Apply the SERVQUAL Instrument to Measure Service Quality for the Adaptation of ICT Technologies: A Case Study of Nursing Homes in Taiwan

Service Quality and Related Factors in Primary Health Care Services: A Cross-Sectional Study

Primary health care services aim to prevent diseases and improve health efficiently and effectively. This study measures perceived service quality in a primary healthcare organization and examines the effect of personality traits on service quality. The cross-sectional study population comprised individuals over the age of 18 who applied to the Bingöl Central Community Health Centre. A total of 460 participants were included in the study between November 2018 and March 2019. The participants completed a face-to-face questionnaire that included socio-demographic characteristics, the SERVQUAL Scale, and an abbreviated form of the revised Eysenck Personality Questionnaire. This study is based on doctoral research in public health. The study found median values for personality trait sub-dimensions as follows: neuroticism: 2, psychoticism: 2.65, extraversion: 4, and lying: 5. The SERVQUAL Score was -0.02. The study revealed that the quality of primary health care services did not meet the participants' expectations. The study findings also indicated that age, educational attainment, and extraverted and psychotic personality traits were significantly associated with the satisfaction of service quality expectations (p < 0.05). It is recommended to provide primary health care services in facilities with good physical characteristics, with sufficient and competent health personnel, and in a timely and accurate manner to improve service quality.

Meeting Patient Expectations: Assessing Medical Service and Quality of Care Using the SERVQUAL Model in Dermatology Patients at a Single Center in Poland

BACKGROUND This study applied the SERVQUAL model, a widely recognized tool for assessing the quality of services, to understand the gap between patient expectations and perceptions of care quality among 413 dermatology patients at a single medical center in Poland. MATERIAL AND METHODS The study cohort included 413 patients: 195 inpatients and 218 outpatients. The SERVQUAL model's 5 dimensions - reliability, assurance, tangibility, empathy, and responsiveness, each including multiple specific items - served as our assessment criteria. Patient responses to these items measured the perceived and expected quality of service. The service quality index (SQ), calculated as the difference between perception and expectation ratings, was the study's primary outcome measure. A negative SQ score was interpreted as patient dissatisfaction. RESULTS The study results showed a negative SQ score, which signified a discrepancy between high patient expectations and the actual perceived quality of service. The largest gap was seen in the tangibility dimension. Differences emerged based on treatment setting, with inpatient and outpatient settings showing varying expectations and perceptions. Patient sex and residential location also influenced the tangibility dimension. Employed patients and patients with diminished quality of life had heightened expectations in certain dimensions, while patients below 36 years of age expressed higher expectations in responsiveness, assurance, and empathy. CONCLUSIONS The findings emphasize the critical role of care quality in shaping patients' satisfaction and perception, particularly in dermatology. Using the SERVQUAL model, this study identified the tangibility dimension as an area needing improvement. This insight serves as a stepping stone toward enhancing patient satisfaction by addressing these unmet expectations.

A comparative study of positive and negative electronic word-of-mouth on the SERVQUAL scale during the COVID-19 epidemic - taking a regional teaching hospital in Taiwan as an example

BACKGROUND

In recent years, studies have shown that electronic WOM (eWOM) directly reflects consumers' post-purchase psychological perception and directly affects repurchase behavior. This information is valued by institutions in various fields. Within the scope of the evaluation of service characteristics, medical service is the least visible and most difficult service attribute to evaluate. Service organizations must have high trust attributes. Therefore, an eWOM review significantly influences people's decision-making process when choosing a healthcare provider. The purpose of this research is to combine eWOM reviews with the SERVQUAL scale in a comparative study of positive and negative eWOM reviews of a regional teaching hospital in Taiwan.

METHODS

This research obtained data from publicly available eWOM reviews on Google Maps of a regional teaching hospital in Taiwan over the past 10 years (from June 24, 2011, to December 31, 2021) using website scraping technology. The semantic content analysis method was used in this study to classify eWOM reviews according to the revised PZB SERVQUAL scale.

