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Decision support system for appointment scheduling and overbooking under patient no-show behavior

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Abstract

Data availability enables clinics to use predictive analytics to improve appointment scheduling and overbooking decisions based on the predicted likelihood of patients missing their appointment (no-shows). Analyzing data using machine learning can uncover hidden patterns and provide valuable business insights to devise new business models to better meet consumers’ needs and seek a competitive advantage in healthcare. The innovative application of machine learning and analytics can significantly increase the operational efficiency of online scheduling. This study offers an intelligent, yet explainable, analytics framework in scheduling systems for primary-care clinics considering individual patients’ no-show rates that may vary for each appointment day and time while generating appointment and overbooking decisions. We use the predicted individual no-show rates in two ways: (1) a probability-based greedy approach to schedule patients in time slots with the lowest no-show likelihood, and (2) marginal analysis to identify the number of overbookings based on the no-show probabilities of the regularly-scheduled patients. We find that the summary measures of profit and cost are considerably improved with the proposed scheduling approach as well as an increase in the number of patients served due to a substantial decrease in the no-show rate. Sensitivity analysis confirms the effectiveness of the proposed dynamic scheduling framework even further.

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Data availability

The dataset generated for the current study is not available to the general public, since it contains confidential data obtained through a license by the authors.

References

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

    Article  Google Scholar 

  • American Academy of Family Physicians. (2021). Primary care. Retrieved 12 May 2022 from http://www.aafp.org/about/policies/all/primary-care.html.

  • Bailey, N. T. J. (1952). A study of queues and appointment times in hospital out-patient departments, with special reference to waiting-times. Journal of the Royal Statistical Society Series B, 14(2), 185–199.

    Google Scholar 

  • Ballou-Nelson, P. (2018). How long are patients waiting for an appointment? MGMA Stat. Retrieved 5 October 2022 from https://www.mgma.com/data/data-stories/how-long-are-patients-waiting-for-an-appointment#:~:text=For primary care single specialties, new patients is 6.3 days.

  • Bean, A. G., & Talaga, J. (1995). Predicting appointment breaking. Journal of Health Care Marketing, 15(1), 29–34.

    Google Scholar 

  • Berg, B. P., Murr, M., Chermak, D., Woodall, J., Pignone, M., Sandler, R. S., & Denton, B. T. (2013). Estimating the cost of no-shows and evaluating the effects of mitigation strategies. Medical Decision Making, 33(8), 976–985.

    Article  Google Scholar 

  • Brenner, S., Zeng, Z., Liu, Y., Wang, J., Li, J., & Howard, P. K. (2010). Modeling and analysis of the emergency department at University of Kentucky Chandler Hospital using simulations. Journal of Emergency Nursing, 36(4), 303–310.

    Article  Google Scholar 

  • Cayirli, T., & Veral, E. (2003). Outpatient scheduling in health care: A review of literature. Production and Operations Management, 12(4), 519–549.

    Article  Google Scholar 

  • Cayirli, T., Veral, E., & Rosen, H. (2008). Assessment of patient classification in appointment system design. Production and Operations Management, 17(3), 338–353.

    Article  Google Scholar 

  • Cayirli, T., & Yang, K. K. (2014). A universal appointment rule with patient classification for service times, no-shows, and walk-ins. Service Science, 6(4), 274–295.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chen, R. R., & Robinson, L. W. (2014). Sequencing and scheduling appointments with potential call-in patients. Production and Operations Management, 23(9), 1522–1538.

    Article  Google Scholar 

  • Daggy, J., Lawley, M., Willis, D., Thayer, D., Suelzer, C., DeLaurentis, P. C., Turkcan, A., Chakraborty, S., & Sands, L. (2010). Using no-show modeling to improve clinic performance. Health Informatics Journal, 16(4), 246–259.

    Article  Google Scholar 

  • Delen, D., Topuz, K., & Eryarsoy, E. (2020). Development of a Bayesian belief network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research, 281(3), 575–587.

    Article  Google Scholar 

  • De los Rios, I. C., & Charnley, F. J. S. (2017). Skills and capabilities for a sustainable and circular economy: The changing role of design. Journal of Cleaner Production, 160, 109–122.

    Article  Google Scholar 

  • Dobson, G., Hasija, S., & Pinker, E. J. (2011). Reserving capacity for urgent patients in primary care. Production and Operations Management, 20(3), 456–473.

