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研究生:蔡國麟
研究生(外文):Kwok-Lun Choi
論文名稱:建立新的糖尿病分級模型
論文名稱(外文):Develop a New Staging Model of Diabetes Mellitus
指導教授:林文德林文德引用關係
指導教授(外文):Wender Lin
學位類別:碩士
校院名稱:長榮大學
系所名稱:醫務管理學研究所
學門:商業及管理學門
學類:醫管學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:61
中文關鍵詞:健保資料庫極端值嚴重度監控風險預測.
外文關鍵詞:National Health Insurance(NHI)Diabetes Mellitusco-morbiditiesOut-patient Department(OPD)International Code of Disease9th versionClinical Modification (ICD 9–CM)utilization analysiscase-mix analysisepidemiologic analysisFree For Service(FFS)Global Budget payment system.
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研究背景及目的
病患疾病嚴重度在醫療過程中的變異,可用作醫療品質評估之依據,而疾病的嚴重程度的級別,能反映出病患在疾病治療前、中、後之健康狀況。為能達到資源有效運用,及提升糖尿病照護品質之監控能力,建立糖尿病分級模型是有須要。

材料及方法
本研究所建立的新分級模型包含下列資料: 1) 病患之診斷資料;2) 病患之處方; 3) 檢驗項目;4) 診療處置及手術資料。為便於比較,新分級模式以及簡易型(單以人口因子:年齡、性別作分級)之分級模式依臨床經驗均歸類為8級。自健保資料庫中,170253位在2001至2002年間有就醫記錄的病患,共截取7174位40歲以上之糖尿病患作為分級對象,分別以新、舊及簡易型三種分級平均住院模式分級,分級結果算出後,再計算其各級之個別平均年度門診醫療支出、全年天數、及其與各評核指標(需急診照護之風險、住院之風險、及在來年死亡之風險)的相關性,並以邏輯回歸計算新舊兩分級模式各等級之勝算比。然後再以C-statistic之統計方式,以為對來年需急診照護之風險、住院之風險、及在來年死亡之風險的預測。而在針對平均年度門診醫療支出及全年平均住院天數之預測上,則以predictive R2來評估。

結果
本研究以二年度需急診照護之風險、住院之風險、來年死亡之風險、2年度門診醫療支出、平均全年住院天數為嚴重度評核指標,在加入更多臨床資料後,新分級結果在上述各指標之相關性上均優於其他兩模式,在上述前三項指標為基準,在對病情嚴重度之解釋力與預測力上,新分級模式之表現較其他兩模式進步,在2001年掛急診及住院的危險預測上,新分級模式表現優於其他兩分級模式,在住院的危險預測上達76.7%;舊分級模式為75.1%,而簡易型分級模式則只有62%。在2002年掛急診及住院的危險預測上,雖然新分級模式的預測力依序也只有62.6%及67.5%,但表現仍優於其他兩分級模式。而在2002年死亡危險的預測上, 新分級模式表現亦優於其他兩分級模式,預測力達74.89%,舊分級模式亦達74.3%。但在預測病患來年之年度門診醫療支出及平均全年住院天數上,經去除極端值後,新模式只略勝於舊模式,且R2值均小於0.1,十分不理想。

結論
新分級模式在對來年急診、住院、死亡、年度門診醫療支出、以及全年住院天數之預測上略有改善,且新分級模式在與上述各指標的相關性上均優於舊型及簡易型,而兩者的效度均不錯,省時又成本低,可使用在大型糖尿病嚴重度監控及風險預測。
Objective
Health care quality can be measured by the changes in the severity of the disease of patients throughout the course of medical management. Disease severity scales ensure more accurate measurement of health status before, during and after medical intervention. In order to assure efficient usage of resources and promote diabetes care quality monitoring, an effective severity evaluation system is needed.

Method and Materials
Our new model includes: 1. diagnostic details. 2. prescription details. 3. laboratory checkup items being ordered. 4. interventions or operations being performed. A total of 7,174 of DM patients with age ≧40 are selected from a population of 170,253 who have had health care consultation records in the year 2001 to 2002. Claim data from the database of the Bureau of National Health Insurance in Taiwan is used. The old, the new and the simple staging model (just using the demographic criteria: age and gender for staging) are used to stage those patients. In order to make comparison easier, modification and restaging of the new staging model is performed. Bases on clinical experience, a total of eight stages are developed. The average annual out patient department (OPD) expenditures and average annual days of admission (due to DM or related disorders), the correlations between stages in different models with those parameters: risk of E/R consultation, risk of admission, and risk of death in the coming year, of each stage of the three models are calculated. The odd ratios of individual stages of three models are measured by logistic regression. The three models are then used to predict the risk of admission, risk of E/R consultation and risk of death in the coming year by C-statistic. The predictive ability of those models in average annual OPD expenditure and admission days is measured by using predictive R2.

Result
In this study, E/R consultation and admission risks, annual admission days and OPD expenditures in the years 2001 and 2002, and mortality in the coming year (2002) are used as parameters for severity evaluation. After the addition of more clinical chart details, the new staging model works better than the other two, the old and the simple model, in the correlations with the above five parameters. In the explanation and prediction of the risk of E/R consultation, risk of admission in 2001, the new model works superior to the other two that the area under the ROC curve is 67.2% and 76.7% respectively. The new model also works better than the other two models in the prediction of the risk of death in the coming year (2002) that the prediction ability is 74.9%. The old model gets 74.3% and the simple model is only 68.2%. However, after the exclusion of extreme cases, it gets only little or minimal improvement in the new model in prediction of the days of admission and the annual OPD expenditure in the coming year and the R2 value are very unsatisfied.

Conclusion
The new model gets some improvement in the ability in the prediction of risk of E/R consultation, risk of admission, risk of death; annual admission days and annual OPD expenditures at the coming year can be achieved and gets better correlations with those parameters being described above. The validity of both the old and the new model is fairly acceptable and can be applied in large scaled studies on DM severity monitoring and risk prediction. Using the two models for disease severity staging is time saving and is very low in cost.
Abstract I
中文摘要 III
Tables and Figures V
Introduction 1
Literatures review 4
Method, Materials and Study Design 13
Result 19
Discussion 22
Conclusion 28
Reference 30
Appendix 32
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