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2024-01-15

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Vol. 9 No. 4 (2024)

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How to Cite

Uymaz, A. O., Uymaz, P., & Akgül, Y. (2024). The shift from disease-centric to patient-centric healthcare: Assessing physicians’ intention to use AI doctors. Environment and Social Psychology, 9(4). https://doi.org/10.54517/esp.v9i4.2308

The shift from disease-centric to patient-centric healthcare: Assessing physicians’ intention to use AI doctors

Ali Osman Uymaz

Department of Human Resources Management, Faculty of Economics, Administrative, and Social Sciences, Alanya Alaaddin Keykubat University

Pelin Uymaz

Department of Nursing, Faculty of Health Sciences, Alanya Alaaddin Keykubat University

Yakup Akgül

Department of Business, Faculty of Economics, Administrative, and Social Sciences, Alanya Alaaddin Keykubat University


DOI: https://doi.org/10.54517/esp.v9i4.2308


Keywords: individual-centric healthcare, artificial intelligence, healthcare, prevention of diseases, PLS-SEM, artificial neural network

Abstract

This study examines physicians’ attitudes toward the intention to use AI doctors in healthcare. Currently, physicians use smart health technologies, health data, and AI in disease-focused research hospitals, and industry regulators hope that AI technology will be extensively used for each person, which means a shift from disease-centric to individual-centric healthcare. Using the theory of technology acceptance and use, a research model was developed to understand physicians’ intentions to use AI doctors for data collection, diagnosis, treatment planning, and patient follow-up. The causal comparison screening technique was used to determine the causes and consequences of physicians’ attitudes, behaviors, ideas, and beliefs. The responses of 478 physicians were evaluated using structural equation modeling and deep learning (an artificial neural network). It was discovered that physicians intend to use AI doctors first for diagnosis and treatment planning, and then for data collection and patient follow-up. According to the findings, the main constructs are performance expectancy, perceived task technology fit, high-tech habits, and hedonic motivation.

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