應用電腦模擬技術於SARS期間醫療等候線之評估

An Application of the Computer Simulation Technique for Evaluation of Waiting Line during the SARS Period

本研究靜、應用等候理論與模擬（simulation ） 工具於SARS 防疫期間，評 估台灣中部地區某一醫學中心體溫量測站之體溫量測情形，以了解樣本醫院 體溫量測站之資源配置與作業流程動態、顧客於等候線平均等候之時間。本 研究運用電腦模擬工具，建立樣本醫院體溫量測站作業流程之模擬模式，並 提出改善方案，以縮短病人等候時間，提升醫療品質與病人滿意度。 研究方法：以數位攝影機收集樣本資料，將資料紀錄彙整後，先以Microsoft Excel 及SPSS 10.0 版進行統計分析，包括描述性統計及單因子變異數 分析，再以Best-Fit 統計套過分析軟體，求出最佳時間分佈模型，最後將所分 析之背景資料透過MedModel 模擬軟體建構體溫量測站運作之模擬模型。 根據研究結果發現：體溫量測系統之瓶頸因素，主要為「人」之不可 預期狀況，包括病人及體溫量測人員，造成體溫量測系統中斷，因此除了加 強體溫量測站之人員宣導及管制，最好準備備用儀器。經由模擬現況與scenanos '暸解樣本醫院體溫量測站人員與儀器配置是充裕的，同時本研究經過 模擬驗證及確認，證明實際情形與模擬結果並無顯著之差異，因此模擬理論 與工具，可以實際應用在醫療機構SARS 期間等候線之評估。

This research applied Waiting-line Theory and the simulation technique to evaluate the body temperature measuring stations of one medical center in central Taiwan during the SARS period. This could help to understand the customer waiting time, disposition of resources, and the operation flow tendency of the body temperature measuring stations in the sample hospital. This research used the computer simulation technique to establish simulation pattern of the working flow in the body temperature measuring stations and proposed the improvement plans. The results can help to reduce patient waiting time and to promote service quality and patient satisfactions. The data collection method: using digital cameras to take picture in sample hospital. After collecting and recording data, the data were first been statistic analysis, including descriptive statistics and single-factor ANOVA by Microsoft Excel and SPSS 10.0 edition. Then the data were fitted by the Best-Fit statistics to get the data distribution model. Finally we constructed the simulation model of body temperature measuring stations by the MedModel simulation software. According research results, we found that the bottleneck factor mainly is 'the person' unexpected situations, including the patients and measuring staffs will cut off the body temperature measuring system. Therefore, besides to strengthen and control patients of the body temperature measuring stations, also prepare the extra instruments. By way of simulation and scenarios, we realized the disposition of personnel and the instruments are sufficient in sample hospital. After the validation and verification of the simulation, there is no significant difference between actual situation and the simulation results. We concluded that the simulation theory and technique could be applied to evaluate patients waiting line of hospitals during the SARS period.