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研究生:林月英
研究生(外文):Yuehing Lin
論文名稱:基層診所申報預審決策支援系統之建立與評估
論文名稱(外文):The Development and Evaluation of the National Health Insurance Claim Preview Support System for the Primary Care Clinics
指導教授:張博論張博論引用關係
指導教授(外文):Polum Chang
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:87
中文關鍵詞:醫療費用決策支援系統族群分析區別分析基層醫療
外文關鍵詞:Health Care ExpenditureDecision Support SystemClustering AnalysisDiscriminant AnalysisPrimary Care
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為了節制醫療資源,健保局在以論量計酬為主軸之支付制度之下所實施之管理措施著重財務平衡,其中加強醫療審查為節制醫療資源的重要措施之一,而加強審查之方法先以檔案分析,找出醫療供給者申報之費用量及單價指標高於同儕者為加強審查之主要對象,而對於其他非加強審查之醫療提供者則以一般方式進行審查。審查之方式仍以事後書面抽樣審查為主,加強審查之方式為將檔案分析結果提供專業審查醫師參考,必要時可進行逐案審查。審查時醫療服務已經完成,無法瞭解當時的病情,且抽樣審查見樹不見林,審查結果據以核刪費用,侵犯醫療提供者的專業自主性,因此導致醫療提供者的不滿。衡諸世界各國之審查方式,有系統的運用檔案分析的技巧,以電腦作業進行統計分析,篩選特殊醫療院所加強審查,其他通過篩選之醫療院所則快速通過審查或不予審查,以尊重醫療提供者的專業自主。
本研究利用檔案分析,將西醫基層醫師1,704位醫師區分為A、B、C三個族群,其中A族群計157位醫師,佔9.21%;B族群計694位醫師,佔40.7%;C族群計853位醫師,佔50.05%,依據研究結果各科各族群之特性,建議族群A應加強審查,族群B可以一般方式審查,族群C則可以免予審查或減量抽審,惟仍應偵測其費用申報變化情形及醫療品質狀況。.
由於現行審查制度專業審查尺度不一,核減率及平均核減金額並無法顯示本研究不同族群之間之差異性。惟百分之五十的C族群醫師可以由該等醫師自主管理;再依申報件數統計,需加強審查之件數占16.93%,一般審查之件數占62.18%,減量抽審或免審的件數占20.89%,依據健保局中區分局之審查時間統計,平均每件門診案件需用2.48分鐘,所節省的審查資源,可以加強審查族群A醫師申報之案件,以提昇審查效率
本研究以區別分析歸類模型對各科醫師分類結果,與族群分析結果之歸類比較,其誤歸機率顯示內科1.8%為最低,其正確率達98.2%,誤歸機率最高之科別為家醫科4.08%,其正確率最低,但亦達95.82%之準。顯示區別分析模式之預測能力強。
To control the medical resources, the Nation Health Insurance Bureau emphasis on the management of financial balance under fee for service payment system, in which to enhance medical review is one of the important policies. The method is to analysis the claim data, to find the hospitals or doctors whose claim data is higher then the others, and examine the cases sampled from they claimed. One of the defects of the examination method is the bias of the sampling, the other is the time lag. The result became to violet the supplier’s professionalism, and lead to dissatisfaction of the suppliers. Comparing to other national health insurance countries, they usually adopted computerized profiling analysis system to grabble special suppliers to review their claim data, whereas other normal suppliers pass quickly without reviewing.In this research, we try to build a computerized profiling analysis system., to classify 1,704 doctors to cluster A, cluster B and cluster C. In which , there are 157 doctors belong to cluster A(9.21%),694 doctors belong to cluster B(40.7%), 853 doctors belong to cluster C(50.05%). According to the characteristics of different clusters, we suggested a different level of review: Cluster A should be reinforced examined; cluster B should be examined in general way; cluster C should pass quickly without examining or reduce the quantity of samples.
The main problem of existing review methods is the large variance between the scale of professional review, so that according to our research, the result of review, reduce rate and reduce amount per clinic can’t reveal the difference between clusters. Nevertheless, fifty percent doctors who belong to cluster C don’t have to be reviewed, only 16.93 percent doctors’ claim cases should be reinforced examined. Therefore, the classifying method leads to a more efficiency review method.
The discriminate model shows that the probability of miss classification is under4.08%. It means that the discriminate model is powerful.
第一章 研究動機與目的
第一節 研究動機
第二節研究目的
第二章 文獻探討
第一節基層醫療現況
第二節我國全民健保醫療費用審核制度之類型
第三節檔案分析
第四節族群分析與區別分析
第五節決策支援系統
第三章 研究方法
第一節研究架構
第二節研究對象
第三節研究步驟
第四節系統開發工具
第四章 研究結果
第一節基層診所申報門診費用分析
第二節建立分類模型
第三節模型之誤歸機率及效益評估
第四節建立使用者介面
第五章 討論與建議
第一節討論
第二節建議
第三節 研究限制
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