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Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling
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Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling
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Journal Article
Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling
2021
Overview
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.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Adult
/ Computer and Information Sciences
/ Female
/ Hospitals, Public - organization & administration
/ Humans
/ LDCs
/ Male
/ Medicine and Health Sciences
/ Multivariate statistical analysis
/ Patient Acceptance of Health Care
/ Patients
/ Research and Analysis Methods
/ Rural Health Services - organization & administration
