensitive approach that integrates training, infrastructure, and policy reforms. The study recommends national adoption of the framework, incorporation into laboratory accreditation processes, and ongoing monitoring and evaluation mechanisms. Furthermore, the research advocates for periodic updates aligned with technological advancements and evolving international standards, providing a sustainable pathway toward uniform, high-quality laboratory testing services that safeguard patient health and optimize clinical decision-making.