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題名 | 應用倒傳遞類神經網路預測醫療機構盈餘之研究=An Empirical Study on Forecasting Hospital Profitability Using Back-propagation Neural Network |
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作 者 | 郭怡婷; 柯伯昇; | 書刊名 | 醫務管理期刊 |
卷期 | 23:2 2022.06[民111.06] |
頁次 | 頁183-197 |
分類號 | 419.22 |
關鍵詞 | 財務報表; 全民健康保險; 迴歸分析; 倒傳遞類神經網路; 敏感度分析; Financial statement; National health insurance; Regression analysis; Back-propagation neural network; Sensitivity analysis; |
語文 | 中文(Chinese) |
DOI引用網址 | 10.6174/JHM.202206_23(2).183 |
中文摘要 | 目的:本研究期望建立準確性高且適合醫院特性的財務預測模型來幫助醫療機構預 測利潤以達永續經營之最終目的。 方法:本研究以全台非公立區域醫院 2013-2017 年財務報表建立獲利預測模型。 比較迴歸分析與倒傳遞類神經網路二者之預測能力並擇優建構預測模型,最後以 2018 年醫事機構財務報表驗證模型之有效性。 結果:研究結果顯示,以倒傳遞類神經網路預測結果之準確性為「合理」。依敏感 度分析顯示醫務收入淨額、人事費用、藥品費用及醫材費用為影響醫務毛利率之重 要參數。 結論:倒傳遞類神經網路透過良好的變數組合及適當的學習訓練,可得到良好的預 測效果。醫院管理者未來選擇財務預測模型時,若樣本特性無法符合迴歸分析之基 本假設,則可考量使用倒傳遞類神經網路,以得到良好的預測效果。 |
英文摘要 | Objectives: This study established a highly accurate financial profitability forecasting model suitable for hospitals to predict future profits in order to achieve the goal of sustainable operations. Methods: This study used the key financial indicators on financial statements of non- public regional hospitals from 2013-2017 in Taiwan to establish a financial profitability forecasting model. This research started with constructing a regression model based on financial theory. After that, we compared the regression model’s forecasting accuracy to it by the Back-propagation neural network model which used the same variables with the regression model, then selected the better model which has less mean absolute percentage error (MAPE). Finally, the validity of the forecasting model was verifi ed by the hospitals’ financial statements of year 2018. Furthermore, the sensitivity analysis was adopted to analyze the degree of influence of the financial explanatory variables on profitability ratio. Results: The results showed that the accuracy of the prediction results by the Back- propagation neural network model was “reasonable” which was better than the regression model’s. Furthermore, sensitivity analysis showed that net medical operating revenue, personnel expenses, drug expenses and medical materials expenses were important financial indicators that affected the gross profit margin of medical services. Conclusions: The Back-propagation neural network obtained a better predicting accuracy through an appropriate combination of variables, proper learning and training than regression analysis. Therefore, the Back-propagation neural network might be a good analysis tool to obtain better forecasting results when hospital managers want to do financial profitability forecasting. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。