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Emergency Departments: “Repairs While You Wait, No Appointment Necessary”

  • Kenneth N. McKay
  • Jennifer E. Engels
  • Sahil Jain
  • Lydia Chudleigh
  • Don Shilton
  • Ashok Sharma
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 184)

Abstract

In this chapter we focus on the detailed, operational modeling of Emergency Departments—the flows and general processes, and not on clinical decision making. On the flow level, an Emergency Department shares a number of characteristics with a general repair shop, and while there are key differences, the flow and resource interrelationships are similar. We use this perspective to assist researchers with the decomposition and analysis of Emergency Departments, as well as the review of detailed research on Emergency Departments. We examine the scope of research efforts, methodologies employed, types of data included in the modeling, and the implementation of research results in practice. The chapter is anchored by an extensive field study at a medium-sized Emergency Department, whose methodology and key results are presented along with insights from the hospital.

Keywords

Emergency Department Wait Time Arrival Pattern Inpatient Ward Nonteaching Hospital 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The chapter has benefited from the critical suggestions of the reviewers and by Dr. Blair Egerdie, Gary Black, Fatih Erenay, John Buzacott, Reha Uzsoy, and Brian Denton. Their suggestions have been very insightful and beneficial. The chapter would not have been possible without the tremendous support and assistance of the hospital and the people working in the Emergency Department. During the study the analysis team received a great deal of help from the nurses, clerks, porters, and physicians. Additional valuable assistance came from other units in the hospital as well. The analysis team consisted of Jennifer Engels, Sahil Jain, and Kenneth McKay. At the time of the study, Jennifer Engels was completing her MASc and volunteered to be a research assistant on the project. Sahil Jain was a senior medical student and participated in the study as part of his educational program.

References

  1. Andersson AK, Omberg M, Svedlund M (2006) Triage in the emergency department—a qualitative study of the factors which nurses consider when making decisions. Nurs Crit Care 11(3): 136–145CrossRefGoogle Scholar
  2. Anthony RN (1965) Planning and control systems: a framework for analysis. Harvard Business School, BostonGoogle Scholar
  3. Asaro PV, Lewis LM, Boxerman SB (2007) The impact of input and output factors on emergency department throughput. Acad Emerg Med 14(3):235–242CrossRefGoogle Scholar
  4. Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr (2003) A conceptual model of emergency department crowding. Ann Emerg Med 42(2):173–180CrossRefGoogle Scholar
  5. Babbage C (1832) On the economy of machinery and manufactures, 2nd edn. Charles Knight, LondonGoogle Scholar
  6. Bagust A, Place M, Posnett JW (1999) Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 319(7203):155–158CrossRefGoogle Scholar
  7. Bailey NTJ (1956) Statistics in hospital planning and design. J R Stat Soc Ser C 5(3):146–157Google Scholar
  8. Beaulieu H, Ferland JA, Gendron B, Michelon P (2000) A mathematical programming approach for scheduling physicians in the emergency room. Health Care Manag Sci 3:193–200CrossRefGoogle Scholar
  9. Bell J (2011) A&E waiting times increase sharply. The Guardian, Tuesday 5 April 2011. Web link: http://www.guardian.co.uk/society/2011/apr/04/accident-emergency-waiting-times-increase. Accessed 25 Nov 2012
  10. Blake JT, Carter MW, Richardson S (1996) An analysis of emergency room wait time issues via computer simulation. INFOR 34(4):263–273Google Scholar
  11. Boldy D (1976) A review of the application of mathematical programming to tactical and strategic health and social services problems. Oper Res Q 27(2):439–448CrossRefGoogle Scholar
