Skip to main content
SpringerLink
Log in

Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center

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

Abstract

With the growth of the population, access to medical care is in high demand, and queues are becoming longer. The situation is more critical when it concerns serious diseases such as cancer. The primary problem is inefficient management of patients rather than a lack of resources. In this work, we collaborate with the Centre Intégré de Cancérologie de Laval (CICL). We present a data-driven study based on a nonblock approach to patient appointment scheduling. We use data mining and regression methods to develop a prediction model for radiotherapy treatment duration. The best model is constructed by a classification and regression tree; its accuracy is 84%. Based on the predicted duration, we design new workday divisions, which are evaluated with various patient sequencing rules. The results show that with our approach, 40 additional patients are treated daily in the cancer center, and a considerable improvement is noticed in patient waiting times and technologist overtime.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Canadian cancer statistics (2017). https://cancer.ca/Canadian-Cancer-Statistics-2017-EN (access in May 2018)

  2. Multivariate adaptive regression splines (marsplines) (2018). http://www.statsoft.com/Textbook/Multivariate-Adaptive-Regression-Splines

  3. Ahmadi-Javid A, Jalali Z, Klassen KJ (2017) Outpatient appointment systems in healthcare: a review of optimization studies. Eur J Oper Res 258(1):3–34

    Article  Google Scholar 

  4. Alaeddini A, Yang K, Reddy C, Yu S (2011) A probabilistic model for predicting the probability of no-show in hospital appointments. Health Care Manag Sci 14(2):146–157

    Article  Google Scholar 

  5. Bailey NT (1952) A study of queues and appointment systems in hospital out-patient departments, with special reference to waiting-times. J R Stat Soc Ser B Methodol 14(2):185–199

    Google Scholar 

  6. Bakker M, Tsui KL (2017) Dynamic resource allocation for efficient patient scheduling: a data-driven approach. J Syst Sci Syst Eng 26(4):448–462

    Article  Google Scholar 

  7. Balshi MS, McGUIRE AD, Duffy P, Flannigan M, Walsh J, Melillo J (2009) Assessing the response of area burned to changing climate in western boreal north america using a multivariate adaptive regression splines (mars) approach. Glob Chang Biol 15(3):578–600

    Article  Google Scholar 

  8. Billings J, Dixon J, Mijanovich T, Wennberg D (2006) Case finding for patients at risk of readmission to hospital: Development of algorithm to identify high risk patients. BMJ 333(7563): 327

    Article  Google Scholar 

  9. Braga P, Portela F, Santos MF, Rua F (2014) Data mining models to predict patient’s readmission in intensive care units. In: ICAART 2014-Proceedings of the 6th international conference on agents and artificial intelligence

  10. Cayirli T, Veral E (2003) Outpatient scheduling in health care: a review of literature. Prod Oper Manag 12(4):519–549

    Article  Google Scholar 

  11. Cayirli T, Veral E, Rosen H (2006) Designing appointment scheduling systems for ambulatory care services. Health Care Manag Sci 9(1):47–58

    Article  Google Scholar 

  12. Conforti D, Guerriero F, Guido R (2008) Optimization models for radiotherapy patient scheduling. 4OR 6(3):263–278

    Article  Google Scholar 

  13. Conforti D, Guerriero F, Guido R (2010) Non-block scheduling with priority for radiotherapy treatments. Eur J Oper Res 201(1):289–296

    Article  Google Scholar 

  14. Conforti D, Guerriero F, Guido R, Veltri M (2011) An optimal decision-making approach for the management of radiotherapy patients. OR Spectrum 33(1):123–148

    Article  Google Scholar 

  15. Denton B, Viapiano J, Vogl A (2007) Optimization of surgery sequencing and scheduling decisions under uncertainty. Health Care Manag Sci 10(1):13–24

    Article  Google Scholar 

  16. Donnan PT, Dorward DW, Mutch B, Morris AD (2008) Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study. Arch Intern Med 168 (13):1416–1422

