题名

尋優適應性類神經模糊推論模式於DRGs取巧行為自動檢測之應用

并列篇名

An Enhanced ANFIS Model for the Detection of DRGs Creeps

DOI

10.6382/JIM.200510.0053

作者

駱至中(Chih-Chung Lo);林錦昌(Jin-Chang Lin)

关键词

醫療資訊學Medical Informatics ; 診斷關聯群前瞻性支付系統Diagnostic Related Groups/Prospective Payment System ; DRG/PPS ; DRGs取巧行為DRGs Creeps ; 模擬退火演算法Simulated Annealing Algorithm ; 適應性類神經模糊推論系統Adaptive Network-based Fuzzy Inf ; Medical Informatics ; Diagnostic Related Groups/Prospective Payment System DRG/PPS ; DRGs Creeps ; Simulated Annealing Algorithm ; Adaptive Network-based Fuzzy Inference System ANFIS

期刊名称

資訊管理學報

卷期/出版年月

12卷4期(2005 / 10 / 01)

页次

53 - 74

内容语文

繁體中文

中文摘要

中央健保局為控制國內醫療費用的成長並有效運用醫療資源,將逐步實施總額預算制度,而「診斷關聯群前瞻性支付」(簡稱DRG/PPS)是新制度中醫療費用分配與支付的基準。為求執行上的公平與正確,如何有效審查進而抑制醫療服務提供者在申請給付時有意或無意間產生的DRGs取巧行為,即成為醫療服務管理的重要課題。此類資訊密集的審查作業目前仍以人工審查為主,實有導入資訊科技的必要。本研究以整合模擬退火演算法與適應性類神經模糊推論系統的方式來建構「尋優適應性類神經模糊推論模式」,並以此智慧型機制進行DRGs取巧行為是否存在的自動檢測。實例驗證的結果顯示:本研究所提模式不僅有高於其他功能相似系統的平均分類正確率(90.10%),更在決策支援方面有較高的透明度及可信賴度。

英文摘要

In order to control the growing costs of healthcare spending, the Bureau of National Health Insurance is adopting the Global Budget System. In this new system, Diagnostic Related Groups/Prospective Payment System (DRG/PPS) is the mechanism for payment evaluation and control. Many healthcare frauds, also known as DRGs creeps, have been found in other countries that have already implemented the similar payment systems. For a fair and effective execution of such payment system, elimination of these frauds becomes an important task for healthcare administration. Detecting DRGs creeps manually is costly. However, costs to tailor packages to fit the detection needs and integrate them into an existing environment are outrageous. In this research, a hybrid soft computing model that integrates simulated annealing and adaptive-network-based fuzzy inference system (ANFIS) is proposed for the automated detection of DRGs creeps. The proposed approach is affordable for healthcare units at all levels. Implementation and evaluation results demonstrate that the proposed hybrid model has improved overall performance in identifying DRGs creeps.

