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    Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/2093

    Title: 預測心臟內科門診病患到診時間之研究
    The Study on the Prediction of OPD Patient's Arrival Time for Internal Heart Department
    Authors: 白佳原;溫梓平;黃筱涵;孫元梅;施艾瑋;黃冠凱
    Jar-Yuan Pai;Tzu-Ping Wen;Shiao-Han Huang;Yuan-Mei Sun;Ai-Wei Shih;Kuan-Kai Huang
    Contributors: 中山醫學大學
    Keywords: 預測;等候時間;複迴歸模型;醫院
    waiting time;Multiple regression;predict;hospital
    Date: 2008-12-01
    Issue Date: 2010-08-11T03:46:27Z (UTC)
    Publisher: 教務處出版組
    Abstract: 由於台灣封閉式之醫院醫師制度,造成了目前醫院的門診病患排隊等候原因,病患必須在門診等候看醫生。然而,太多病患在等候區不只會浪費病人的時間,而且還可能增加醫院成本。雖然樣本醫院超過50%的病患做了預約,但資訊系統仍無法確切告知病患何時到醫院。為了減少門診病患之等候時間,本研究依據每位醫師看診風格與習慣,監測門診病患等候時間與到診時間,作出適合的預測模式。樣本醫院在2007年11月推動實施ISO,預測心臟內科門診病患到診時間為ISO品質目標之一。研究方法:由於大型教學醫院願意提供必要之數據,故被選為樣本醫院。1927筆病患的到達及等候時間被收集,進行複迴歸分析。開診至病患看診的時間為非獨立變數,獨立變數為診號、醫師編號、初複診、VIP號、看診時段,以上變數進入複迴歸模型。本研究使用STATISTICA Version 7.1進行統計,例如F檢定和複迴歸分析。研究結果:最後得到的最適當之複迴歸模型為:Y=a0+a1X1+a2X2,R=45.5%, adjusted R^2=20.7%, F檢定顯示此模型為適當的。結論與建議:樣本醫院的MIS(Management Information System)系統結合本研究之複迴歸模型,可預測出更有效的病患等候時間,同時可增加即時通知病人到診時間:例如透過醫院網頁公告、電話通知,鼓勵門診病患利用電話或網路預約掛號。
    Due to the closed staff system of Taiwan's hospitals, patients usually have to wait in line to see doctors in the outpatient department (OPD). Too many patients in the waiting area not only waste patients' time, it also increases hospital cost. Although more than 50% of patients waiting in to see their physicians have already made reservations because the system cannot tell the patients when to come to the hospital. Following each physician's style and interview pattern, this study estimated the time that patients should come into the OPD. Methods: At a large teaching hospital, we collected the arrival and waiting times of 1927 patients. We then conducted multiple regression model using patients waiting time as a dependent variable and the patient attendance number, physicians, newly diagnosed patients, patients with special condition (VIP), and the doctor's office shift as independent variables. Statistica Version 7.1 was used to perform F test and multiple regressions. Results: The proper multiple model to determine patient waiting time was Y=a0+a1X1+a2X2 Y, where a0 was intercept, X1 patient attendance number, X2 physician number, R=45.5%, adjusted R^2=20.7%. The F tests showed the model to be proper. Conclusions and Suggestions: Management information systems (MIS) can be merged with the regression models used in this research. This can be used to further refine predictive ability of the models and it can be used when patients make reservation on the website or by telephone.
    URI: https://ir.csmu.edu.tw:8080/handle/310902500/2093
    Relation: 中山醫學雜誌, v19 n.2 p187-197
    Appears in Collections:[教務處] 期刊論文

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