参考文献:1. Boyle A, Beniuk K, Higginson I, Atkinson P (2012) Emergency department crowding: time for interventions and policy evaluations. Emerg Med Int 2. Moskop, JC, Sklar, DP, Geiderman, JM, Schears, RM, Bookman, KJ (2009) Emergency department crowding, part 2鈥揵arriers to reform and strategies to overcome them. Ann Emerg Med 53: pp. 612-617 2008.09.024" target="_blank" title="It opens in new window">CrossRef 3. Moskop, JC, Sklar, DP, Geiderman, JM, Schears, RM, Bookman, KJ (2009) Emergency department crowding, part 1鈥揷oncept, causes, and moral consequences. Ann Emerg Med 53: pp. 605-611 2008.09.019" target="_blank" title="It opens in new window">CrossRef 4. (2007) Hospital-based emergency care: at the breaking point, Institute of Medicine, Committee on the Future of Emergency Care in the United States Health System: Washington, D.C. p. 1鈥?24 5. Powell, ES, Khare, RK, Venkatesh, AK, Roo, BD, Adams, JG, Reinhardt, G (2012) The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med 42: pp. 186-196 2010.06.028" target="_blank" title="It opens in new window">CrossRef 6. Olshaker, JS (2009) Managing emergency department overcrowding. Emerg Med Clin North Am 27: pp. 593-603 2009.07.004" target="_blank" title="It opens in new window">CrossRef 7. Hoot, NR, Aronsky, D (2008) Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med 52: pp. 126-136 2008.03.014" target="_blank" title="It opens in new window">CrossRef 8. Welch, SJ, Asplin, BR, Stone-Griffith, S, Davidson, SJ, Augustine, J, Schuur, J (2011) Emergency department operational metrics, measures and definitions: results of the second performance measures and benchmarking summit. Ann Emerg Med 58: pp. 33-40 2010.08.040" target="_blank" title="It opens in new window">CrossRef 9. United States General Accounting Office (2003) Hospital emergency crowded conditions vary among hospitals and communities 10. Asplin, BR, Magid, DJ, Rhodes, KV, Solberg, LI, Lurie, N, Camargo, CA (2003) A conceptual model of emergency department crowding. Ann Emerg Med 42: pp. 173-180 2003.302" target="_blank" title="It opens in new window">CrossRef 11. Fatovich, DM, Hirsch, RL (2003) Entry overload, emergency department overcrowding, and ambulance bypass. Emerg Med J 20: pp. 406-409 20.5.406" target="_blank" title="It opens in new window">CrossRef 12. Fatovich, DM, Nagree, Y, Sprivulis, P (2005) Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia. Emerg Med J 22: pp. 351-354 2004.018002" target="_blank" title="It opens in new window">CrossRef 13. Abraham, J, Reddy, MC (2010) Challenges to inter-departmental coordination of patient transfers: a workflow perspective. Int J Med Inform 79: pp. 112-122 2009.11.001" target="_blank" title="It opens in new window">CrossRef 14. Peck J, Benneyan J, Gaehde S, Nightingale D, Boston VHA (2012) Models for using predictions to facilitate hospital patient flow, in Healthcare Systems Process Improvment Conference: p. 1鈥? 15. Leegon J, Aronsky D (2006) Impact of different training strategies on the accuracy of a Bayesian network for predicting hospital admission. AMIA Annual Symposium Proceedings: p. 474鈥?78 16. Leegon J, Jones I, Lanaghan K, Aronsky D (2005) Predicting hospital admission for Emergency Department patients using a Bayesian network. AMIA Annual Symposium Proceedings: p. 1022 17. Li J, Guo L, Handly N (2009) Hospital Admission Prediction Using Pre-hospital Variables, in 2009 I.E. International Conference on Bioinformatics and Biomedicine. IEEE. p. 283鈥?86 18. Li, J, Guo, L, Handly, N, Mai, AA, Thompson, DA (2012) Semantic-enhanced models to support timely admission prediction at emergency departments. Netw Model Anal Health Inform Bioinform 1: pp. 161-172 21-012-0014-6" target="_blank" title="It opens in new window">CrossRef 19. Sun, Y, Heng, BH, Tay, SY, Seow, E (2011) Predicting hospital admissions at emergency department triage using routine administrative data. Acad Emerg Med 18: pp. 844-850 2712.2011.01125.x" target="_blank" title="It