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<p>Artificial intelligence (AI) is rapidly transforming the healthcare sector, revolutionizing decision-making processes and operational efficiency. However, despite its transformative potential, unintended consequences are emerging, including techno-stressors that negatively impact users’ well-being. By adopting the stressor–strain–outcome (SSO) theoretical model, this research explores how do AI-related techno-stressors contribute to techno-exhaustion in the healthcare sector. Data were gathered through an e-survey involving 221 participants. This research makes four key contributions: introducing and empirically validating the novel techno-stressor "techno-paralysis”; employing a preliminary neurophysiological experiment to establish its link with techno-exhaustion; expanding the SSO model by integrating techno-paralysis to address indecisiveness-related stress in behavioral functioning; and proposing a calibration graph that acts as an optimizer for the decision deliberation process in healthcare.</p>

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Forecasting AI-induced technostress in healthcare: identifying techno-paralysis in decision-making

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Rachid Jabbouri,
  • Helmi Issa,
  • Jad Jaber

摘要

Artificial intelligence (AI) is rapidly transforming the healthcare sector, revolutionizing decision-making processes and operational efficiency. However, despite its transformative potential, unintended consequences are emerging, including techno-stressors that negatively impact users’ well-being. By adopting the stressor–strain–outcome (SSO) theoretical model, this research explores how do AI-related techno-stressors contribute to techno-exhaustion in the healthcare sector. Data were gathered through an e-survey involving 221 participants. This research makes four key contributions: introducing and empirically validating the novel techno-stressor "techno-paralysis”; employing a preliminary neurophysiological experiment to establish its link with techno-exhaustion; expanding the SSO model by integrating techno-paralysis to address indecisiveness-related stress in behavioral functioning; and proposing a calibration graph that acts as an optimizer for the decision deliberation process in healthcare.