醫院手術室效能影響因素與效能評估方法之發展

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：51

、訪客IP：18.118.253.117

姓名

陳姵君(Pei-Chun Chen) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

醫院手術室效能影響因素與效能評估方法之發展
(A Study of Performance Indicators and Influence Factors in the Surgical Rooms of Hospitals)

相關論文

★ 企業流程為核心的食品產業運籌體系規劃:低溫物流部份	★ 以企業流程方法規劃整體飛航後勤維修體系之研究
★ 成衣產業導入ERP運籌管理方案之個案研究	★ 光電產業試產基地之生產最佳化模式：以光投影機為例
★ TFT LCD產業獲利因素之探討	★ 科技事業進行合併/讓售之決策過程與成效之個案探討
★ 企業治理、風險及遵循解決方案導入之個案研究-以職責分離資訊系統為例	★ 品質機能展開與多準則決策於設備開發應用
★ ERP導入品質因素對IFRS轉換專案之影響	★ ERP投資金額對服務品質及導入後IT治理目標之分析
★ ERP 導入問題對專案的影響	★ IFRS轉換對員工退休金計畫影響
★ IFRS轉換問題對IFRS效益的影響	★ 電子產業新產品開發參考模式之發展
★ 應用資料科學方法提昇國防裝備可靠度之研究－以防空系統為例	★ 企業資訊方案行銷歷程之探討-以MES為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

台灣自全民健保實施後，民眾選擇就醫的第一考量不再是成本，而是轉向能夠提供最高品質、最佳醫療服務的醫療機構。因此，醫院管理的重點漸漸由早期所強調的醫療品質，進一步延伸至經營品質的概念。由於醫院手術室擁有精密且昂貴設備，且其所擁有的設備資源占全醫院預算的9~10%（Viapiano. et al. 2000），所以本研究致力於手術室的績效改善，找出手術室的績效指標與影響手術室績效的因素。
資料來源為台灣某醫學中心之手術室資料，運用迴歸分析及決策樹為基礎的分析方法進行分析及比較。以迴歸分析找出關鍵影響因素，發現全身麻醉且手術複雜度較高的病患，其手術費可能較高，且麻醉費、手術時間及手術時間的變異，會隨著手術項目個數的平方成長。此外，使用決策樹中的分類樹方法，找出預測績效指標的重要影響因素，手術費、麻醉費及手術時間最重要的影響因素皆為是否全身麻醉，而手術時間的變異程度之最重要的影響因素則為手術複雜度。院方可依此作為排程順序的考量，或是後續醫療品質及流程優化的重點。

摘要(英)

After the implementation of Taiwan’s national health insurance system, people choosing hospitals by its quality and service instead of expenditure. Recently, the hot topics in hospital management have changed from medical quality to management quality. Because there are delicate and expensive devices which occupied 9% to 10% of the budgets over the hospital in surgical rooms, this paper focus on the performance improvement of the surgical rooms, aiming on finding the factors that influence the performance.
We used both regression and decision tree methods to analyze data from one of the medical centers in Taiwan and compared the results. By the regression method, we discovered that general anesthesia patients with more procedures might bring more surgery revenue. In addition, by using classification and regression tree (C&R tree) to find the most important factor when predicting performance, we found that whether general anesthesia patients is the most important factor predicting surgery revenue, anesthesia revenue, and surgery duration time, while the complexity of the surgery affects the variance of surgery the most. The results provided could be the reference of surgery scheduling and sequencing, or process optimization for better healthcare quality.

關鍵字(中)

★ 手術室
★ 效能評估
★ 迴歸分析
★ 決策樹
★ 分類樹

關鍵字(英)

論文目次

一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 論文結構 2
二、相關研究 3
2-1 醫院品質與成本 3
2-2 手術室排程 4
三、研究模型之發展 10
3-1 手術室病患就醫過程分析 12
3-2 資料前處理 13
3-3 以迴歸分析為基礎的方法 14
3-4 以決策樹為基礎的方法 16
四、實證結果與分析 18
4-1 個案醫院簡介 18
4-2 分析結果 18
五、結論及建議 38
5-1 模型結果比較 38
5-2 實務貢獻 38
5-3 研究限制 39
5-4 未來研究方向建議 40
參考文獻 41
附錄 44
一、適合度檢定 44
二、多變量方法 44
三、迴歸分析 46

