臺灣博碩士論文加值系統

研究背景與目的： 在許多已開發或開發中國家，腦中風都是造成死亡、長期住院、或慢性殘障失能的重要原因，其急性醫療照顧與後續的復健治療、長期照護費用都相當可觀。腦中風於急性期的醫療費用主要來自住院期間花費；了解住院天數與費用的主要影響因素，有助於腦中風臨床醫療照護模式、健保支付制度及相關衛生福利政策的調整與研擬。
研究方法與步驟： 本研究向國家衛生研究院申請四組共二十萬人之「健保承保抽樣歸人檔」，以分析腦中風患者之健保醫療利用。該「承保抽樣歸人檔」以健保承保資料為抽樣母群體，依簡單隨機抽樣決定樣本(人)，依此擷取各該人自1996年後之所有醫療利用相關資料（包括門診、住院及特約藥局）。本研究從抽樣歸人檔1997~2002年的住診申報資料中，選取住院主診斷碼符合”急性腦血管疾病”之個案。本研究觀察的應變項分別是住院天數與住院健保醫療費用，而自變項則歸類為患者人口學特質、臨床特質與醫院特質等因素；以單變項統計分析與多變項迴歸分析來探討影響腦中風患者住院天數與費用的主要因素。
研究結果： 總計有2639位急性腦中風患者納入本研究分析。各類型中風之平均急性病房住院天數為：蜘蛛膜下腔出血15.3天、腦內出血17.2天、腦梗塞11.8天，其它腦血管病變6.1天。而涵蓋急性與復健病房在內的平均住院天數則分別為：蜘蛛膜下腔出血44.0天、腦內出血49.1天、腦梗塞37.4天，其它腦血管病變27.0天。逐步複迴歸分析顯示預測模型可解釋住院天數變異量的41.3%，以及住院醫療費用變異量的54.5%。對住院天數預測力最佳的前五名依次為被研判具有行動或日常功能障礙、接受手術處置、院內死亡（負面因子）、住院期間曾發生感染或吸入性肺炎，以及住進台北地區的醫院等。至於對住院健保醫療費用預測力最佳的前五名則依次為手術費用、腦內出血類型、被研判具有行動或日常功能障礙、住進醫學中心，以及住院期間發生感染或吸入性肺炎等。其他對住院天數與健保醫療費用同樣具顯著預測力、但相對較次要的因素包括：年齡、性別、曾使用呼吸器、被研判具有語言或吞嚥功能障礙、醫療合併症、及醫院權屬別等。若以住進急性病房、且後續未再轉入其他病房或醫院的2358位急性患者而言，有245位（10.4%）符合本研究定義之超長住院（住院天數超過23天）；這些少數患者耗用了所有患者住院人日總和的38.9%以及所有住院醫療費用總和的47.8%。
結論與建議：本研究建議應鼓勵腦中風病房或單位的設置，及早對急性腦中風患者提供整合性、跨醫療專業團隊的服務，應有助於穩定患者的醫療狀況、降低功能障礙程度、減少併發症的產生，因此能有效縮短住院天數及其後續社會經濟負擔。健保局規劃中的住院診斷關聯群前瞻性支付制度實施在即，由於中風嚴重程度與功能障礙程度被認為是影響急性住院期間資源耗用的重要因素，應被納入疾病嚴重度分類及支付費率之考量。此外，建議儘速建構完整的急性後期照護與長期照護體系，以強化急性醫療資源的使用效率，以及提供腦中風患者更連續、更具成本效益的照護模式。而合宜的出院準備服務計畫應有助於減少不必要的超長住院，並促進中風患者及早返家、重新融入社會。

Background and Objectives: As a major cause of mortality, prolonged hospitalization, and chronic disability, stroke imposed considerable physical and socioeconomic burden. The direct cost of stroke was largely dependent on the length of initial hospital stay. It is important to understand the determinants of inpatient length of stay (LOS) and costs for acute stroke, and how they may be modified by specific treatments, care arrangement or payment schemes. This study aimed to analyze inpatient length of stay, costs and their predictive factors systematically for acute hospitalized stroke patients in Taiwan. The magnitude and associated factors for prolonged hospital stay (LOS ³ 23 days) during acute care phase of stroke were also examined.
