Development of a Multidimensional Quality Assurance Framework for Medical Laboratory Diagnostics

Thesis Abstract

Health systems increasingly depend on accurate and reliable medical laboratory diagnostics to inform clinical decisions, public health surveillance, and patient safety; however, variability in laboratory processes, quality control practices, and governance across institutions undermines diagnostic excellence and trust. This study addresses the gap by developing a comprehensive, multidimensional quality assurance (QA) framework that integrates technical, organizational, and governance dimensions to enhance reliability, timeliness, and interpretability of laboratory results in medical diagnostics. The aim is to construct and validate a theory-driven QA framework capable of guiding policy, practice, and continuous improvement across diverse laboratory settings. Specific objectives include (1) to identify and synthesize core quality assurance constructs across pre-analytical, analytical, and post-analytical phases; (2) to articulate an integrated framework that aligns ISO 15189 standards, Lean Six Sigma principles, and institutional governance models; (3) to evaluate the framework’s predictive validity for diagnostic error reduction using empirical data; (4) to examine contextual factors (resource availability, staffing, training, and information systems) that influence QA performance; and (5) to propose implementation guidelines and metric sets for monitoring and sustaining QA improvements. The study adopts a mixed-methods design underpinned by the Dynamic Capability Theory and the Total Quality Management (TQM) paradigm, with a triangulation approach to ensure robustness of findings. A sequential explanatory design is employed. In the qualitative phase, purposive sampling targets 20 quality managers and 40 senior clinical laboratorians across five tertiary and five secondary laboratories. Data are generated through semi-structured interviews, document analysis, and process walkthrough observations, with thematic analysis guided by Braun and Clarke’s approach to identify QA dimensions, enablers, and barriers. In the quantitative phase, a cross-sectional survey is administered to 250 laboratory professionals, sampled to reflect varying volumes and specializations; instrument validity is established via expert review and pilot testing (n=30), and reliability is assessed using Cronbach’s alpha. Laboratory performance indicators are sourced from six months of routine data, including turnaround time, specimen rejection rates, proficiency testing scores, and incident reports. For data analysis, structural equation modeling (SEM) tests the hypothesized relationships among quality dimensions, while multilevel regression analyzes contextual moderators. Content validity is supplemented by confirmatory factor analysis to validate the proposed multidimensional construct. The expected findings include (i) a validated multidimensional QA framework comprising interdependent dimensions—technical accuracy, pre-analytical integrity, post-analytical communication, governance and compliance, workforce competence, information system integrity, and continuous improvement culture; (ii) empirical evidence that integrated QA dimensions predict reductions in diagnostic errors and improvements in turnaround times and proficiency testing outcomes; (iii) identification of critical contextual determinants (leadership engagement, investment in automation, and standardized information governance) that strengthen QA performance; and (iv) a practical metric set and action-oriented implementation guidance for real-world adoption. The theoretical contribution lies in operationalizing Dynamic Capability Theory and TQM into a cohesive, actionable QA model for medical laboratory diagnostics, bridging theory and practice with empirically grounded constructs and measurable outcomes. Practically, the framework offers stakeholders a scalable tool for benchmarking, prioritizing QA investments, and guiding accreditation processes while supporting continuous improvement cycles. The anticipated contributions to knowledge include (1) a novel integrative QA framework tailored to medical laboratory diagnostics that covers all stages of the diagnostic pathway, (2) validated measurement instruments and a structural model linking QA dimensions to operational performance, and (3) pragmatic recommendations for harmonizing international standards with local practice through a governance-driven implementation plan. The study concludes that adopting a multidimensional QA framework yields tangible improvements in diagnostic reliability and patient safety, provided there is sustained leadership commitment, disciplined data governance, and ongoing professional development. Recommendations emphasize systematic adoption across institutions, investment in interoperable information systems, standardized pre- and post-analytical procedures, and periodic revalidation of the framework to respond to evolving technologies and clinical demands.