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Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions

Flexible Services and Manufacturing Journal volume 28, pages 120–147 (2016)Cite this article

Abstract

This paper presents a case study which uses simulation to analyze patient flows in a hospital emergency department in Hong Kong. We first analyze the impact of the enhancements made to the system after the relocation of the Emergency Department. After that, we developed a simulation model (using ARENA) to capture all the key relevant processes of the department. When developing the simulation model, we faced the challenge that the data kept by the Emergency Department were incomplete so that the service-time distributions were not directly obtainable. We propose a simulation–optimization approach (integrating simulation with meta-heuristics) to obtain a good set of estimate of input parameters of our simulation model. Using the simulation model, we evaluated the impact of possible changes to the system by running different scenarios. This provides a tool for the operations manager in the Emergency Department to “foresee” the impact on the daily operations when making possible changes (such as, adjusting staffing levels or shift times), and consequently make much better decisions.

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References

  1. Abo-Hamad W, Arisha A (2013) Simulation-based framework to improve patient experience in an emergency department. Eur J Oper Res 224(1):154–166

    Article  Google Scholar 

  2. Ahmed MA, Alkhamis TM (2009) Simulation optimization for an emergency department healthcare unit in Kuwait. Eur J Oper Res 198(3):936–942

    Article  MATH  Google Scholar 

  3. Babes M, Sarma GV (1991) Out-patient queues at the Ibn-Rochd health centre. J Oper Res Soc 42(10):845–855

    Article  MATH  Google Scholar 

  4. Baesler FF, Jahnsen HE, DaCosta M (2003) The use of simulation and design of experiments for estimating maximum capacity in an emergency room. In: Proceedings of the 2003 winter simulation conference, pp 1903–1906

  5. Blunt E (1998) Role and productivity of nurse practitioners in one urban emergency department. J Emerg Nurs 24(3):234–239

    Article  Google Scholar 

  6. Brailsford SC, Harper PR, Patel B, Pitt M (2009) Analysis of the academic literature on simulation and modelling in health care. J Simul 3:130–140

    Article  Google Scholar 

  7. Census and Statistics Department, the Government of the Hong Kong Special Administrative Region (2013) End-year population for 2012. http://www.censtatd.gov.hk/hkstat/sub/so20.jsp. Accessed 25 Oct 2013

  8. Centeno MA, Giachetti R, Linn R, Ismail AM (2003) A simulation-ilp based tool for scheduling ER staff. In: Proceedings of the 2003 winter simulation conference, pp 1930–1938

  9. Chamberlain JM, Klig J (2001) Extending the physician’s reach: physician assistants, nurse practitioners, and trauma technologists. Clin Pediatr Emerg Med 2(4):239–246

    Article  Google Scholar 

  10. Cinlar E (1975) Introduction to stochastic processes. Printice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  11. Cochran JK, Roche KT (2009) A multi-class queuing network analysis methodology for improving hospital emergency department performance. Comput Oper Res 36(5):1497–1512

    Article  MATH  Google Scholar 

  12. Connelly LG, Bair AE (2004) Discrete event simulation of emergency department activity: a platform for system-level operations research. Acad Emerg Med 11(11):1177–1185

    Article  Google Scholar 

  13. Davies R, Davies HTO (1994) Modelling patient flows and resource provision in health systems. Omega 22(2):123–131

    Article  Google Scholar 

  14. De Angelis V, Felici G, Impelluso P (2003) Integrating simulation and optimisation in health care centre management. Eur J Oper Res 150(1):101–114

    Article  MATH  Google Scholar 

  15. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  16. Donner A (1982) The relative effectiveness of procedures commonly used in multiple regression analysis for dealing with missing values. Am Stat 36(4):378–381

    Google Scholar 

  17. Draeger MA (1992) An emergency department simulation model used to evaluate alternative nurse staffing and patient population scenarios. In: Proceedings of the 1992 winter simulation conference, pp 1057–1064

  18. Eick SG, Massey WA, Whitt W (1993) The physics of the Mt/G/ queue. Oper Res 41(4):731–742

    Article  MathSciNet  MATH  Google Scholar 

  19. Enders CK (2010) Applied missing data analysis. Guilford Press, New York

    Google Scholar 

  20. Evans GW, Gor TB, Unger E (1996) A simulation model for evaluating personnel schedules in a hospital emergency department. In: Proceedings of the 1996 winter simulation conference, pp 1205–1209