  12. Buzacott JA, Shanthikumar JG (1993) Stochastic models of manufacturing systems. Prentice-Hall, New JerseyGoogle Scholar
  13. CIHI—Canadian Institute for Health Information (2011) Wait times in Canada—a comparison by province. CIHIGoogle Scholar
  14. Carter MW, Lapierre SD (2001) Scheduling emergency room physicians. Health Care Manag Sci 4:347–360CrossRefGoogle Scholar
  15. Carter MW, Blake JT (2005) Using simulation in an acute-care hospital: easier said than done. In: Brandeau M, Sainfort F, Pierskalla W (eds) Handbook of operations research in health care. Kluwer, Boston, pp 191–215Google Scholar
  16. Chan L, Reilly KM, Salluzzo RF (1997) Variables that affect patient throughput times in an academic emergency department. Am J Med Qual 12(4):183–186CrossRefGoogle Scholar
  17. Chisholm CD, Collison EK, Nelson DR, Cordell WH (2000) Emergency department workplace interruptions: are emergency physicians “interrupt-driven” and “multitasking”. Acad Emerg Med 7(11):1239–1243CrossRefGoogle Scholar
  18. Coiera E, Tombs V (1998) Communication behaviours in a hospital setting: an observational study. BMJ 315:673–676CrossRefGoogle Scholar
  19. Coleman JV, Errera P (1963) The general hospital emergency room and its psychiatric problems. Am J Public Health 53–8:1294–1301Google Scholar
  20. Connelly LG, Bair AE (2004) Discrete event simulation of emergency department activity: a platform for system-level operations research. Acad Emerg Med 11:1177–1185CrossRefGoogle Scholar
  21. Crane J, Noon C (2011) The definitive guide to emergency department operational improvement. Productivity Press, New YorkGoogle Scholar
  22. Dobson G, Lee HH, Sainathan A, Tilson V (2012) A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility. MSOM 14(4): 584–599CrossRefGoogle Scholar
  23. Downey LA, Zun LS (2007) Determinates of throughput times in the emergency department. J Health Manag 9(1):51–58CrossRefGoogle Scholar
  24. Duguay C, Chetouane F (2007) Modeling and improving emergency department systems using discrete event simulation. Simulation 83(4):311–320CrossRefGoogle Scholar
  25. Facchin P, Rizzato E, Romanin-Jacur G (2010) Emergency department generalized flexible simulation model. In: 2010 IEEE workshop on health care management (WHCM). Venice, Italy, pp 1–6Google Scholar
  26. Fairbanks RJ, Bisantz AM, Sunm M (2007) Emergency department communication links and patterns. Ann Emerg Med 50(4):396–406CrossRefGoogle Scholar
  27. Fetter RB, Thompson JD (1965) The simulation of hospital systems. Oper Res 13(5):689–711CrossRefGoogle Scholar
  28. Fisher M (2007) Strengthening the empirical base of operations management. MSOM 9(4): 368–382CrossRefGoogle Scholar
  29. Flagle CD (2002) Some origins of operations research in the health services. Oper Res 50(1):52–60CrossRefGoogle Scholar
  30. Fletcher A, Halsall D, Huxham S, Worthington D (2007) The DH accident and emergency department model: a national generic model used locally. J Oper Res Soc 58:1554–1562CrossRefGoogle Scholar
  31. Fletcher A, Worthington D (2009) What is a ‘generic’ hospital model?—a comparison of ‘generic’ and ‘specific’ hospital models of emergency patient flows. Health Care Manag Sci 12:374–391CrossRefGoogle Scholar
  32. Fomundam S, Herrmann J (2007) A survey of queuing theory applications in health care. ISR technical report, pp 2007–2024Google Scholar
  33. Fries BE (1979) Bibliography of operations research in health care systems: an update. Oper Res 27(2):408–419CrossRefGoogle Scholar
  34. GAO (2009) Hospital emergency departments—crowding continues to occur, and some patients wait longer than recommended time frames. Report, United States Government Accountability OfficeGoogle Scholar
  35. Gascon V, Villeneuve S, Michelon P, Ferland JA (2000) Scheduling the flying squad nurses of a hospital using a multi-objective programming model. Ann Oper Res 96:149–166CrossRefGoogle Scholar
