Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

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.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

“…In the literature AI technologies are associated with compassion through automated healthcare quality assessment. Specific examples are the use of natural language processing and patient’s online social media comments to capture service feedback information from diverse groups of service users ( Doing-Harris et al, 2017 ; Rahim et al, 2021 ); automated analysis of patient and family feedback captured by interactive patient care technology in hospitals ( Clavelle et al, 2019 ); a large-scale network study of online health communities to inform future policy interventions for patients’ self-care ( Panzarasa et al, 2020 ). At the clinical level, automated evaluation of psychotherapy skills using speech and language technologies can augment experts’ capabilities in training, supervision, and quality assurance of services ( Xiao et al, 2015 ; Flemotomos et al, 2022 ).…”

Appeal of word of mouth: Influences of public opinions and sentiment on ports in corporate choice of import and export trade in the post-COVID-19 era

“…However, such research is obviously not deep enough to explore the in-depth emotional connotations and the wide perspectivesin public opinions. There has also emerged a trend of replacing text content analysis through machine learning methods (A Rahim et al, 2021 ) and video content analysis ( Southwick et al, 2021 ).…”

The quality of Syrian healthcare services during COVID-19: A HEALTHQUAL approach

“…A number of studies attempted to add, reduce or change the SERVQUAL dimensions to accommodate different settings such as Carmen ( 22 ), Bowers et al ( 23 ), Jun et al ( 24 ), Shelton ( 25 ), Doran and Smith ( 26 ), Mostafa ( 27 ), Scobie et al ( 28 ), Evans and Lindsay ( 29 ), Yesilada and Direktor ( 30 ). Rahim et al ( 31 ) used machine learning to build a sentiment analyzer and service quality classifier, instead of questionnaire, to automatically classifies the sentiment and SERVQUAL dimensions using comments from 48 official public hospitals' Facebook pages.…”

“…However, other studies argued that the reverse is true ( 39 , 43 , 44 ). Rahim et al ( 31 ) founnd that patients in Malaysia were generally satisfied with the services provided by public hospitals though they did not compare with private hospitals.…”