臺灣博碩士論文加值系統

背景與目的：藉風險校正合理設定個人的支付金額，可以避免論人計酬制度所帶來就醫公平與風險選擇的問題。風險校正模型端賴良好的風險校正因子來從事未來醫療費用的推估、合理反映個人醫療需求。由於診斷和處方用藥資訊包含豐富的臨床訊息，以診斷因子和用藥處方因子進行校正已逐漸成為國際研究的主要方向。我國早期風險校正研究結果顯示國內之診斷因子，具有相當之預測力，較具發展性。我國醫療費用結構，藥品費用約佔門診醫療支出的1/3，而門診藥費的成長，又以慢性病藥費的增加為主。故本研究欲發展整合用藥處方與診斷資訊之風險校正模式，期望提供前瞻式預算分配參考。
材料與方法：本研究利用全民健保投保對象之基本資料，以及特約醫療院所2000年與2001年對於西醫醫療費用所申報之診斷資料與用藥處方資料，建構健康狀態風險計價模式。運用先前利用因子、PCG因子、我國學者所發展TSAG與TPIPDCG因子，與本研究自行發展的診斷與用藥處方資訊整合因子，共建立九個風險校正模型。並以R2和預測比評估其對個人醫療費用及特定群體醫療費用之預測力。
結果：運用較多的醫療資訊，可以使風險校正模型具有更好的預測能力。各模型中，以具有TSAG因子與診斷與用藥處方整合因子表現最好；整合因子的預測表現略優於TSAG因子。整合因子相較單以門診診斷或是門診用藥處方，的確具有更佳之預測力。以特定慢性疾病分類來看，整合因子在某些慢性病類別上，有較佳的預測比。
結論：整合慢性病用藥與診斷資訊，確可更進一步提升風險校正模型預測能力。同時在特定之慢性病族群上，也具有更精確之預測比，有助於風險選擇的抑制。由於並未針對本土之醫療型態，修正國外用藥處方因子，未來應可針對我國用藥型態修正，並與次診斷資訊予以整合。

Objective：Using risk adjustment to set personal premium reasonably can avoid the problem of risk selection and ensure the equity of access to care. By appropriate risk adjusters, a risk adjustment method can not only predict personal medical expense but also reasonably reflect medical need. Diagnosis–based adjusters and prescribed drugs adjusters have attracted the research attention for their rich clinical messages. Results of early studies in Taiwan demonstrated outstanding predictability and potentiality. Reviewing medical expense structure in Taiwan, one-third of outpatient medical expense is spent on prescription drugs, and chronic disease prescriptions account for the main portion in drug expenses. This study intends to refine diagnostic risk adjusters with prescription information to improve predictability of risk adjustment models in Taiwan.
Data and methods：With detailed enrollment data, medical expense data, diagnostic and prescription data of contracted institution in 2000 and 2001, this study constructed health-based risk assessment models. Using PCG adjusters, Taiwan’s outpatient and inpatient diagnosis-based adjusters, and the adjusters combining diagnostic and prescription data, this study constructed nine risk adjustment models and evaluated the predictions to medical expenses of individuals and specific subgroups.
Principal findings：More clinical information improves the predictability. In all models, the models with the TSAGs and the adjuster combining diagnostic and prescription data outperformed other methods, and the combined adjusters slightly outperform TASG adjusters, Compared with either diagnostic or prescription information, combined information improved the predictability of risk adjustment models. In particular, in specific chronic disease groups, the combined adjusters demonstrated a better predictive ratio.
Conclusion：Using prescription information to refine diagnosis-based risk by splitting it into refined cost group can improve the risk adjustment model not only in all models but also predictive ratio of specific chronic disease group, which helped to avoid risk selection. This study didn’t modify prescribed drug adjusters by local condition. Future research on modifying by local prescription habits and combining with secondary diagnostic information is suggested.

目 錄

致謝••••••••••••••••••••••••••••••••••••••••i
中文摘要••••••••••••••••••••••••••••••••••••ii
英文摘要••••••••••••••••••••••••••••••••••••iii
目錄••••••••••••••••••••••••••••••••••••••••v
表目錄••••••••••••••••••••••••••••••••••••••vi
第一章 緒論•••••••••••••••••••••••••••••••••1
第二章 文獻探討••••••••••••••••••••••••••••••4
第三章 研究方法••••••••••••••••••••••••••••••11
第四章 研究結果••••••••••••••••••••••••••••••15
第五章 討論•••••••••••••••••••••••••••••••••19
參考文獻•••••••••••••••••••••••••••••••••••••24
表格•••••••••••••••••••••••••••••••••••••••••29

表目錄
表一、三個版本CDS的比較•••••••••••••••••••••••••29
表二、用藥因子風險校正研究•••••••••••••••••••••••29
表三、不同版本Rx model的比較•••••••••••••••••••••31
表四、研究樣本年齡性別分層比較•••••••••••••••••••32
表五、研究樣本使用各項服務比例與金額••••••••••••••33
表六、PCG用藥處方群組分類示例與樣本數分佈••••••••••••••••••••••••••••••••••34
表七、TSAG用藥處方群組分類示例與樣本數分佈•••••••••••••••••••••••••••••••35
表八、TPIPDCG分類示例與樣本數分佈•••••••••••••••••••••••••••••••••••••••••••••••••36
表九、整合因子分類示例與樣本數分佈••••••••••••••••••••••••••••••••••••••••••••••••37
表十、風險計價模式預測力分析•••••••••••••••••••••••••••••••••••••••••••••••••••••••••••39
表十一、風險計價模式預測比分析••••••••••••••••••••••••••••••••••••••••••••••••••••••••40

中文部分
中央健保局，全民健康保險統計，歷年資料。
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張睿詒、賴秋伶：風險校正因子：論人計酬醫療費用預測之基礎。台灣衛誌2004；23（2）：91-9。
蔡偉德、羅紀瓊：「論人計酬」之支付標準探討。經濟論文2000；28：231-61。
謝其政：利用全民健康保險資料建立風險計價模式之初探。臺灣大學醫療機構管理研究所碩士論文。
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英文部分

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