Content area
Aim
To systematically evaluate the impact of artificial intelligence (AI) technologies on reducing medication errors in nursing practice, focusing on tools such as clinical decision support systems (CDSS), smart infusion pumps, barcode scanning and automated prescription validation.
BackgroundMedication errors are a persistent threat to patient safety and a major burden on healthcare systems. Nurses, who are central to the medication administration process, remain vulnerable to human error. AI offers new opportunities to enhance safety through real-time decision support and predictive analytics.
DesignA systematic review following PRISMA 2020 guidelines and using a mixed-methods approach to integrate quantitative outcomes with qualitative insights from nursing practice.
MethodsStudies published in English between January 2013 and March 2024 were retrieved from PubMed, ScienceDirect and CINAHL. Eligibility was guided by the PICO framework. Quality appraisal tools appropriate to study designs were applied.
ResultsTwelve studies were included. CDSS reduced operating room errors by up to 95 %, while smart infusion pumps reduced IV medication errors by approximately 80 %. Prescription validation tools led to a 55 % reduction in prescribing errors. AI-driven alert filtering decreased non-actionable alerts by 45 %. Qualitative data revealed both appreciation of AI’s utility and concerns about algorithmic bias, system usability and trust.
ConclusionsAI technologies significantly improve medication safety in nursing. However, successful implementation depends on nurse training, system integration, ethical safeguards and workflow alignment. Further experimental studies are needed to validate efficacy and address barriers such as alert fatigue, algorithm transparency and adoption resistance.
Details
Patients;
Decision Support Systems;
Literature Reviews;
Nurses;
Influence of Technology;
Records (Forms);
Mixed Methods Research;
Meta Analysis;
Reference Materials;
Qualitative Research;
Search Strategies;
Information Seeking;
Artificial Intelligence;
Nursing;
Automation;
Database Management Systems;
Algorithms;
Individual Characteristics
Transparency;
Safeguards;
Infusion devices;
Operating rooms;
Prescribing;
Human error;
Efficacy;
Automation;
Health care industry;
Medical errors;
Artificial intelligence;
Algorithms;
Professional practice;
Patient safety;
Safety;
Systematic review;
Drugs;
Clinical decision making;
Critical incidents;
Clinical nursing;
Drug administration;
Decision support systems;
Predictive analytics;
Fatigue;
Ethics;
Errors;
Nurses;
Resistance;
Support networks;
Validity;
Adoption of innovations;
Health services;
Nursing;
Medical decision making;
Patients
; Ahmad, Muhammad Waleed 3 1 Department of Software Engineering, Zarqa University, Faculty of Information Technology, Zarqa, Jordan
2 Medical and Surgical Nursing Department, Faculty of Nursing, University of Tabuk, Tabuk, Saudi Arabia
3 Tianjin university, China