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Outpatient capacity allocation considering adding capacity to match high patient demand

  • Bowen Jiang
  • Jiafu Tang
  • Chongjun Yan
Article

Abstract

This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under this policy, the doctor is allowed to add capacity if all regular capacity have been booked. A capacity allocation model is formulated for both possible no-show routine patients and all show-up same-day patients. The purpose is to determine the number of capacity can be added and how to allocate regular capacity among routine patients and same-day patients, towards maximizing the expected profit, which is composed of the expected income minus the cost of weighted expected doctor’s overload work caused by the adding capacity policy and the cost of rejecting patients. To achieve the aims, we prove the expected profit monotonously decreases when the number of additional capacity exceeds a threshold, and present a two-tier enumeration search algorithm to find the global optimal solution based on the proof. Numerical results indicate that the proposed policy performs well under different levels of demand higher than supply. The optimal number of the additional capacity is hardly affected by varying total expected patient demand. Additionally, under the change of no-show rate, the number of regular capacity allocated to routine patients becomes more stable, compared with the optimal scheme without considering adding capacity policy.

Keywords

Appointment adding capacity policy no-show high demand two-tier enumeration 

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Notes

Acknowledgments

This research is supported in part by the National Natural Science Foundation of China under Grant 71420107028, in part by Hong Kong Research Grant Council under Grant T32-102/14-N and in part by the National Natural Science Foundation of China under Grant 71501027.

