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Assessing the Early Stage of eHealth Adoption: A Case Study From a Community Hospital in Thailand

Author

Listed:
  • Noppon Choosri

    (Chiang Mai University, Thailand)

  • Waritsara Jitmun

    (Chiang Mai University, Thailand)

  • Pathathai Na Lumpoon

    (Chiang Mai University, Thailand)

  • Supavas Sitthithanasakul

    (Chiang Mai University, Thailand)

  • Sompob Saralamba

    (Mahidol University, Thailand)

  • Krid Thongbunjob

    (Kohka Hospital, Thailand)

  • Pongsatorn Chumsang

    (Kohka Hospital, Thailand)

Abstract

In this paper, the authors implement and determine the success the eHealth adoption for queue management when it was first deployed for a community hospital setting in Thailand. The electronic queue system was first implemented to improve conventional operations; then extensive evaluations were conducted to measure the effectiveness for each stakeholder. The healthcare staff shared a common perception that the new system could reduce their workload and increase the efficacy of queue fairness. The overall patient satisfaction and actual waiting time patients spent at the nurse interview station improved significantly. The majority of the patients agreed that the notification for attention from the computerized system is more effective. The community healthcare has strong potential to adopt the eHealth system. Being more automated enabled a reduced burden of administration jobs and significantly reduced waiting times for patients. Patients responded that they had greater satisfaction after the introduction of the electronic queue system.

Suggested Citation

  • Noppon Choosri & Waritsara Jitmun & Pathathai Na Lumpoon & Supavas Sitthithanasakul & Sompob Saralamba & Krid Thongbunjob & Pongsatorn Chumsang, 2022. "Assessing the Early Stage of eHealth Adoption: A Case Study From a Community Hospital in Thailand," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-9, January.
  • Handle: RePEc:igg:jrqeh0:v:11:y:2022:i:1:p:1-9
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    References listed on IDEAS

    as
    1. Leeflang, P.S.H. & Wittink, Dick R., 2000. "Building models for marketing decisions: past, present and future," Research Report 00F20, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. repec:dgr:rugsom:00f20 is not listed on IDEAS
    3. Wang, Yichuan & Kung, LeeAnn & Byrd, Terry Anthony, 2018. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 3-13.
    Full references (including those not matched with items on IDEAS)

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