臺灣博碩士論文加值系統

義大醫院於2011年3月29日正式啟用實驗室自動化系統，有別於前人僅就各單獨項目進行探討，本人則選擇對全部項目進行研究。經由該實驗室蒐集自動化前(2010年7月至2011年3月)與自動化後(2011年4月至2011年12月)18個月數據，一共1,826,444筆檢驗項目報告之週轉時間，計算每月週轉時間之平均值，再將所得18個平均值按自動化前、後分為2組進行雙因子變異數分析後，結果顯示在95%信賴水準(P=.05)之下，自動化前、後週轉時間顯著不同，週轉時間自88.8分鐘降至74.4分鐘，下降14.4分鐘，下降比率為16.2%。而將各月週轉時間區分為急檢和常規樣本2類，計算每月週轉時間之平均值，再分別按自動化前、後分為2組進行雙因子變異數分析後，結果顯示自動化前、後急檢樣本週轉時間無顯著不同(P=.05)；而自動化前、後常規樣本週轉時間顯著不同(P=.05)，且下降29.0分鐘，下降比率為21.3%。此結果應代表該實驗室自動化前、後各檢驗項目報告之週轉時間縮短的原因，主要因為常規樣本週轉時間縮短。此可由急檢樣本週轉時間對於原週轉時間之相關係數為0.36；常規樣本週轉時間對於原週轉時間之相關係數為0.99，而獲得驗證。當將各月週轉時間選取時間排序前90%數據，計算每月週轉時間之平均值，再分別按自動化前、後分為2組進行雙因子變異數分析後，結果顯示自動化前、後90%週轉時間顯著不同(P=.05)，且下降10.8分鐘，下降比率為15.3%。經與原週轉時間分析結果比較之下，顯示以90%週轉時間進行分析時，將導致對自動化後效率提升的低估，即將16.2%低估為15.3%；而自動化前、後TAT Outlier平均值亦顯著不同，代表拖尾情形改善且下降47.0分鐘，下降比率為18.5%，但因拖尾部分僅占10%，無法完整呈現週轉時間分布狀態，故建議以100%TAT數據進行效率分析。而將各月週轉時間區分為生化類和血清類樣本2類，計算每月週轉時間之平均值，再分別按自動化前、後分為2組進行雙因子變異數分析後，結果顯示在95%信賴水準之下，自動化前、後生化類TAT平均值顯著不同，且下降13.7分鐘，下降比率為16.1%；而自動化前、後血清類TAT平均值顯著不同，且下降18.2分鐘，下降比率為14.7%。此結果應代表該實驗室自動化前、後各檢驗項目報告之週轉時間縮短的原因，同時來自生化類及血清類樣本週轉時間縮短之貢獻。此可由生化類及血清類樣本週轉時間對於原週轉時間之相關係數分別為1.00及0.96(P=.05)而獲得驗證。我們也發現當報告錯誤率降低時院內員工對檢驗客服務滿意度則上升；反之亦然。

A Laboratory Automation System (LAS) was implemented in E-Da hospital since 29 March 2011. We chose every result-reported data of all determination items instead of single item. We collected 1,826,444 reported data on laboratory turnaround time (TAT) from E-Da hospital during both pre- (July 2010 to March 2011) and post-LAS (April to December 2011). All data were classed with months and 18 TAT means were calculated. A Two-Factor analysis of variance was applied to the means and we found a significant difference, from 88.8 min. to 74.4 min., between pre- and post-LAS under a 95% confidence level. This showed a result of TAT improvement of 14.4 min. (16.2%) after LAS implemented. The data were seperated into Stat or Routine sets after classed with months, and means of each set of data were calculated. The result of Two-Factor analyses of variance on the separated Stat or Routine sets revealed that the Stat TAT did not change significantly (P=.05) and the Routine TAT, as a result of improvement of 29.0 min. (21.3%), did. This showed an improvement of Routine TAT contributes to the improvement of TAT. The former conclusion could be demostrated that Routine TAT correlated best with the TAT (r2=0.99, P=.05) and Stat TAT correlated poor with the TAT (r2=0.36, P=.05). As to 90% TAT analysis, it seemes that an improvement from 14.4 min. to 10.8 min. (16.2% to 15.3%) will be underestimated if 90% TAT was sifted from original TAT and analyzed. And a TAT Outlier analysis, tail size, seems to improve better, reduced 47 min. (18.5%), than 90% TAT analysis. We suggest a total TAT analysis instead of 90% TAT or Outlier ones. Both Clinical Chemistry (CC) and Immune Assay (IA) TAT did change significantly (P=.05), as a result of improvement of 13.7 min. (16.1%) and 18.2 min. (14.7%) each other, when the data were seperated into CC or IA sets. This showed an improvement of TAT after automation was contributed by both the improvement of CC and IA TAT. It could be also demostrated by the best correlation relationship of CC and IA TAT with the TAT (r2=1.00 and 0.96, P=.05). We also found fewer laboratory errors after automation and this result consisted with staff satisfacation. In other words, the staff of the hospital were much satisfied with LAS when a fewer laboratory errors was found from the reports.

