Exploring the impact of artificial intelligence integration on medication error reduction: A nursing perspective

Area of Science:

• Nursing Informatics
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Medication errors pose a significant risk to patient safety and increase healthcare costs.
• Nurses are integral to medication administration and susceptible to human error.
• AI technologies offer novel solutions for improving medication safety through advanced analytics and decision support.

Purpose of the Study:

• To systematically evaluate the impact of AI technologies on reducing medication errors in nursing.
• Focus on specific AI tools: clinical decision support systems (CDSS), smart infusion pumps, barcode scanning, and automated prescription validation.

Main Methods:

• Systematic review adhering to PRISMA 2020 guidelines.
• Mixed-methods approach integrating quantitative and qualitative data.
• Searched PubMed, ScienceDirect, and CINAHL (Jan 2013-Mar 2024), applying PICO framework and quality appraisal.

Main Results:

• Included twelve studies, demonstrating significant error reductions: CDSS (up to 95%), smart infusion pumps (approx. 80%), prescription validation (55%).
• AI-driven alert filtering reduced non-actionable alerts by 45%.
• Qualitative insights highlighted AI utility alongside concerns regarding bias, usability, and trust.

Conclusions:

• AI technologies substantially enhance medication safety in nursing practice.
• Successful adoption hinges on comprehensive nurse training, seamless system integration, ethical frameworks, and workflow compatibility.
• Further research is essential to validate efficacy and overcome implementation barriers like alert fatigue and algorithm transparency.