opens in new window">CrossRef 20. Xie, B (2013) Development and validation of models to predict hospital admission for emergency department patients. Int J Stat Med Res 2: pp. 55-65 21. Peck, JS, Benneyan, JC, Nightingale, DJ, Gaehde, SA (2012) Predicting emergency department inpatient admissions to improve same-day patient flow. Acad Emerg Med 19: pp. E1045-E1054 2712.2012.01435.x" target="_blank" title="It opens in new window">CrossRef 22. Stover-Baker, B, Stahlman, B, Pollack, M (2012) Triage nurse prediction of hospital admission. J Emerg Nurs 38: pp. 306-310 2011.10.003" target="_blank" title="It opens in new window">CrossRef 23. Cameron A, Rodgers K, Ireland A, Jamdar R, McKay GA (2014) A simple tool to predict admission at the time of triage. Emerg Med J 24. Khouja, M (1999) The Single-period (news-vendor) problem: literature review and suggestions for future research. Omega Int J Manag Sci 27: pp. 537-553 CrossRef 25. Wachtel, RE, Dexter, F (2010) Review article: review of behavioral operations experimental studies of newsvendor problems for operating room management. Anesth Analg 110: pp. 1698-1710 213/ANE.0b013e3181dac90a" target="_blank" title="It opens in new window">CrossRef 26. Olivares, M, Terwiesch, C, Cassorla, L (2008) Structural estimation of the newsvendor model: an application to reserving operating room time. Manag Sci 54: pp. 41-55 287/mnsc.1070.0756" target="_blank" title="It opens in new window">CrossRef 27. Biyu He, FD, Macario, A, Zenios, S (2012) The timing of staffing decisions in hospital operating rooms: incorporating workload heterogeneity into the newsvendor problem. Manuf Serv Oper Manag 14: pp. 99-114 28. Green, LV, Savin, S, Savva, N (2013) 鈥淣ursevendor problem鈥? personnel staffing in the presence of endogenous absenteeism. Manag Sci 59: pp. 2237-2256 287/mnsc.2013.1713" target="_blank" title="It opens in new window">CrossRef 29. Ding, R, McCarthy, ML, Desmond, JS, Lee, JS, Aronsky, D, Zeger, SL (2010) Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression. Acad Emerg Med 17: pp. 813-823 2712.2010.00812.x" target="_blank" title="It opens in new window">CrossRef 30. Sun, Y, Teow, KL, Heng, BH, Ooi, CK, Tay, SY (2012) Real-time prediction of waiting time in the emergency department, using quantile regression. Ann Emerg Med 60: pp. 299-308 2012.03.011" target="_blank" title="It opens in new window">CrossRef 31. Wiler, JL, Handel, DA, Ginde, AA, Aronsky, D, Genes, NG, Hackman, JL, Hilton, JA, Hwang, U, Kamali, M, Pines, JM, Powell, E, Sattarian, M, Fu, R (2012) Predictors of patient length of stay in 9 emergency departments. Am J Emerg Med 30: pp. 1860-1864 2012.03.028" target="_blank" title="It opens in new window">CrossRef 32. Casalino E, Wargon M, Peroziello A, Choquet C, Leroy C, Beaune S, Pereira L, Bernard J, Buzzi JC (2013) Predictive factors for longer length of stay in an emergency department: a prospective multicentre study evaluating the impact of age, patient's clinical acuity and complexity, and care pathways. Emerg Med J: EMJ 33. Kocher, KE, Meurer, WJ, Desmond, JS, Nallamothu, BK (2012) Effect of testing and treatment on emergency department length of stay using a national database. Acad Emerg Med 19: pp. 525-534 2712.2012.01353.x" target="_blank" title="It opens in new window">CrossRef 34. Rathlev, NK, Chessare, J, Olshaker, J, Obendorfer, D, Mehta, SD, Rothenhaus, T, Crespo, S, Magauran, B, Davidson, K, Shemin, R, Lewis, K, Becker, JM, Fisher, L, Guy, L, Cooper, A, Litvak, E (2007) Time series analysis of variables associated with daily mean emergency department length of stay. Ann Emerg Med 49: pp. 265-271 2006.11.007" target="_blank" title="It opens in new window">CrossRef 35. Faddy, M, Graves, N, Pettitt, A (2009) Modeling length of stay in hospital and other right skewed data: comparison of phase-type, gamma and log-normal distributions. Value Health 12: pp. 309-314 24-4733.2008.00421.x" target="_blank" title="It opens in new window">CrossRef 