臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：運用知識發現法及賽局理論進行醫師診療形態管理之研究
作者：林純美
作者(外文)：Chun-Mei Lin
校院名稱：國立成功大學
系所名稱：工業與資訊管理學系碩博士班
指導教授：林清河
學位類別：博士
出版日期：2007
主題關鍵詞：自組織映射神經網路；協調賽局；賽局理論；決策支援系統；知識發現；Decision Support Systems (DSS)；Game Theory；Self-Organizing Maps (SOM) neural networks；Knowledge Discovery in Database (KDD)；Coordination Game
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:1
健康照護費用支出的成長已為世界各國重要的政治議題，且健康照護費用過渡的成長將排擠其他部門的費用。而醫師的診療行態是影響健康照護費用支出的重要因素之一，不適當的診療形態造成醫療費用的支出增加，因此對於醫師診療形態的適度管理，可以減緩健康照護費用支出的成長。
為了協助保險人(第三者支付或利害關係人)了解及辨識醫療供給者的異常診療型態，以管控醫療費用的支出，本研究首先以知識發現法進行醫師診療形態分群管理，並建立各類型的管理重點與優先順序；其次，運用協調賽局理論建立決策支援系統揭露同儕比較的訊息結果，以促使醫師自我修正偏離常模的診療型態。
結果顯示，知識發現法有效組織與量化各類型的診療形態，以利保險人對於異常的診療型態進一步管理。另一方面，透過決策支援系統揭露同儕比較的訊息，提供醫師協調的機制以整合外在訊息於處方箋開立的決策過程。
在實務的應用上，醫療給付者面對日益增加的醫療費用，可運用診療型態分群於日常費用審核過程的分類及異常篩選，以降低管理成本，並善用協調工具揭露同儕比較訊息讓醫療供給者自我修正行診療行為，進而降低不必要的醫療費用支出。
Health expenditures have rapidly risen to the top of the political agenda in many countries. Health costs can crowd out expenditures in other sectors of the economy. Physicians and their actions account for most health care spending, and unreasonable health expenditures and variations in physicians’ practice raise a number of problems.
To overcome unusual health expenditures, better managerial physician practice patterns should be developed. In order to help the third-party payers to recognize and describe novel or unusual general practitioners (GP’s) practice patterns to control health care expenditures, we developed a complete scheme for the management of physician practice patterns from different stakeholder (i.e. health care provider or third-party insurance payer) perspectives.
First, we develop a hybrid of the KDD (knowledge discovery in database), methodology to segment GPs’ practice patterns from the their medical claims. This will help the third-party payers to recognize and describe novel or unusual practice patterns, while the features and characteristics of health expenditures will be extracted from the huge database. Second, it is a promising strategy to use game theory to build a data warehouse decision support system (DWDSS) and to embed the critical factors involved in a coordination game equilibrium. This supports GPs in evaluating prescription rates of antibiotics to help self–management and investigate whether GPs will reshape their patterns of practice due to the influence of peer-comparison.
The results of segmentation of GPs’ practice patterns were better health care fraud detection and use of investigative resources by recognizing and quantifying the underlying features of these patterns. From the health care provider’s perspective, using the game-theory modeling, built into the DWDSS, has made significant contributions to managing GPs’ practice patterns and encouraging GPs toward patterns favored by the third-party insurance payer. DWDSS provides a useful coordinating instrument for GPs to integrate relevant information into their decision-making process with regard to prescriptions.
The payer or stakeholder can use these practice patterns and coordinating instruments to manage inappropriate or unusual behavior, and thus develop different claim auditing procedures to overcome the growth in health expenditures, which has advantages for both beneficiaries and third party-payers.
1.Ahluwalia, J. S., Weisenberger, M. L., Bernard, A. M. and McNagny, S. E. (1996), “Changing physician prescribing behavior: a low-cost administrative policy that reduced the use of brand name non-steroidal anti-inflammatory drugs”, Preventive Medicine, 25, 668-672.
2.Alm, J. and McKee, M. (2004), “Tax compliance as a coordination game”, Journal of Economic Behavior & Organization, 54(3), 297-312.
3.Alvanzo, A. H., Cohen, G. M. and Nettleman, M. (2003), ”Changing physician behavior: half-empty or half-full?” Clinical Governance, 8(1), 69-78.
4.Barnum, H., Kutzin, J. and Saxenian, H. (1995), “Incentives and provider payment methods”, International Journal of Health Planning and Management, 10, 23-45.
5.Becerra-Fernandez, I., Zanakis, S. H. and Walczak, S. (2002), “ Knowledge discovery techniques for predicting country investment risk”, Computers & Industrial Engineering, 43, 787-800.
