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An Empirical Study of Hospital’s Outpatient Loyalty From a Medical Center in Taiwan

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  • Shu-Hui Chao
  • Mu-Kuan Chen
  • Hsin-Hung Wu

Abstract

In a highly competitive medical industry, hospitals can continue to create medical values and competitive advantages using data mining technologies to identify patients’ needs and provide the medical services needed by various patients. This research focuses on the outpatients in a medical center in Taiwan and adopts recency, frequency, and monetary (RFM) model, self-organizing maps, and K -means method to construct a set of data exploration procedures so that the hospital can use the reference to deal with the related patient management issues, where R , F , and M measure the RFM spent for each outpatient in Year 2016. The results show that 321,908 outpatients can be classified into 12 groups and further categorized into loyal outpatients, new outpatients, and lost outpatients. The similarities and differences among groups can be further analyzed to allow hospital management to provide differentiation strategies to its patients. That is, with the model illustrated in this study, the hospital can establish a better and long-term relationship with its patients by increasing patient loyalty.

Suggested Citation

  • Shu-Hui Chao & Mu-Kuan Chen & Hsin-Hung Wu, 2021. "An Empirical Study of Hospital’s Outpatient Loyalty From a Medical Center in Taiwan," SAGE Open, , vol. 11(2), pages 21582440211, April.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:2:p:21582440211004125
    DOI: 10.1177/21582440211004125
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    References listed on IDEAS

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    1. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
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