Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network.
Nowadays the ability to provide outpatient services with exceptional quality is paramount to long-term survival of hospitals, as the revenues from outpatient services are predicted to equal or exceed inpatient revenues in the near future. Identifying the relative weight of different dimensions of healthcare quality service which concur together to determine outpatients satisfaction is very important, as it can help healthcare managers to allocate resources more efficiently and identify managerial actions able to guarantee higher levels of patients' satisfaction. This study proposes the use of Artificial Neural Network (ANN) as a knowledge discovery technique for identifying the service quality factors that are important to outpatient. An ANN model is developed on data from a panel of outpatients of public healthcare services.
Center for Value Management, DAPIT, University of Basilicata, Via dell'Ateneo Lucano, 10, 85100, Potenza, Italy. daniela.carlucci@unibas.it
Daniela
D
Carlucci
Paolo
P
Renna
Giovanni
G
Schiuma
D006302:Health Services Research; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010045:Outpatients; D017060:Patient Satisfaction; D011786:Quality Control