Improving rural medication safety with AI: a scoping review

Early Abstract:

Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings.
Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data was analysed thematically to obtain insights on AI interventions across the medication process. 
Results: AI technologies have been integrated into every stage of medication management right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that were affected; (3) how effective these technologies are in minimizing errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance which present major obstacles.
Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance. Making required infrastructural investments, strengthening our staff, and establishing robust ethical and regulatory governance frameworks considering the difficulties of rural implementation, would help us to fully use this potential. Future studies should priorities long-term results, including the community, and emphasis creating culturally sensitive AI solutions to guarantee these technologies are adequately integrated into underprivileged areas. In rural healthcare, AI offers strong potential to improve medication safety through automation, data-driven monitoring, and clinical support. To realise this, investment in infrastructure, workforce capacity, and ethical governance is essential. Rural-specific barriers must be addressed.
Keywords: artificial intelligence, healthcare technology, medication errors, patient safety, rural health, scoping review.