Methods: Patients admitted consecutively between 1 January 1997 and 31 December 2002 with the principal diagnoses of acute cerebrovascular accidents were identified from the claims data of a nationally representative cohort of 200,000 National Health Insurance (NHI) enrollees. Dependent variables were inpatient length of stay and medical costs for acute stroke. Independent variables included patient demographics, clinical characteristics and hospital characteristics. Univariate and multivariate analyses were performed to analyze the main predictors of LOS and costs during an acute stroke admission.
Results: In total, 2639 stroke subjects were eligible for analysis. The mean acute LOS was 15.3 days for SAH, 17.2 days for ICH, 11.8 days for CI, and 6.1 days for other unspecified CVD. The mean total LOS (combined acute and rehabilitation stays) was 44.0 days for SAH, 49.1 days for ICH, 37.4 days for CI, and 27.0 days for other unspecified CVD. Multi-variate analysis revealed 41.3% of the total variance in acute LOS and 54.5% of the total variance in acute costs for stroke was explained by each stepwise regression model. After adjusting for other factors, coding of physical/ADL dependency, surgical operation, in-hospital death (negative factor), occurrence of infection or aspiration pneumonia, and admission to a hospital located in Taipei region were the strongest predictors for the acute LOS. Furthermore, surgical fee, ICH, coding of physical/ADL dependency, admission to a medical center, and occurrence of infection or aspiration pneumonia were the strongest predictors for the acute care costs. Other factors, such as: age, gender, use of mechanical ventilation, coding of speech/swallowing disorders, comorbidity, and hospital ownership had significant but less important impact on acute hospital use. Of the sub-sample of 2358 subjects who admitted to acute wards without further transfer to other wards or hospitals, only 245 subjects (10.4%) had prolonged hospital stay (LOS³ 23 days), but they accounted for 38.9% of the total person-hospital days and 47.8% of the total inpatient medical expenses of all the stroke subjects.
Conclusions and Recommendations: An early, well-organized multidisciplinary team care is postulated for its potential to minimize functional dependency, prevent complications; and hence reduce the LOS and subsequent social-economic burden of stroke. It is suggested that the degree of neurological impairment or functional disability be incorporated into the Taiwan-DRG based prospective payment scheme for acute stroke care to ensure fairer reimbursement. NHI payment reform as well as establishment of a more integrated and affordable post-acute or long-term care system should be policy priorities to effectively reduce unnecessary acute hospital use and to ensure a seamless stroke care. Good discharge planning is crucial for efficient hospital use and successful reintegration into the community.

