Perception of Patient Safety and Error Reduction Through a Clinical Decision Support System (AI)
DOI:
https://doi.org/10.5294/aqui.2026.26.2.1Keywords:
Artificial intelligence, patient safety, nursing, clinical decision support systems, human factors, explainable artificial intelligenceAbstract
Objective: To evaluate the impact of implementing artificial intelligence (AI) tools on patient safety perception and the reduction of clinical errors in care at two hospitals in Peru. Materials and Methods: A quasi-experimental quantitative design was used, with a non-randomized control group. Measurements were taken before and after the intervention. The sample consisted of 80 nurses (experimental group n=40; control group n=40), selected by purposive non-probability sampling from two public hospitals in Latin America. The experimental group used an AI-based Clinical Decision Support System (CDSS-AI) for 12 weeks. Validated tools were used to assess nurses’ self-reported nursing errors and patient safety perception. Results: CDSS-AI intervention had a positive impact on participants in the experimental group, significantly improving patient safety perception and reducing the reported incidence of clinical errors. However, technical, ethical, and organizational challenges remain, exacerbated by the limited availability of explainable AI (XAI) models and insufficient training for healthcare workers in safely integrating these technologies into their workflows. Conclusion: Integrating AI into nursing services can be an important tool for improving patient safety, provided it is implemented within a clear ethical and regulatory framework, prioritizing algorithmic transparency, and encouraging active participation of professionals in the design and oversight of these systems.
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