RESULTS

Statistical analysis was conducted. During the COVID-19 pandemic, positive reviews showed a downward trend. Among the five determinants of the SERVQUAL of PZB, positive eWOM reviews performed best in "assurance" with a positive review rate of 60.00%, followed by 42.11% for "reliability". For negative eWOM reviews, "assurance" performed the worst with a positive rate of 72.34%, followed by "responsiveness" at 28.37% and "reliability" at 26.95%.

CONCLUSION

Since the onset of COVID-19 in 2020, negative eWOM has increased significantly and exceeded the amount of positive eWOM. Regardless of positive and negative reviews, what patients care most about is "assurance" of the professional attitude and skills of medical staff, which urgently needs to be strengthened. In addition, good "reliability" will help to develop positive eWOM. However, "responsiveness" as indicated by poor service waiting time can easily lead to the spread of negative eWOM. Hospital management should focus on these service-oriented qualities.

Patient satisfaction with quality of care in public hospitals in Albania

Healthcare service quality and hospitalized patient satisfaction in Albania have never truly received the necessary attention. The aim of this study is the assessment of key elements of service quality and their relationship with patients' satisfaction with receiving healthcare at public health institutions in Albania. The study examines five key dimensions of quality such as responsiveness, reliability, assurance, tangibles, and staff empathy to assess properly the healthcare quality and patient satisfaction in Albania based on the SERVQUAL instrument, which measures the differences between patient's perceptions of service quality received, and their expectations in terms of these five dimensions. A total of 800 randomly selected patients were interviewed in the main cities of Albania in public hospitals. A respective literature review was also a necessity to determine not only the appropriate methodology to be applied, together with the right quality dimensions to use but also for a better understanding of the link between service quality and patient satisfaction. The determination of the main quality factors which contribute to patient satisfaction, as well as, their order of importance, is another key aspect of the study, which finalizes with appropriate recommendations that might help quality improvement in future. The analysis shows that overall patient satisfaction is affected in public hospitals by all dimensions of service quality. Therefore, patients' perspective is extremely important in assessing healthcare service quality and should be taken into consideration by healthcare managers and policymakers in Albania, whenever significant reforms will be undertaken to improve the quality of services in this sector.

Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions.

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

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.

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.

The Effect of Quality Service Towards Outpatients Satisfaction at Poasia Community Health Centre

The quality of outpatient services can be measured by the SERVQUAL model that considers five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. This study aimed to look at the effect of service quality on outpatient satisfaction at the Poasia community health center's pharmacy. The subjects in this study were 33 outpatients. The analysis technique used is gap analysis to compare patients' expectations and reality, normality test data based on questionnaire patient satisfaction tested for validation. This study's results indicate the average value of the gap in the Poasia community health center's pharmacy, amounting to -0.44. From the value of the gap, the lowest gap value in the pharmacy center of Poasia is the place of service and the provision of inadequate drug information of -0.88. Based on the paired t-test where a significant value >0.05, Ho is rejected, and Ha accepted, which means a significant difference between the quality of service and patients' satisfaction in the Pharmacy of Puskesmas Poasia Kendari City. This study concludes that there is a significant gap between expectations and reality at the Poasia community health center's pharmacy.

Certificate-Of-Need Regulation and Healthcare Service Quality: Evidence from the Nursing Home Industry

This quantitative study investigates the effect of certificate-of-need (CON) regulation on the quality of care in the nursing home industry. It uses county-level demographic data from the 48 contiguous US states that are extracted from the American Community Survey (ACS) and cover the years 2012, 2013, and 2014. In doing so, it employs a new set of service quality variables captured from a variety of county-level data sources. Instrumental variables results indicate that health survey scores for nursing homes that are computed by healthcare professionals are about 18-24% lower, depending on the type of nursing home under consideration, in states with CON regulation. We also find that the presence of CON regulation leads to a substitution of lower-quality certified nursing assistant care for higher-quality licensed practical nurse care, regardless of the type of nursing home under consideration.