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A service analytic approach to studying patient no-shows

Author

Listed:
  • Murtaza Nasir

    (University of Massachusetts Lowell)

  • Nichalin Summerfield

    (University of Massachusetts Lowell)

  • Ali Dag

    (Creighton University)

  • Asil Oztekin

    (University of Massachusetts Lowell)

Abstract

Patients who fail to show up for an appointment are a major challenge to medical providers. Understanding no-shows and predicting them are keys to developing a proactive strategy in healthcare operations. In this study, we propose a data analytics framework to explore the underlying factors of no-shows via various machine learning models to predict whether a patient is a no-show. The analytics results reveal key patterns in no-show patients. We also propose a methodology to integrate the prediction model with a Bayesian inference system to create an overbooking decision support tool that allows variable overbooking rates in different time windows.

Suggested Citation

  • Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    • Handle: RePEc:spr:svcbiz:v:14:y:2020:i:2:d:10.1007_s11628-020-00415-8
    DOI: 10.1007/s11628-020-00415-8
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    References listed on IDEAS

    as
    1. Yen-Chun Chou & Howard Hao-Chun Chuang, 2018. "A predictive investigation of first-time customer retention in online reservation services," Service Business, Springer;Pan-Pacific Business Association, vol. 12(4), pages 685-699, December.
    2. Yu-Li Huang & Paulina Zuniga & Justin Marcak, 2014. "A cost-effective urgent care policy to improve patient access in a dynamic scheduled clinic setting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(5), pages 763-776, May.
    3. Florez-Lopez, Raquel, 2007. "Modelling of insurers' rating determinants. An application of machine learning techniques and statistical models," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1488-1512, December.
    4. 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.
    5. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    6. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    7. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    8. Vera L. Miguéis & Ana S. Camanho & José Borges, 2017. "Predicting direct marketing response in banking: comparison of class imbalance methods," Service Business, Springer;Pan-Pacific Business Association, vol. 11(4), pages 831-849, December.
    9. 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.
    10. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    11. Nan Liu, 2016. "Optimal Choice for Appointment Scheduling Window under Patient No-Show Behavior," Production and Operations Management, Production and Operations Management Society, vol. 25(1), pages 128-142, January.
    12. Nan Liu & Serhan Ziya, 2014. "Panel Size and Overbooking Decisions for Appointment-Based Services under Patient No-Shows," Production and Operations Management, Production and Operations Management Society, vol. 23(12), pages 2209-2223, December.
    13. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    14. Seung Jun Lee & Gregory R. Heim & Chelliah Sriskandarajah & Yunxia Zhu, 2018. "Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No†Shows," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 28-48, January.
    15. Chi, Hoi-Ming & Ersoy, Okan K. & Moskowitz, Herbert & Ward, Jim, 2007. "Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 180(1), pages 174-193, July.
    16. Y Huang & P Zuniga, 2014. "Effective cancellation policy to reduce the negative impact of patient no-show," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(5), pages 605-615, May.
    17. Y Huang & P Zuniga, 2012. "Dynamic overbooking scheduling system to improve patient access," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(6), pages 810-820, June.
    18. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    19. Ruiz, Edilberto & Nieto, Fabio H., 2000. "A note on linear combination of predictors," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 351-356, May.
    20. 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.
    21. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    22. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    23. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    24. Jianzhe Luo & Vidyadhar G. Kulkarni & Serhan Ziya, 2012. "Appointment Scheduling Under Patient No-Shows and Service Interruptions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 670-684, October.
    25. Refael Hassin & Sharon Mendel, 2008. "Scheduling Arrivals to Queues: A Single-Server Model with No-Shows," Management Science, INFORMS, vol. 54(3), pages 565-572, March.
    26. 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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