Health organizations have widely adopted social media for health promotion, public health communication conveyance, and organizational promotion activities. However, little published data exists on the factors that facilitate health information diffusion in South East Asia, especially Malaysia compared with Western countries. This study aimed to investigate factors associated with good engagement rates among internet users on the Facebook (FB) page of Ministry of Health Malaysia. In this observational study, 2123 FB posts were randomly selected. Data dated from 1 November 2016 to 31 October 2017 was gathered from the Facebook Insight. The logistic regression model was applied to identify factors associated with good engagement rates. This study found that a FB post with a good engagement rate was significantly associated with a health education post (Adjusted Odd Ratio (AOR): 3.80, 95% Confidence Interval CI: 3.02–4.78, p < 0.001), a risk communication post (AOR: 1.77, 95% CI: 1.39–2.26, p < 0.001), a post in the afternoon (AOR: 1.76, 95% CI: 1.34–2.31, p < 0.001) or in the evening (AOR: 1.48, 95% CI: 1.20–1.82, p < 0.001), and a video format (AOR: 3.74, 95% CI: 1.44–9.71, p = 0.007). Therefore, we present the first comprehensive analysis of health information engagement among internet users in Malaysia. The growing trends of online health information-seeking behaviors and demand for the availability of validated health information require effective strategies by public health organizations to disseminate health information and achieve better audience engagement on social media.

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.

Objectives: This study aimed to determine Facebook post characteristics and factors associated with good engagement rates among netizens on the Facebook (FB) page of Ministry of Health Malaysia.Methods: 2123 FB posts were randomly selected in this cross-sectional study. The logist ic regression model was applied to identify factors associated with good engagement rates.Results: Majority of the FB post characteristics were organizational promotion content, photo type and posted between midnight and early morning. An FB post with a good engagement rate was significantly associated with a health education post (Adjusted Odd Ratio (AOR): 3.80, 95% Confidence Interval CI: 3.02–4.78, p < 0.001), a risk communication post (AOR: 1.77, 95% CI: 1.39–2.26, p < 0.001), a post in the afternoon (AOR: 1.76, 95% CI: 1.34–2.31, p < 0.001) or in the evening (AOR: 1.48, 95% CI: 1.20–1.82, p < 0.001), and a video format (AOR: 3.74, 95% CI: 1.44–9.71, p = 0.007).Conclusion: We present the first comprehensive analysis of social media engagement and health communication analysis in Malaysia. The dynamic of health communication and rapid changes of health technology recently require health organizations to constantly upgrading their health promotion capacity and facilities. Therefore, they can effectively disseminate qualit y health information, achieving better audience engagement and subsequently improving health literacy among netizens on social media.International Journal of Human and Health Sciences Supplementary Issue: 2019 Page: 32

Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time.

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.

BACKGROUND Health organizations have widely adopted social media for health promotion, public health communication conveyance and organizational, promotional activities. However, limited information is available on the factors that facilitate the health information diffusion in Malaysia, an Asia country with different socio-economic, cultural events and average internet penetration rate compared to Western countries. OBJECTIVE This study aims to look at factors associated with good engagement rates among netizens using Facebook (FB) Page of the Ministry of Health (MOH) Malaysia. METHODS Two thousand one hundred twenty-three FB posts randomly selected in the cross-sectional study design. The sample was determined based on the study objective using two proportion formula. Data gathered from Facebook Insight of MOH’s FB Page from November 2016 to October 2017. The logistic regression model was applied to identify factors associated with good engagement rate. RESULTS The most prevalent type of health information was the organizational promotion (n=766) while the majority of posts were posted between midnight till early morning (n=870) and the most frequent types of the post was the photo (n=1366). This study found that good engagement rate significantly associated with health education post (AOR 3.80, 95% CI 3.02, 4.78, P<0.001), risk communication post (AOR 1.77, 95% CI 1.39, 2.26, P<0.001), post in afternoon (AOR 1.76, 95% CI 1.34, 2.31, P<0.001), or in evening (AOR: 1.48, 95% CI 1.20, 1.82, P<0.001) and video post (AOR: 3.74, 95% CI 1.44, 9.71, P= 0.007). The engagement was negatively associated with FB post at morning, and utilization of different FB type of post namely link, share video or photo. CONCLUSIONS Understanding of engagement factors on social media such as Facebook may improve the dissemination of health information among netizens by health organizations.

As the global battle against COVID-19 rages on, Malaysia’s concerted effort in stemming the spread is commendable. This study characterized the epidemiology of COVID-19 aiming towards understanding the disease in a local setting for better preparation and management. A nation-based e-COVID reporting system was used to collect data on laboratory-confirmed COVID-19 cases in Kelantan from January to July 2020. Information from investigation reports was also reviewed. Analyses comprised of the estimation of incidence and case-fatality rate, summary of demographic and clinical characteristics including the age and sex distributions, construction of the epidemiological curve and choropleth map, and appraisal of healthcare usage. Multiple logistic regression was used to determine the risk factors for Intensive Care Unit (ICU) admission. A total of 166 cases reported in Kelantan until July 2020. Cases peaked during March before steadily declining and were concentrated in the capital. The age-adjusted incidence rate was 9.4/100,000 populations with a case-fatality rate of 2.4%. The median age was 37 years and 78% were male. The predominant symptoms were fever and cough while 25% of cases were asymptomatic. About 57% of cases were identified by active case detection and 97% had exposure risk. Potentially infected cases were isolated within a median of 7 days after exposure, even before the diagnosis. All cases were hospitalized with a median of 14 bed days, while 12% admitted to ICU, and 3% required mechanical ventilators. Significant factors for ICU admission were older age (AOR: 1.05, 95% CI: 1.02, 1.09, P = 0.001) and diabetes mellitus (AOR 4.55, 95% CI: 1.36, 15.25, P = 0.014). Although all ages appeared susceptible to COVID-19, older age and diabetic patients were more vulnerable. Kelantan’s targeted approaches of prompt identification and isolation of potentially infected individuals have been effective in limiting the transmission, allowing sufficient healthcare capacity in managing the pandemic.

While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital’s Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.