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Predictive analytics model for healthcare planning and scheduling

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  • Harris, Shannon L.
  • May, Jerrold H.
  • Vargas, Luis G.

Abstract

Patients who fail to attend their appointments complicate appointment scheduling systems. The accurate prediction of no-shows may assist a clinic in developing operational mitigation strategies, such as overbooking appointment slots or special management of patients who are predicted as being highly likely to not attend. We present a new model for predicting no-show behavior based solely on the binary representation of a patient's historical attendance history. Our model is a parsimonious, pure predictive analytics technique, which combines regression-like modeling and functional approximation, using the sum of exponential functions, to produce probability estimates. It estimates parameters that can give insight into the way in which past behavior affects future behavior, and is important for clinic planning and scheduling decisions to improve patient service. Additionally, our choice of exponential functions for modeling leads to tractable analysis that is proved to produce optimal and unique solutions. We illustrate our approach using data from patients’ attendance and non-attendance at Veteran Health Administration (VHA) outpatient clinics.

Suggested Citation

  • Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    • Handle: RePEc:eee:ejores:v:253:y:2016:i:1:p:121-131
    DOI: 10.1016/j.ejor.2016.02.017
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    1. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    2. Startz, Richard, 2008. "Binomial Autoregressive Moving Average Models With an Application to U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 1-8, January.
    3. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    4. C Vasilakis & A H Marshall, 2005. "Modelling nationwide hospital length of stay: opening the black box," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 862-869, July.
    5. K J Glowacka & R M Henry & J H May, 2009. "A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1056-1068, August.
    6. Bo Zeng & Ayten Turkcan & Ji Lin & Mark Lawley, 2010. "Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities," Annals of Operations Research, Springer, vol. 178(1), pages 121-144, July.
    7. H. Xie & T. J. Chaussalet & P. H. Millard, 2005. "A continuous time Markov model for the length of stay of elderly people in institutional long‐term care," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 51-61, January.
    8. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
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