Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
Journal:
PloS one
PMID:
34559840
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.
Authors
Keywords
Adolescent
Adult
Bangladesh
Female
Forecasting
Hospitals, Public
Humans
Latent Class Analysis
Machine Learning
Male
Middle Aged
Models, Statistical
Patient Acceptance of Health Care
Patient Satisfaction
Personal Satisfaction
Rural Health Services
Rural Population
Surveys and Questionnaires
Telemedicine
Young Adult