  36. Gaur KN (2008) Operations research in hospitals. In: Srinivasan AV (ed) Managing a modern hospital, 2nd edn. Sage, Thousand OaksGoogle Scholar
  37. Gendreau M, Ferland J, Gendron B, Hail N, Jaumard B, Lapierre S, Pesant G, Soriano P (2006) Physician scheduling in emergency rooms. In: Proceedings of practice and theory of automated timetabling conference (PATAT) 2006. Brno, Czech Republic, pp 2–14Google Scholar
  38. Green L (2006) Queueing analysis in health care. In: Hall RW (ed) Patient flow: reducing delay in health care delivery, Springer International Series. Springer, New York, pp 281–307Google Scholar
  39. Green LV, Soares J, Giglio JF, Green RA (2006) Using queueing theory to increase the effectiveness of emergency department provider staffing. Acad Emerg Med 13(1):61–68CrossRefGoogle Scholar
  40. Gunal MM, Pidd M (2006) Understanding accident and emergency department performance using simulation. In: Proceedings of 2006 winter simulation conference. Monterey, US, pp 446–452Google Scholar
  41. Hall RW (ed) (2006) Patient flow: reducing delay in health care delivery, Springer international series. Springer, New YorkGoogle Scholar
  42. Hamilton WF (1974) Systems analysis in emergency care planning. Med Care 12(2):152–162CrossRefGoogle Scholar
  43. Han JH, France DJ, Levin SR, Jones ID, Storrow AB, Aronsky D (2010) The effect of physician triage on emergency department length of stay. J Emerg Med 39–2:227–233CrossRefGoogle Scholar
  44. Handyside AJ, Morris D (1967) Simulation of emergency bed occupancy. Health Serv Res Fall-Winter 2(3):287–297Google Scholar
  45. Hannan EL (1975) Planning an emergency department holding unit. Socio Econ Plann Sci 9(5):179–188CrossRefGoogle Scholar
  46. Holroyd BR, Bullard MJ, Latoszek K, Gordon D, Allen S, Tam S, Blitz S, Yoon P, Rowe BH (2007) Impact of triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Acad Emerg Med 14:702–708CrossRefGoogle Scholar
  47. Hoffman B (2006) Emergency rooms: the reluctant safety net. In: Stevens RA, Rosenberg CE, Burns LR (eds) History & health policy in the United States: putting the past back in. Rutgers University Press, New Jersey, pp 250–272Google Scholar
  48. Hoot NR, LeBlanc LJ, Jones I, Levin SR, Zhou C, Gadd CS, Aronsky D (2008) Forecasting emergency department crowding: a discrete event simulation. Ann Emerg Med 52(2):116–125CrossRefGoogle Scholar
  49. Innes GD, Stenstrom R, Grafstein E, Christenson JM (2005) Prospective time study derivation of emergency physician workload predictors. Can J Emerg Med 7(5):299–308Google Scholar
  50. Jacobson SH, Hall SN, Swisher JR (2006) Discrete-event simulation of health care systems. In: Hall RW (ed) Patient flow: reducing delay in health care delivery, Springer International Series. Springer, New York, pp 211–252Google Scholar
  51. Jayaprakash N, O’Sullivan R, Bey T, Ahmed SS, Lotfipour S (2009) Crowding and delivery of health care in emergency departments: the European perspective, Western. J Emerg Med 10(4):233–239Google Scholar
  52. Jones SS, Evans RS (2008) An agent based simulation tool for scheduling emergency department physicians. In: AMIA 2008 symposium proceedings. Washington, US, pp 338–342Google Scholar
  53. Jones SS, Thomas A, Evans RS, Welch SJ, Haug PJ, Snow GL (2008) Forecasting daily patient volumes in the emergency department. Acad Emerg Med 15:159–170CrossRefGoogle Scholar
  54. Jun JB, Jacobson SH, Swisher JR (1999) Application of discrete-event simulation in health care clinics: a survey. J Oper Res Soc 50(2):109–123Google Scholar
  55. Kolesar P (1970) A Markovian model for hospital admission scheduling. Manag Sci 16(6): B384–B396CrossRefGoogle Scholar
  56. Komashie A, Mousavi A (2005) Modeling emergency departments using discrete event simulation techniques. In: Proceedings of 2005 winter simulation conference. Orlando, US, pp 2681–2685Google Scholar
