Skip to main content
SpringerLink
Log in

A queueing network model to analyze the impact of parallelization of care on patient cycle time

  • Published:
Health Care Management Science Aims and scope Submit manuscript

Abstract

The total time a patient spends in an outpatient facility, called the patient cycle time, is a major contributor to overall patient satisfaction. A frequently recommended strategy to reduce the total time is to perform some activities in parallel thereby shortening patient cycle time. To analyze patient cycle time this paper extends and improves upon existing multi-class open queueing network model (MOQN) so that the patient flow in an urgent care center can be modeled. Results of the model are analyzed using data from an urgent care center contemplating greater parallelization of patient care activities. The results indicate that parallelization can reduce the cycle time for those patient classes which require more than one diagnostic and/or treatment intervention. However, for many patient classes there would be little if any improvement, indicating the importance of tools to analyze business process reengineering rules. The paper makes contributions by implementing an approximation for fork/join queues in the network and by improving the approximation for multiple server queues in both low traffic and high traffic conditions. We demonstrate the accuracy of the MOQN results through comparisons to simulation results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. X-ray is also a bottleneck, but only for those patient classes that require x-ray.

References

  1. Dansky K, Miles J (1997) Patient satisfaction with ambulatory healthcare services: waiting time and filing time. Hosp Health Service Admin 42(2):165–177

    Google Scholar 

  2. Mowen JC, Licata W, McPhail J (1993) Waiting in the emergency room: how to improve patient satisfaction. J Health Care Mark 13(2):26–33

    Google Scholar 

  3. Huang X (1994) Patient attitude towards waiting in an outpatient clinic and its applications. Health Serv Manag Res 7:2–8

    Google Scholar 

  4. Wartman SA (1983) Patient understanding and satisfaction as predictors of compliance. Med Care 21:886–891

    Article  Google Scholar 

  5. MacStravic RS (1991) Beyond patient satisfaction: building patient loyalty. Health Administration Press, Ann Arbor, MI

    Google Scholar 

  6. Hammer M, Champy J (1993) Reengineering the corporation: a manifesto for business revolution. Harper Business, New York

    Google Scholar 

  7. Mansar SL, Reijers HA (2005) Best practices in business process redesign: validation of a redesign framework. Computer Industry 56:457–471

    Article  Google Scholar 

  8. Miller D (2005) Going lean in health care in innovation series 2005. Institute for Healthcare Improvement (IHI), Cambridge, MA

    Google Scholar 

  9. Pande PS, Neuman RP, Cavanagh RR (2000) The six sigma way: how GE, Motorola, and other top copmanies are honing their performance. McGraw-Hill Professional Publishing, New York

    Google Scholar 

  10. Reijers HA, Mansar SL (2005) Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33:283–306

    Article  Google Scholar 

  11. Buzacott JA (1996) Commonalities in reengineered business processes: models and issues. Manage Sci 42(5):768–783

    Google Scholar 

  12. Seidmann A, Sundararajan A (1997) The effects of task and information asymmetry on business process redesign. Int J Prod Econ 23(3):579–595

    Google Scholar 

  13. Marshall A, Vasilakis C, El-Darzi E (2005) Length of stay-based patient flow models: recent developments and future directions. Health Care Manage Sci 8:213–220

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. El-Darzi E (1998) A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department. Health Care Manage Sci 1:143–150

    Article  Google Scholar 

  16. Pooch UW, Wall JA (1993) Discrete event simulation a practical study. CRC Press, Boca Raton

    Google Scholar 

  17. Koizumi N, Kuno E (2005) Modeling patient flows using a queuing network with blocking. Health Care Manage Sci 8:49–60

    Article  Google Scholar 

  18. Taylor BW, Keown AJ (1980) A network Analysis of an inpatient/outpatient department. J Oper Res Soc 31(2):169–180

    Article  Google Scholar 

  19. Hershey JC, Weiss EN, Cohen MA (1981) A stochastic service network model with application to hospital facilities. Oper Res 29(1):1–22

    Article  Google Scholar 

  20. Brahimi M, Worthington DJ (1991) Queuing models for outpatient appointment systems: a case study. J Oper Res Soc 42:733–746

    Article  Google Scholar 

  21. Doi M, Chen Y-M (1997) A queueing model in which arrival times are scheduled. Oper Res Lett 21:249–253

    Article  Google Scholar 

  22. Wang PP (1993) Static and dynamic scheduling of customer arrivals to a single-server system. Naval Res Logist 40:345–360

