Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network

A cross-sectional study on exploring the antecedents of patient's revisit intention: Mediating role of trust in the hospital among patients in India

BACKGROUND

In the healthcare domain, patients' trust in the hospital plays an instrumental role in determining the behavioral intention of the patient. This article attempts to investigate the impact of service quality perception on behavioral intention with the mediating effect of trust in the hospital and patient satisfaction.

METHODS

This research was carried out in multispecialty hospitals located in Bangalore Urban and Mysore districts of Karnataka during August 2021. This was a questionnaire-based study and the sample size was 242. Statistical Package for the Social Science (SPSS) 27.0 and SmartPLS 3.0 software was used to analyze the data.

RESULTS

The findings revealed that perceived service quality significantly influences trust through patient satisfaction (observed partial mediation) and patient satisfaction significantly impacts behavioral intention through trust (observed partial mediation).

CONCLUSION

This study empowers hospital managers to understand the factors influencing behavioral intention. Healthcare professionals must ensure that good quality service is delivered to enhance patient satisfaction and trust in adverse services, which influence behavioral intention among the patients.

How satisfied are patients with nursing care and why? A comprehensive study based on social media and opinion mining

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonetheless, one of the key aspects is the performance of nurses as they closely interact with patients on many occasions. Nurses carry out many tasks that could be assessed to understand the patient's satisfaction and consequently, the effectiveness of the offered services. To assess their performance, traditionally, expensive, and time-consuming methods such as questionnaires and interviews have been used; nevertheless, the development of social networks has allowed the patients to convey their opinions in a free and public manner. For that reason, in this study, a comprehensive analysis has been performed based on patients' opinions collected from a feedback platform for health and care services, to discover the topics about nurses the patients are more interested in. To do so, a topic modeling technique has been proposed. After this, sentiment analysis has been applied to classify the topics as satisfactory or unsatisfactory. Finally, the results have been compared with what the patients think about doctors. The results highlight what topics are most relevant to assess the patient satisfaction and to what extent. The results remark that the opinion about nurses is, in general, more positive than about doctors.

Assessing the quality of hospital outpatient services in Iran: a systematic review and meta-analysis

BACKGROUND

Quality healthcare services are considered one of the most effective vehicles for healthcare managers to achieve organizational goals. Therefore, this study aimed to combine the findings of comparable studies to identify consistencies and contradictions in the quality of outpatient services in Iran.

METHODS

The current systematic review and meta-analysis study was conducted in 2022 according to PRISMA guideline. All relevant English and Persian studies were searched in databases, including Web of Sciences, PubMed, Scopus, Scientific Information Database, and Magiran. No year restriction was applied. The quality of the studies was assessed by the 22-item Strengthening the Reporting of Observational Studies in Epidemiology checklist. The meta-analysis was conducted by using Open Meta Analyst, and between-study heterogeneity was investigated with I-squared statistic.

RESULTS

Of the 106 retrieved articles, seven studies with a total sample size of 2600 were included in the meta-analysis. The pooled estimate of mean for overall perception was 3.95 (95% CI: 3.34- 4.55, P< 0.001, I2= 99.97), while the pooled estimate of the mean for the overall expectation was 4.43 (95% CI: 4.11- 4.75, P< 0.001, I2= 99.93). The highest and lowest perception mean scores were related to tangibility (3.52, Gap= -0.86) and responsiveness (3.30, Gap= -1.04) dimensions.

CONCLUSION

Responsiveness was identified as the weakest dimension. Therefore, managers are recommended to design suitable workforce-development programs which focus on the provision of timely and prompt services, polite and courteous interactions with patients, and prioritization of patients' needs. Moreover, training public sector practitioners along with incentives can fill up the existing gaps.