致謝 i
目錄 ii
表目錄 iv
圖目錄 vi
中文摘要 vii
Abstract ix
1. INTRODUCTION 1
2. LITERATURE REVIEW 5
2.1. Studies on Initial Hospital Stay and Costs for Stroke 5
2.2. Studies on Prediction of the Length of Stay and Costs for Stroke Patients 9
2.3. Cost-effectiveness for Stroke Rehabilitation Services 13
3. METHODS 15
3.1. Database 15
3.2. Study Subjects 16
3.3. Key Variables of Interest 18
3.4. Statistical Analysis 22
4. RESULTS 24
4.1. Descriptive Statistics 24
4.2. Univariate Analysis of Inpatient Length of Stay and Medical Costs by Patient and Hospital Characteristics 26
4.3. Predictive Effects of Patient and Hospital Characteristics on Inpatient length of Stay by Stepwise Multiple Regression Analysis 29
4.4. Predictive Effects of Patient and Hospital Characteristics on Inpatient Costs by Stepwise Multiple Regression Analysis 30
4.5. Factors Associated with the Likelihood of Prolonged Hospital Stay (LOS≧31 days) by Univariate and Multiple Logistic Regression Analysis 31
5. DISCUSSION 33
5.1. Cross-country Comparison of Inpatient Length of Stay and Costs for Acute Stroke Patients 34
5.2. Predictive Factors for Inpatient LOS, Costs and Prolonged Hospital Stay for Stroke Patients 37
5.3. Clinical and Policy Implications for Stroke Care 43
5.4. Study Limitations 47
6. CONCLUSIONS AND RECOMMENDATIONS 49
REFERENCES 50
表 目 錄
Table 1: Basic characteristics for patients admitted to acute wards Vs. admitted/transferred to rehabilitation wards (N=2,639) 59
Table 2: Basic characteristics of sub-sample admitted to acute wards without transfer to other wards or hospitals (N=2,358) 60
Table 3: Inpatient length of stay (LOS, days) by patient demographics for patients admitted to acute wards Vs. admitted /transferred to rehabilitation wards (N=2,639) 61
Table 4: Inpatient LOS by patient clinical characteristics for patients admitted to acute wards Vs. admitted/ transferred to rehabilitation wards (N=2,639) 62
Table 5: Inpatient medical costs by patient demographics for patients admitted to acute wards Vs. admitted/ transferred to rehabilitation wards (N=2,639) 63
Table 6: Inpatient medical costs by patient clinical characteristics for patients admitted to acute wards Vs. admitted/ transferred to rehabilitation wards (N=2,639) 64
Table 7: Inpatient LOS and medical costs by patient demographics for sub-sample admitted to acute wards without transfer to other wards or hospitals (N=2,358) 65
Table 8: Inpatient LOS and medical costs by patient clinical characteristics for sub-sample admitted to acute wards without transfer to other wards or hospitals (N=2,358) 66
Table 9: Inpatient LOS and medical costs by Charlson comorbidity categories for sub-sample admitted to acute wards without transfer to other wards or hospitals (N=2,358) 67
Table 10: Inpatient LOS and medical costs by admission hospital characteristics for sub-sample admitted to acute wards without transfer to other wards or hospitals (N=2,358) 68
Table 11: Stepwise Multiple Regression Analysis Predicting Acute LogLOS* (N=2,358 ; Adjusted R2=.407) 69
Table 12: Stepwise Multiple Regression Analysis Predicting Acute LogLOS* (N=2,358 ; Adjusted R2=.413) (Enter individual Charlson comorbidity item instead of CCI into the model) 70
Table 13: Stepwise Multiple Regression Analysis Predicting Acute LogCosts* (N=2,358 ; Adjusted R2=.539) 71
Table 14: Stepwise Multiple Regression Analysis Predicting Acute LogCosts* (N=2,358 ; Adjusted R2=.545) (Enter individual Charlson comorbidity item instead of CCI into the model) 72
Table 15 LOS and Costs in Acute Hospital Stay: Divided by PHS and Non-PHS (N=2,358) 73
Table 16: Comparison of Proportion of PHS among Patients of Different Characteristics (N=2,358) 74
Table 17 Factors Associated with Prolonged Hospital Stay (LOS ³ 23 days) by Multiple Logistic Regression Analysis* (N=2,358) 75
圖 目 錄
Fig.1 Subject Flow Chart 76
Fig.2 Research Framework 77
Fig.3 Basic Characteristics of Subjects 78
Fig.4 Acute LOS (N=2499) and Total LOS (combined acute and rehabilitation stay) (N=140) Stratified by Stroke Type 79
Fig.5 Acute LOS (N=2499) Stratified by Stroke Severity Proxies 80
Fig.6 Acute LOS (N=2499) Stratified by Complications / Comorbidity 81
Fig.7 Acute LOS Stratified by Hospital Characteristics (N=2,358) 82

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