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

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  • Daniela Carlucci
  • Paolo Renna
  • Giovanni Schiuma

Abstract

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. Copyright Springer Science+Business Media, LLC 2013

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  • Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.
    • Handle: RePEc:kap:hcarem:v:16:y:2013:i:1:p:37-44
    DOI: 10.1007/s10729-012-9211-1
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    References listed on IDEAS

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    Cited by:

    1. Guilan Kong & Lili Jiang & Xiaofeng Yin & Tianbing Wang & Dong-Ling Xu & Jian-Bo Yang & Yonghua Hu, 2018. "Combining principal component analysis and the evidential reasoning approach for healthcare quality assessment," Annals of Operations Research, Springer, vol. 271(2), pages 679-699, December.
    2. Tuzkaya, Gülfem & Sennaroglu, Bahar & Kalender, Zeynep Tuğçe & Mutlu, Meltem, 2019. "Hospital service quality evaluation with IVIF-PROMETHEE and a case study," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    3. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    4. Ferreira, D.C. & Marques, R.C. & Nunes, A.M. & Figueira, J.R., 2018. "Patients’ satisfaction: The medical appointments valence in Portuguese public hospitals," Omega, Elsevier, vol. 80(C), pages 58-76.
    5. C. R. Vishnu & E. N. Anilkumar & R. Sridharan & P. N. Ram Kumar, 2023. "Statistical characterization of managerial risk factors: a case of state-run hospitals in India," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 812-834, June.
    6. Ferreira, Diogo Cunha & Marques, Rui Cunha & Nunes, Alexandre Morais & Figueira, José Rui, 2021. "Customers satisfaction in pediatric inpatient services: A multiple criteria satisfaction analysis," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    7. Brian Nkwinda & Wanda Jacobs & Charlene Downing, 2019. "Patient Satisfaction With Caring at a District Hospital in Malawi," Global Journal of Health Science, Canadian Center of Science and Education, vol. 11(1), pages 1-15, January.

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