  21. Fetter RB, Thompson JD (1965) The simulation of hospital systems. Oper Res 13(5):689–711

    Article  Google Scholar 

  22. Ford BL (1983) An overview of hot-deck procedures. In: Incomplete data in sample surveys, vol 2 (Part IV), pp 185–207

  23. García ML, Centeno MA, Rivera C, DeCario N (1995). Reducing time in an emergency room via a fast-track. In: Proceedings of the 1995 winter simulation conference, pp 1048–1053

  24. Green LV, Soares J, Giglio JF, Green RA (2006) Using queuing theory to increase the effectiveness of emergency department provider staffing. Acad Emerg Med 13(1):61–68

    Article  Google Scholar 

  25. Günal MM, Pidd M (2010) Discrete event simulation for performance modelling in health care: a review of the literature. J Simul 4(1):42–51

    Article  Google Scholar 

  26. 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(5):116–125

    Article  Google Scholar 

  27. Hospital Authority of Hong Kong Special Administrative Region (2014) Guide to Accident & Emergency (A&E) Service. https://www.ha.org.hk/visitor/ha_serviceguide_details.asp?Content_ID=10051&IndexPage=200066&Lang=ENG&Ver=HTML. Accessed 23 April 2014

  28. Huang J, Carmeli B, Mandelbaum A (2012) Control of patient flow in emergency departments, or multiclass queues with deadlines and feedback. Working Paper, National University of Singapore

  29. Jahangirian M, Naseer A, Stergioulas L, Young T, Eldabi T, Brailsford S, Patel B, Harper P (2012) Simulation in health-care: lessons from other sectors. Oper Res 12:45–55

    Google Scholar 

  30. Jun JB, Jacobson SH, Swisher JR (1999) Application of discrete-event simulation in health care clinics: a survey. J Oper Res Soc 50(2):109–123

    Article  MATH  Google Scholar 

  31. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  32. Kleijen JPC (1999) Validation of models: statistical techniques and data availability. In: Proceedings of the 1999 winter simulation conference, pp 647–1654

  33. Kumar AP, Kapur R (1989) Discrete simulation application-scheduling staff for the emergency room. In: Proceedings of the 1989 winter simulation conference, pp 1112–1120

  34. Lane DC, Monefeldt C, Rosenhead JV (2000) Looking in the wrong place for healthcare improvements: a system dynamics study of an accident and emergency department. J Oper Res Soc 51(5):518–531

    Article  MATH  Google Scholar 

  35. Leemis LM (1991) Nonparametric estimation of the cumulative intensity function for a nonhomogeneous Poisson process. Manag Sci 37(7):886–900

    Article  MATH  Google Scholar 

  36. Lenz ER, Mundinger MON, Kane RL, Hopkins SC, Lin SX (2004) Primary care outcomes in patients treated by nurse practitioners or physicians: two-year follow-up. Med Care Res Rev 61(3):332–351

    Article  Google Scholar 

  37. May JH, Strum DP, Vargas LG (2000) Fitting the lognormal distribution to surgical procedure times. Decis Sci 31(1):129–148

    Article  Google Scholar 

  38. McGee LA, Kaplan L (2007) Factors influencing the decision to use nurse practitioners in the emergency department. J Emerg Nurs 33(5):441–446

    Article  Google Scholar 

  39. Medscape (2013) Emergency medicine physician compensation report. http://www.medscape.com/features/slideshow/compensation/2013/emergencymedicine. Accessed 7 May 2014

  40. Niska R, Bhuiya F, Xu J (2010) National hospital ambulatory medical care survey: 2007 emergency department summary. Natl Health Stat Rep 26:1–31

    Google Scholar 

  41. Rais A, Viana A (2011) Operations research in healthcare: a survey. Int Trans Oper Res 18(1):1–31

    Article  MathSciNet  Google Scholar 

  42. Rohleder TR, Lewkonia P, Bischak DP, Duffy P, Hendijani R (2011) Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Manag Sci 14(2):135–145

    Article  Google Scholar 

  43. Rossetti MD, Trzcinski GF, Syverud SA (1999) Emergency department simulation and determination of optimal attending physician staffing schedules. In: Proceedings of the 1999 winter simulation conference, pp 1532–1240

