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

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...

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Autores principales:	Khondker Mohammad Zobair, Louis Sanzogni, Luke Houghton, Md Zahidul Islam
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d12b8828345e42418df5e35d9c81f535
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spelling	oai:doaj.org-article:d12b8828345e42418df5e35d9c81f5352021-12-02T20:06:09ZForecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.1932-620310.1371/journal.pone.0257300https://doaj.org/article/d12b8828345e42418df5e35d9c81f5352021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257300https://doaj.org/toc/1932-6203Many 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.Khondker Mohammad ZobairLouis SanzogniLuke HoughtonMd Zahidul IslamPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257300 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Khondker Mohammad Zobair Louis Sanzogni Luke Houghton Md Zahidul Islam Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
description	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.
format	article
author	Khondker Mohammad Zobair Louis Sanzogni Luke Houghton Md Zahidul Islam
author_facet	Khondker Mohammad Zobair Louis Sanzogni Luke Houghton Md Zahidul Islam
author_sort	Khondker Mohammad Zobair
title	Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
title_short	Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
title_full	Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
title_fullStr	Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
title_full_unstemmed	Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
title_sort	forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d12b8828345e42418df5e35d9c81f535
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Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.

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