    Article  Google Scholar 

  • Dogru, A. K., & Melouk, S. H. (2019). Adaptive appointment scheduling for patient-centered medical homes. Omega—the International Journal of Management Science, 85, 166–181.

    Article  Google Scholar 

  • Feldman, J., Liu, N., Topaloglu, H., & Ziya, S. (2014). Appointment scheduling under patient preference and no-show behavior. Operations Research, 62(4), 794–811.

    Article  Google Scholar 

  • Ferro, D. B., Brailsford, S., Bravo, C., & Smith, H. (2020). Improving healthcare access management by predicting patient no-show behavior. Decision Support Systems, 138, 113398.

    Article  Google Scholar 

  • Fone, D., Hollinghurst, S., Temple, M., Round, A., Lester, N., Weightman, A., Roberts, K., Coyle, E., Bevan, G., & Palmer, S. (2003). Systematic review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health, 25(4), 325–335.

    Article  Google Scholar 

  • Gerchak, Y., Gupta, D., & Henig, M. (1996). Reservation planning for elective surgery under uncertain demand for emergency surgery. Management Science, 42(3), 321–334.

    Article  Google Scholar 

  • Glowacka, K. J., Henry, R. M., & May, J. H. (2009). A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling. Journal of the Operational Research Society, 60(8), 1056–1068.

    Article  Google Scholar 

  • Green, L. V., & Savin, S. (2008). Reducing delays for medical appointments: A queueing approach. Operations Research, 56(6), 1526–1538.

    Article  Google Scholar 

  • Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Transactions, 40(9), 800–819.

    Article  Google Scholar 

  • Gupta, S., & Kim, H. W. (2008). Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities. European Journal of Operational Research, 190(3), 818–833.

    Article  Google Scholar 

  • Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., Atun, R., & Car, J. (2013). Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database of Systematic Reviews, 2013(12), DC007458. https://doi.org/10.1002/14651858.CD007458.pub3

    Article  Google Scholar 

  • Harris, S. L., & Samorani, M. (2020). On selecting a probabilistic classifier for appointment no-show prediction. Decision Support Systems, 142, 113472.

    Article  Google Scholar 

  • Hassin, R., & Mendel, S. (2008). Scheduling arrivals to queues: A single-server model with no-shows. Management Science, 54(3), 565–572.

    Article  Google Scholar 

  • Ho, C.-J., & Lau, H.-S. (1992). Minimizing total cost in scheduling outpatient appointments. Management Science, 38(12), 1750–1764.

    Article  Google Scholar 

  • Huang, Y., & Zuniga, P. (2012). Dynamic overbooking scheduling system to improve patient access. Journal of the Operational Research Society, 63(6), 810–820.

    Article  Google Scholar 

  • Hung, G. R., Whitehouse, S. R., O’Neill, C., Gray, A. P., & Kissoon, N. (2007). Computer modeling of patient flow in a pediatric emergency department using discrete event simulation. Pediatric Emergency Care, 23(1), 5–10.

    Article  Google Scholar 

  • Jacobson, S. H., Hall, S. N., & Swisher, J. R. (2013). Discrete-event simulation of health care systems. In R. Hall (Ed.), Patient flow: Reducing delay in healthcare delivery (2nd ed., pp. 273–309). Springer.

    Chapter  Google Scholar 

  • Jiang, B., Tang, J., & Yan, C. (2019). A stochastic programming model for outpatient appointment scheduling considering unpunctuality. Omega—the International Journal of Management Science, 82, 70–82.

    Article  Google Scholar 

  • Jiang, R., Shen, S., & Zhang, Y. (2017). Integer programming approaches for appointment scheduling with random no-shows and service durations. Operations Research, 65(6), 1638–1656.

    Article  Google Scholar 

  • Johnson, B. J., Mold, J. W., & Pontious, J. M. (2007). Reduction and management of no-shows by family medicine residency practice exemplars. Annals of Family Medicine, 5(6), 534–539.

    Article  Google Scholar 

  • Kaandorp, G. C., & Koole, G. (2007). Optimal outpatient appointment scheduling. Health Care Management Science, 10(3), 217–229.

    Article  Google Scholar 

  • Kheirkhah, P., Feng, Q., Travis, L. M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16, 13.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Klassen, K. J., & Yoogalingam, R. (2014). Strategies for appointment policy design with patient unpunctuality. Decision Sciences, 45(5), 881–911.

