Exploring Human Factors Dynamics of Trust in Healthcare Automation Leading to Safety and Adoption

Just one of many healthcare-related problems that compromises patient safety (medication dispensing errors) remain a persistent threat to patient health, safety, and organizational security, despite the growing availability of automated pill verification and recognition technologies. Human factors science emphasizes the critical role of trust in mediating interactions between clinicians and automation within complex sociotechnical systems. Trust is dynamic, shaped by perceptions of performance, process transparency, and purpose, and can be miscalibrated through either over-reliance or disuse. Existing scholarship highlights the potential for automation to reduce pharmaceutical error rates, yet significant gaps remain regarding how trust in automation (TiA) is formed, eroded, and repaired within authentic clinical workflows. Compounding this threat is a parallel challenge: healthcare organizations are not consistently operating as learning organizations, entities capable of continuously acquiring, disseminating, and acting on new knowledge. Without structured learning processes, feedback loops, and adaptive improvement behaviors, distrust in automation becomes entrenched, inhibiting technology adoption and perpetuating preventable risk. Understanding the conditions under which automation is trusted or rejected is a critical research need at the intersection of human performance, organizational resilience, and patient safety. The consequences are substantial: medication errors have been reported to range from 2% to 94%, contribute to more than 90,000 deaths, and impose approximately $42 billion in global costs annually. This qualitative, literature-driven inquiry synthesizes theoretical frameworks from human–machine interaction, technology acceptance, and organizational learning to examine trust formation among pharmacists and healthcare workers. Findings suggest that the absence of structured learning processes, feedback loops, and adaptive improvement behaviors fosters institutional distrust and slows the adoption of safety-enhancing technologies. Two research problems exist: (1) safety-compromising events in healthcare, such as medication-dispensing errors, and (2) the uncommon presence of structured learning among healthcare organizations that address building trust in automation. The study underscores the need for healthcare organizations to operate as learning organizations capable of iterative improvement and knowledge sharing. By integrating multidisciplinary perspectives, this work advances conceptual clarity in the under-researched domain of trust in healthcare automation and provides a scholarly foundation for future empirical evaluation. Insights from this inquiry are used to propose a new theoretical concept that advances trust in healthcare automation through organizational learning (OL). The Learning to Trust and Adopt Automation Technology (LTAAT) concept model offers principles that promote intentional, structured organizational learning as indispensable to any TiA model.