請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18528
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
dc.contributor.author | Yin-Hsia Chou | en |
dc.contributor.author | 周吟霞 | zh_TW |
dc.date.accessioned | 2021-06-08T01:09:59Z | - |
dc.date.copyright | 2014-09-03 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2014-08-18 | |
dc.identifier.citation | [1] Adan, I. J., and Vissers, J. M., “Patient Mix Optimisation in Hospital Admission Planning: A Case Study,” International Journal of Operations and Production Management , 22 (4), 445 -461 (2002).
[2] Anderson, H. R., ” Increase in hospital admissions for childhood asthma: trends in referral, severity, and re-admissions from 1970 to 1985 in a health region of the United Kingdom,” Thorax, 44, 614-619 (1989). [3] Ayvaz, N. and Huh, W., “Allocation of hospital capacity to multiple types of patients,” Journal of Revenue and Pricing Management, 9(5), 386–398 (2010). [4] Benneyan, J. C., “An Introduction to Using Computer Simulation in Healthcare: Patient Wait Case Study,” Journal of the Society for Health Systems , 5, 1-15 (1997). [5] Brumelle, S., and Walczak, D., “Dynamic Airline Revenue Management with Multiple Semi-Markov Demand,” Operations Research , 51 (1), 137-148 (2003). [6] Carr, S. and Duenyas, I., “Optimal admission control and sequencing in a make-to-stock/make-to-order production system,” Operations Research, 48(5), 709–720 (2000). [7] Chambers M., Clarke, A. “Measuring readmission rates,“ British Medical Journal, 301, 1134–1136 (1990). [8] Clarke, A., “Are re-admissions avoidable? “ British Medical Journal, 301, 1136-1138 (1990). [9] Ding, Q., Kouvelis, P. and Milner, J., “Dynamic pricing through customer discounts for optimizing multi-class customers demand fulfillment,” Operations Research, 54(1), 169–183 (2006). [10] Duenyas, I. “Single facility due date setting with multiple customer classes,” Operations Research, 48(5), 709–720 (2000). [11] El-Haber, S., and El-Taha, M., “Dynamic Two-Leg Airline Seat Inventory Control with Overbooking, Cancellations and No-Shows,” Journal of Revenue and Pricing Management, 3 (2), 43-170(2004). [12] Gerchak, Y., Gupta, D. and Henig,M. “Reservation Planning for Elective Surgery under Uncertain Demand for Emergency Surgery” Management Science, 42(3), 321-334 (1996). [13] Gorunescu, F., McClean, S. I. and Millard, P. H. “A queueing model for bed-occupancy management and planning of hospitals” Journal of the Operational Research Society, 53, 19–24(2002). [14] Green,L.V., Savin, S., Wang, B. “Managing Patient Service in a Diagnostic Medical” Operations Research, 54(1) (2006) [15] Gunal, M., Pidd, M., “Discrete event simulation for performance modeling in health care: a review of the literature,” Journal of Simulation, 4, 42-51 (2010). [16] Gupta, D. and Wang, L., “Manufacturing capacity revenue management”, http://www.me.umn.edu/labs/scorlab/ (2008). [17] Harper, R., Shahani, A. “Modeling for the planning and management of bed capacities in hospitals,” Journal of the Operational Research Society, 53(1), 11–18, (2002) [18] Helm, J.E., Beygi,S.A., Oyen,M.P.V., “Design and Analysis of Hospital Admission Control for Operational Effectiveness,” Production and Operations Management 20(3), 359–374 (2002) [19] Holloran, T. and Byrn, J., “United Airlines station manpower planning system,” Interfaces, 16, 39-50 (1986) . [20] Jencks, S.F., Williams, M.V., Coleman, E.A., “Rehospitalizations among Patients in the Medicare Fee-for-Service Program,” New England Journal of Medicine, 360, 1418-1428 (2009). [21] Kim, S.C., Horowitzb, I., “Scheduling hospital services: the efficacy of elective-surgery quotas,” Omega, 30, 335 – 346 (2002) [22] Koidea, T., and Ishii, H., “The Hotel Yield Management with Two Types of Room Prices, Overbooking and Cancellations,” International Journal of Production Economics , 93-94 (8), 417-428(2005). [23] Kolisch, R., and Sickinger, S., “Providing Radiology Health Care Services to Stochastic Demand of Different Customer Classes,” OR Spectrum, 30 (2), 375-395 (2008). [24] Kunnumkal, S., and Topaloglu, H., “A New Dynamic Programming Decomposition Method for the Network Revenue Management Problem with Customer Choice Behavior,” Production and Operations Management, 19 (5), 575-590 (2010). [25] Lai, K.