  57. Lane DC, Monefedlt C, Rosenhead JV (2000) Looking in the wrong place for health care improvements: a systems dynamics study of an accident and emergency department. J Oper Res Soc 51(5):518–531Google Scholar
  58. Laxmisan A, Hakimzada R, Sayan OR, Green RA, Zhang J, Patel VL (2007) The multitasking clinician: decision-making and cognitive demand during and after team handoffs in emergency care. Int J Med Inform 76:801–811CrossRefGoogle Scholar
  59. Levin S, France DJ, Hemphill R, Jones I, Chen KY, Rickard D, Makowski R, Aronsky D (2006) Tracking workload in the emergency department. Hum Factors 48(3):526–539CrossRefGoogle Scholar
  60. Levin S, Aronsky D, Hemphill R, Slagle J, France DJ (2007) Shifting toward balance: measuring the distribution of workload among emergency physician teams. Ann Emerg Med 50(4): 419–423CrossRefGoogle Scholar
  61. Lynn SG, Kellermann AL (1991) Critical decision making: managing the emergency department in an overcrowded hospital. Ann Emerg Med 20(3):287–292CrossRefGoogle Scholar
  62. Malakooti B, Malakooti NR, Yang Z (2004) Integrated group technology, cell formation, process planning, and production planning with application to the emergency room. Int J Prod Res 42(9):1769–1786CrossRefGoogle Scholar
  63. Mayhew L, Smith D (2008) Using queuing theory to analyze the Government’s 4-h completion time target in accident and emergency departments. Health Care Manag Sci 11:11–21CrossRefGoogle Scholar
  64. McKay KN (1987) Conceptual framework for job shop scheduling, M.A.Sc. Dissertation, University of WaterlooGoogle Scholar
  65. McKay KN (1992) Production planning and scheduling: a model for manufacturing decisions requiring judgement. Ph.D. Dissertation, University of WaterlooGoogle Scholar
  66. McKay KN (2011) Inter-domain translational research on planning and scheduling—operating rooms versus job shops. Int J Plann Scheduling 1(1/2):42–57CrossRefGoogle Scholar
  67. Mentis, H.M. (2010) Emotion awareness and invisibility in an emergency room: a socio-technical dilemma. Ph.D. Dissertation, Pennsylvania State UniversityGoogle Scholar
  68. Mentis H, Reddy M, Rosson MB (2010) Invisible emotion: information and interaction in an emergency room. CSCW 2010:311–320Google Scholar
  69. Ministry of Health NZ (2011) Targeting emergencies—shorter stays in emergency departments. ReportGoogle Scholar
  70. NHS National Services Scotland (2011) Emergency department activity. ReportGoogle Scholar
  71. Newell DJ (1954) Provision of emergency beds in hospitals. Br J Prev Soc Med 8:77–80Google Scholar
  72. Newell DJ (1965) Unusual frequency distributions. Biometrics 21(1):159–168CrossRefGoogle Scholar
  73. Paul SA, Reddy MC, Deflitch CJ (2010) A systematic review of simulation studies investigating emergency department overcrowding. Simulation 86(8–9):559–571Google Scholar
  74. Pierskalla WP, Brailer DJ (1994) Applications of operations research in health care delivery. In: Pollock SM et al (eds) Handbooks in OR & MS, vol 6. Elsevier Science, Amsterdam, pp 469–505Google Scholar
  75. Pinedo ML (2009) Planning and scheduling in manufacturing and services. Springer, New YorkCrossRefGoogle Scholar
  76. Rais A, Viana A (2010) Operations research in health care: a survey. Int Trans Oper Res 18:1–31CrossRefGoogle Scholar
  77. Saunders CE, Makens PK, Leblanc LJ (1989) Modeling emergency department operations using advanced computer simulation systems. Ann Emerg Med 18(2):134–140CrossRefGoogle Scholar
  78. Samaha S, Armel WS, Starks DW (2003) The use of simulation to reduce the length of stay in an emergency department. In: Proceeding of the 2003 winter simulation conference. New Orleans, US, pp 1907–1911Google Scholar
  79. Seila AF, Brailsford S (2009) Opportunities and Challenges in Health Care Simulation. In: Advancing the Frontiers of Simulation, Alexopoulos C et al (eds) Intl Series in Operations Research & Management Science, Springer, 133:195–229Google Scholar
