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

Comparative Analysis of Patient Satisfaction Surveys-A Crucial Role in Raising the Standard of Healthcare Services

(1) Background: The study aimed to assess the patients' perception of the quality of the medical staff's care, the hotel's services, and the hospital's overall impression as well as to determine the best rating scale through a comparative analysis of patient satisfaction questionnaires. (2) Methods: A retrospective study was performed based on satisfaction questionnaires addressed to the patients hospitalized in the Orthopedics and Traumatology departments of the County Clinical Emergency Hospital Oradea between 2015 and 2019. Three different types of questionnaires were used during the study period, with the number of questions varying between 30 (variant A) and 37 (variant C). The evaluation was done using the Likert scales with three, four, or five answer variables. (3) Results: The items that we found to be present in all three categories of surveys and for which at least two different questionnaire variants used the Likert scales with various answer variables were chosen. In terms of the treatment given by the medical staff, hotel services, and the overall perception of the hospital, the patients perceive a higher level of quality. (4) Conclusions: The level of patient overall satisfaction or general impression about the hospital is strongly dependent on the quality of medical care provided by the doctors and the specific hotel conditions of the hospital. The quality assessment using the Likert rating scale with five binary variables is more accurate.

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

Syria is a developing country that face enormous healthcare challenges that aggravated with the outbreak of COVID-19. In the study, we evaluate the perceived healthcare service quality based on hospital type, public and private, using five HEALTHQUAL dimensions. We find that service quality in Syrian private hospitals is perceived better that in public hospitals. However, neither type of hospitals scores exceptionally high in any of the examined HEALTHQUAL dimensions. On the contrary, both hospitals score extremely low in the Improvement dimension. We argue that crowdedness environment, medical staff availability and their low salaries, pricing policies as well as the health insurance system, are to blame for such low perceived quality.

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

With the advent of the post-COVID-19 era, corporate managers of import and export trade are now more sensitive in their daily work, and their decisions are more likely to be influenced by the emotional bias of public opinions, especially regarding cooperation with trade ports of frequent circulation. Therefore, how to manage public opinion and sentiment in the post-COVID-19 era will be a new opportunity and challenge for the marketing management of ports. For the above considerations, through the same frequency verification between public opinions and sentiment on ports and corporate choice of import and export trade, and through analysis of the influence mechanism, the present study demonstrates the positive effects of public opinions and sentiment on ports in corporate choice of import and export trade in the post-COVID-19 era, verifies the significance of shaping word of mouth in port management, puts forward the great role of public opinions and sentiment in the cognitive and emotional empathy in the choice of import and export trade, and provides theoretical guidance for port managers' strategic choices in the post-COVID-19 era.

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.

evaluation of patient satisfaction in outpatient departments of selected eye care hospitals in the Hyderabad City, Telangana State

The degree of agreement between a patient's expectations of optimal treatment and their evaluation of the actual care they got has been defined as the level of patient satisfaction. Medical service quality and cost-effectiveness, doctor conduct and attitude, the cost of services, hospital infrastructural facilities, physical comfort and psychological care, as well as empathy for clients' testimonies all go towards a patient's satisfaction with their healthcare facility experience. To determine how satisfied patients are with the out-patient department (OPD) services at five different eye care facilities in Hyderabad, Telangana, and the variables influencing patient satisfaction. To conduct the study, the out-patient department of LV Prasad Eye Institute: Banjara Hills, Vasavi Eye Care & Diabetic Center: Chandanagar, Aravind Eye Hospital & Institute: Mehdipatnam, Sri Madhavi Eye Care Hospital: Habsiguda, Dr. Agarwal Eye Hospital: Panjagutta in Hyderabad city, Telangana state was visited by researchers from 01.04.2021 to 30.06.2021. To choose patients for the out-patient section of the hospital, a systematic random technique was adopted.

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

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.