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

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.

Keywords

• Artificial Intelligence
• Healthcare Management
• Operational Efficiency
• Meta-Analysis

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

Abstract

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.

Keywords

• sağlık yönetimi
• yapay zeka
• meta analiz

References

1. Adams, J. G., & Walls, R. M. (2021). Supporting the health care workforce during the COVID-19 global epidemic. JAMA, 323(15), 1439–1440.
2. Al-Muhanna, A., Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Subbarayalu, A. V., ... & Al-Muhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951. https://doi.org/10.3390/jpm13060951
3. Bates, D. W., Levine, D., Syrowatka, A., Kuznetsova, M., Craig, K. J. T., & Rhee, K. (2020). The potential of artificial intelligence to improve patient safety: A scoping review. Journal of the American Medical Informatics Association, 27(12), 1903–1911.
4. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Wiley. Chen, P., Wu, L., & Wang, L. (2023). AI fairness in data management and analytics: A review on challenges, methodologies and applications. Applied Sciences, 13(18), 10258. https://doi.org/10.3390/app131810258
5. Choudhury, A., & Asan, O. (2021). Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Medical Informatics, 8(7), e18599. https://doi.org/10.2196/18599
6. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
7. De Micco, F., Di Palma, G., Ferorelli, D., De Benedictis, A., Tomassini, L., Tambone, V., ... & Scendoni, R. (2025). Artificial intelligence in healthcare: Transforming patient safety with intelligent systems—A systematic review. Frontiers in Medicine, 11, 1522554. https://doi.org/10.3389/fmed.2024.1522554
8. Dos Santos, R. P., Silva, D., Menezes, A., ... (2021). Automated surveillance of healthcare-associated infections using AI algorithms. Infect Prev Pract, 3, 100167.

9. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 624–634. https://doi.org/10.1136/bmj.315.7109.629
10. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
11. Faiyazuddin, M., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., ... & Sah, R. (2025). The impact of artificial intelligence on healthcare: A comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Science Reports, 8, e70312. https://doi.org/10.1002/hsr2.70312
12. Gerdes, L. U., & Hardahl, C. (2013). Text mining electronic health records to identify hospital adverse events. Studies in Health Technology and Informatics, 192, 1145.
13. Gupta, M. K., Rajachar, V., & Prabha, C. (2015). Medical tourism: A new growth factor for Indian healthcare industry. International Journal of Research in Medical Sciences, 3(9), 2161–2163.
14. Haddaway, N. R., Collins, A. M., Coughlin, D., & Kirk, S. (2015). The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PLoS ONE, 10(9), e0138237.
15. Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2020). A targeted real-time early warning score (TREWScore) for septic shock. Health Services Research, 55(3), 373–381.
16. Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2021). Cochrane handbook for systematic reviews of interventions (Version 6.2).
17. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), e000101. https://doi.org/10.1136/svn-2017-000101
18. Juang, L.-H., & Wu, M.-N. (2015). Fall down detection under smart home system. Journal of Medical Systems, 39, 107.
19. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24, 1716–1720. https://doi.org/10.1038/s41591-018-0213-5
20. Ladios-Martin, M., Cabañero-Martínez, M. J., Fernández-de-Maya, J., ... (2022). Developing a predictive model of inpatient fall risk using machine learning. Journal of Nursing Management, 30, 3777–3786.
21. Li, Y., Liu, Y., Hong, Z., Wang, Y., & Lu, X. (2022). Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke. Computer Methods and Programs in Biomedicine, 225, 107093.
22. Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. PLoS Medicine, 6(7), e1000100.
23. Lin, S., Khoo, J., & Schillinger, E. (2016). Next big thing: Integrating medical scribes into academic medical centres. BMJ Simulation & Technology Enhanced Learning, 2, 27–29. https://doi.org/10.1136/bmjstel-2015-000054
24. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., ... & Denniston, A. K. (2019). Deep learning performance compared to healthcare professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297. https://doi.org/10.1016/s2589-7500(19)30123-2
25. Lu, Z., Sim, J. A., Wang, J. X., et al. (2021). Using natural language processing and machine learning methods to characterize unstructured patient-reported outcomes: Validation study. Journal of Medical Internet Research, 23, e26777.
26. Ménard, T., Barmaz, Y., Koneswarakantha, B., Bowling, R., & Popko, L. (2019). Enabling data-driven clinical quality assurance: Predicting adverse event reporting in clinical trials using machine learning. Drug Safety, 42, 1045–1053.
27. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535.
28. Nashwan, A. J., Alkhawaldeh, I. M., Shaheen, N., et al. (2023). The use of artificial intelligence to determine body iron content: A scoping review. Blood Reviews, 62, 101133.
29. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
30. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1, 18. https://doi.org/10.1038/s41746-018-0029-1
31. Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., ... & O’Brien, C. (2020). Real-world integration of a sepsis deep learning technology into routine clinical care: Implementation study. JMIR Medical Informatics, 8(7), e15182. https://doi.org/10.2196/15182
32. Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., ... & Blike, G. (2016). Allocation of physician time in outpatient practice: A time and motion study in 4 specialties. Annals of Internal Medicine, 165, 753–760.
33. Tinoco, A., Evans, R. S., Staes, C. J., Lloyd, J. F., Rothschild, J. M., & Haug, P. J. (2011). Comparison of computer-based monitoring and manual chart review for adverse drug events. Journal of the American Medical Informatics Association, 18(4), 491–497. https://doi.org/10.1136/amiajnl-2011-000187
34. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
35. Wells, G. A., Shea, B., O’Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2014). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
36. Wiley. (n.d.). Wiley Online Library. https://doi.org/10.1002/9781119536604
37. Xie, Y., Nguyen, Q. D., Hamzah, H., et al. (2020). Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: An economic analysis modelling study. The Lancet Digital Health, 2, e240–e249.
38. Yıldırım, K. (2024). Hastanelerde finansal sürdürülebilirlik analizi. Journal of Healthcare Management and Leadership, 1(1), 32–45. https://doi.org/10.35345/johmal.1435805
39. Young, I. J. B., Luz, S., & Lone, N. (2019). A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. International Journal of Medical Informatics, 132, 103971. https://doi.org/10.1016/j.ijmedinf.2019.103971