6.Becker, D., Kessler, D. and McClellan, M. (2005), “Detecting Medicare abuse, Journal of Health Economics”, 24, 189-210.
7.Berndt, D. J., Hevner, A. R. and Studnicki, J. (2003), “The Catch data warehouse: support for community health care decision-making”, Decision Support Systems, 35, 367-384.
8.Bhargava, H. K., Power, D. J. and Sun, D. (2005), “Progress in Web-based decision support technologies”, Decision Support Systems (in press), doi:10.1016/j.dss.2005.07.002
9.Blumstein, J. F. (1996), “Rationalizing the fraud and abuse statute”, Health affairs, 15(4), 118-128.
10.Brandts, J. and Charness, G. (2003), “Truth or consequences: an experiment”, Management Science, 49(1), 116-130.
11.Brodley, C. E., Lane, T. and Stough, T. M. (1999), “Knowledge discovery and data mining”, American Scientist, 87(1), 54-61.
12.Brownell, M. D., Roos, N. P. and Roos, R. L. (2001), “Monitoring health reform: a report card approach”, Social Science & Medicine, 52, 657-670.
13.Burton, A. and Sefton, M. (2004), “Risk, pre-play communication and equilibrium”, Games and Economic Behavior, 46, 23-40.
14.Camerer, C. F. (2003), Behavioral game theory: experiments in strategic interaction, Princeton, N.J.: Princeton University Press.
15.Chang, S. C., Chang, H. J. and Lai, M. S. (1999), “Antibiotic usage in primary care units in Taiwan”, International Journal of Antimicrobial Agents, 11, 23-30.
16.Chang, S. C., Shiub, M. N. and Chen, T. J. (2001), “Antibiotic usage in primary care units in Taiwan after the institution of national health insurance”, Diagnostic Microbiology and Infectious Disease, 40, 137-143.
17.Chen, S. H. and Wang, P. P. (2003), Searching financial patterns with self-organizing maps. Computational Intelligence in Economics and Finance, New York: Springer-Verlag.
18.Cheng, T. M. (2003), “Taiwan's New National Health Insurance Program: genesis and experience so far”, Health Affairs, 22(3), 61-76.
19.Chiang, T.L. (1997), “Taiwan's 1995 Health care reform”, Health Policy, 39(3), 225-239.
20.Chinburapa, V., Larson, L. N., Brucks, M., Draugalis, J. L., Bootman, J. L. and Puto, C. (1993), “Physician prescribing decisions: the effects of situational involvement and task complexity on information acquisition and decision making”, Social Science & Medicine, 36, 1473-1482.
21.Colman, A. M. (1995), Game theory and its applications in the social and biological sciences (2nd ed.), Boston, Mass.: Oxford, Butterworth-Heinemann.
22.Cooper, R., DeJong, D., Forsythe, R. and Ross, T. (1990), “Selection criteria in coordination games: some experimental results”, American Economic Review, 80, 218-233.
23.Cooper, R., DeJong, D., Forsythe, R. and Ross, T. (1992), “Communication in coordination games”, Quarterly Journal of Economics, 53, 739-771.
24.Davies, H. T. and Lampel, J. (1998), “Trust in performance indicators?” Quality in Health Care 7 (3), 159–162.
25.Delesie, L. and Croes, L. (2000), “Operations research and knowledge discovery: a data mining method applied to health care management”, International Transactions in Operational Research, 7, 159-170.
26.Denig, P., Haaijer-Ruskamp, F. M. and Zijsling, D. H. (1988), “How physicians choose drugs”, Social Science & Medicine, 27, 1381-1386.
27.Dixit, A. and Skeath, S. (1999), Games of Strategy, New York: WW Norton and Company.
28.Dong, H., Bogg, L., Rehnberg, C. and Diwan, V. (1999), “Association between health insurance and antibiotics prescribing in four counties in rural China”, Health Policy, 48, 29-45.
29.Dranove, D., Kessler, D., McClellan, M. and Satterthwaite, M. (2003), “Is more information better? The effects of "report cards" on health care providers”, Journal of Political Economy, 111(3), 555-588.
30.Dyer, R. F. and Forman, E. H. (1992), “Group decision support with the analytic hierarchy process”, Decision Support Systems, 8, 99-124.
31.Elgood, C. (1984), Handbook of Management Games (2nd ed.), England: Gower (Aldershot).
32.Farrell, J. (1993), “Meaning and credibility in cheap-talk games”, Games Economic Behavior, 5, 514-531.
33.Fayyad, U. and Stolorz, P. (1997), “Data mining and KDD: promise and challenges”, Future Generation Computer Systems, 13, 99-115.