主题分类 基礎與應用科學 > 資訊科學
社會科學 > 管理學
参考文献
  1. 黃興進(2002)。醫療資訊管理系統研究議題之探討。資訊管理學報,9,101-116。
    連結:
  2. 黃興進(2002)。醫療資訊管理系統研究議題之探討。資訊管理學報,9,101-116。
    連結:
  3. Bonissone, P.P.,Chen, Y.T.,Goebel, K.,K1iedkar, P.S.(1999).Hybrid Soft Computing Systems: Industrial and Commercial Applications.Proceedings of the IEEE,87(9),1641-1667.
  4. Brasil, L.Ml, de Azevedo, F.M.,Barreto, J.M.(2001).Hybrid Expert System for Decision Supporting in the Medical Area: Complexity and Cognitive Computing.International Journal of Medical Informatics,63(1-2),1930.
  5. Dote, Y.,Ovaska, S.J.(2001).Industrial Applications of Soft Computing: A Review.Proceedings of the IEEE,89(9),1243-1265.
  6. Fenton, W.G.,McGinnity, T.M.,Maguire, L.P.(2001).Fault Diagnosis of Electronic Systems Using Intelligent Techniques: A Review.IEEE Transactions on Systems, Man, and Cybernetics-Part C,31(3),269-281.
  7. Herrera F.,Lozano M.(2001).Adaptive Genetic Algorithms Based on CoeVolution with Fuzzy Behaviors.IEEE Transactions on Evolutionary Computation,5(2),149-165.
  8. Ishibuchi, H.,Nakashima, T.(2001).Effect of Rule Weights in Fuzzy Rule-Based Classification Systems.IEEE Transactions on Fuzzy Systems,9(4),506-515.
  9. Jang, J. S.(1993).ANFIS: Adaptive-Network-based Fuzzy Inference System.IEEE Transactions. on Systems, Man, and Cybernetics,23(3),665-685.
  10. Jordan, T.J.(2002).Understanding Medical Informatics: A Guide to Informatics & Decision Making.New York:McGraw-Hill.
  11. Kang, S.,Woo, H.(2000).Evolutionary Design of Fuzzy Rule Base for Nonlinear System Modeling and Control.IEEE Transactions on Fuzzy Systems,8(1),37-45.
  12. Kasabov, N.K.(1996).Learning Fuzzy Rules and Approximate Reasoning in Fuzzy Neural Networks and Hybrid Systems.Fuzzy Sets and Systems,82,135-149.
  13. Kwok, H.F.,Linkens, D.A.,Mahfouf, M.,Mills, G.H.(2003).Rule-base Derviation for Intensive Care Ventilator Control using ANFIS.Artficial Intelligence in Medicine,29(3),185-201.
  14. Loia, V.,Sessa, S.A.,Staiano, R.(2000).Merge Fuzzy Logic, Neural Networks, and Genetic Computation in the Design of a Decision-Support System.International Journal of Intelligent Systems,15,575-594.
  15. Simborg, D.W.(1981).DRG Creeps.New England Journal of Medicine,304,1602-1604.
  16. Sintchenko, V.,Coiera, E.W.(2003).Which Clinical Decisions Benefit from Automation? A Task Complexity Approach.International Journal of Medical Informatics,70(2-3),309-316.
  17. Sushmita, M.(2001).FRBF: A Fuzzy Radial Basis Function Network.Neural Computing & Applications,10,244-252.
  18. Sushmita, M.,Hayashi, Y.(2000).Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework.IEEE Transactions on Neural Networks,11(3),748-768.
  19. 林雨靜(2001)。財團法人國家政策研究基金會財團法人國家政策研究基金會,未出版
  20. 莊逸洲、黃祟哲(2000)。醫療機構管理制度。華杏出版。
  21. 賴憲堂、楊志良、范碧玉(1998)。全民健康保險下疾病分類編碼品質與相關影響因素研究。中華公共衛生雜誌,17(4),337-347。
  22. 賴憲堂、韓揆、張鳳智(1996)。DRGs償付制度與醫院疾病編碼取巧行為之可能性。醫院,29(3),31-46。
  23. 駱至中、戴丁榮、王鄭總、林錦昌(2003)。自動檢測DRG取巧行為之遺傳模糊專家系統。第四屆產業資訊管理學術暨新興科技實務研討會論文集
  24. 戴丁榮、駱至中、劉明華(2001)。DRG取巧行為之智慧型與自動化檢測。中國二業工程學會九十年度年會暨學術研討會論文集
  25. 韓揆(2001)。互斥、非互斥?診斷組合(DRGs)分類結構之辦。臺灣衛誌,20(4),259-264。
被引用次数
  1. 錢才瑋、林為森(2016)。利用試題反應理論挑出健保住院費用的異常。醫務管理期刊,17(4),334-350。
  2. 葉怡成、黃冠傑、陳重志(2008)。具自適應核形狀參數的徑向基底函數網路。資訊管理學報,15(2),135-154。