IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v253y2016i1p121-131.html
   My bibliography  Save this article

Predictive analytics model for healthcare planning and scheduling

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716300376
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.02.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Libo, 2018. "Predicting online invitation responses with a competing risk model using privacy-friendly social event data," European Journal of Operational Research, Elsevier, vol. 270(2), pages 698-708.
    2. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    3. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    4. Henry Lenzi & Ângela Jornada Ben & Airton Tetelbom Stein, 2019. "Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    5. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    6. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2019. "Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center," Health Care Management Science, Springer, vol. 22(4), pages 768-782, December.
    7. Bowen Jiang & Jiafu Tang & Chongjun Yan, 2019. "A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 150-182, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kazim Topuz & Timothy L. Urban & Robert A. Russell & Mehmet B. Yildirim, 2024. "Decision support system for appointment scheduling and overbooking under patient no-show behavior," Annals of Operations Research, Springer, vol. 342(1), pages 845-873, November.
    2. Samorani, Michele & LaGanga, Linda R., 2015. "Outpatient appointment scheduling given individual day-dependent no-show predictions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 245-257.
    3. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G. & Foster, Krista M., 2020. "The effect of cancelled appointments on outpatient clinic operations," European Journal of Operational Research, Elsevier, vol. 284(3), pages 847-860.
    4. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    5. Jaelynn Oh & Xuanming Su, 2022. "Optimal Pricing and Overbooking of Reservations," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 928-940, March.
    6. Qu, Xiuli & Peng, Yidong & Shi, Jing & LaGanga, Linda, 2015. "An MDP model for walk-in patient admission management in primary care clinics," International Journal of Production Economics, Elsevier, vol. 168(C), pages 303-320.
    7. Christos Zacharias & Tallys Yunes, 2020. "Multimodularity in the Stochastic Appointment Scheduling Problem with Discrete Arrival Epochs," Management Science, INFORMS, vol. 66(2), pages 744-763, February.
    8. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    9. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    10. Agrawal, Deepak & Pang, Guodong & Kumara, Soundar, 2023. "Preference based scheduling in a healthcare provider network," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1318-1335.
    11. Bruce Jones & Sally McClean & David Stanford, 2019. "Modelling mortality and discharge of hospitalized stroke patients using a phase-type recovery model," Health Care Management Science, Springer, vol. 22(4), pages 570-588, December.
    12. Samuel Davis & Nasser Fard, 2020. "Theoretical bounds and approximation of the probability mass function of future hospital bed demand," Health Care Management Science, Springer, vol. 23(1), pages 20-33, March.
    13. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    14. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    15. Sharan Srinivas & A. Ravi Ravindran, 2020. "Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers," Health Care Management Science, Springer, vol. 23(3), pages 360-386, September.
    16. William P. Millhiser & Emre A. Veral, 2019. "A decision support system for real-time scheduling of multiple patient classes in outpatient services," Health Care Management Science, Springer, vol. 22(1), pages 180-195, March.
    17. Feng Xiao & Kin Keung Lai & Chun Kit Lau & Bhagwat Ram, 2024. "Robust Overbooking for No-Shows and Cancellations in Healthcare," Mathematics, MDPI, vol. 12(16), pages 1-18, August.
    18. Ruiwei Jiang & Siqian Shen & Yiling Zhang, 2017. "Integer Programming Approaches for Appointment Scheduling with Random No-Shows and Service Durations," Operations Research, INFORMS, vol. 65(6), pages 1638-1656, December.
    19. Dogru, Ali K. & Melouk, Sharif H., 2019. "Adaptive appointment scheduling for patient-centered medical homes," Omega, Elsevier, vol. 85(C), pages 166-181.
    20. Li Luo & Ying Zhou & Bernard T. Han & Jialing Li, 2019. "An optimization model to determine appointment scheduling window for an outpatient clinic with patient no-shows," Health Care Management Science, Springer, vol. 22(1), pages 68-84, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:253:y:2016:i:1:p:121-131. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.