Statistical characterization of managerial risk factors: a case of state-run hospitals in India

Public healthcare institutions are the crucial component in the social and economic development of a nation, particularly India. However, public hospitals in India confront multiple operational risk factors that compromise patient satisfaction. Although all the risk factors are essentially critical, the impact potential of any risk factor is ultimately determined by its ability to induce other risk factors. The current research derives motivation from these scenarios and investigates the characteristics of crucial operational risk factors experienced in the public healthcare sector in a South Indian state. Extensive questionnaire-based surveys were conducted among civilians and healthcare professionals in two phases, i.e., prior to the COVID-19 crisis and during the COVID-19 crisis, for identifying significant risk factors. The collected data is analysed using statistical techniques like exploratory factor analysis (EFA) and partial least squares based structural equation modelling (PLS-SEM) to characterise the inter-relationships between risk factors. The research discloses the translational effect of administrative/infrastructure constraints in public hospitals in compromising the operational performance indirectly through human-related issues rather than having a direct influence. More precisely, the presented model indicates that risk factors like the physical infrastructure limitations and shortage of staff will overburden the existing employees, resulting in human-related issues, including attitudinal issues of employees and community mistrusts and misbelieves. The results reveal seemingly resolvable budget allocation issues, but at the same time alarms the authorities to execute immediate countermeasures. Ultimately, this research seeks to empower public hospital administrators with interesting insights and managerial implications drawn from the statistical models.

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Purpose

This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

Design/methodology/approach

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

Findings

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

Research limitations/implications

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

Practical implications

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

Originality/value

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

Health Services and Patient Satisfaction in IRAN during the COVID-19 Pandemic: A Methodology Based on Analytic Hierarchy Process and Artificial Neural Network

The aim of this study is to identify and classify the most important factors affecting patient satisfaction in the COVID-19 pandemic crisis considering economic effects. This is an analytical study using the analytic hierarchy process (AHP) method and ANN-MLP (Artificial neural network based on multilayer perceptron model as a supervised learning algorithm) as an innovative methodology. The questionnaire was completed by 72 healthcare experts (N = 72). The inter-class correlation (ICC) coefficient value was confirmed in terms of consistency to determine sampling reliability. The findings show that interpersonal care and organizational characteristics have the greatest and least influence, respectively. Furthermore, the observations confirm that the highest and lowest effective sub-criteria, respectively, are patient safety climate and accessibility. Based on the study’s objective and general context, it can be claimed that private hospitals outperformed public hospitals in terms of patient satisfaction during the COVID-19 pandemic. Focusing on performance sensitivity analysis shows that, among the proposed criteria to achieve the study objective, the physical environment criterion had the highest difference in private and public hospitals, followed by the interpersonal care criterion. Furthermore, we used a multilayer perceptron algorithm to assess the accuracy of the model and distinguish private and public hospitals as a novelty approach. Overfitting results in finding an MLP model which is reliable, and the accuracy of the model is acceptable.

Developing and training artificial neural networks using bootstrap data envelopment analysis for best performance modeling of sawmills in Ontario

Purpose

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

Design/methodology/approach

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

Findings

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

Originality/value

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Introduction

A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure.

Methods

We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria.

Results

Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer.

Conclusion

The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

Research on Teleconsultation service quality based on multi-granularity linguistic information: the perspective of regional doctors

Background

Due to the increasing complexity in socioeconomic environments and the ambiguity in human cognition, decision makers prefer to give linguistic cognitive information with different granularities according to their own preferences. Consequently, to consider the uncertainty and preferences in the evaluation process, a method based on Multi-Granularity Linguistic Information (MGLI) for evaluating teleconsultation service quality is proposed, which provides a new research direction for scientific evaluation and improvement of teleconsultation service quality.

Methods

Firstly, this paper explored a service quality evaluation system from the perspective of regional doctors. And then considering the uncertainty and preferences of decision makers, MGLI was used to optimize the index system according to the similarity degree between the linguistic evaluation information and a given linguistic term set. Finally, the empirical research was conducted using Henan Province Telemedicine Center of China (HTCC) as an example to identify the direction for improving the service quality in teleconsultation.