  44. Rinne H (2008) The Weibull distribution: a handbook. CRC Press, Boca Raton, FL

    Book  Google Scholar 

  45. Rubin DB (2009) Multiple imputation for nonresponse in surveys. Wiley, New York

    Google Scholar 

  46. Saghafian S, Hopp WJ, Van Oyen MP, Desmond JS, Kronick SL (2012) Patient streaming as a mechanism for improving responsiveness in emergency departments. Oper Res 60(5):1080–1097

    Article  MATH  Google Scholar 

  47. Saghafian S, Hopp WJ, Van Oyen MP, Desmond JS, Kronick SL (2012) Complexity-based triage: a tool for improving patient safety and operational efficiency. Working Paper, Arizona State University

  48. Siddharthan K, Jones WJ, Johnson JA (1996) A priority queuing model to reduce waiting times in emergency care. Int J Health Care Qual Assur 9(5):10–16

    Article  Google Scholar 

  49. Sinreich D, Jabali O (2007) Staggered work shifts: a way to downsize and restructure an emergency department workforce yet maintain current operational performance. Health Care Manag Sci 10(3):293–308

    Article  Google Scholar 

  50. Sinreich D, Marmor Y (2005) Emergency department operations: the basis for developing a simulation tool. IIE Trans 37(3):233–245

    Article  Google Scholar 

  51. Spangler WE, Strum DP, Vargas LG, May JH (2004) Estimating procedure times for surgeries by determining location parameters for the lognormal model. Health Care Manag Sci 7(2):97–104

    Article  Google Scholar 

  52. U.S. News (2014) The 100 Best Jobs. http://money.usnews.com/careers/best-jobs/rankings/the-100-best-jobs. Accessed 7 May 2014

  53. Wai AK, Chor CM, Lee AT, Sittambunka Y, Graham CA, Rainer TH (2009) Analysis of trends in emergency department attendances, hospital admissions and medical staffing in a Hong Kong university hospital: 5-year study. Int J Emerg Med 2(3):141–148

    Article  Google Scholar 

  54. Wang T, Guinet A, Belaidi A, Besombes B (2009) Modelling and simulation of emergency services with ARIS and Arena. Case study: the emergency department of Saint Joseph and Saint Luc Hospital. Prod Plan. Control 20(6):484–495

    Google Scholar 

  55. Wang B, McKay K, Jewer J, and Sharma A (2013) Physician shift behavior and its impact on service performances in an emergency department. In: Proceedings of the 2013 winter simulation conference, pp 2350–2361

  56. 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(4):1073–1083

    Article  Google Scholar 

  57. Zhang H, Li H, Tam CM (2004) Fuzzy discrete-event simulation for modeling uncertain activity duration. Eng Constr Archit Manag 11(6):426–437

    Article  Google Scholar 

  58. Zhang H, Tam CM, Li H (2005) Modeling uncertain activity duration by fuzzy number and discrete-event simulation. Eur J Oper Res 164(3):715–729

    Article  MATH  Google Scholar 

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Acknowledgments

The research of the first author is supported by Macao Science and Technology Development Fund 088/2013/A3. The work of the fourth author is partially supported by GRF grant 414313 from the Hong Kong Research Grants Council. The authors would like to thank Mr. Stones Wong, Operations Manager of the Emergency Department of the Prince of Wales Hospital, for his assistance in data collection. The authors also thank the referees for their helpful comments on this article.

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Affiliations

  1. Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Yong-Hong Kuo

  2. PriceWaterhouseCoopers Advisory Spa, Milan, Italy

    Omar Rado

  3. Luxottica Group Spa, Pederobba, Treviso, Italy

    Benedetta Lupia

  4. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Janny M. Y. Leung

  5. Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Colin A. Graham

Authors
  1. Yong-Hong Kuo
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  2. Omar Rado
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  3. Benedetta Lupia
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  4. Janny M. Y. Leung
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  5. Colin A. Graham
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Correspondence to Yong-Hong Kuo.

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Kuo, YH., Rado, O., Lupia, B. et al. Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flex Serv Manuf J 28, 120–147 (2016). https://doi.org/10.1007/s10696-014-9198-7

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Keywords

  • Health-care management
  • Patient flows
  • Simulation
  • Meta-heuristics
  • Simulated annealing
  • Simulation optimization
  • Parameter estimation