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Article

Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

1
Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia
2
Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia
3
Unit of Biostatistics and Research Methodology, Health Campus, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Mahmudur Rahman
Healthcare 2021, 9(10), 1369; https://doi.org/10.3390/healthcare9101369
Received: 22 August 2021 / Revised: 27 September 2021 / Accepted: 12 October 2021 / Published: 14 October 2021
Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study’s objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time. View Full-Text
Keywords: patient satisfaction; service quality; SERVQUAL; Facebook; machine learning; patient online review; Malaysia patient satisfaction; service quality; SERVQUAL; Facebook; machine learning; patient online review; Malaysia
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MDPI and ACS Style

Rahim, A.I.A.; Ibrahim, M.I.; Musa, K.I.; Chua, S.-L.; Yaacob, N.M. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021, 9, 1369. https://doi.org/10.3390/healthcare9101369

AMA Style

Rahim AIA, Ibrahim MI, Musa KI, Chua S-L, Yaacob NM. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare. 2021; 9(10):1369. https://doi.org/10.3390/healthcare9101369

Chicago/Turabian Style

Rahim, Afiq Izzudin A., Mohd I. Ibrahim, Kamarul I. Musa, Sook-Ling Chua, and Najib M. Yaacob. 2021. "Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook" Healthcare 9, no. 10: 1369. https://doi.org/10.3390/healthcare9101369

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