Development of a Framework for Point-of-Ccare Lab Error Reduction in Resource-Limited Settings

Thesis Abstract

In resource-limited healthcare systems, point-of-care (POC) laboratory testing is increasingly deployed to expedite clinical decision-making, yet it is frequently compromised by operational errors, leading to inaccurate results, compromised patient safety, and reduced trust in diagnostic processes. This study aims to develop a validated framework for reducing POC laboratory errors in such settings by integrating human factors engineering, quality management principles, and contextually appropriate training. The specific objectives are (1) to identify and categorize the prevalent error types across POC testing workflows in regional clinics; (2) to evaluate existing quality assurance and error-reduction practices using a multi-site assessment; (3) to develop a contextually tailored framework combining standard operating procedures, error-tracking mechanisms, competency-based training, and supervisory stewardship; (4) to pilot-test the framework in three district laboratories and refine it through iterative feedback; and (5) to propose an implementation plan with metrics for sustainability and scalability. The study will adopt a mixed-methods design underpinned by activity theory and the Normalization Process Theory to illuminate how POC workflows are enacted, resisted, and normalized in routine practice. The population includes laboratory personnel, nurses, and clinicians involved in POC testing across five district health facilities. A purposive sampling strategy will select 60 laboratory staff across two phases, complemented by 20 in-depth interviews with senior clinicians and quality officers, and 10 focus group discussions with frontline operators. Quantitative data will be gathered through structured observation checklists, a validated POC error taxonomy instrument, and pre-post competency assessments, while qualitative data will be collected via semi-structured interview guides and focus group protocols. Instrument validity will be established through expert panel review and pilot testing in one non-study site, with reliability assessed via Cohen’s kappa for inter-rater agreement and Cronbach’s alpha for internal consistency. Data analysis will include descriptive statistics to quantify error frequencies, chi-square tests to examine associations between personnel roles and error types, and regression analysis to identify predictors of error rate reductions following the framework implementation. For qualitative data, thematic analysis will be conducted using a deductive-inductive approach, with coding anchored in the theoretical constructs of activity theory and Normalization Process Theory, and triangulated with quantitative findings. A process evaluation will monitor fidelity, dose–response, and acceptability of the framework during the pilot phase. The anticipated findings include a delineation of high-risk steps in POC workflows (e.g., specimen handling, device operation, result transcription), identification of gaps in training and supervision, and evidence that the proposed framework—comprising standardized SOPs, a simple error-tracking log, targeted competency modules, regular supervisory audits, and an accessible dashboard for feedback—substantially reduces error rates by at least 25% within six months of implementation. Secondary outcomes are improvements in operator confidence, reduced turnaround times, and enhanced documentation quality. The study will also elucidate contextual enablers and barriers to sustainable adoption, such as supply chain reliability, workload distribution, and leadership engagement. This research will contribute to knowledge by offering a theory-informed, practically implementable framework for error reduction in POC laboratories that can be adapted to similar low-resource environments. It advances the integration of human factors with quality management in laboratory medicine and provides empirical evidence on the efficacy of targeted training, supervision, and information feedback mechanisms in lowering diagnostic errors. The main conclusion is that a context-adapted, multi-component framework that prioritizes standardization, continuous monitoring, and leadership-supported governance can meaningfully reduce POC laboratory errors in resource-limited settings. Recommendations include scaling the framework through regional training centers, embedding the error-tracking system into national quality assurance programs, ensuring consistent supply chain support for testing devices, and conducting longitudinal studies to assess long-term sustainability and patient-centered outcomes.