    Article  Google Scholar 

  17. Friedman JH (1991) Multivariate adaptive regression splines. The annals of statistics 19(1):1–67

    Article  Google Scholar 

  18. Glowacka KJ, Henry RM, May JH (2009) A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling. J Oper Res Soc 60(8):1056–1068

    Article  Google Scholar 

  19. Golmohammadi D, Radnia N (2016) Prediction modeling and pattern recognition for patient readmission. Int J Prod Econ 171:151–161

    Article  Google Scholar 

  20. Gupta D (2007) Surgical suites’ operations management. Prod Oper Manag 16(6):689–700

    Article  Google Scholar 

  21. Gupta D, Denton B (2008) Appointment scheduling in health care: challenges and opportunities. IIE Trans 40(9):800–819

    Article  Google Scholar 

  22. Harris SL, May JH, Vargas LG (2016) Predictive analytics model for healthcare planning and scheduling. Eur J Oper Res 253(1):121–131

    Article  Google Scholar 

  23. Henrard S, Speybroeck N, Hermans C (2015) Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia. Haemophilia 21(6):715–722

    Article  Google Scholar 

  24. Huang Y, Hanauer DA (2014) Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inform 5(3):836–860

    Article  Google Scholar 

  25. Huang YL, Bach SM (2016) Appointment lead time policy development to improve patient access to care. Appl Clin Inform 7(4):954–968

    Article  Google Scholar 

  26. Huang YL, Marcak J (2013) Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization. Health Syst 2(2):93–102

    Article  Google Scholar 

  27. Kim SH, Whitt W, Cha WC (2018) A data-driven model of an appointment-generated arrival process at an outpatient clinic. INFORMS J Comput 30(1):181–199

    Article  Google Scholar 

  28. Koh HC, Tan G, et al. (2011) Data mining applications in healthcare. J Healthc Inf Manag 19(2):64–72

    Google Scholar 

  29. Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39(4):261–283

    Article  Google Scholar 

  30. Lagoe RJ, Noetscher CM, Murphy MP (2001) Hospital readmission: predicting the risk. J Nurs Care Qual 15(4):69–83

    Article  Google Scholar 

  31. Larose DT (2005) Discovering knowledge in data: An introduction to data mining; Daniel T. Larose. Hoboken, N.J: Wiley-Interscience

  32. Legrain A, Fortin MA, Lahrichi N, Rousseau LM (2015) Online stochastic optimization of radiotherapy patient scheduling. Healthc Manag Sci 18(2):110–123

    Article  Google Scholar 

  33. Lotfi V, Torres E (2014) Improving an outpatient clinic utilization using decision analysis-based patient scheduling. Socio Econ Plan Sci 48(2):115–126

    Article  Google Scholar 

  34. Mandelbaum A, Momcilovic P, Trichakis N, Kadish S, Leib R, Bunnell CA Data-driven appointment-scheduling under uncertainty: The case of an infusion unit in a cancer center. Working paper

  35. Rokach L, Maimon O (2005) Decision trees. Springer, Berlin, pp 165–192

    Google Scholar 

  36. Tomar D, Agarwal S (2013) A survey on data mining approaches for healthcare. Int J Bio-Sci Bio-Technol 5(5):241–266

    Article  Google Scholar 

  37. Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231

    Article  Google Scholar 

  38. van Walraven C, Wong J, Hawken S, Forster AJ (2012) Comparing methods to calculate hospital-specific rates of early death or urgent readmission. Can Med Assoc J 184(15):E810–E817

Download references

Author information

Authors and Affiliations

  1. Ecole Polytechnique de Montréal, CP 6079 Succ. Centre-ville, Montréal, H3C3A7, Canada

    Dina Bentayeb, Nadia Lahrichi & Louis-Martin Rousseau

Authors
  1. Dina Bentayeb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Lahrichi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Louis-Martin Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadia Lahrichi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bentayeb, D., Lahrichi, N. & Rousseau, LM. Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center. Health Care Manag Sci 22, 768–782 (2019). https://doi.org/10.1007/s10729-018-9459-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-018-9459-1

Keywords

Search

Navigation