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The Suitability of Artificial Neural Networks in Service Quality Control and Forecasting

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Lean Thinking for Healthcare

Part of the book series: Healthcare Delivery in the Information Age ((Healthcare Delivery Inform. Age))

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Abstract

There has been considerable research into service quality over the last couple of decades. Services, however, as intangible, perishable, and heterogenic transactions are very difficult to quantify and measure, and little success has been reported on a systematic approach in modeling of quality of service transactions (with SERVQUAL and its derivatives as the notable exception). In this chapter, we propose artificial neural networks (ANNs) to monitor quality of service transaction as a dynamic and real-time control and forecasting system. ANNs are widely used in many engineering fields to model and simulate complex systems. The resulting near-perfect models are particularly suited for applications where real-world complexities make it difficult or even impossible to mathematically model and control the system. The proposed approach alleviates restrictions and limitations of applying questionnaire-based static methods, even in cases where there are large number of correlated attributes as well as obscure and unobservable quality characteristics. We illustrate with a case vignette in a healthcare context, thereby demonstrating the suitability of such techniques for healthcare delivery a vital, at times lifesaving service.

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Author information

Authors and Affiliations

  1. School of Business IT and Logistics, RMIT University, 2476, Melbourne, VIC, 3001, Australia

    Mohammad Rezazadeh Niavarani

  2. Epworth HealthCare & Department of BITL & HIRi, RMIT University, 2476, Melbourne, VIC, 3001, Australia

    Nilmini Wickramasinghe Ph.D., M.B.A.

Authors
  1. Mohammad Rezazadeh Niavarani
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  2. Nilmini Wickramasinghe Ph.D., M.B.A.
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Correspondence to Mohammad Rezazadeh Niavarani .

Editor information

Editors and Affiliations

  1. RMIT University, School of Business Information Technolog, Melbourne VIC, Australia

    Nilmini Wickramasinghe

  2. University of Southern Queensland, Toowoomba, Queensland, Australia

    Latif Al-Hakim

  3. Department of Urology, Northwestern University, Chicago, Illinois, USA

    Chris Gonzalez

  4. DeGroote School Business, McMaster University, Hamilton, Ontario, Canada

    Joseph Tan

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Niavarani, M.R., Wickramasinghe, N. (2014). The Suitability of Artificial Neural Networks in Service Quality Control and Forecasting. In: Wickramasinghe, N., Al-Hakim, L., Gonzalez, C., Tan, J. (eds) Lean Thinking for Healthcare. Healthcare Delivery in the Information Age. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8036-5_3

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  • DOI: https://doi.org/10.1007/978-1-4614-8036-5_3

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  • Print ISBN: 978-1-4614-8035-8

  • Online ISBN: 978-1-4614-8036-5

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