-K., and Ng, W.-L., “A Stochastic Approach to Hotel Revenue Optimization,” Computers and Operations Research, 32 (5), 1059-1072 (2005). [26] Langabeer II, J. R., Health Care Operations Management: A Quantitative Approach to Business and Logistics, Jones & Bartlett Learning.(2008) [27] Littlewood, K., “Forecasting and Control of Passenger Bookings,” AGIFORS Symposium Proceedings, 12, 95-117 (1972). [28] Liu, N., Ziya, S., and Kulkarni, V. G., “Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations,” Manufacturing and Service Operations Management, 2, 247-364 (2010). [29] Maglaras, C. and Zeevi, A., “Pricing and design of differentiated services: Approximate analysis and structural insights,” Operations Research, 53(2), 242–262 (2005). [30] Min, D. and Yih, Y., “An elective surgery scheduling problem considering patient priority,” Computers & Operations Research, 37, 1091-1099 (2010). [31] Mooney, C. Z., Monte Carlo Simulation, 116, Sage Publications(1997). [32] Patrick, J. and Puterman, M.L. “Improving resource utilization for diagnostic services through flexible inpatient scheduling: A method for improving resource utilization,” Journal of Operations Research Society, 58(1), 235–245 (2007). [33] Patrick, J., Puterman, M. L., and Queyranne, M., “Dynamic Multipriority Patient Scheduling for a Diagnostic Resource,” Operations Research, 56 (6), 1507-1525 (2008). [34] Patrick, J. “A Markov decision model for determining optimal outpatient scheduling,” Health Care Management science, 5(2), 91-102 (2012). [35] Petrou, S., and Wolstenholme, J. “A Review of Alternative Approaches to Healthcare Resource Allocation,” Pharmacoeconomics, 18(1), 33-43 (2000). [36] Ratcliffe, A., Gilland, W., and Marucheck, A., “Revenue Management for Outpatient Appointments: Joint Capacity Control and Overbooking with Class-Dependent No-Shows,” Flexible Services and Manufacturing Journal, Online First (2011). [37] Reed, R.L., Pearlman, R.A., Buchner, D.M., “Risk Factors for Early Unplanned Hospital Readmission in the Elderly,” Journal of General Internal Medicine, 6, 223-228 (1991). [38] Rovin, S., “Medicine and Business: Bridge the Gap”, Aspen Publisher. [39] Schutz, H.-J., & Kolisch, R., “Approximate Dynamic Programming for Capacity Allocation in the Service Industry,” European Journal of Operational Research, 218 (1), 239-250 (2012). [40] Subramanian, J., Stidham Jr, S., and Lautenbacher, C. J., “Airline Yield Management with Overbooking, Cancellations, and No-Shows,” Transportation Science, 33 (2), 147-167 (1999). [41] Tsai, K.L., Leey, A. C., and Patrick, A. R., “Hospital re-admissions: an empirical analysis of quality management in Taiwan,” Health Services Management Research, 14, 92-103 (2001). [42] White, D. L., Froehle, C. M., Kenneth, J. K., “The Effect of Integrated Scheduling and Capacity Policies on Clinical Efficiency” Production and Operations Management, 20(3), 442–455 (2011). [43] Young, J.P., “A queuing theory approach to the control of hospital inpatient census” Unpublished Doctoral dissertation, IE department, The Johns Hopkins University. (1962) [44] Yang, K.J., Cheng, S., Hu, P. M., Liaw, S.J., Hu W. S., Chin, H. K., “Causes of Unplanned Readmission within 14 Days – A Preliminary Study” Taiwan Journal of Family Medicine, 17(4), 199-209 (2007). [45] 吳重慶、葉淑娟、葉宏明、顏裕庭、黃明和,”醫療品質”,秀傳醫學雜誌,3(1),25-29,(2001)。 [46] 莊念慈、黃國哲、許怡欣、郭乃文、魏中仁,”醫院因應總額支付制度之策略方案及其相關因素探討”,臺灣公共衛生雜誌,23(2),150-8,(2004)。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18528 | - |
