Source-linked AI summary
Improving Rural Medication Safety with AI: A Scoping Review
Jeong-ah Kim, Muhammad Ashad Kabir, Daniel Terry, Maryam Rouhi
TL;DR
Medication errors remain a serious healthcare threat, and evidence on AI for rural medication safety is limited. This scoping review examined studies from nine countries and found transformative potential for AI to enhance medication safety in resource-constrained settings.
Problem
Medication errors remain a serious threat across healthcare and can occur at any stage of the medication process.
Method
A scoping review explored AI applications and effectiveness for enhancing patient safety and reducing medication errors.
Results
Findings from nine countries highlighted AI’s transformative potential for enhancing medication safety in resource-constrained healthcare settings.
Takeaways & Limitations
Smart infusion devices and related AI tools offer scalable, data-driven solutions that can reduce medication errors.
Takeaways & Limitations
The review included a limited number of eligible studies despite aiming to capture a broad range of AI applications in rural healthcare.
Abstract
from arXiv · showhide
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 the 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 were 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 are affected; (3) how effective these technologies are in minimising 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 still 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.
1. Introduction
Medication errors are a persistent global healthcare threat, especially in rural areas with limited resources, inadequate facilities, and workforce shortages. This scoping review addresses a knowledge gap by examining how AI can improve medication safety in rural healthcare settings, including implementation challenges and potential benefits.
- Problem context: Medication errors occur across the medication process and remain a global healthcare challenge despite safety protocols.Errors may cause inappropriate medication use or patient harm and can occur during prescribing, dispensing, and administration.
- Problem context: 7-9% of medication orders are estimated to contain errors, with approximately 1% of these errors resulting in patient harm.Medication errors alone cost $20–45 billion annually in the United States.
- Rural context: Rural healthcare faces heightened medication-safety risks because resources and facilities are limited and healthcare professionals are scarce.These conditions have increased recognition of the need for technology-driven solutions to reduce medication errors.
- AI rationale: AI has been adopted across healthcare and has potential to enhance patient safety, particularly by reducing medication errors.The paper describes AI as enabling computers to replicate human learning and analysis.
- Study aim and contribution: This scoping review examines AI applications, effectiveness, benefits, and implementation challenges for medication safety in rural healthcare settings.It focuses on the intersection of AI and medication-error prevention, including prescription checking, e-prescribing, limited connectivity, alert fatigue, and rural adoption barriers.
2. What evidence exists regarding the effectiveness of AI in reducing medication errors in rural areas? · 3. What are the challenges and barriers to implementing AI in these settings? · 2. Methods
The paper used a scoping review to examine AI applications and effectiveness in reducing medication errors and improving patient safety in rural healthcare. It applied a PCC framework, PRISMA-ScR guidance, systematic database searching, and thematic narrative synthesis.
- 2. Methods: The review examined AI applications and effectiveness for improving patient safety and reducing medication errors in rural healthcare settings.
- 2. Methods: A scoping-review design was selected to capture diverse uses, viewpoints, and study types relevant to rural artificial intelligence applications.
- 2. Methods: The review used the Population, Concept, And Context approach to support a systematic and comprehensive literature examination.
- 2.1. Search Strategy:: Searches conducted on October 2, 2024, covered EBSCO, Emcare (Ovid), MEDLINE, and ProQuest Consumer Health Database for articles published from 2012 to 2025.
- 2.1. Search Strategy:: The search targeted medication errors, artificial intelligence or related technologies, and rural, remote, or rural-community settings.
- 2.2. Data Extraction and Analysis:: Records were deduplicated in Covidence, screened independently by two reviewers, and disagreements were resolved through team consensus.
- 2.2. Data Extraction and Analysis:: Eligible studies underwent extraction and thematic synthesis, with themes assessed by two team members and key study information captured through narrative review.
- 2.2. Data Extraction and Analysis:: The analysis critically examined the breadth, character, and scope of current research to identify ideas, knowledge gaps, and methodological developments.
3. Results
The results synthesized 12 primary studies from a larger set of 191 records and identified four themes concerning AI types, medication-process stages, effectiveness, and implementation barriers. AI and automated technologies improved medication safety across prescribing, dispensing, administration, and post-administration monitoring, while workflow, integration, and implementation challenges remained.
- 3. Results: The scoping review included 12 primary studies and identified four overarching themes: AI types, targeted medication stages, effectiveness, and implementation barriers.The review process began with 191 records.
- Implementation barriers: Implementation barriers included alert fatigue, mistimed workflow interruptions, technical and EHR integration problems, unclear procedures, and nonadjustable interfaces.Successful implementation also required attention to people, tasks, tools, environment, organizational factors, customization, training, and seamless workflow integration.
