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

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.

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