臺灣博碩士論文加值系統

醫療照護產業是服務業中具有獨特性質的產業，全球大多數已開發國家都面臨相似的困境：維持提供高品質的醫療照護前提下，同時為每個國民提供可負擔的醫療照護服務。隨著醫學科技在近代以前所未有的速度進展，醫療照護在規模和複雜性上不斷提升，迫使醫療照護系統從原本僅提供治療患者服務，轉變為同時需要來自衛生保健，醫療專家，醫院管理者甚至整個系統中的保險政策制定者共同投注心力的系統。
本研究從醫療管理的角度出發，以醫院病床管理為主體，分為三個階段：第一個階段研究主題為住院流程、出院流程及住院管理資訊系統的可用性評估，研究結果發現與住院長度預測相關的決策支援功能為使用者認為住院管理資訊系統最迫切應具備的系統功能；第二個階段研究主題為個案醫院心臟疾病患者住院長度預測之研究，透過線性回歸與類神經網路建立預測模型，並比較不同入院階段的住院長度預測表現；第三階段進一步應用個案醫院的住院長度預測模型建立程序及方法至全國性健保研究資料庫。

Most developed countries face the continuous pressure to keep offering the best and affordable health care services to every citizen, especially for counties with universal health care coverage. One of the most challenging issue in the definition of service quality and the cost structure in the healthcare industry lies in the lack of transparency or even common ground when comparing among clinics and hospitals. For acute-care inpatient facilities, the length of stay (LOS) is considered as a general measure associated with the complexity of treatment needed for individual patients.
In this research, a questionnaire-based approach is first proposed to evaluate the use of HIS in terms of usability factors. The perception of the satisfaction and expectation in these factors is then investigated using the generalized version of importance-performance analysis, or referred to as dynamic importance-performance analysis. The artificial neural network (ANN) models is constructed to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei. The National Health Insurance Research Database (NHIRD) of Taiwan is then applied to demonstrate the feasibility and the predictivity of the national-level LOS prediction model.

摘　要 i
ABSTRACT ii
誌　謝 iv
目　錄 v
圖目錄 vii
表目錄 ix
第 1 章 緒論 1
1.1. 研究背景 1
1.2. 研究目的與議題 5
1.2.1. 研究架構與流程 6
第 2 章 文獻回顧 7
2.1. 醫療資訊系統 7
2.1.1. 醫療資訊系統使用 8
2.1.2. 影響醫療資訊系統使用因素 9
2.2. 資訊系統可用性 12
2.2.1. 可用性 12
2.2.2. 醫療資訊科技可用性評估方法 14
2.2.3. 可用性問卷調查 16
2.3. LOS預測 17
2.3.1. 影響LOS的因素 17
2.3.2. 不同階段LOS預測應用於評估疾病風險 17
2.3.3. 心臟疾病的LOS預測 18
第 3 章 醫療資訊系統可用性評估 20
3.1. 住院管理資訊系統可用性評估 20
3.1.1. 住院管理資訊系統可用性問卷設計 21
3.1.2. 住院管理資訊系統可用性問卷調查結果 22
3.1.3. 住院管理資訊系統可用性問卷調查結果小結 26
3.2. 出院管理資訊系統可用性動態重視度表現值分析 27
3.2.1. 動態重視度表現值分析 28
3.2.2. 出院作業流程 30
3.2.3. 出院管理資訊系統可用性問卷設計 32
3.2.4. 出院管理資訊系統可用性問卷調查結果 32
3.2.5. 出院管理資訊系統可用性問卷調查結果小結 38
第 4 章 個案醫院心臟疾病患者LOS預測 39
4.1. 研究方法 39
4.1.1. 資料來源與資料前處理 39
4.1.2. 統計分析 41
4.1.3. 類神經網路結構 43
4.2. 研究結果 46
4.2.1. 冠狀動脈硬化患者預測結果 47
4.2.2. 心肌梗塞與心臟衰竭患者預測結果 48
4.2.3. 類神經網路模型驗證 49
4.2.4. 討論與小結 51
第 5 章 全國性心臟疾病患者LOS預測 53
5.1. 全國性健康保險資料庫 53
5.1.1. 全國性醫療系統 53
5.1.2. 台灣全民健康保險研究資料庫(NHIRD) 54
5.2. 資料收集與前處理 58
5.2.1. 資料申請與收集期間 58
5.2.2. 資料前處理 59
5.3. 敘述統計 64
5.3.1. 異常值診斷與篩選 64
5.3.2. 敘述統計分析 66
5.4. 統計分析 74
5.4.1. 皮爾森相關係數分析 74
5.4.2. 全國性心臟疾病患者LOS統計迴歸模型 76
5.5. 全國性住院長度預測模型 81
5.6. 討論與小結 85
第 6 章 研究結論與建議 87
6.1. 個別研究結果回顧與討論 87
6.1.1. 具LOS預測相關決策支援功能的住院管理資訊系統 87
6.1.2. 結構化的單一醫院入院前LOS預測可有效預測 88
6.1.3. 應用NHIRD建構全國性LOS預測模型可行且有效 88
6.2. 研究限制與未來研究方向 89
6.2.1. 資料合併的適切性 89
6.2.2. 疾病本身的複雜度與分類演進 90
6.2.3. 全國性LOS預測模型應用於醫院績效評估工具 92
參考文獻 94
附錄一、住院管理資訊系統可用性問卷調查 受訪者樣本結構 102
附錄二、出院管理資訊系統可用性問卷調查 受訪者樣本結構 103
附錄三、主診斷對應ICD-9-CM代碼與名稱 104

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