Artificial intelligence (AI) is increasingly recognized not only as a clinical decision support tool but also as a transformative technology for improving administrative and managerial processes in healthcare. This meta-analytic study evaluates the quantitative impact of AI on patient safety and operational efficiency within the context of healthcare management. A systematic review was conducted on 32 quantitative studies published between 2015 and 2025, which assessed AI’s effects on key indicators such as medical error rates, adverse events, patient wait times, and resource utilization. The analysis included data from 145,872 patients across 78 healthcare facilities. Using a random-effects model, pooled effect sizes revealed that AI implementation significantly improved patient safety, reducing medical errors by 22% (OR = 0.78; 95% CI 0.65–0.93) and lowering the incidence of adverse events with a moderate effect size (Cohen’s d = 0.45). In terms of operational efficiency, AI contributed to an 18% reduction in patient wait times and a 14% optimization in bed occupancy rates (SMD = 0.58; 95% CI 0.41–0.75). Subgroup analyses demonstrated that machine learning systems outperformed rule-based algorithms, and that public hospitals benefited more from AI-driven efficiency gains compared to private hospitals. However, the observed effect sizes were notably smaller in low-resource settings, highlighting contextual limitations. These findings emphasize that AI offers not only technological innovation but also strategic value for healthcare managers aiming to enhance system performance and patient outcomes. This study provides robust, evidence-based guidance for decision-makers and underscores the importance of investing in scalable, ethically grounded, and context-specific AI strategies within healthcare systems.
Ufuk Burak Karcıoğlu (Mon,) studied this question.
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