參考文獻

Coley, K. C., Williams, B. A., DaPos, S. V., Chen, C., & Smith, R. B. (2002). Retrospective evaluation of unanticipated admissions and readmissions after same day surgery and associated costs. J Clin Anesth, 14, 349-353.
Devi, S., Rao, K., & Sangeetha, S. (2010). Prediction of Surgery Times and Scheduling of Operation Theaters in Optholmology Department. Journal of Medical Systems, 1-16.
Dexter, F., Abouleish, A. E., Epstein, R. H., Whitten, C. W., & Lubarsky, D. A. (2003). Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesth Analg, 97, 1119-1126.
Dexter, F., Macario, A., Penning, D. H., & Chung, P. (2002). Development of an appropriate list of surgical procedures of a specified maximum anesthetic complexity to be performed at a new ambulatory surgery facility. Anesth Analg, 95, 78-82.
Dimick, J. B., Staiger, D. O., Baser, O., & Birkmeyer, J. D. (2009). Composite measures for predicting surgical mortality in the hospital. Health Aff (Millwood), 28, 1189-1198.
Eijkemans, M. J. C., van Houdenhoven, M., Nguyen, T., Boersma, E., Steyerberg, E. W., & Kazemier, G. (2010). Predicting the Unpredictable A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon’s Estimate. Anesthesiology, 112, 41-49.
Eisenstein, E. L., Bethea, C. F., Muhlbaier, L. H., Davidian, M., Peterson, E. D., Stafford, J. A., & Mark, D. B. (2005). Surgeons’ economic profiles: can we get the "right" answers? Journal of Medical Systems, 29, 111-124.
Fleisher, L. A., Pasternak, L. R., & Lyles, A. (2007). A novel index of elevated risk of inpatient hospital admission immediately following outpatient surgery. Arch Surg, 142, 263-268.
Hollingsworth, J. M., Ye, Z., Strope, S. A., Krein, S. L., Hollenbeck, A. T., & Hollenbeck, B. K. (2010). Physician-ownership of ambulatory surgery centers linked to higher volume of surgeries. Health Aff (Millwood), 29, 683-689.
Houdenhoven, M., Hans, E., Klein, J., Wullink, G., & Kazemier, G. (2007). A Norm Utilisation for Scarce Hospital Resources: Evidence from Operating Rooms in a Dutch University Hospital. Journal of Medical Systems, 31, 231-236.
Iregui, M., Ward, S., Sherman, G., Fraser, V. J., & Kollef, M. H. (2002). Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator-associated pneumonia. CHEST, 122, 262-268.
Kumar, A., & Shim, S. J. (2006). Simulating staffing needs for surgical instrument distribution in hospitals. J Med Syst, 30, 363-369.
Litvak, E., & Bisognano, M. (2011). More patients, less payment: increasing hospital efficiency in the aftermath of health reform. Health Aff (Millwood), 30, 76-80.
May, J. H., Spangler, W. E., Strum, D. P., & Vargas, L. G. (2011). The Surgical Scheduling Problem: Current Research and Future Opportunities. Production and Operations Management, 20, 392-405.
McManus, M. L., Long, M. C., Cooper, A., Mandell, J., Berwick, D. M., Pagano, M., & Litvak, E. (2003). Variability in surgical caseload and access to intensive care services. Anesthesiology, 98, 1491-1496.
O’Neill, L., & Dexter, F. (2007). Tactical increases in operating room block time based on financial data and market growth estimates from data envelopment analysis. Anesth Analg, 104, 355-368.
Overdyk, F. J., Harvey, S. C., Fishman, R. L., & Shippey, F. (1998). Successful strategies for improving operating room efficiency at academic institutions. Anesthesia & Analgesia, 86, 896-906.
Ozkarahan, I. (2000). Allocation of surgeries to operating rooms by goal programing. J Med Syst, 24, 339-378.
Pakdil, F., & Harwood, T. N. (2005). Factors That Influence Efficiency in Performing ENT Cases: A Qualitative and Quantitative Analysis. Journal of Medical Systems, 29, 285-301.
Schuster, M., Standl, T., Wagner, J. A., Berger, J., Reimann, H., & Am Esch, J. S. (2004). Effect of different cost drivers on cost per anesthesia minute in different anesthesia subspecialties. Anesthesiology, 101, 1435-1443.
Sobolev, B., Harel, D., Vasilakis, C., & Levy, A. (2008). Using the Statecharts paradigm for simulation of patient flow in surgical care. Health Care Management Science, 11, 79-86.
Sobolev, B. G., Sanchez, V., & Vasilakis, C. (2011). Systematic Review of the Use of Computer Simulation Modeling of Patient Flow in Surgical Care. Journal of Medical Systems, 35, 1-16.
Strum, D. P., May, J. H., & Vargas, L. G. (2000). Modeling the uncertainty of surgical procedure times - Comparison of log-normal and normal models. Anesthesiology, 92, 1160-1167.
Strum, D. P., Sampson, A. R., May, J. H., & Vargas, L. G. (2000). Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology, 92, 1454-1466.
Wickizer, T. M. (1991). Effect of hospital utilization review on medical expenditures in selected diagnostic areas: an exploratory study. Am J Public Health, 81, 482-484.
Wullink, G., Van Houdenhoven, M., Hans, E. W., van Oostrum, J. M., van der Lans, M., & Kazemier, G. (2007). Closing emergency operating rooms improves efficiency. J Med Syst, 31, 543-546.

指導教授

呂俊德(Jun-Der Leu)

審核日期

2013-1-9

推文