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Journals / Journal of Healthcare Management and Leadership / 2025 / Sayı: 1

THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE MANAGEMENT ON PATIENT SAFETY AND OPERATIONAL EFFICIENCY: A META-ANALYTIC EVALUATION

YAPAY ZEKANIN SAĞLIK YÖNETİMİNDEKİ ROLÜ HASTA GÜVENLİĞİ VE OPERASYONEL VERİMLİLİK ÜZERİNDEKİ ETKİSİ: META-ANALİTİK BİR DEĞERLENDİRME

Ufuk Burak Karcıoğlu(ISTINYE UNIVERSITY)0009-0006-9131-6407
Pages
8–21
Publication Date
2025-09-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Lab & Field Experiment
📍Scope:2015 Verileri
⚙️Variables:sağlık yönetimi · yapay zeka · meta analiz

Abstract

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.

Özet

Yapay zeka (YZ), yalnızca klinik karar destek sistemleri ile sınırlı kalmayan, aynı zamanda sağlık hizmetlerinin yönetsel boyutlarını da dönüştüren yenilikçi bir teknolojidir. Bu çalışmada, YZ'nin sağlık yönetimi bağlamında hasta güvenliği ve operasyonel verimlilik üzerindeki etkileri meta-analitik yöntemle değerlendirilmiştir. Araştırma kapsamında, 2015 ile 2025 yılları arasında yayımlanmış, nicel veriler içeren 32 bilimsel çalışma sistematik olarak incelenmiştir. Seçilen çalışmalar, YZ’nin tıbbi hata oranları, olumsuz olaylar, hasta bekleme süreleri ve kaynak kullanımı gibi göstergeler üzerindeki etkilerini ölçen analizleri içermektedir. Rastgele etkiler modeli kullanılarak yapılan istatistiksel analizler sonucunda, YZ'nin hasta güvenliğini anlamlı düzeyde artırdığı, tıbbi hata oranlarında %22’lik bir azalma (OR = 0,78; %95 GA [0,65–0,93]) ve olumsuz olay sıklığında orta düzeyde bir düşüş (Cohen’s d = 0,45) sağladığı belirlenmiştir. Operasyonel verimlilik açısından ise hasta bekleme sürelerinde %18 oranında azalma ve yatak doluluk oranlarında %14’lük bir iyileşme (SMD = 0,58; %95 GA [0,41–0,75]) gözlenmiştir. Alt grup analizlerinde, makine öğrenmesi tabanlı sistemlerin, kural temelli algoritmalara göre daha yüksek etkililik gösterdiği; kamu hastanelerinde YZ’nin, özel hastanelere kıyasla operasyonel verimlilik üzerindeki etkisinin daha belirgin olduğu ortaya çıkmıştır. Bununla birlikte, düşük kaynaklı sağlık ortamlarında YZ uygulamalarının sınırlı düzeyde etki yarattığı anlaşılmıştır. Bulgular, YZ’nin sağlık yöneticileri açısından yalnızca teknolojik bir araç değil, aynı zamanda hasta güvenliğini artırma ve hizmet süreçlerini optimize etme açısından stratejik bir unsur olduğunu göstermektedir. Bu çalışma, sağlık sistemlerinin dijital dönüşüm süreçlerine bilimsel dayanak sunarken, YZ'nin sürdürülebilir ve ölçeklenebilir kullanımına ilişkin politika geliştirme ihtiyacını da ortaya koymaktadır.

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Cite This Article

Karcıoğlu, U. B. (2025). THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE MANAGEMENT ON PATIENT SAFETY AND OPERATIONAL EFFICIENCY: A META-ANALYTIC EVALUATION. Journal of Healthcare Management and Leadership, Sayı: 1, 8–21. https://doi.org/10.35345/johmal.1670414

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