Machine learning for medication error detection: a scoping review protocol

BackgroundMedication errors pose a significant threat to public health. Despite efforts by health agencies and the implementation of various interventions, such as staff training, medication reconciliation, and automation, the persistence of these incidents highlights the need for more effective, scalable solutions. In recent years, machine learning (ML) has emerged as a promising approach in healthcare, offering potential to detect and predict medication errors, through data-driven insights. ObjectiveThis scoping review aims to systematically map the existing literature on ML-based approaches to predict or detect medication errors across all stages of the medication use process. The review seeks to identify the range of ML applications in this domain, characterize methodological trends, and highlight current knowledge gaps. The findings will provide a structured and accessible overview for both clinicians and researchers, supporting the development of safer, more data-informed medication practices. Method and analysisThe review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Structured searches will be performed in PubMed, Embase, and Web of Science. Predefined inclusion and exclusion criteria will be used to identify eligible studies. Key information - including ML model, data sources and type, evaluation methods, and clinical context - will be extracted and analyzed using descriptive statistics, visualizations, thematic analysis, and narrative synthesis. Study registrationThis protocol has been registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/38SFY).