應用資料分析及探勘技術於健保醫療費用管控及申報異常篩選作業

An Integrated Model of Data Analysis and Knowledge Discovery for the Expense Control And Management of National Health Insurance

劉偉倫(Wei-Lun Liu)

指導教授 ： 賴國華

元智大學/資訊學院/資訊工程學系/碩士(2001年)

摘要

全民健保之主要目的在於提昇醫療品質，並分擔就醫時的財務風險，減少國人就醫之財務障礙，但健保開辦至今以來，財務問題一直是最大隱憂，其原因包含保險制度、民眾就醫習慣、及醫療資源浪費等問題，而其中造成健保財務惡化的最大原因就是醫療資源的浪費。
針對醫療費用的管控，本研究提出一費用分析架構藉以即時分析醫療費用掌控醫療院所的申報概況，對於異常院所及醫師的管控，則利用異常監控管理系統追蹤管理，使其改善異常情形。經由健保局的實際驗證，確實分析出異常浮報的院所，並有效的掌控醫療院所申報情形，縮短分析作業時程，改善異常情形進而減少醫療費用的浪費。另一方面，透過分析流程管理系統記錄使用者分析異常院所的步驟，進而獲取分析經驗，提供分析醫療費用時的支援決策功能。
另外，本研究整合貝氏分類、決策樹、Fuzzy Pattern Clustering與Fuzzy c-Means Clustering等資料探勘技術，提出一異常篩選架構應用於申報異常案件之篩選，並以健保真實資料進行實作驗證，研究成果顯示資料分類率高達98.64%，敏感度達90.1%，證實此異常篩選架構的確可行，對於現行健保局抽樣審查作業有極大的幫助。

關鍵字

費用分析 ； 異常監控 ； 分析流程 ； 異常篩選 ； 資料探勘

並列摘要

The main purpose of National Health Insurance (NHI) is to promote the medical quality, to share the financial risk of medical treatment, and to alleviate the financial burden of each individual. However, since it has been established, the financial shortfall of NHI has been the focal point of many debates. The reason includes the consumers'' behaviors, the waste of health resources, and the insurance system itself. Among these various causes, the most trouble one is the abuse of medical resources.
In this thesis, aiming at the control of medical expenditure, not only do we propose an integrated model of data analysis and knowledge discovery for the expense control of NHI, but also develop a management model for monitoring the anomalous claims of medical services. A prototype implemented in the NHI reveals that the claims of medical services could be effectively controlled. Besides, shortening the time of analysis process, improving the abnormal situation, and decreasing the waste of medical expenditure are all bonuses to this system. On the other hand, we can also record the process of analyzing abnormal hospitals to get experiences, which could be offered as a decision support while we analyze the medical expenditure.
Finally, the simulation results show that the data classification rate is up to 98.64%, and the sensitivity is up to 90.1%. These results present that this automatic selection of abnormal medical expense filings model is effective. It will do great help to the present NHI in the sampling audit operation.

並列關鍵字

Data Analysis ； Knowledge Discovery ； medical expenditure

參考文獻

[3] Curet, O., Jackson, M. and Tarar, A, “Designing And Evaluating A Case-Based Learning And Reasoning Agent In Unstructured Decision Making,” IEEE International Conference on Volume: 4, pp. 2487 —2492, 1996.

[4] E. Trauwaert, On The Meaning of Dunn’s Partition Confficient for Fuzzy Clusters, Fuzzy Sets and Systems pp217~242, 1998.

[6] George J. Klir , “Fuzzy Sets and Fuzzy Logic Theory and Applications,” Prentice-Hall International, Inc. ,pp357~378

[7] J.C. Bezdek. Pattern recognition with fuzzy objective function algorithms. Plenum, New York, 1981.

[9] J.C.Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybern., 3:32, 1974.