34.Ford, E. W., Wells, R. and Bailey, B. (2004), “Sustainable network advantages: a game theoretic approach to community-based health care coalition”, Health Care Management Review, 29(2), 159-169.
35.Forman, E. H. and Selly, M. A. (2001), Decision by objectives: how to convince others that you are right. NJ: River Edge.
36.Fu, H. H., Tsai, H. H., Lin, C. W. and Wei, D. (2004), “Application of a single sampling plan for auditing medical-claim payments made by Taiwan national health insurance”, Health Policy, 70(2), 185-195.
37.Goodwin, L., VanDyne, M., Talbert, S. and Lin, S. (2003), “Data Mining issues and opportunities for building nursing knowledge”, Journal of Biomedical Informatics, 36, 379-388.
38.Grand, A. L., Hogerzeil, H. V. and Haaijer-Ruskamp, F. M. (1999), “Intervention research in rational use of drugs: a review”, Health Policy and Planning, 14 (2), 89-102.
39.Grumbach, K., Osmond, D., Vranizan, K., Jaffe, D. and Bindman, A. B. (1998), “Primary care physicians' experience of financial incentives in managed-care systems”, The New England Journal of Medicine, 339 (21), 1516-1521.
40.Grytten, J. and Sorensen, R. (2003), “Practice variation and physician-specific effects”, Journal of Health Economics, 22, 403-418.
41.Liang, H., Xue,Y. and Berger, B. A.(2005), “Web-based intervention support system for health promotion”, Decision Support Systems (in press), doi:10.1016/j.dss.2005.02.001.
42.Bhargava, H. K., Power, D. J. and Sun, D. (2005), “Progress in Web-based decision support technologies”, Decision Support Systems (in press), doi:10.1016/j.dss.2005.07.002
43.Hair, J. F., Anderson, R., Tatham, R. and Black, W. (1992), Data Analysis with Readings. New York: Macmillan.
44.Han, J. and Kamber, M. (2006), Data mining: concepts and techniques. San Francisco: Morgan Kaufmann.
45.He, H., Wang, J., Craco, W. and Hawkins, S. (1997), “Application of neural networks to detection of medical fraud”. Expert Systems with Applications, 13(4), 329-336.
46.Hellinger F. J. (1996), “The impact of financial incentives on physician behavior in managed care plans: a review of the evidence”. Medical Care Research and Review, 53(3), 294-314.
47.Hibbard, J. H., Stockared, J. and Tusler, M. (2003), “Does publicizing hospital performance stimulate quality improvement efforts?”, Health Affairs, 22(2), 84-94.
48.Hibbard, J. H., Stockared, J. and Tusler, M. (2005), “Hospital performance reports: impact on quality, market share and reputation”, Health Affairs, 24(4), 1150-1161.
49.Jaegher, K. D. and Jegers, M. (2001), “The physician-patient relationship as a game of strategic information transmission”, Health Economics, 10, 651-668.
50.Jan, S. (2003), “A perspective on the analysis of credible commitment and myopia in health sector decision making”, Health Policy, 63(1), 269- 278.
51.Jin, H., Shum, W.H., Leung, K. S. and Wong, M. L. (2004), “Expanding self-organizing map for data visualization and cluster analysis”, Information Sciences, 163, 157-173.
52.Jolliffe, I. T. (2002), Principal Component Analysis (2nd ed.), New York: Springer-Verlag.
53.Kiang, M. Y. and Kumar, A. (2004), “A comparative analysis of an extended SOM network and K-means analysis”, International Journal of Knowledge-Based and Intelligent Engineering Systems, 8(1), 9-15.
54.Kim, C. S., Kristopaitis, R. J., Stone, E., Pelter, M., Sandhu, M. and Weingarten, S. R. (1999), “Physician education and report cards: do they make the grade? Results from a randomized controlled trial”, The American Journal of Medicine, 107(6), 556-560.
55.Kohli, R., Piontek, F., Ellington, T., VanOsdol, T., Shepard, M. and Brazel, G. (2001), “Managing customer relationships through E-business decision support applications: a case of hospital-physician collaboration”, Decision Support Systems, 32, 171-187.
56.Kohonen, T. (1995), Self- organizing maps, Berlin: Springer.
57.Kunin, C. M. (1993), “Resistance to antimicrobial drugs: a world-wide calamity”, Annual Internal Medicine, 118, 557-61.
58.Kuo, R. J., An, Y. L., Wang, H. S. and Chung, W. J. (2006), “Integration of self-organizing feature maps neural network and genetic k-means algorithm for market segmentation”, Expert Systems with Applications, 30(2), 313-324.
