Abstract
Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.
Highlights
IntroductionThe patient-centered approach (PCA) has become a critical component in the development and enhancement of health services and patient care
We suggest that each Malaysian public hospital create a separate or official Facebook page and monitor what their patients say on social media
We demonstrate how monitoring Facebook reviews with machine learning methods offers valuable, real-time data that is not available via conventional quality measures or surveys
Summary
The patient-centered approach (PCA) has become a critical component in the development and enhancement of health services and patient care. It values the important input of medical consumers in order to develop aspects of healthcare services that improve patients’. Consumers and patients have been more involved in talks among stakeholders and health care task groups in recent years. With the goal of actively including health consumers in the transformation and reconstruction of quality care activities, debate persists about whether PCA methods should be adopted or if conventional organizational requirements seem to take precedence [1].
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