Skip to main content

An assessment of the interruption level of doctors in outpatient appointment scheduling

Abstract

Interruptions to the server in an outpatient clinic environment have received limited attention in the appointment scheduling literature. However, explicitly modeling interruptions on the part of the doctor may have an impact on the optimal appointment schedule and consequently, on patient waiting times. This is explored with a simulation optimization model that is based on data from time studies and interviews with medical professionals from multiple outpatient clinics. The results show a “plateau-dome” scheduling rule for practical implementation to be robust for low interruption rates and a traditional dome pattern for higher levels of interruptions. In addition, if clinic operations are such that doctors are able to adjust their behavior to complete all work during the session, then the schedule is invariant to changes in the interruption rate.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Andradόttir S (2002) Simulation optimization: integrating research and practice. INFORMS J Comput 14:216–219

    Article  Google Scholar 

  2. Azadivar F, Tompkins G (1999) Simulation optimization with qualitative variables and structural model changes: a genetic algorithm approach. Eur J Oper Res 113:169–182

    Article  Google Scholar 

  3. Blanco White MJ, Pike MC (1964) Appointment systems in outpatients’ clinics and the effect on patients’ unpunctuality. Med Care 2:133–145

    Article  Google Scholar 

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

    Google Scholar 

  5. Cayirli T, Veral E (2003) Outpatient scheduling in health care: a review of literature. Prod Oper Manag 12:519–549

    Google Scholar 

  6. Cayirli T, Veral E, Rosen H (2006) designing appointment scheduling systems for ambulatory -care services. Health Care Manage Sci 9:47–58

    Article  Google Scholar 

  7. Cayirli T, Veral E, Rosen H (2008) assessment of patient classification in appointment system design. Prod Oper Manag 17:338–353

    Google Scholar 

  8. Denton B, Gupta D (2003) A sequential bounding approach for optimal appointment scheduling. IIE Trans 35:1003–1016

    Article  Google Scholar 

  9. Fetter R, Thompson J (1966) Patients’ waiting time and doctors’ idle time in the outpatient setting. Health Serv Res 1:66–90

    Google Scholar 

  10. Fries B, Marathe V (1981) Determination of optimal variable-sized multiple-block appointment systems. Oper Res 29:324–345

    Article  Google Scholar 

  11. Fu M (2002) Optimization for simulation: theory vs. practice. INFORMS J Comput 14:192–215

    Article  Google Scholar 

  12. Glover F (1994) Genetic algorithms and scatter search: unsuspected potentials. Stat Comput 4:131–140

    Article  Google Scholar 

  13. Ho C, Lau H (1992) Minimizing total cost in scheduling outpatient appointments. Manage Sci 38:1750–1764

    Article  Google Scholar 

  14. Ho C, Lau H (1999) Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems. Eur J Oper Res 112:542–553

    Article  Google Scholar 

  15. Huang G, Linton J, Yeomans JS, Yoogalingam R (2005) Policy planning under uncertainty: efficient starting populations for simulation optimization methods applied to municipal solid waste management. J Environ Manag 77:22–34

    Article  Google Scholar 

  16. Jansson B (1966) Choosing a good appointment system: a study of queues of the type (D/M/1). Oper Res 14:292–312

    Article  Google Scholar 

  17. Kelton WD, Sadowski RP, Sturrock D (2007) Simulation with Arena (4th ed). McGraw-Hill, USA

    Google Scholar 

  18. Klassen KJ, Rohleder TR (1996) Scheduling outpatient appointments in a dynamic environment. J Oper Manag 14:83–101

    Article  Google Scholar 

  19. Klassen KJ, Rohleder TR (2004) Outpatient appointment scheduling with urgent clients in a dynamic, multi-period environment. Int J Serv Ind Manag 15:167–186

    Article  Google Scholar 

  20. Klassen KJ, Yoogalingam R (2009) Improving performance in outpatient appointment services with a simulation optimization approach. Prod Oper Manag. Forthcoming, available online in January 2009.

  21. Law AM, Kelton WD (2000) Simulation modeling and analysis (3rd ed). McGraw-Hill, New York, NY

    Google Scholar 

  22. Lehaney B, Clarke SA, Paul RJ (1999) A case of intervention in an outpatients department. J Oper Res Soc 50:877–891

    Article  Google Scholar 

  23. Linton J, Yeomans JS, Yoogalingam R (2002) Policy planning using genetic algorithms combined with simulation: the case of municipal solid waste. Environ Plann B, Plann Des 29:757–778

    Article  Google Scholar 

  24. Liu L, Liu X (1998) Block appointment systems for outpatient clinics with multiple doctors. J Oper Res Soc 49:1254–1259

    Article  Google Scholar 

  25. Lopez-Garcia L, Posada-Bolivar A (1999) A simulator that uses Tabu search to approach the optimal solution to stochastic inventory models. Comput Ind Eng 37:215–218

    Article  Google Scholar 

  26. Marti R, Laguna M, Glover F (2006) Principles of scatter search. Eur J Oper Res 169:359–372

    Article  Google Scholar 

  27. Mercer A (1960) A queuing problem in which arrival times of the customers are scheduled. J R Stat Soc Ser B 22:108–113

    Google Scholar 

  28. O’Keefe R (1985) Investigating outpatient departments: implementable policies and qualitative approaches. J Oper Res Soc 36:705–712

    Article  Google Scholar 

  29. OptQuest (2007) OptTek Systems, Inc. Available at http://www.opttek.com/index.htm. Accessed May 2007

  30. Pegden CD, Rosenshine M (1990) Scheduling arrivals to queues. Comput Oper Res 17:343–348

    Article  Google Scholar 

  31. Pierreval H, Tautou L (1997) Using evolutionary algorithms and simulation for the optimization of manufacturing systems. IIE Trans 29:181–189

    Google Scholar 

  32. Rising E, Baron R, Averill B (1973) A system analysis of a university health service outpatient clinic. Oper Res 21:1020–1047

    Article  Google Scholar 

  33. Robinson L, Chen R (2003) Scheduling doctors’ appointments: Optimal and empirically-based heuristic policies. IIE Trans 35:295–307

    Article  Google Scholar 

  34. Rohleder TR, Klassen KJ (2000) Using client-variance information to improve dynamic appointment scheduling performance. Omega 28:293–305

    Article  Google Scholar 

  35. Stein W, Côté M (1994) Scheduling arrivals to a queue. Comput Oper Res 22:607–614

    Article  Google Scholar 

  36. Teleb R, Azadivar F (1994) A methodology for solving multi-objective simulation optimization problems. Eur J Oper Res 72:135–145

    Article  Google Scholar 

  37. Vissers J (1979) Selecting a suitable appointment system in an outpatient setting. Med Care 12:1207–1220

    Article  Google Scholar 

  38. Vissers J, Wijngaard J (1979) The outpatient appointment system: design of a simulation study. Eur J Oper Res 3:459–463

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Wang P (1997) Optimally scheduling n customer arrival times for a single-server system. Comp Oper Res 24:703–716

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Reena Yoogalingam.

Appendix A

Appendix A

Table 6 Best AS—interruptions add work

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Klassen, K.J., Yoogalingam, R. An assessment of the interruption level of doctors in outpatient appointment scheduling. Oper Manag Res 1, 95–102 (2008). https://doi.org/10.1007/s12063-008-0013-z

Download citation

Keywords

  • Appointment scheduling
  • Outpatient clinics
  • Simulation optimization