59.Kwon O. (2006), “The potential roles of context-aware computing technology in optimization-based intelligent decision-making”, Expert systems with applications, 31, 629-421.
60.Lambert, B. L., Salmon, J. W., Stubbings, J., Gilomen-Study, G., Valuck, R. J. and Kezlarian K. (1997), “Factors associated with antibiotic prescribing in a managed care setting: an exploratory investigation”. Social Science & Medicine, 45(12), 1767-1779.
61.Lavrač, N. (1999), “Selected techniques for data mining in medicine”, Artificial intelligence in medicine, 16, 3-23.
62.Liang, H., Xue,Y. and Berger, B. A. (2006), “Web-based intervention support system for health promotion”, Decision Support Systems, 42(1), 435-449.
63.Lee, J. H. and Park, S. C. (2005), “Intelligent profitable customer segmentation system based on business intelligence tools”, Expert Systems with Applications, 29, 145-152.
64.Lee, K. C. and Chung, N. (2005), “A Web DSS approach to building an intelligent internet shopping mall by integrating virtual reality and avatar”, Expert Systems with Applications, 28, 333-346.
65.Liang, H., Xue, Y. and Berger, B. A. (2005), “Web-based intervention support system for health promotion”, Decision Support Systems (in press), doi:10.1016/j.dss.2005.02.001.
66.Lopez-Rubio, E., Munoz-Perez, J. and Gomez-Ruiz, J. A. (2004), “Principal components analysis self-organizing map”, Neural Networks, 17, 261-270.
67.Lovell, N. H. and Celler, B. G. (1999), “Information technology in primary health care”. International Journal of Medical Informatics, 55(1), 9-22.
68.Lu, J. F. and Hsiao, C. W. (2003), “Does universal health insurance make healthcare unaffordable? Lessons from Taiwan”, Health Affairs, 22(3), 77-88.
69.Tremblay, M. C., Fuller, R. Berndt, D. and Studnicki, J. (2006),” Doing more with more information: changing healthcare planning with OLAP tools”, Decision Support Systems (in press), doi:10.1016/j.dss.2006.02.008.
70.Zack, M. H. (2006), “The role of decision support systems in an indeterminate world, Decision Support Systems” (in press), doi:10.1016/j.dss.2006.09.003.
71.Major, J. A. and Riedinger, D. R. (2002), “FED: a hybrid knowledge/ statistical-based system for the detection of fraud”, Journal of Risk and Insurance, 69(3), 309-315.
72.March, S. T. and Hevner, A. R. (2007), “Integrated decision support systems: a data warehousing perspective”, Decision Support Systems, 43(3), 1031-1043.
73.Maryniuk, G.A. (1990), “Practice variation: learned and socio-economic factors”, Advances dental research, 4, 19-24.
74.Mason A. and Street A. (2005), “Publishing outcome data: is it an effective approach?” Journal of Evaluation in Clinical Practice, 12 (1), 37-48.
75.McNamara, P. (2006), “Provider-specific report cards: a tool for health sector accountability in developing countries”, Health policy and planning, 21(2), 101-109.
76.Nemati H. R., Steiger D. M., Iyer L. S. and Herschel R. T. (2002), “Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing”, Decision Support Systems, 33, 143-161.
77.Nguyen, X. N. (1996), “Physician volume response to price controls”, Health Policy, 35, 189-204.
78.O'Brien, B. J. (1988), “A game-theoretic approach to donor kidney sharing”, Social Science and Medicine, 26, 1109-1116.
79.Okunade, A. A. and Suraratdecha, C. (2006), “The pervasiveness of pharmaceutical expenditure inertia in the OECD countries”, Social Science Medicine, 63(1), 225-238.
80.Pedersen M. K. and Larsen M. H. (2001),” Distributed knowledge management based on product state models - the case of decision support in health care administration”, Decision Support Systems, 31, 139-158.
81.Radyowijati, A. and Haak, H. (2003), ” Improving antibiotic use in low-income countries: an overview of evidence on determinants”, Social Science and Medicine, 57(4), 733-744.
82.Rasmusen, E. (1992), Games and information: an introduction to game theory, Cambridge University Press: Blackwell.
83.Roiger, R. J. and Geatz, M. W. (2003), Data mining: a tutorial-based primer. Boston: Addison Wesley.
84.Rousseeuw, P. J. (1987), “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”, Journal of Computational and Applied Mathematics, 20, 53-65.
85.Ruland, C. M. and Bakken, S. (2002), “Developing, implementing, and evaluating decision support systems for shared decision making in patient care: a conceptual model and case illustration”, Journal of Biomedical Informatics, 35(5-6), 313-21.