Results

This study found that the number of consulting rooms, attitude of operators, consultation duration, charges, and attitude of experts are the key factors affecting the quality of teleconsultation service.

Conclusions

Suggestions for improving the quality of teleconsultation service are put forward in terms of optimizing the allocation of consulting rooms, improving regional doctors’ experience and standardizing charging standards, which provides a new direction for improving the quality of teleconsultation service.

What Patients Value in Physicians: Analyzing Drivers of Patient Satisfaction Using Physician-Rating Website Data

BACKGROUND

Customer-oriented health care management and patient satisfaction have become important for physicians to attract patients in an increasingly competitive environment. Satisfaction influences patients' choice of physician and leads to higher patient retention and higher willingness to engage in positive word of mouth. In addition, higher satisfaction has positive effects on patients' willingness to follow the advice given by the physician. In recent years, physician-rating websites (PRWs) have emerged in the health care sector and are increasingly used by patients. Patients' usage includes either posting an evaluation to provide feedback to others about their own experience with a physician or reading evaluations of other patients before choosing a physician. The emergence of PRWs offers new avenues to analyze patient satisfaction and its key drivers. PRW data enable both satisfaction analyses and implications on the level of the individual physician as well as satisfaction analyses and implications on an overall level.

OBJECTIVE

This study aimed to identify linear and nonlinear effects of patients' perceived quality of physician appointment service attributes on the overall evaluation measures that are published on PRWs.

METHODS

We analyzed large-scale survey data from a German PRW containing 84,680 surveys of patients rating a total of 7038 physicians on 24 service attributes and 4 overall evaluation measures. Elasticities are estimated from regression models with perceived attribute quality as explanatory variables and overall evaluation measures as dependent variables. Depending on the magnitude of the elasticity, service attributes are classified into 3 categories: attributes with diminishing, constant, or increasing returns to overall evaluation.

RESULTS

The proposed approach revealed new insights into what patients value when visiting physicians and what they take for granted. Improvements in the physicians' pleasantness and friendliness have increasing returns to the publicly available overall evaluation (b=1.26). The practices' cleanliness (b=1.05) and the communication behavior of a physician during a visit (b level between .97 and 1.03) have constant returns. Indiscretion in the waiting rooms, extended waiting times, and a lack of modernity of the medical equipment (b level between .46 and .59) have the strongest diminishing returns to overall evaluation.

CONCLUSIONS

The categorization of the service attributes supports physicians in identifying potential for improvements and prioritizing resource allocation to improve the publicly available overall evaluation ratings on PRWs. Thus, the study contributes to patient-centered health care management and, furthermore, promotes the utility of PRWs through large-scale data analysis.

Evaluation of service quality from patients' viewpoint

Background

Measuring patients’ perception from health service quality as an important element in the assessment of service quality has attracted much attention in recent years. Therefore, this study was conducted to find out how the patients evaluated service quality of clinics at teaching hospitals affiliated with Tehran University of Medical Sciences in Iran.

Methods

This cross-sectional study was conducted in Tehran in 2017 and 400 patients were randomly selected from four hospitals. Data were collected using a questionnaire, the validity and reliability of which were confirmed in previous study. In order to analyze the data, T-test, ANOVA, and Pearson correlation coefficient were calculated using SPSS 23.

Results

The results indicated that among eight dimensions of health service quality, the patients were more satisfied with physician consultation, services costs and admission process. The highest and lowest mean scores were related to physician consultation (Mean = 4.17), and waiting time (Mean = 2.64), in that order. The total mean score of service quality was 3.73 (± 0.51) out of 5. Outpatient services were assessed as good, moderate and weak by 57.5, 40 and 2.5% of the patients, respectively. There was a significant relationship between the positive perception of service quality and reason for admission, source of recommendation, gender, education level, health status, and waiting time in the clinics (p <  0.05).

