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博碩士論文 etd-0115107-120258 詳細資訊
Title page for etd-0115107-120258
論文名稱
Title
心臟相關疾病之共病與醫療資源耗用之相關研究
Correlates of Comorbidity, Medical Resources Consumption and Cardiovascular Disease
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
67
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2006-12-11
繳交日期
Date of Submission
2007-01-15
關鍵字
Keywords
資源耗用、風險、Charlson共病指數、共病
Charlson comorbidity index, Risk, Comorbidity, Resource consumption
統計
Statistics
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中文摘要
本研究目的為利用Charlson共病指數對心臟疾病高醫療利用者進行資料分析,探討影響醫療費用之風險因子。研究樣本取自於南部某醫學中心2003年至2005年之44,940位心臟疾病患者,使用線性迴歸方法。
研究結果顯示心臟疾病患者對醫療資源的利用會隨著年齡的成長而增加;共病指數權重愈高者耗用之醫療服務資源愈多;住院天數和看診次數會受到共病指數權重之影響,共病指數權重愈高者,住院天數愈長或看診次數愈多。各自變數之變異數膨脹因素VIF均介於在1 – 1.343之間,表示模型各自變數彼此間不具有高度複相關性,即無相對影響效果。
藉由此研究,除可供醫院疾病管理之參考外,亦可運用於風險因子對醫療資源利用預測之參考,並與保險支付制度結合,對資源做合理分配。
Abstract
Objective: To explore correclational relationship between the risk factors and medical resources consumption in cardiovascular disease patients.
Methods: A database containing 44940 cardiovascular disease patients in a southern Taiwan Medical center from year 2003 to 2005 was chosen as studying sample. By applying Charlson Comorbidity Index as one of the major risk factors to these cardiovascular disease patients, then using liner regression to analyze the data for their relationship with medical resources consumption.
Results: Medical resources consumption increases as the cardiovascular disease patients become older. The higher the comorbidity index weight with the patient, the more the patient consumes medical resources. A patient’s comorbidity index weight has great influence to the length of hospital stay of the patient and the frequency of clinic visit of the patient. The higher the comorbidity index weight a patient he or she, is likely to stay in hospital longer or to see doctors more often. The variance inflation factor (VIF) of each independent variable is ranged between 1 and 1.343, which means there are no high correlations between independent variables in the modes. In other words, there is no correlative influence effect that would invalidate the assumption.
Conclusion: The findings of this study can be a good reference to hospital disease management. In addition, they can be applied to predicting the risk factor in medical resources consumption. Also they can be adapted into medical insurance payment system, thereby improving medical resource distribution.
目次 Table of Contents
表目錄 V
圖目錄 VI
誌謝 VII
摘要 IX
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 16
第三節 研究流程 17
第二章 文獻探討 18
第一節 總額支付制度與醫療服務利用 18
第二節 國外共病對醫療資源利用之相關研究 21
第三章 研究方法 23
第一節 研究設計 23
第二節 研究限制 30
第四章 研究結果與討論 32
第一節 描述性統計 32
第二節 心臟疾病醫療利用之描述性分析 35
第三節 心臟疾病醫療利用之變異分析 45
第四節 討論 49
第五章 結論與建議 50
第一節 結論 50
第二節 建議 53
參考文獻 54
參考文獻 References
參考文獻
中文部份
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李玉彝(1994)年齡與醫療保健費用支出的關係,國立中正大學,碩士論文。
張睿詒等(1998)以風險校正模型探討控制全民健康保險醫療費用之研究,行政院衛生署委託研究計畫,計畫編號:DOH87-NH-010。
李隆安、藍忠孚、吳肖琪(1999)人口老化對全民健康保險醫療利用與費用影響之評估研究,行政院衛生署八十七年度委託研究計畫,計畫編號:DOH87-NH-003。
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英文部份
Richard, A. Deyo., Daniel, C. Cherkin & Marcia, A. Ciol. ( 1992 ), Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases, J Clin Epidemiol, 45(4),613-619.
William D’Hoore, Andre′Bouckaert,. & Charles Tilquin (1996). Practical considerations on the Use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol, 49(12),1429-1433.
Charlson, ME., Pompei P, Ales KL, Mackenzie CR.. (1987), A new method of classifying prognostic comorbidity in longitudinal studies:development and validation, J Chron Dis, 40,373-383.
Barbara Starfield, MD,.MPH, FRCGP, Klaus W. Lemke, PhD, Terence Bernbardt, BA, Steven S. Foldes, PhD, Christopher B. Forrest, MD, PhD & Jonathan, P. Weiner, DrPH. (2003), Comorbidity:implications for the importance of primary care in ‘Case’ management, Annals of Family Medicine, 1(1),8-14.
Laura A. Petersen, MD,.MPH, Kenneth Pietz, PhD, Le Chauncy D. Woodard, MD, MPH, & Margaret Byrne, PhD. (2005), Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes, Medical Care, 43(1),61-67.
John S. Hughes, MD, Richard F. Averill, MS, Jon Eisenhandler, PhD, Norbert I. Goldfield, MD, John Muldoon, MHA, John M. Neff, MD, & James C. Gay, MD (2004), Clinical risk groups(CRGs)a classification system for risk-adjusted capitation-based payment and health care management, Medical Care, 42(1), 81-90.
Schauffler, H.H., J. Howland, and J. Cobb (1992), Using chronic disease risk factors to adjust Medicare capitation payments? Health Care Financing Review, 14(1), 79-90.
Andersonk G. F., J.C. Cantor, E.P. Steinberg, and J. Holloway, 1986? Capitation pricing: Adjusting for prior utilization and physician discretion? Health Care Financing Review, 8(2), 27-34.
Eill, R. P., G. C. Pope, L. I. Iezzoni, J.Z. Ayanian, et al. (1996), Diagnostic-Based risk adjustment for Medicae capitation payments? Health Care Financing Review, 17(3),101-127.
Howland, J., J. Stokes, S. C.Crane, and A. J. Belanger(1987), Adjusting capitation using chronic disease risk factors: A preliminary study? Health Care Financing Review, 9(2),15-23.
Green J, Wintfeld N, Sharkey P, Passman LJ.(1990), The importance of severity of illness in assessing hospital mortality. JAMA, 263:241-246.

參考網站:
衛生統計資訊網http://www.doh.gov.tw/statistic/index.htm
中央健康保險局http://www.nhi.gov.tw/
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