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Journals / Kastamonu Üniversitesi Sağlık Bilimleri Fakültesi Dergisi / 2026 / Cilt: 5 Sayı: 2

Algorithmic Governance And Predictive Artificial Intelligence In Hospital Workforce Management: A Narrative Review Of Burnout Prevention Strategies

HASTANE İŞGÜCÜ YÖNETİMİNDE ALGORİTMİK YÖNETİŞİM VE ÖNGÖRÜCÜ YAPAY ZEKA: TÜKENMİŞLİK ÖNLEME STRATEJİLERİNE YÖNELİK BİR ANLATISAL İNCELEME

Ufuk Burak Karcıoğlu(ISTINYE UNIVERSITY)0009-0006-9131-6407
Pages
76–90
Publication Date
2026-08-31
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AI Summary & Key Takeaways

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

Overall, predictive AI may serve as a tool for identifying burnout-related organizational risks at an earlier stage and supporting targeted workforce interventions; however, the current evidence does not justify a definitive causal claim that predictive AI prevents burnout.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Healthcare worker burnout remains a major organizational challenge that adversely affects employee well-being, quality of care, and workforce sustainability.
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Key Findings & Data:Persistent staffing shortages, high workloads, unpredictable working schedules, and increasing administrative demands suggest that individual-level interventions alone may be insufficient to address the organizational determinants of burnout.
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Conclusion & Impact:Overall, predictive AI may serve as a tool for identifying burnout-related organizational risks at an earlier stage and supporting targeted workforce interventions; however, the current evidence does not justify a definitive causal claim that predictive AI prevents burnout.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Sağlık çalışanı tükenmişliği · tahmini yapay zeka · algoritmik yönetişim

Abstract

Healthcare worker burnout remains a major organizational challenge that adversely affects employee well-being, quality of care, and workforce sustainability. Persistent staffing shortages, high workloads, unpredictable working schedules, and increasing administrative demands suggest that individual-level interventions alone may be insufficient to address the organizational determinants of burnout. This narrative review examines the potential role of predictive artificial intelligence and algorithmic governance in addressing organizational and workload-related factors associated with burnout in hospital workforce management and evaluates both the potential benefits and risks of these technologies. The available literature suggests that AI-enabled workforce management may improve workload predictability, reduce selected administrative burdens, and support workforce planning. Accordingly, the potential contribution of predictive AI to burnout mitigation should be understood not as a technological effect in isolation, but as part of a broader organizational approach supported by appropriate working conditions and robust algorithmic governance. Overall, predictive AI may serve as a tool for identifying burnout-related organizational risks at an earlier stage and supporting targeted workforce interventions; however, the current evidence does not justify a definitive causal claim that predictive AI prevents burnout. Future research should examine these relationships through longitudinal and empirical studies across different healthcare systems, professional groups, and organizational resource settings, while also evaluating potential unintended consequences of AI implementation.

Özet

Sağlık çalışanlarında tükenmişlik, çalışanların iyilik hâlini, sağlık hizmetlerinin kalitesini ve kurumların iş gücü sürdürülebilirliğini olumsuz etkileyen önemli bir örgütsel sorun olmaya devam etmektedir. Özellikle personel yetersizliği, yüksek iş yükü, düzensiz çalışma saatleri ve artan idari yük gibi sistemik faktörler, tükenmişlik riskinin azaltılmasında yalnızca bireysel müdahalelerin yeterli olmayacağını göstermektedir. Bu çalışmanın amacı, hastane iş gücü yönetiminde tahmini yapay zekâ ve algoritmik yönetişimin tükenmişlikle ilişkili örgütsel ve iş yükü kaynaklı faktörleri ele alma potansiyelini incelemek ve bu teknolojilerin uygulanmasına ilişkin fırsatları ve riskleri bütüncül bir çerçevede değerlendirmektir. Bu anlatısal derlemede PubMed, Scopus, Web of Science ve Google Scholar veri tabanlarında 2017–2025 yılları arasında yayımlanan ilgili literatür taranmış; tahmini yapay zekânın personel talebinin öngörülmesi, iş yükünün izlenmesi, vardiya planlaması, yorgunluk riskinin belirlenmesi ve iş gücü kaynaklarının optimize edilmesi gibi kullanım alanları ile algoritmik yönetişimin şeffaflık, açıklanabilirlik, adalet, hesap verebilirlik ve insan gözetimi boyutları incelenmiştir. Mevcut literatür, yapay zekâ destekli iş gücü yönetimi uygulamalarının iş yükünün öngörülebilirliğini artırma, idari yükü azaltma ve personel planlamasını destekleme açısından potansiyel yararlar taşıdığını göstermektedir. Sonuç olarak, tahmini yapay zekâ hastane iş gücü yönetiminde tükenmişlikle ilişkili risklerin erken belirlenmesini ve örgütsel müdahalelerin desteklenmesini sağlayabilecek bir araç olarak değerlendirilebilir; ancak mevcut kanıtlar, doğrudan bir nedensel tükenmişlik önleme etkisi ileri sürmek için yeterli değildir. Gelecek araştırmaların farklı sağlık sistemi bağlamlarında, meslek gruplarında ve kurumsal kaynak düzeylerinde uzunlamasına ve ampirik çalışmalarla bu ilişkinin etkinliğini ve olası olumsuz sonuçlarını değerlendirmesi gerekmektedir.

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Cite This Article

Karcıoğlu, U. B. (2026). Algorithmic Governance And Predictive Artificial Intelligence In Hospital Workforce Management: A Narrative Review Of Burnout Prevention Strategies. Kastamonu Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, Cilt: 5 Sayı: 2, 76–90. https://doi.org/10.59778/sbfdergisi.1856431

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