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Journals / Verimlilik Dergisi / 2026 / Cilt: 60 Sayı: 1

Prioritization of Social Impacts of Artificial Intelligence-Based Automation Systems on Healthcare Workers in Healthcare Institutions Using the AHP Method

Sağlık Kurumlarında Yapay Zekâ Tabanlı Otomasyon Sistemlerinin Sağlık Çalışanları Üzerindeki Sosyal Etkilerinin AHP Yöntemiyle Önceliklendirilmesi

Ersin Kocaman(TEKİRDAĞ NAMIK KEMAL ÜNİVERSİTESİ)0000-0002-3825-1548
Pages
83–98
Publication Date
2026-01-28
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AI Summary & Key Takeaways

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

This study encourages the consideration of human factors in the design and implementation of technological solutions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose: The study aims to determine and prioritize the social impacts of artificial intelligence-based automation systems in healthcare institutions from the perspective of healthcare professionals.
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Key Findings & Data:Findings: The findings show that the consistency of λmax: 9.34304 (CI: 0.04288, CR: 0.02957) is acceptable and the intended model can be established.
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Conclusion & Impact:This study encourages the consideration of human factors in the design and implementation of technological solutions.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Sağlık Kurumları · Sağlık Çalışanları · Yapay Zekâ

Abstract

Purpose: The study aims to determine and prioritize the social impacts of artificial intelligence-based automation systems in healthcare institutions from the perspective of healthcare professionals. Methodology: The study adopted a descriptive quantitative approach based on a combination of literature review and the Analytical Hierarchy Process (AHP), a multi-criteria decision-making technique. The study sample consisted of eight experts. During the data collection process, criteria were first identified through a literature review, followed by prioritization. Data analysis was conducted using the AHP calculation steps in Microsoft Excel. Findings: The findings show that the consistency of λmax: 9.34304 (CI: 0.04288, CR: 0.02957) is acceptable and the intended model can be established. Of the 9 criteria determined, Job Security Anxiety has the highest weight (𝑤: 0.21829). This is followed by Technological Stress (𝑤: 0.20034) and Role Ambiguity and Change in Professional Identity (𝑤: 0.15595). The lowest weights belong to the criteria Algorithmic Trust/ Transparency Perception (𝑤: 0.05527), Privacy/ Surveillance Anxiety (𝑤: 0.05072) and Distrust of the Algorithm/Rejection Tendency (𝑤: 0.03889). Originality: This study makes a unique contribution to the literature on digital transformation in healthcare by presenting an empirically based model for prioritizing the social impacts of AI automation from the perspective of healthcare professionals. This study encourages the consideration of human factors in the design and implementation of technological solutions.

Özet

Amaç: Çalışma sağlık kurumlarında yapay zekâ tabanlı otomasyon sistemlerinin sosyal etkilerini sağlık çalışanları açısından belirlemeyi ve önceliklendirmeyi amaçlamaktadır. Yöntem: Çalışmada, literatür taraması ile çok kriterli karar verme tekniklerinden biri olan Analitik Hiyerarşi Süreci (AHP) yönteminin kombinasyonuna dayalı tanımlayıcı nicel bir yaklaşım benimsenmiştir. Çalışmanın örneklemini 8 uzman oluşturmaktadır. Veri toplama sürecinde önce literatür incelemesiyle kriterler belirlenmiş ardından kriterlerin önceliklendirmesi yapılmıştır. Veri analizi Microsoft Excel programında AHP hesaplama basamakları takip edilerek yapılmıştır. Bulgular: Bulgular, λmax: 9,34304 (CI: 0,04288, CR: 0,02957) kabul edilebilir tutarlılığı ve amaçlanan modelin kurulabilir olduğunu göstermektedir. Belirlenen 9 kriterden İş Güvencesi Kaygısı en yüksek ağırlığa (𝑤: 0,21829) sahiptir. Bunu Teknolojik Stres (𝑤:0,20034) ve Rol Belirsizliği ve Mesleki Kimlikte Değişim (𝑤: 0,15595) takip etmektedir. En düşük ağırlıklar, Algoritmik Güven/ Şeffaflık Algısı (𝑤: 0,05527), Mahremiyet/ İzlenme Kaygısı (𝑤: 0,05072) ve Algoritmaya Güvensizlik/ Reddetme Eğilimi (𝑤: 0,03889) kriterlerine aittir. Özgünlük: Çalışma, sağlık çalışanları perspektifinden yapay zekâ otomasyonunun sosyal etkilerinin önceliklendirilmesi için ampirik temelli bir model sunarak sağlık hizmetlerinde dijital dönüşüm literatürüne özgün bir katkı sunmaktadır. Bu doğrultuda teknolojik çözümlerin tasarımında ve uygulanmasında insan faktörünün hesaba katılması desteklenmektedir.

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Cite This Article

Kocaman, E. (2026). Prioritization of Social Impacts of Artificial Intelligence-Based Automation Systems on Healthcare Workers in Healthcare Institutions Using the AHP Method. Verimlilik Dergisi, Cilt: 60 Sayı: 1, 83–98. https://doi.org/10.51551/verimlilik.1746569

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