IEEE J Biomed Health Inform. 2018 Nov;22(6):1796-1806. doi: 10.1109/JBHI.2018.2852495. Epub 2018 Jul 2.
Driven by the automation technologies and health informatics of Industry 4.0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.
在中国，受工业 4.0 的自动化技术和健康信息学的推动，医院已经部署了一个完整的医疗服务访问自动化系统/平台。老年人通常没有太多的互联网知识，他们通常会寻求第三方来帮助他们从网络或 APP 上获取医疗服务，这导致了黄牛可以通过不正当手段抢占所有医疗服务预约，并以更高的价格转售给老年人的意外情况。此外，由于医疗流程的复杂性、临时性和多场景性质，医生很难识别黄牛。在本文中，提出了一种用于识别和创建移动医疗服务中黄牛用户组的新方法。该方法利用和扩展了移动系统事件日志中的最新数据分析方法来识别用户组。基于用户组，通过从分层用户-事件集群中识别代表性事件案例来提取用户配置文件。在移动医疗 APP 的事件日志中选择测试集进行了全面评估。结果表明，该方法在移动医疗 APP 中的黄牛检测中具有准确性和有效性。此外，还在一家实际医院部署了一个完整的案例研究，以确保其实用性、功效和可靠性。