誌謝..................................................................................................................................i
中文摘要.........................................................................................................................iii
Abstract .......................................................................................................................... v
目 錄.............................................................................................................................vi
圖目錄............................................................................................................................vii
表目錄...........................................................................................................................viii
第一章 緒論...........................................................................................................1
第一節 研究背景與動機 .....................................................................................1
第二節 研究目的 .................................................................................................2
第二章 文獻探討...................................................................................................4
第一節 自動化檢驗方式的流程介紹...................................................................4
第二節 實驗室自動化之現況………….............................................................12
第三節 探討各相關自動化系統有關效率之評估方式.....................................15
第四節 探討醫院實驗室效率之評估方式….....................................................17
第三章 研究方法….............................................................................................20
第一節 研究架構 ...............................................................................................20
第二節 研究設計 ...............................................................................................20
第三節 研究資料收集….....................................................................................21
第四章 研究結果與討論.....................................................................................24
第一節 自動化前後之TAT(Turn Around Time，週轉時間)分析....................24
第二節 自動化前後報告更改率及客戶滿意度分析.........................................40
第三節 綜合討論………………….....................................................................47
第五章 結論與建議.............................................................................................58
參考文獻.........................................................................................................................61
中文部份 .......................................................................................................................61
英文部份 .......................................................................................................................62
附 錄..................................................................................................................附錄1

中文部分：
1. 黃芳玫，羅紀瓊，”台灣地區低收入戶醫療照護初探”，人口、就業與福利 1996，中央研究院經濟研究所。
2. 袁建中，”績效評估模式規劃計畫期末報告”，2001 台灣經濟部中小企業處。
3. 陳敏，陳磊，藍小鵬，”LAS 與 LIS的系統整合於優化，中國醫療衛生裝備期刊第31卷07期 2010。
4. 方世榮，”統計學”，1987五南圖書出版公司。
5. 彭游，吳水丕，”生物統計學”，1994 合計圖書出版社。
英文部分：
1.CAP (College of American Pathologists) Today (2012). Laboratory automation systems and workcells.
2.Hawker, C. D., Roberts, W. L., Garr, S. B., Hamilton, L. T., Penrose, J. R., Ashwood, E. R., & Weiss, R. L. (2002), , Automated Transport and Sorting System in a Large Reference Laboratory: Part 2. Implementation of the System and Performance Measures over Three Years, Clinical Chemistry 48, 10.
3.Charnes, A., Cooper, W.W. & Rhodes, E., “Measuring the Efficiency of Decision Making Unit (1978), European Journal of Operational Research, 2(6), 429-444.
4.Lam, C. W. & Jacob, E. (2010), Implementing Laboratory Automation System Experience of a Large Clinical Laboratory, JALA, 8, 1.
5.Fornell, C., Johnson, M. D., Anderson, E. W., J. Cha, & Bryant, B. E. (1996), The American customer Satisfaction index: nature, purpose, and findings, Journal of Marketing, 60, 7-18.
6.Seaberg, Richard S., Stallone, Robert O. & Statland, Bernard E. (2000), The Role of Total Laboratory Automation in a Consolidated Laboratory Network, American Association For Clinical Chemistr.
7.Hawkins, Robert C. (2007), Turnaround Time”, Clin Biochem Rev 28.
8.Kaplan, Robert S. & Norton, David P. (1992), The Balanced Scorecard: Measures that Drive Performance, Harvard Business Review 1-2
9.Melanson, Stacy E. F.; Lindeman, Neal I.; Jarolim, Petr (2007), Selecting Automation for the Clinical Chemistry Laboratory”, Arch Pathol Lab Med, 131, 7.