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Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling

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  • Khondker Mohammad Zobair
  • Louis Sanzogni
  • Luke Houghton
  • Md Zahidul Islam

Abstract

Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for their physical and emotional illnesses. This study investigates the antecedents of patients’ satisfaction relating to telemedicine adoption in rural public hospitals settings in Bangladesh through the adaptation of Expectation Disconfirmation Theory extended by Social Cognitive Theory. This research advances a theoretically sustained prediction model forecasting patients’ satisfaction with telemedicine to enable informed decision making. A research model explores four potential antecedents: expectations, performance, disconfirmation, and enjoyment; that significantly contribute to predicting patients’ satisfaction concerning telemedicine adoption in Bangladesh. This model is validated using two-staged structural equation modeling and artificial neural network approaches. The findings demonstrate the determinants of patients’ satisfaction with telemedicine. The presented model will assist medical practitioners, academics, and information systems practitioners to develop high-quality decisions in the future application of telemedicine. Pertinent implications, limitations and future research directions are endorsed securing long-term telemedicine sustainability.

Suggested Citation

  • Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
    • Handle: RePEc:plo:pone00:0257300
    DOI: 10.1371/journal.pone.0257300
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    2. Ankush Kadu & Manwinder Singh & Kingsley Ogudo, 2022. "A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    3. Ong, Ardvin Kester S. & Kurata, Yoshiki B. & Castro, Sophia Alessandra D.G. & De Leon, Jeanne Paulene B. & Dela Rosa, Hazel V. & Tomines, Alex Patricia J., 2022. "Factors influencing the acceptance of telemedicine in the Philippines," Technology in Society, Elsevier, vol. 70(C).
    4. Ulrike Baum & Frauke Kühn & Marcel Lichters & Anne-Katrin Baum & Renate Deike & Hermann Hinrichs & Thomas Neumann, 2022. "Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model," IJERPH, MDPI, vol. 19(20), pages 1-22, October.

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