博碩士論文 etd-0612103-091248 詳細資訊


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姓名 楊婉秀(Wan-Shiou Yang) 電子郵件信箱 ryang@mis.nsysu.edu.tw
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 博士(Ph.D.) 畢業時期 91學年第2學期
論文名稱(中) 流程萃取以偵測醫療詐欺及濫用之研究
論文名稱(英) A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse
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    摘要(中) 隨著生活品質的改善與醫療資訊的普及,民眾愈來愈重視身體健康,對醫療資源的使用也日益頻繁,因此,對醫療保險的需求日益高漲。在各國不同的制度下,民眾或透過私人保險的購買,或透過國家整體醫療保險的參與,分擔高額醫療費用的風險,以取得醫療服務。在不同的醫療保險制度中,按量計酬(Fee for Service)是一種常見的費用給付方式。在按量計酬的方式下,病人於醫療機構先取得醫療服務,醫療機構再依據所提供的各項診斷、治療服務,逐項向保險機構提出費用申請。因此,醫療機構如果申報較多的醫療服務,便可能取得較多的給付,而使得按量計酬常成為醫療機構浪費、謊報醫療服務的誘因。面對可能的浪費、詐欺行為,保險機構因而常聘請專家以審查醫療案例。然而,專家審查的方式,耗費大量的時間、人力成本,對於大量的保險案例(例如,國家整體醫療保險),往往無法負荷。本研究著眼於此,引入流程分析的概念,提出整體分析架構與方法,透過系統化、自動化的方式,偵測可能的醫療浪費、詐欺行為。
    摘要(英) With the intensive need for health insurances, health care service providers’ fraud and abuse have become a serious problem. The practices, such as billing services that were never rendered, performing medically unnecessary services, and misrepresenting non-covered treatments as medically necessary covered treatments, etc, not only contribute to the problem of rising health care expenditure but also affect the health of patients. We are therefore motivated to investigate the detection of service providers’ fraudulent and abusive behavior.
    In this research, we introduce the concept of clinical pathways and thereby propose a framework that facilitates automatic and systematic construction of adaptable and extensible detection systems. For the purposes of building such detection systems, we study the problems of mining frequent patterns from clinical instances, selecting features that have more discriminating power and revising detection model to have higher accuracy with less labeled instances.
    The performance of the proposed approaches has been evaluated objectively by synthetic data set and real-world data set. Using the real-world data set gathered from the National Health Insurance (NHI) program in Taiwan, the experiments show that our detection model has fairly good prediction power. Comparing to traditional expense driven approach, more importantly, our detection model tends to capture different fraudulent scenarios.
    關鍵字(中)
  • 資料探勘
  • 醫療詐欺及濫用
  • 醫療流程
  • 關鍵字(英)
  • Clinical pathways
  • Data mining
  • Health care fraud and abuse
  • 論文目次 TABLE OF CONTENTS
    ABSTRACT………...………………………………………………………………v
    LIST OF FIGURES………………………………………………………………….vi
    LIST OF TABLES….………………………………………………………………viii
    1 Introduction………………………………………………………………………1
    1.1 Motivation………………………………………………………….………1
    1.2 Problem statement and the proposed approach………………………………2
    1.3 Overview of the research……………………………………………………3
    2 The Problem and the related work………………………………………………4
    2.1 Health care fraud and abuse………………………………………………….4
    2.2 Current status…………………………………………………………………8
    2.3 Clinical pathways…………………………………………………………….9
    2.4 Research framework………………………………………………………...15
    3 Structure pattern discovery……………………………………………………….18
    3.1 Related works……………………………………………………………….19
    3.2 Formalization of structure pattern discovery problem…………………...…20
    3.3 Structure pattern discovery algorithms……………………………………23
    3.3.1 TP-Graph algorithm…………………………………………………23
    3.3.2 TP-Itemset algorithm……………………………………………...…34
    3.3.3 TP-Sequence algorithm……………………………………………...36
    3.4 Performance evaluation……………………………………………………41
    3.4.1 Generation of synthetic data………………………….………...….41
    3.4.2 Effects of minimum support thresholds……………………………43
    3.4.3 Effects of instance characteristics………………………………...…45
    3.4.4 Scale-up experiments………………………………………………47
    3.5 Summary……………………………………………………………………49
    4 Feature selection……………………………………………………….…………50
    4.1 Related works………………………………………………………………51
    4.2 Formalization of feature selection problem……………………………….53
    4.3 Feature selection algorithms………………………………………………57
    4.4 Performance evaluation……………………………………………………67
    4.4.1 Data collection and preprocessing…………………………………...68
    4.4.2 Induction method…………………………………………...………69
    4.4.3 Evaluation criteria……………………….………………………….71
    4.4.4 Evaluation results………………………………………………….72
    4.5 Summary……………………………………………………………………78
    5 Model Revision……………………………………………………….…………80
    5.1 Related works………………………………………………………………81
    5.2 Formalization of model revision problem…………………………………84
    5.3 Model revision algorithms…..……………………………………………85
    5.3.1 Selecting unlabeled examples……………………………………..86
    5.3.2 Combining resulting classifiers……………………………………93
    5.4 Performance evaluation……………………………………………………94
    5.4.1 Data collection and induction algorithms………………………….94
    5.4.2 Evaluation results………………………………………………95
    5.5 Summary…………………………………………………………………101
    6 Conclusion……………………………………………………………………..103
    6.1 Summary…………………………………………………………………103
    6.2 Contributions………………………………………………………………104
    6.3 Limitations………………………………………………………………105
    6.4 Future works……………………………………………………………...106
    APPENDIX A……………………………………………………………………....108
    LIST OF REFERENCES……………………………………………………….109
    LIST OF PUBLICATIONS……………………………………………………….115
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    口試委員
  • 林信惠 - 召集委員
  • 劉敦仁 - 委員
  • 洪炯宗 - 委員
  • 葉淑娟 - 委員
  • 魏志平 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2003-06-05 繳交日期 2003-06-12

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