Conclusion

The majority of the patients had a positive experience with visiting clinics and perceived service provision as good. In fact, patients’ perceptions of physician consultation, provision of information to patients and the environment of delivering services, are the most important determinants of service quality in clinics.

A multi-year SEM model predicting the impact of behavior attributes on overall patient satisfaction

Purpose

Pressure from competition; inflexible third-party reimbursements; greater demand from government, regulatory and certifying agencies; discerning patients; and the quest of healthcare entities for greater profitably place demands and high expectations for service quality impacting overall patient experience. Extending a prior multivariate, single-period model of varied medical practices predicting patient experience to a three-year time period to understand whether there was a change in overall assessment using data analytics. The paper aims to discuss these issues.

Design/methodology/approach

SEM was employed on a per year and aggregated, three-year basis to gain insights into qualitative psychometric constructs predicting overall patient experience and strength of the relationships.

Findings

Statistically significant differences were uncovered between years indicating the strength of the relationships of latent variables on overall performance.

Research limitations/implications

Study focused on data gathered from a questionnaire mailed to patients who visited various outpatient medical clinics in a rural community with over 4,000 responses during the three-year study period. A higher percentage of female respondents over the age of 45 may limit the generalizability of the findings.

Practical implications

Practitioners can gain a broader understanding of different factors influencing overall patient experience. Administrative processes associated with the primary care provider are inconsequential. Patients are not as concerned with patient flow as they are with patient safety and health.

Originality/value

This research informs healthcare quality management of psychometrics and analytics to improve the overall patient experience in outpatient medical clinics.

Slack resources and quality performance: case of a mega health care organization

Purpose

Prior studies have examined the relationship between budgetary slack and short-termism of management within a profit-seeking business environment. The purpose of this paper is to examine the dynamics of slack resources in relation to quality performance of heath care services delivered by a publicly funded organization.

Design/methodology/approach

A longitudinal regression analysis of resource utilization, productivity and the quality of health care services delivered is performed to reveal evidence about the underlying dynamics of heterogeneous slack resources. It attempts to study slack resources in the case of a “mega” health care service provider based in Hong Kong.

Findings

The results suggest that the organization’s cost containment culture, with a strategic focus on productivity measures, has augmented cost effectiveness; however, not all slack resources would enhance quality performance.

Originality/value

This study of a mega health care service provider complements the prior studies of slack resources and points out the challenges of proactively managing any slack resources toward quality performance beyond productivity.

Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases

The research aims to explore the association between behavioral habits and chronic diseases, and to identify a portfolio of risk factors for preventive healthcare. The data is taken from the Behavioral Risk Factor Surveillance System (BRFSS) database of the Centers for Disease Control and Prevention, for the year 2012. Using SPSS Modeler, we deploy neural networks to identify strong positive and negative associations between certain chronic diseases and behavioral habits. The data for 475,687 records from BRFS database included behavioral habit variables of consumption of soda and fruits/vegetables, alcohol, smoking, weekly working hours, and exercise; chronic disease variables of heart attack, stroke, asthma, and diabetes; and demographic variables of marital status, income, and age. Our findings indicate that with chronic conditions, behavioral habits of physical activity and fruit and vegetable consumption are negatively associated; soda, alcohol, and smoking are positively associated; and income and age are positively associated. We contribute to individual and national preventive healthcare by offering a portfolio of significant behavioral risk factors that enable individuals to make lifestyle changes and governments to frame campaigns and policies countering chronic conditions and promoting public health.

Service quality of hospital outpatient departments: patients' perspective

PURPOSE

Assessment of patient perceptions of health service quality as an important element in quality assessments has attracted much attention in recent years. The purpose of this paper is to assess the service quality of hospital outpatient departments affiliated to Shahid Beheshti University of Medical Sciences from the patients' perspective.

