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

• Published in: PloS one Vol. 16; no. 9; p. e0257300
• Main Authors: Zobair, Khondker Mohammad, Sanzogni, Louis, Houghton, Luke, Islam, Md Zahidul
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.09.2021; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Artificial intelligence; Artificial neural networks; Bangladesh - epidemiology; Biology and Life Sciences; Cognitive ability; Computer and Information Sciences; Consumers; Consumption; Decision making; Developing countries; Diabetic retinopathy; Evaluation; Female; Forecasting; Forecasts and trends; Health care industry; Hospitals, Public - organization & administration; Humans; Industry forecasts; Information systems; Innovations; Latent Class Analysis; LDCs; Learning algorithms; Machine Learning; Male; Mathematical models; Medicine and Health Sciences; Middle Aged; Modelling; Models, Statistical; Multivariate statistical analysis; Neural networks; Patient Acceptance of Health Care; Patient Satisfaction; Patients; People and Places; Personal Satisfaction; Physical Sciences; Physicians; Prediction models; Quality of service; Research and Analysis Methods; Rural areas; Rural Health Services - organization & administration; Rural Population; Structural equation modeling; Surveys and Questionnaires; Sustainability; Technology Acceptance Model; Telemedicine; Telemedicine - methods; Telemedicine - trends; Young Adult; Bangladesh; Australia; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257300

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.