A Phenomenological Exploration of Medical Practitioners' Experiences in Adopting Artificial Intelligence and Machine Learning Technologies in Hospitals
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Abstract
Digital health systems have become increasingly vital in healthcare, transforming how practitioners and patients interact. While previous studies have explored technological adoption in healthcare, few have examined the lived experiences of practitioners and patients within these platforms, particularly in telemedicine. A critical gap exists in understanding the subjective meanings and experiences behind these interactions. This study uses a phenomenological approach to explore the experiences of medical practitioners and patients in using telemedicine platforms, focusing on their perceptions, challenges, and insights. In-depth interviews with 25 participants were conducted, revealing key themes such as communication barriers, trust-building in digital spaces, and the perceived benefits of telemedicine in patient care These findings offer several practical implications for improving the design and implementation of telemedicine platforms. For instance, addressing communication barriers by incorporating user-friendly interfaces and language support tools can enhance accessibility and usability for diverse populations. Building mechanisms to foster trust in digital interactions—such as robust data privacy measures and transparent protocols—can strengthen user confidence. Moreover, understanding the perceived benefits from a patient-centered perspective can guide policymakers and healthcare providers in promoting telemedicine adoption, especially in underserved or remote areas.. These findings offer valuable insights into the human factors influencing digital health adoption and provide a more nuanced understanding of telemedicine's impact on healthcare delivery. The study contributes to bridging the gap between technological innovation and patient-practitioner interaction, with implications for improving telemedicine platforms and enhancing user experience in future research.
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