DESIGN/METHODOLOGY/APPROACH

This cross-sectional study was conducted in 2014 in Tehran, Iran. The study samples included 500 patients who were selected by multi-stage random sampling from four hospitals. The data collection instrument was a questionnaire consisting of 50 items, and the validity and reliability of the questionnaire were confirmed. For data analysis, exploratory and confirmatory factor analysis, Friedman test, and descriptive statistics were used through LISREL 8.54 and SPSS 18 applications.

FINDINGS

Eight significant factors were extracted for outpatient service quality, which explained about 67 per cent of the total variance. Physician consultation, information provided to the patient, and the physical environment of the clinic were the three determining factors of the quality of outpatient services. The highest and lowest perceptions were related to physician consultation and perceived waiting time dimension, respectively. The mean score of patients' perception of outpatient service quality was 3.89 (±0.60). About 59.5 per cent of patients assessed the quality of outpatient services as good, 38.2 per cent as moderate, and 2.3 per cent as poor. Practical implications - The instrument developed for this study is valid and reliable, and it can help hospital managers to identify the areas needing improvement and correction.

ORIGINALITY/VALUE

According to the findings of this study, the majority of patients had a positive experience with outpatient departments of teaching hospitals, and the services provided in these centres were of adequate quality, based on patient assessments.

Best performance modeling using complementary DEA-ANN approach

Purpose

– The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Design/methodology/approach

– Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

Findings

– The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Research limitations/implications

– Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

Originality/value

– This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.

THE EFFECT OF OUTPATIENT SERVICE QUALITY ON PATIENT SATISFACTION IN TEACHING HOSPITALS IN IRAN

Aim:

The quality of services plays a primary role in achieving patient satisfaction. The main purpose of this study was to explore the effect of outpatient service quality on patient satisfaction in teaching hospitals in Iran.

Methods:

this cross-sectional study was conducted in 2014. The study sample included 500 patients were selected with systematic random method from the outpatient departments (clinics) of four teaching hospitals in Tehran. The survey instrument was a questionnaire consisted of 44 items, which were confirmed its reliability and validity. The data were analyzed by using descriptive statistics, Pearson’s correlation, and multivariate regression methods with the SPSS.18 software.

Results:

According to the findings of this study, the majority of patients had a positive experience in the outpatient departments of the teaching hospitals and thus evaluated the services as good. Perceived service costs, physician consultation, physical environment, and information to patient were found to be the most important determinants of outpatient satisfaction.

Conclusion:

The results suggest that improving the quality of consultation, providing information to the patients during examination and consultation, creating value for patients by reducing costs or improving service quality, and enhancing the physical environment quality of the clinic can be regarded as effective strategies for the management of teaching hospitals toward increasing outpatient satisfaction.

Patient perceptions of quality: analyzing patient satisfaction surveys

Purpose

– Healthcare facilities are entering an era of increased oversight and heightened expectations concerning both reduced costs and measureable quality. The US Affordable Care Act requires healthcare organizations to collect certain metrics, including patient assessments of quality, in order to monitor and improve the quality of healthcare. These metrics are used as a basis for graduated insurance reimbursements, and are available to consumers as an aid in selecting healthcare providers and insurance plans. The purpose of this paper is to provide healthcare providers with the analytic capabilities to better understand quality of care from the patient’s point of view.

Design/methodology/approach

– This research examines patient satisfaction data from a multi-specialty Medical Practice Group, and uses regression analysis and paired comparisons to provide insight into patient perceptions of care quality.

Findings

– Results show that variables related to Access, Moving Through the Visit, Nurse/Assistant, Care Provider and Personal Issues significantly impact overall assessments of care quality. In addition, while gender and type of care provider do not appear to have an impact on overall patient satisfaction, significant differences do exist based on age group, specialty of the physician and clinic type.

Originality/value

– This study differs from most academic research as it focusses on medical practices, rather than hospitals, and includes multiple clinic types, medical specialties and physician types in the analysis. The study demonstrates how analytics and patient perceptions of quality can inform policy decisions.