Modeling Customer Evaluations of the Quality of Health Care Using Artificial Neural Network (Case Study of Birjand University of Medical Sciences)

Subject Areas : medical documents

Zahra Hashemi ¹ ( MA student of Information Technology Management, Islamic Azad University, Tehran Electronic, Tahran, Iran )
Marzieh Faridi Masuleh ² ( PhD Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran )

Received: 2016-12-29 Accepted : 2017-04-12 Published : 2017-02-19

Keywords: Hospital, Service Quality, Artificial Neural Network, Customer Satisfaction,

Abstract :

Introduction: The service quality is always one of the managerial concerns to supply customer’s satisfaction. Preparing qualified service needs to exact knowledge about the key factors of service quality and their effectiveness in the level of customer’s satisfaction. So implementing the different methods of measuring service quality could make it more explicit the unknown aspects of this factor effectiveness on the satisfaction. So the aim of this study was to evaluating the health care quality methods with artificial neural network approach. Methods: This study was a descriptive-correlation and an applied research. The statistical population of research consists of customers in hospitals of medical sciences Birjand University with an indefinite number. Referring to Cochran sampling formula a number of 385 individuals were selected using in access approach and validated questionnaires of study distributed among them. To measure the service quality it used the 4 approaches of weighted and un-weighted SERVQL and SERVPRF and the effect of service quality dimensions in each 4 approach were evaluated on the satisfaction. In this study to analyze the data is used of Spss software and the results of four methods to measure service quality using artificial neural networks have been studied. Results: The results showed that the method of measuring the quality of services achieved the lowest level of error for SERVQUAL 0.18 Weighted number That measure the quality of service in terms of weight SERVQUAL model using artificial neural networks have been more accurate in predicting customer satisfaction. Conclusions: methods of measuring service quality have different performance in predicting customer’s satisfaction under the scale of measuring service quality. Also the artificial neural networks regarding to implement predicting algorithm, may contain weaker forecast rather than classic statistical methods. Introduction: uality of service has always been one of the main concerns of managers in providing customer satisfaction. So, employing different methods to measure the effectiveness of this agent's unknown aspects of service quality can be more transparent on customer satisfaction. Methodology: This study was conducted cross-correlation functional investigation. The population of Birjand University of Medical Sciences were all customers that their number was not specified. According to Cochran sampling about 385 of them were selected based on availability of validated questionnaires were distributed among them. To measure the service quality it used the 4 approaches of weighted and un-weighted SERVQL and SERVPRF and the effect of service quality dimensions in each 4 approach were evaluated on the satisfaction. The data were analyzed using multi-layered artificial neural networks.Findings:The results showed that the method of measuring the quality of services achieved the lowest level of error for SERVQUAL 0.18 Weighted number That measure the quality of service in terms of weight SERVQUAL model using artificial neural networks have been more accurate in predicting customer satisfacti Conclusion: methods of measuring service quality have different performance in predicting customer’s satisfaction under the scale of measuring service quality. Also the artificial neural networks regarding to implement predicting algorithm, may contain weaker forecast rather than classic statistical methods

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