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題名：風險分類與健保個人醫療費用預測
書刊名：管理評論
作者：許碩芬 引用關係／林兆欣 引用關係／楊雅玲
作者(外文)：Hsu, Shuofen／Lin, Chaohsin／Yang, Ya-ling
出版日期：2006
卷期：25:4
頁次：頁27-48
主題關鍵詞：風險分類；自組織映射網路；倒傳遞網路；醫療費用預測；Risk classification；SOM；BPN；Prediction of medical expenses
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0)
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  • 點閱點閱:20
台灣健保已經全面實施總額預算支付制度，以有效抑制醫療費用的成長，且在未來有朝向多元健保人體制之規劃。為了改善總額預算額度分配及競爭型健保市場的公平性和效率性，本文結合人工智慧方法中的自組織映射網路(Self-Organizing Map network, SOM)和倒傳遞網路(Back-Propagation Network, BPN)以預測個人醫療費用，作為健保預算分配依據，並作為未來引進多元健保人制度時，計算風險調整保費的基礎。 本文除利用上述研究方法來預測個人醫療費用外，並比較各種模型之預測能力，以驗證此研究方法的可行性。結果發現：(1)鑑於健保資料分配的特殊性，進行風險分類可增加模型之預測能力。(2)全民健保保險對象之風險分類可以SOM分為兩類。(3)結合SOM和BPN比單獨使用BPN或線性迴歸模型之預測能力高。
In order to solve the trade-off relationship between the Law of Large Numbers and homogeneity principal in risk classification, we proposed a methodology by combing two neural network model s, i .e., Self-Organizing Map network (SOM) and Back-Propagation Network (BPN). This paper uses empirical data from Taiwan's Nat ional Health Insurance (NHI) to demonstrate that the healthcare expenses budget assignment under global budget system and the cream-skimming problem under multi-carrier health market can be solved and hence fairness and efficiency can be improved by combining SOM and BPN. In addition, this paper examines the predictive power of different medical expenses predict ion models. Main results obtained from this paper are as follows: (1) risk classification can improve the predictive power of forecasting model. (2) Individuals of NHI can be classified into two subgroups by SOM. (3)The predictive power of combination model of SOM and BPN is better than using BPN alone or linear regression alone.
期刊論文
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會議論文
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3.Hsu, S. F.、Lin, C. H.、Yang, Y. L.(2005)。A Review and Comparison of Risk-adjusted Capitation Formula。0。327-338。  new window
4.Murao, H.、Nishikawa, I.、Kitamura, S.(1993)。A Hybrid Neural Network System for the Rainfall Estimation Using Satellite Imagery。1993 International Joint Conference on Neural Networks，1211-1214。  new window
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研究報告
1.林兆欣、許碩芬、林明俊、蘇恩德(2003)。依保險對象罹病風險建立全民健保醫療費用預測模型。0。  延伸查詢new window
圖書
1.Kohonen, T.(1989)。Self-organization and Associative Memory。Self-organization and Associative Memory。Berlin, Germany：Springer-Verlag。  new window
2.Abdi, H.、Valentin, D.、Edelman, B.(1999)。Neural Networks。Neural Networks。Thousand Oaks, CA。  new window
3.Deboeck, G.、Kohonen, T.(1998)。Visual Explorations in Finance with Self-organization Maps。New York, NY：Springer-Verlag。  new window
其他
1.全民健康保險醫療費用協定委員會(2005)。全民健康保險醫療費用總額支付制度問答輯，https://www.mohw.gov.tw/dl-3397-6af7f852-0d4f-4d1a-9eee-599fccb03c05.html。  延伸查詢new window
2.中央健康保險局(2005)。2003全民健康保險統計動向，0。  延伸查詢new window
圖書論文
1.van de Ven, W. P. M. M.、Ellis, R. P.(2000)。Risk adjustment in competitive health plan markets。Handbook of Health Economics。Amsterdam：Elsevier。  new window
 
 
 
 
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