86.Rygielski, C., Wang, J. C. and Yen, D.C. (2002), “Data mining techniques for customer relationship management”, Technology in Society, 24, 483-502.
87.Ryu, T. W. and Eick, C. F. (2005), “A database clustering methodology and tool”, Information Sciences, 171, 29-59.
88.Saaty, T. L. (1980), The analytic hierarchy process: planning, priority setting, resource allocation, New York: McGraw-Hill.
89.Schaffer, C. M. and Green, P. E. (1998), “Cluster-based market segmentation: some further comparisons of alternative approaches”, Journal of the Market Research Society, 40 (2), 155-163.
90.Schelling, T. C. (1960). The Strategy of Conflict. Cambridge, MA: Harvard University Press.
91.Schectman, J. M., Schorling, J. B., Nadkarni, M. M. and Voss, J. D. (2005), “ Determinants of physician use of an ambulatory prescription expert system”, International Journal of Medical Informatics 74, 711–717.
92.Shahian, D. M., Normand, S., Torchiana, D. F., Lewis, S. M., Pastore, J. O., Kuntz, R. E. and Dreyer, P. I. (2001), “Cardiac surgery report cards: comprehensive review and statistical critique”, Annals of Thoracic Surgery, 72, 2155-2168.
93.Shiell, A. and Chapman, S. (2000), “The inertia of self-regulation: a game-theoretic approach to reducing passive smoking in restaurants”, Social Science & Medicine, 51, 1111-1119.
94.Simon, H. A. (1960), The new science of management decision (2nd. ed.), New York: Harpen & Row.
95.Slovensky, D., Fottler, M. and Houser, H. (1998), “Developing an outcomes report card for hospitals: a case study and implementation guideline”, Journal of Healthcare Management, 43(1), 15-30.
96.Sokol, L., Garcia, B., Rodriguez, J., West, M. and Johnson, K. (2001), “Using data mining to find fraud in HCFA health care claims”, Topics in Health Information Management, 22(1), 1-13.
97.Tremblay, M. C., Fuller, R., Berndt, D. and Studnicki, J. (2006), “Doing more with more information: changing healthcare planning with OLAP tools, Decision Support Systems, (in press) doi:10.1016/j.dss.2006.02.008.
98.Van Huyck, J. B., Battalio, R. C. and Beil, R. (1990), “Tacit coordination games, strategic uncertainty and coordination failure”, American Economic Review, 80, 234-248.
99.Vellido, A., Lisboa, P. J. G. and Meehan, K. (1999), “Segmentation of the on-line shopping market using neural networks”, Expert Systems with Applications, 17, 303-314.
100.Vesanto, J. and Alhoniemi, E. (2000), “Clustering of the self-organizing map”, IEEE Transactions on Neural Networks, 11(3), 586-600.
101.Viaence, S., Dedene, G. and Derrig, R. A. (2005), “Auto claim fraud detection using Bayesian learning neural networks”, Expert Systems with Applications, 29(3), 653-666.
102.Wagner, J. M., Shimshak, D. G. and Novak, M. A. (2003), “Advances in physician profiling: the use of DEA”, Socio-Economic Planning Sciences, 37, 141-163.
103.Waters, H. R. and Hussey, P. (2004), “Pricing health services for purchasers-a review of methods and experiences”, Health Policy, 70, 175-184.
104.Watson H. J., Fuller C. and Ariyachandra T. (2004), “Data warehouse governance: best practices at Blue Cross and Blue Shield of North Carolina”, Decision Support Systems, 38, 435-450.
105.Wensing, M., Broge, B., Kaufmann-Kolle, P., Andres, E. and Szecsenyi, J. (2004), “Quality circles to improve prescribing patterns in primary medical care: what is their actual impact?”, Journal of Evaluation in Clinical Practice, 10(3), 457-466.
106.World Health Organization (2000), The world health report 2000 - health systems: improving performance. Geneva: World Health Organization.
107.Wu, S. and Chow, T. W. S. (2004), “Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density”, Pattern Recognition, 37(2), 175-188.
108.Yang,W. S. and Hwang, W. S. (2006), “A process-mining framework for the detection of healthcare fraud and abuse”, Expert Systems with Applications, 31(1), 56-68.
109.Zanakis, S. H. and Becerra-Fernandez, I. (2005), “Competitiveness of nations: A knowledge discovery examination”, European Journal of Operational Research, 166, 185-211.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：1470（臺灣：1464，外國：6） ／ 本年度總使用人次：1855660 ／ 訪客人次（自102年09月）：67649520
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上