    Article  Google Scholar 

  23. Woodside CM, Hunt RM (1977) Medical facilities planning using general queuing network analysis. IEEE Transactions SMC7(11):793–799

    Google Scholar 

  24. Brandwajn A (1988) An approximation method for tandem queues with blocking. Oper Res 36(1):73–84

    Google Scholar 

  25. Dittus RS, Klein RW (1996) Medical resident work schedules: design and evaluation by simulation. Manage Sci 42:1–16

    Google Scholar 

  26. Narahari Y, Sundarrajan P (1995) Performability analysis of fork-join queueing systems. Oper Res Society 1995(46):1237–1251

    Google Scholar 

  27. Varki E, Merchant A (1996) Analysis of balanced fork-join systems. in ACM SIGMETRICS Conference on the Measurement and Modeling of Computer Systems. Philadelphia, PA

  28. Nelson R, Tantawi AN (1988) Approximate analysis of fork/join synchronization in parallel queues. IEEE Trans Comput 37:739–746

    Article  Google Scholar 

  29. Balsamo S, Donatiello L (1998) Bound performance models of heterogeneous parallel processing systems. IEEE Trans Parallel Distrib Syst 9:1041–1057

    Article  Google Scholar 

  30. Flatto L (1985) Two parallel queues created by arrivals with two demands. SIAM J Appl Math 44:1041–1054

    Article  Google Scholar 

  31. Baccelli F (1985) Two parallel queues created by arrivals with two demands: the M/G/2 symmetrical case, in Report INRIA

  32. Baccelli F, Makowski A (1985) Simple computable bounds for the fork-join queue. Proceeding Conference Information Science Systems 1985, pp 436–451

  33. Baccelli F, Makowski A (1989) The fork-join queue and related systems with synchronization constraints: stochastic ordering and computable bounds. Appl Probab 21:629–661

    Article  Google Scholar 

  34. Whitt W (1983) The queuing network analyzer. Bell Syst Tech J 62:2779–2815

    Google Scholar 

  35. Whitt W (1993) Approximations for the G1/G/m queue. Prod Oper Manag 2:114–151

    Google Scholar 

  36. Bitran G, Morabito R (1994) Open queuing networks: optimization and performance evaluation models for discrete manufacturing systems. Prod Oper Manag 5:163–193

    Google Scholar 

  37. Brideau L (2004) Flow: why does it matter? Frontiers of Health Service Management 20(4):47–51

    Google Scholar 

  38. Harper PR (2002) A framework for operational modeling of hospital resources. Health Care Manage Sci 5:165–173

    Article  Google Scholar 

  39. Reiser M, Kobayashi H (1974) Accuracy of diffusion approximations for some queueing systems. IBM J Res Develop 18:110–125

    Article  Google Scholar 

  40. Bitran G, Tirupati D (1988) Multiproduct queueing networks with deterministic routing: Decomposition approach and the notion of interference. Manage Sci 34:75–101

    Article  Google Scholar 

  41. Albin SL (1982) Poisson approximations for superposition arrival processes in queues. Manage Sci 28:126–138

    Google Scholar 

  42. Bose SK (2002) An introduction to queuing system. Springer, New York, p 287

    Google Scholar 

  43. Rao PC, Suri R (2000) Performance analysis of an assembly station with input from multiple fabrication lines. Prod Oper Manag 9:283–303

    Google Scholar 

  44. Chinnaswamy MR (2005) Aggregation approaches for incorporating e-mail processing history in queueing models of customer contact centers. Oklahoma State University, Oklahoma p 1–172

  45. Sargent RG (2004) Validation and verification of simulation models. in Proceedings of the 2004 Winter Simulation Conference. Washington, DC

  46. Gittell JH (2002) Coordinating mechanisms in care provider groups: relational coordination as a moderator and input uncertainty as a moderator of performance effects. Manage Sci 48:1408–1427

    Article  Google Scholar 

  47. Giachetti RE, Nunez AN (2006) Quantifying the amount of coordination work as a function of the task. Florida International University, Florida

Download references

Author information

Authors and Affiliations

  1. Industrial & Systems Engineering, Florida International University, EC 3104, 10555 W. Flagler Street, Miami, FL, 33174, USA

    Lixiang Jiang & Ronald E. Giachetti

Authors
  1. Lixiang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ronald E. Giachetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronald E. Giachetti.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, L., Giachetti, R.E. A queueing network model to analyze the impact of parallelization of care on patient cycle time. Health Care Manage Sci 11, 248–261 (2008). https://doi.org/10.1007/s10729-007-9040-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-007-9040-9

Keywords

Search

Navigation