  80. Shingo H (2010) Emergency medicine in Japan. Keio J Med 59(4):131–139CrossRefGoogle Scholar
  81. Shiver JM, Eitel D (2009) Optimizing emergency department throughput. Taylor Francis, New YorkGoogle Scholar
  82. Sinreich D, Marmor YN (2005) Emergency department operations: the basis for developing a simulation tool. IIE Trans 37:233–245CrossRefGoogle Scholar
  83. Sleptchenko A, van der Heijden MC, van Harten A (2005) Using repair priorities to reduce stock investment in spare part networks. Eur J Oper Res 163:733–750CrossRefGoogle Scholar
  84. Soni K, Saxena K (2011) A study of applicability of waiting line model in health care: a systematic review. Int J Manag Tourism 19(1):75–91Google Scholar
  85. Taylor J (2006) Don’t bring me your tired, your poor: the crowded state of America’s emergency departments. Natl Health Policy ForumIssue Brief July 7, 2006 (811): 1–24Google Scholar
  86. Ting HH, Lee TH, Soukup J, Cook EF, Tosteson ANA, Brand DA, Rouan GW, Goldman L (1991) Impact of physician experience on triage of emergency room patients with acute chest pain at three teaching hospitals. Am J Med 91:401–408CrossRefGoogle Scholar
  87. Topping A, Campbell D (2010) Waiting targets for accident and emergency to be scrapped. The Guardian, Thursday 10 June 2010. Web link: http://www.guardian.co.uk/politics/2010/jun/10/accident-and-emergency-waiting-time-nhs. Accessed 25 Nov 2012
  88. Turner J, Mehrotra S, Daskin MS (2010) Perspectives on health care resource management problems. In: Sodhi MS, Tang CS (eds) A long view of research and practice in operations research and management science, International series in operations research & Management Science. Springer, New York, p 148Google Scholar
  89. Ullman R, Block JA, Stratmann WC (1975) An emergency room’s patients: their characteristics and utilization of hospital services. Med Care 13(12):1011–1020CrossRefGoogle Scholar
  90. Van Sambeek JRC, Cornelissen FA, Bakker PJM, Krabbendam JJ (2010) Models as instruments for optimizing hospital processes: a systematic review. Int J Health Care Qual Assur 23(4): 356–377CrossRefGoogle Scholar
  91. Weinerman ER, Edwards HR (1964) ‘Triage’ system shows promise in management of emergency department load. J Am Hospitals Assoc 38(4):55–62Google Scholar
  92. Weinerman ER, Rutzen SR, Pearson DA (1965) Effects of medical “triage” in hospital emergency service. Yale Studies Ambulatory Med Care 80(5):389–399Google Scholar
  93. Wiler JL, Griffey RT, Olsen T (2011) Review of modeling approaches for emergency department patient flow and crowding research. Acad Emerg Med 18:1371–1379CrossRefGoogle Scholar
  94. Wilper AP, Woolhandler S, Lasser KE, McCormick D, Cutrona SL, Bor DH, Himmelstein DU (2008) Waits to see an emergency department physician: U.S. trends and predictors, 1997–2004. Health Aff 27(2):w84–w95CrossRefGoogle Scholar
  95. Woloshynowych M, Davis R, Brown R, Vincent C (2007) Communication patterns in a UK emergency department. Ann Emerg Med 50(5):407–413CrossRefGoogle Scholar
  96. Yang CC, Lin WT, Chen HM, Shi YH (2009) Improving scheduling of emergency physicians using data mining analysis. Expert Syst Appl 36:3378–3387CrossRefGoogle Scholar
  97. Yeh JY, Lin WS (2007) Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department. Expert Syst Appl 32:1073–1083CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kenneth N. McKay
    • 1
  • Jennifer E. Engels
    • 2
  • Sahil Jain
    • 3
  • Lydia Chudleigh
    • 4
  • Don Shilton
    • 5
  • Ashok Sharma
    • 6
  1. 1.Department of Management SciencesUniversity of WaterlooWaterlooCanada
  2. 2.Manulife Financial Corporation, Business System AnalystKitchenerCanada
  3. 3.TorontoCanada
  4. 4.VP Quality and Performance Management, St. Mary’s General HospitalKitchenerCanada
  5. 5.St. Mary’s General HospitalKitchenerCanada
  6. 6.St. Mary’s General Hospital, Grand River Hospital, Joint Chief of StaffKitchenerCanada

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