Attitudes towards automation in medical laboratories and perceived job security: Implications for digital health transformation in Africa

Introduction: The global shift towards digital health transformation has accelerated the adoption of automation in medical laboratories, particularly in Africa, where healthcare systems are increasingly leveraging technology to improve diagnostic accuracy and efficiency. However, the rapid integration of automation, including Total Laboratory Automation (TLA), has sparked concerns about job security, skill displacement, and workforce adaptation among laboratory professionals. Understanding these dynamics is critical for ensuring that digital health transformation in Africa is both inclusive and sustainable.
Objective: This study aims to explore the attitudes of medical laboratory professionals towards automation and assess its perceived impact on job security, with a focus on implications for digital health transformation in Africa.
Methods: A cross-sectional mixed-methods design will be employed, combining quantitative surveys and qualitative key informant interviews. Data will be collected from 370 laboratory professionals across public and private medical laboratories in Nigeria and Kenya using a pretested, validated tool based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative data will be analyzed using R/SPSS, with categorical variables summarized as frequencies and percentages, and numerical variables reported as means (SD) or medians (IQR). Comparisons will be made using Chi-square, t-tests, or Mann-Whitney U tests, with statistical significance set at p < 0.05. Qualitative data will be analyzed using content analysis supported by INVIVO to identify recurring themes.
Expected Outcomes: The study anticipates uncovering dual perspectives on automation, with professionals recognizing its potential to enhance efficiency and reduce errors, while also expressing concerns about job displacement and the need for upskilling. The findings are expected to reveal a correlation between automation levels and perceived job insecurity, particularly in highly automated laboratories.
Significance: This study will provide critical insights for policymakers and healthcare administrators in Africa, offering recommendations for implementing automation alongside strategies to address workforce concerns. These include targeted retraining programs, psychological support, and policy frameworks to ensure automation complements, rather than displaces, the human workforce.
Keywords: Automation, Medical Laboratories, Job Security, Digital Health Transformation, Africa, Workforce Adaptation.