dc.description.abstract | 本研究即探討具有回流性質的等候系統之允入機制,醫療產業中之住院部門即為一具回流性質系統,病患出院後可能會於短時間內再次住院,因此對於醫療機構而言有兩類不同的病患需求,初住院病患與再住院病患。再住院病患的病情通常較初次住院的病患來的嚴重,需要立即的照護,因此基於人道立場醫院應保留資源給潛在再住院病患,然而保留太多或太少皆會對整個醫療系統產生負面影響。
本研究即以動態規劃為方法,提出一動態的病患允入機制,考量兩類需求來到、服務率等不確定性,於各期不同的狀態下求得最佳允入水準。並提出回流醫療系統病患允入模型(Re-entrant Healthcare System Inpatients Admission, R-HSIA),其為一啟發式動態允入模型,用以趨近動態規劃之最佳解。為驗證RHSIA模型求解後所得策略之可行性,我們建構一模擬醫院允入控制流程程式,並與其他常見之醫院病患允入策略(先到先服務法FCFS、各科各自決策法SE)進行實驗設計,將本研究求得之R-HSIA允入決策與模擬結果比較分析。模擬結果說明本啟發式模型,在醫療供給與需求相近時啟發式允入模型效用優於其他兩種方法,並可見其之可行性與穩健性。另外,本研究所提出的R-HSIA模型不但能處理較複雜的資源分配問題,同時也能加快求解速度。最後我們也進行程式模擬來驗證模型的正確性與可行性。 | zh_TW |
dc.description.abstract | This research studies flexible resources allocation problem in re-entrant healthcare systems. For healthcare service, inpatient department is an example of re-entrant system. Inpatients may return shortly after original discharge from the hospital. Therefore, for the inpatient department, there are two types of demands: initial admission and readmission patients. In practice, readmitted patients are usually sicker than initial patients. Thus, for humanity consideration, hospital should give readmission patients higher priority. However, the real demand of readmission patients is usually unpredictable. In order to reserve resources for potential readmission demand, hospital might need to control the number of initial admission patients. Nevertheless, either too much or too little resource reservation has negative impact on healthcare institute. Too much reservation may generate higher resource idle cost, and too little reservation may cause higher rejection cost of readmission patients. To solve this resource allocation problem, it is necessary to develop an efficient admission control mechanism for healthcare service providers.
In this research, we propose a dynamic admission control model to allocate a central pool of resources between initial admissions and readmissions patients. We also consider uncertainties of initial admission demands, readmission demands, and number of high risk discharges in the model. At the beginning of each period, we have to decide the number of initial patients can be admitted. By adopting a stochastic dynamic programming approach, we obtain an optimal admission policy that can achieve minimal total cost in the long term. At last, we develop a heuristic model, re-entrant healthcare system inpatients admission model, as a simpler alternative to the optimal policy. Our models are validated in extensive simulation study. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T01:09:59Z (GMT). No. of bitstreams: 1 ntu-102-R00546002-1.pdf: 1846691 bytes, checksum: 69b4886209e834b2d6f524fd329dd806 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 第一章 緒論 1
1.1 研究背景與動機 1 1.2 研究目的 2 1.3 研究流程 2 第二章 文獻回顧 5 2.1 製造服務系統之資源配置 5 2.1.1 製造產業之資源配置 5 2.1.2 旅遊產業之資源配置 6 2.2 醫療服務產業 7 2.2.1 醫療資源配置 8 2.2.2 病床管理 11 2.2.3 再住院病患 12 2.3 小結 13 第三章 問題描述與模型建構 14 3.1 問題描述 14 3.2 符號定義 15 3.3 研究假設 17 3.4 單科動態規劃模型 17 3.4.1 動態規劃模型 17 3.4.2 動態模型求解步驟 21 3.5 多科動態規劃模型 21 第四章 回流醫療系統病患允入模型 26 4.1 符號定義 26 4.2 回流醫療系統病患允入模型(R-HSIA)建構 27 4.3 模擬邏輯 32 4.4 其他允入機制 34 4.5 範例 35 4.5.1 單一科別允入範例 35 4.5.2 兩科別允入範例 37 第五章 實驗設計與參數分析 39 5.1 實驗因子 39 5.2 實驗結果分析 44 5.3 績效差異顯著檢定 53 5.3.1 檢定流程說明 53 5.3.2 假設檢定結果 54 第六章 結論與未來展望 62 6.1 結論 62 6.2 未來展望 63 參考文獻 64 附錄一:模擬結果說明(Run2) 68 附錄二:模擬結果 70 | |
dc.language.iso | zh-TW | |
dc.title | 具回流性質之醫療系統動態資源配置 | zh_TW |
dc.title | Dynamic Admission Control and Flexible Resource Allocation for Re-entrant Healthcare Service | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳文智(Wen-Chih Chen),喻奉天,洪一薰 | |
dc.subject.keyword | 醫療服務,動態規劃,產能配置,再住院病患,允入控制, | zh_TW |
dc.subject.keyword | Healthcare,inpatient department,stochastic dynamic programming,admission control,readmission, | en |
dc.relation.page | 91 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2014-08-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-102-1.pdf 目前未授權公開取用 | 1.8 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。