- Reported effectiveness and outcomes: 83.33% accuracy was reported for flagging inconsistent prescriptions, while one system produced a low alert burden of 0.4% of orders.The alert system’s alerts were 85% clinically valid, 80% useful, and prompted order changes in 43% of cases.
- Medication process stages: AI interventions addressed medication safety from prescribing and dispensing through administration and post-administration monitoring.Applications included clinical decision support, automated dispensing, infusion pump programming, barcode systems, and monitoring of unstructured clinical notes.
- Reported effectiveness and outcomes: 53% fewer medication administration errors were observed overall, including 79.1% fewer incorrect doses and 93.7% fewer incorrect drugs (P < 0.01).The intervention also reduced medication administration errors by 53% overall.
4. Discussion
AI shows strong potential to improve medication safety in resource-constrained rural settings through error reduction, monitoring, decision support, and expanded specialist access. However, implementation requires infrastructure, funding, training, governance, stakeholder engagement, and stronger evidence from real-world studies.
- Benefits and opportunities: AI tools can reduce medication errors while improving prescribing, dispensing, real-time monitoring, and adaptive decision-making in rural care.Reported reductions exceeded 50% in some specific contexts, although effectiveness depends on study design, setting, integration, data quality, and user engagement.
- Benefits and opportunities: AI can analyse unstructured clinical data to identify adverse medication events, support clinical judgment, and foster proactive safety improvement.These capabilities are particularly valuable where clinicians have limited support or incomplete patient information, with continuous learning and feedback loops supporting a safety culture.
- Benefits and opportunities: Telepharmacy and AI-assisted prescription validation may extend specialist support to remote areas and narrow urban-rural differences in healthcare quality.The review links this scalability to broader efforts to strengthen primary care and achieve universal health coverage.
- Implementation challenges: Rural implementation is constrained by unreliable connectivity, non-interoperable EHRs, limited technical support, financial costs, clinician resistance, inadequate training, alert fatigue, and weak governance.Ethical risks include privacy, algorithmic bias, and cultural insensitivity; representative datasets and user-centred design are needed for responsible adoption.
- Implications and future research: Policymakers should support infrastructure, workforce development, and regulation, while practitioners should treat AI as a collaborative tool and researchers should conduct longitudinal studies.Implementation science can tailor solutions to local resources, needs, and cultural contexts, and stakeholder engagement is critical to making systems effective and acceptable.
- Limitations: Evidence remains difficult to generalise because eligible studies were few, designs and outcomes were heterogeneous, metrics were not standardised, and many studies were small pilots or simulations.Excluding grey literature may also have omitted practical insights from ongoing projects and government initiatives.
5. Conclusion
AI has transformative potential to improve medication safety in rural healthcare by reducing medication errors and supporting clinical decision-making. Safe, effective, and equitable adoption requires addressing infrastructural, financial, human, ethical, and governance challenges.
- 5. Conclusion: AI technologies, from clinical decision support systems to smart infusion devices, offer scalable, data-driven approaches to reduce medication errors in resource-constrained rural settings.These technologies can also support clinical decision-making.
- 5. Conclusion: Realising AI’s potential requires overcoming infrastructural, financial, and human barriers, including limited digital infrastructure, clinician resistance, and alert fatigue.Ethical concerns also require attention.
- 5. Conclusion: Robust policy frameworks, targeted investments, and inclusive implementation strategies are needed to address rural AI adoption challenges.These measures should respond to digital infrastructure limitations, clinician resistance, alert fatigue, and ethical concerns.
- 5. Conclusion: Coordinated technology deployment, workforce training, and governance development are essential for integrating AI safely, effectively, and equitably.These efforts are especially important as rural healthcare systems transition toward digital health.
Ethics
Because the study used only publicly available data and did not involve human participants, ethical approval was not required.
- Ethics: Ethical approval was not required because the study involved no human participants and used only publicly available data.The ethics determination was based on the study’s data sources and participant status.
Funding
The study received no external funding.
- No external funding was received for this study.
CRediT authorship contribution statement
The authors contributed across conceptualisation, methodology, investigation, analysis, writing, supervision, and visualisation. Contributions were distributed among Jeong-ah Kim, Ashad Kabir, Daniel Terry, and Maryam Rouhi.
- Jeong-ah Kim contributed to conceptualisation, investigation, visualisation, methodology, formal analysis, and drafting and revising the manuscript.
- Ashad Kabir contributed to conceptualisation, supervision, methodology, and manuscript review and editing.
- Daniel Terry contributed to conceptualisation, methodology, and manuscript review and editing.
- Maryam Rouhi contributed to methodology, investigation, formal analysis, and manuscript review and editing.