IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v11y2021i2p21582440211004125.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440211004125
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440211004125?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Chen, Yanhong & Liu, Luning & Zheng, Dequan & Li, Bin, 2023. "Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    3. Liu, Feng & Zhao, Shaoqiong & Li, Yang, 2017. "How many, how often, and how new? A multivariate profiling of mobile app users," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 71-80.
    4. Carlos Fernández-Loría & Maxime C. Cohen & Anindya Ghose, 2023. "Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform," Information Systems Research, INFORMS, vol. 34(2), pages 698-720, June.
    5. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    6. Shu-Hui Chao & Mu-Kuan Chen & Hsin-Hung Wu, 2021. "An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan," SAGE Open, , vol. 11(3), pages 21582440211, July.
    7. Lu, Huidi & van der Lans, Ralf & Helsen, Kristiaan & Gauri, Dinesh K., 2023. "DEPART: Decomposing prices using atheoretical regression trees," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 781-800.
    8. Noorizadeh, Abdollah & Kuosmanen, Timo & Peltokorpi, Antti, 2021. "Effective purchasing reallocation to suppliers: insights from productivity dynamics and real options theory," International Journal of Production Economics, Elsevier, vol. 233(C).
    9. Hyeokkoo Eric Kwon & Sanjeev Dewan & Wonseok Oh & Taekyung Kim, 2023. "Self-Regulation and External Influence: The Relative Efficacy of Mobile Apps and Offline Channels for Personal Weight Management," Information Systems Research, INFORMS, vol. 34(1), pages 50-66, March.
    10. Mina Ameri & Elisabeth Honka & Ying Xie, 2024. "Watching intensity and media franchise engagement," Quantitative Marketing and Economics (QME), Springer, vol. 22(3), pages 291-356, September.
    11. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    12. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    13. Nobuhiko Terui & Shohei Hasegawa & Greg M. Allenby, 2015. "A Threshold Model for Discontinuous Preference Change and Satiation," TMARG Discussion Papers 122, Graduate School of Economics and Management, Tohoku University.
    14. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
    15. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    16. Rodrigo Rivera-Castro & Polina Pilyugina & Evgeny Burnaev, 2020. "Topological Data Analysis for Portfolio Management of Cryptocurrencies," Papers 2009.03362, arXiv.org.
    17. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    18. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    19. Annika Baumann & Johannes Haupt & Fabian Gebert & Stefan Lessmann, 2019. "The Price of Privacy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 413-431, August.
    20. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:11:y:2021:i:2:p:21582440211004125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.