References

  1. [1]
    Bailey, N.T.J. (1952). A study of queues and appointment systems in hospital outpatient departments, with special reference to waiting times. Journal of the Royal Statistical Society, 14: 185–199.Google Scholar
  2. [2]
    Cayirli, T. & Veral, E. (2003). Outpatient scheduling in health care: a review of literature. Production and Operations Management, 12 (4): 519–549.CrossRefGoogle Scholar
  3. [3]
    Cayirli, T., Veral, E. & Rosen, H. (2006). Designing appointment scheduling systems for ambulatory care services. Health Care Management Science, 9 (1): 47–58.CrossRefGoogle Scholar
  4. [4]
    Cayirli, T., Yang, K.K. & Quek, S.A. (2012). A universal appointment rule in the presence of no-shows and walk-ins. Production and Operations Management, 21 (4): 682–697.CrossRefGoogle Scholar
  5. [5]
    Chakraborty, S., Muthuraman, K. & Lawley, M. (2010). Sequential clinical scheduling with patient no-shows and general service time distributions. IIE Transactions, 42 (5): 354–366.CrossRefGoogle Scholar
  6. [6]
    Chen, R.R. & Robinson, L.W. (2014). Sequencing and scheduling appointments with potential call-in patients. Production and Operations Management, 23 (9): 1522–1538.CrossRefGoogle Scholar
  7. [7]
    Dobson, G., Hasija, S. & Pinker, E.J. (2011). Reserving capacity for urgent patients in primary care. Production and Operations Management, 20 (3): 456–473.CrossRefGoogle Scholar
  8. [8]
    Erdelyi, A. & Topaloglu, H. (2009). Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods. IIE Transactions, 41(6):498–510.CrossRefGoogle Scholar
  9. [9]
    Feldman, J., Liu, N., Topaloglu, H. & Ziya, S. (2014). Appointment scheduling under patient preference and no-show behavior. Operations Research, 62 (4): 794–811.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    Feldman, J. B. & Topaloglu, H. (2015). Capacity constraints across nests in assortment optimization under the nested logit model. Operations Research, 63 (4): 812–822.MathSciNetCrossRefzbMATHGoogle Scholar
  11. [11]
    Gocgun, Y. & Ghate, A. (2012). Lagrangian relaxation and constraint generation for allocation and advanced scheduling. Computers and Operations Research, 39 (10): 2323–2336.MathSciNetCrossRefzbMATHGoogle Scholar
  12. [12]
    Gupta, D. & Denton, B. (2008). Appointment scheduling in health care: challenges and opportunities. IIE Transactions, 40 (9): 800–819.CrossRefGoogle Scholar
  13. [13]
    Hassin, R. & Mendel, S. (2008). Scheduling arrivals to queues: a single server model with no-shows. Management Science, 54 (3): 565–572.CrossRefzbMATHGoogle Scholar
  14. [14]
    Izady, N. (2015). Appointment capacity planning in specialty clinics: a queueing approach. Operations Research, 63 (4): 916–930.MathSciNetCrossRefzbMATHGoogle Scholar
  15. [15]
    Kaandorp, G. & Koole, G. (2007). Optimal outpatient appointment scheduling. Health Care Management Science, 10 (3): 217–229.CrossRefGoogle Scholar
  16. [16]
    Kim, S. & Giachetti, R.E. (2006). A stochastic mathematical appointment overbooking model for healthcare providers to improve profits. IEEE Transactions on Systems, Man and Cybernetics Part A-Systems and Humans, 36 (6): 1211–1219.CrossRefGoogle Scholar
  17. [17]
    Klassen, K.J. & Yoogalingam, R. (2009). Improving performance in outpatient appointment services with a simulation optimization approach. Production and Operations Management, 18 (4): 447–458.CrossRefGoogle Scholar
  18. [18]
    Kocabıyıkoğlu, A., Popescu, I. & Stefanescu, C. (2014). Pricing and revenue management: the value of coordination. Management Science, 60 (3): 730–752.CrossRefGoogle Scholar
  19. [19]
    Koeleman, P.M. & Koole, G.M. (2012). Optimal outpatient appointment scheduling with emergency arrivals and general service times. IIE Transactions on Healthcare Systems Engineering, 2 (1): 14–30.CrossRefGoogle Scholar
  20. [20]
    LaGanga, L.R. & Lawrence, S.R. (2007). Clinic overbooking to improve patient access and increase provider productive. Decision Science, 38 (2): 251–276.CrossRefGoogle Scholar
  21. [21]
    Lakshmi, C. & Iyer, S.A. (2013). Application of queueing theory in health care: a literature review. Operations Research for Health Care, 2 (1): 25–39.Google Scholar
  22. [22]
    Lee, S., Min, D., Ryu, J. & Yih, Y. (2013). A simulation study of appointment scheduling in outpatient clinics open access and overbooking. Simulation: Transactions of the Society for Modeling and Simulation International, 89 (12): 1459–1473.CrossRefGoogle Scholar
  23. [23]
    Liu, N. & Ziya, S. (2014). Panel size and overbooking decisions for appointment-based services under patient no-shows. Production and Operations Management, 23 (12): 2209–2223.CrossRefGoogle Scholar
  24. [24]
    McMullen, M.J. & Netland, P.A. (2015). Lead time for appointment and the no-show rate in an ophthalmology clinic. Clinical Ophthalmology, 9: 513–516.Google Scholar
  25. [25]
    Murray, M. & Tantau, C. (2000). Same-day appointments: exploding the access paradigm. Family Practice Management, 7 (8): 45–50.Google Scholar
  26. [26]
    Murray, M. & Berwick, D.M. (2003). Advance access: reducing waiting and delays in primary care. The Journal of the American Medical Association, 289(8): 1035–1039.CrossRefGoogle Scholar
  27. [27]
    Muthuraman, K. & Lawley, M. (2008). A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Transactions, 40 (9): 820–837.CrossRefGoogle Scholar
  28. [28]
    Qu, X., Rardin, R.L., Williams, J.A.S. & Willis, D.R. (2007). Matching daily healthcare provider capacity to demand in advanced access scheduling systems. European Journal of Operational Research, 183 (2): 812–826.CrossRefzbMATHGoogle Scholar
  29. [29]
    Qu, X., Rardin, R.L., Williams, J.A.S. & Willis, D.R. (2011). Single versus hybrid time horizons for open access scheduling. Computers and Industrial Engineering, 60 (1): 56–65.CrossRefGoogle Scholar
  30. [30]
    Qu, X., Rardin, R.L. & Williams, J.A.S. (2012). A mean variance model to optimize the fixed versus open appointment percentages in open access scheduling systems. Decision Support Systems, 53 (3): 554–564.CrossRefGoogle Scholar
  31. [31]
    Ratcliffe, A., Gilland, W. & Marucheck, A. (2012). Revenue management for outpatient appointments: joint capacity control and overbooking with class-dependent no-shows. Flexible Services and Manufacturing Journal, 24 (4): 516–548.CrossRefGoogle Scholar
  32. [32]
    Robinson, L.W. & Chen, R.R. (2010). A comparison of traditional and open-access policies for appointment scheduling. Manufacturing and Service Operations Management, 12 (2): 330–346.CrossRefGoogle Scholar
  33. [33]
    Schuetz, H.J. & Kolisch, R. (2012). Approximate dynamic programming for capacity allocation in the service industry. European Journal of Operational Research, 218 (1): 239–250.MathSciNetCrossRefzbMATHGoogle Scholar
  34. [34]
    Schuetz, H.J. & Kolisch, R. (2013). Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service. Annals of Operations Research, 206 (1): 401–423.MathSciNetCrossRefzbMATHGoogle Scholar
  35. [35]
    Steinbauer, J.R., Korell, K., Erdin, J. & Spann, S.J. (2006). Implementing open-access scheduling in an academic practice. Family Practice Management, 13 (3): 59–64.Google Scholar
  36. [36]
    Tang, J., Yan, C. & Cao, P. (2014). Appointment scheduling algorithm considering routine and urgent patients. Expert Systems with Applications, 41 (10): 4529–4541.CrossRefGoogle Scholar
  37. [37]
    Truong, V.A. (2015). Optimal advance scheduling. Management Science, 61 (7): 1584–1597.CrossRefGoogle Scholar
  38. [38]
    Turkcan, A., Zeng, B., Muthuraman, K. & Lawley, M. (2011). Sequential clinical scheduling with service criteria. European Journal of Operational Research, 214 (3): 780–795.CrossRefzbMATHGoogle Scholar
  39. [39]
    Wright, C.P., Groenevelt, H. & Shumsky, R.A. (2010). Dynamic revenue management in airline alliances. Transportation Science. 44 (1): 15–37.CrossRefGoogle Scholar
  40. [40]
    Zeng, B., Turkcan, A., Lin, J. & Lawley, M. (2010). Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities. Annals of Operations Research. 178 (1): 121–144.MathSciNetCrossRefzbMATHGoogle Scholar
  41. [41]
    National Health and Family Planning Commission of the People’s Republic of China. (2014). Statistical bulletin on the development of health and family planning in China in 2013. http://www.moh.gov.cn/guihuaxxs/s10742/220140/886f82dafa344c3097f1d16581a1bea2.shtml. Cited May 30, 2014.Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Institute of Systems EngineeringNortheastern UniversityShenyangChina
  2. 2.College of Management Science and EngineeringDongbei University of Finance and EconomicsDalianChina

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