36. Marazzi, A, Paccaud, F, Ruffieux, C, Beguin, C (1998) Fitting the distributions of length of stay by parametric models. Med Care 36: pp. 915-927 CrossRef 37. Gardiner JC (2013) Modeling heavy-tailed distributions in healthcare utilization by parametric and Bayesian methods. J Stat Theory Pract 38. Falvo, T, Grove, L, Stachura, R, Vega, D, Stike, R, Schlenker, M, Zirkin, W (2007) The opportunity loss of boarding admitted patients in the emergency department. Acad Emerg Med 14: pp. 332-337 2712.2007.tb02017.x" target="_blank" title="It opens in new window">CrossRef 39. Health Management Associates (2011) Impact of proposed budget cuts to community- based mental health services, in Texas Conference of Urban Counties 40. Kingman, JFC (1962) On queues in heavy traffic. J R Stat Soc Ser B Methodol 24: pp. 383-392 41. Kulkarni, SS, Magazine, MJ, Raturi, AS (2004) Risk pooling advantages of manufacturing network configuration. Prod Oper Manag 13: pp. 186-199 2004.tb00154.x" target="_blank" title="It opens in new window">CrossRef 42. Technology VHI (2010) VistA Emergency Department Intergration Software User Guide Version 1.0, Department of Veteran Affairs Health Systems Desing and Development 43. Tipping, ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1: pp. 211-244 44. Aronsky, D, Kendall, D, Merkley, K, James, BC, Haug, PJ (2001) A comprehensive set of coded chief complaints for the emergency department. Acad Emerg Med 8: pp. 980-989 2712.2001.tb01098.x" target="_blank" title="It opens in new window">CrossRef 45. The Pattern Recognition Toolbox (PRT) for MATLAB, CoVar Research: http://www.newfolderconsulting.com 46. Hand, DJ, Till, RJ (2001) A simple generalization of the area under the ROC curve to multiple class classification problems. Mach Learn 45: pp. 171-186 23/A:1010920819831" target="_blank" title="It opens in new window">CrossRef 47. The R Project for Statistical Computing: http://www.r-project.org 48. Cox, DR (1972) Regression models and life-tables. J R Stat Soc Ser B Methodol 34: pp. 187-220 49. Partovi, SN, Nelson, BK, Bryan, ED, Walsh, MJ (2001) Faculty triage shortens emergency department length of stay. Acad Emerg Med 8: pp. 990-995 2712.2001.tb01099.x" target="_blank" title="It opens in new window">CrossRef 50. Russ, S, Jones, I, Aronsky, D, Dittus, RS, Slovis, CM (2010) Placing physician orders at triage: the effect on length of stay. Ann Emerg Med 56: pp. 27-33 2010.02.006" target="_blank" title="It opens in new window">CrossRef 51. Nestler, DM, Fratzke, AR, Church, CJ, Scanlan-Hanson, L, Sadosty, AT, Halasy, MP, Finley, JL, Boggust, A, Hess, EP (2012) Effect of a physician assistant as triage liaison provider on patient throughput in an academic emergency department. Acad Emerg Med 19: pp. 1235-1241 2010" target="_blank" title="It opens in new window">CrossRef
刊物类别:Business and Economics
刊物主题:Economics Operation Research and Decision Theory Health Administration Health Informatics and Administration Management Econometrics Business
出版者:Springer Netherlands
ISSN:1572-9389
文摘
Emergency departments (ED) in hospitals are experiencing severe crowding and prolonged patient waiting times. A significant contributing factor is boarding delays where admitted patients are held in ED (occupying critical resources) until an inpatient bed is identified and readied in the admit wards. Recent research has suggested that if the hospital admissions of ED patients can be predicted during triage or soon after, then bed requests and preparations can be triggered early on to reduce patient boarding time. We propose a cost sensitive bed reservation policy that recommends optimal bed reservation times for patients. The policy relies on a classifier that estimates the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. Results from testing the proposed bed reservation policy using data from a VA Medical Center are very promising and suggest significant cost saving opportunities and reduced patient boarding times.