Artificial Intelligence Awareness, Readiness and Ethical Concerns among Health Logistics Personnel in Public Secondary Health Facilities: Evidence from Kaduna Metropolis, Nigeria

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Background: Artificial intelligence (AI) is increasingly recognized as a transformative technology capable of improving healthcare supply chain performance through enhanced forecasting, inventory management, and decision-making. However, successful implementation depends on the awareness, readiness, and perceptions of the personnel expected to interact with AI-enabled systems. Despite growing interest in AI adoption within healthcare, limited evidence exists regarding the preparedness of health logistics personnel, particularly in low- and middle-income countries. Methods: A mixed-methods cross-sectional study was conducted among a small, facility-based sample of 47 health logistics personnel in public secondary health facilities in Kaduna Metropolis. Data were collected using a structured questionnaire comprising closed-ended Likert-scale items and open-ended questions. Quantitative data were analysed using descriptive statistics, while qualitative responses were analysed thematically. The Technology Acceptance Model (TAM) guided the assessment of technology acceptance. Results: Awareness of AI was high, with 95.7% of respondents indicating prior awareness of the Awareness of AI was high, with 95.7% of respondents reporting prior knowledge of AI. Readiness for AI adoption was also high, with 93.6% willing to learn how to use AI tools, 95.7% indicating that they could adapt to AI or digital tools with proper training, and 95.7% open to using AI to support logistics work. TAM findings showed high perceived usefulness, with item mean scores ranging from 4.62 to 4.68, perceived ease of use ranging from 4.38 to 4.57, and behavioural intention ranging from 4.36 to 4.45 on a five-point scale. Major ethical concerns included data privacy (87.3%), potential reduction in the need for some staff (74.5%), and AI-related errors that may be difficult to detect (53.2%). Qualitative findings highlighted perceived benefits such as improved efficiency, reduced workload, and better decision-making, alongside concerns about infrastructure, training gaps, data protection, and workforce displacement. Conclusion: Health logistics personnel in public secondary health facilities in Kaduna Metropolis demonstrate high awareness and strong readiness for AI adoption. While the workforce appears receptive to AI-enabled logistics systems, addressing ethical concerns, strengthening digital competencies, and improving infrastructure will be essential for successful implementation. The findings provide valuable evidence for policymakers and healthcare managers seeking to advance responsible AI integration within public health supply chains.