    Article  Google Scholar 

  • Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT Press.

    Google Scholar 

  • Kong, Q., Li, S., Liu, N., Teo, C.-P., & Yan, Z. (2020). Appointment scheduling under time-dependent patient no-show behavior. Management Science, 66(8), 3480–3500.

    Article  Google Scholar 

  • Kuo, Y. H., Balasubramanian, H., & Chen, Y. (2020). Medical appointment overbooking and optimal scheduling: Tradeoffs between schedule efficiency and accessibility to service. Flexible Services and Manufacturing Journal, 32(1), 72–101.

    Article  Google Scholar 

  • Kyrimi, E., McLachlan, S., Dube, K., Neves, M. R., Fahmi, A., & Fenton, N. (2021). A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artificial Intelligence in Medicine, 117, 102108.

    Article  Google Scholar 

  • LaGanga, L. R., & Lawrence, S. R. (2007). Clinic overbooking to improve patient access and increase provider productivity. Decision Sciences, 38(2), 251–276.

    Article  Google Scholar 

  • LaGanga, L. R., & Lawrence, S. R. (2012). Appointment overbooking in health care clinics to improve patient service and clinic performance. Production and Operations Management, 21(5), 874–888.

    Article  Google Scholar 

  • Lee, S. J., Heim, G. R., Sriskandarajah, C., & Zhu, Y. (2018). Outpatient appointment block scheduling under patient heterogeneity and patient no-shows. Production and Operations Management, 27(1), 28–48.

    Article  Google Scholar 

  • Leeftink, G., Martinez, G., Hans, E. W., Sir, M. Y., & Pasupathy, K. S. (2022). Optimising the booking horizon in healthcare clinics considering no-shows and cancellations. International Journal of Production Research, 60(10), 3201–3218.

    Article  Google Scholar 

  • Li, Y., Tang, S. Y., Johnson, J., & Lubarsky, D. A. (2019). Individualized no-show predictions: Effect on clinic overbooking and appointment reminders. Production and Operations Management, 28(8), 2068–2086.

    Article  Google Scholar 

  • Liu, J., & KC, D. (2023). Nudging patient choice: Reducing no-shows using waits framing messaging. Operations Research, 71(3), 1004–1020.

    Article  Google Scholar 

  • Liu, N. (2016). Optimal choice for appointment scheduling window under patient no-show behavior. Production and Operations Management, 25(1), 128–142.

    Article  Google Scholar 

  • Liu, N., Ziya, S., & Kulkarni, V. G. (2010). Dynamic scheduling of outpatient appointments under patient no-shows and cancellations. Manufacturing & Service Operations Management, 12(2), 347–364.

    Article  Google Scholar 

  • Mahjoob, A., Alfadhli, Y., & Omachonu, V. (2023). Healthcare waste and sustainability: Implications for a circular economy. Sustainability, 15(10), 7788.

    Article  Google Scholar 

  • Mardani, A., Weber, G. W., & Khan, S. A. R. (2022). Guest editorial: Editorial for special issue on the role of big data on the transition to circular economy and sustainable operations management. Journal of Enterprise Information Management, 35(4/5), 949–954.

    Article  Google Scholar 

  • McLachlan, S., Dube, K., Hitman, G. A., Fenton, N. E., & Kyrimi, E. (2020). Bayesian networks in healthcare: Distribution by medical condition. Artificial Intelligence in Medicine, 107, 101912.

    Article  Google Scholar 

  • Murray, M. (2005). Answers to your questions about same-day scheduling. Family Practice Management, 12(3), 59–64.

    Google Scholar 

  • Muthuraman, K., & Lawley, M. (2008). A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Transactions, 40(9), 820–837.

    Article  Google Scholar 

  • Nadkarni, S., & Shenoy, P. P. (2001). A Bayesian network approach to making inferences in causal maps. European Journal of Operational Research, 128(3), 479–498.

    Article  Google Scholar 

  • Nadkarni, S., & Shenoy, P. P. (2004). A causal mapping approach to constructing Bayesian networks. Decision Support Systems, 38(2), 259–281.

    Article  Google Scholar 

  • Norris, J. B., Kumar, C., Chand, S., Moskowitz, H., Shade, S. A., & Willis, D. R. (2014). An empirical investigation into factors affecting patient cancellations and no-shows at outpatient clinics. Decision Support Systems, 57, 428–443.

    Article  Google Scholar 

  • Pan, X., Geng, N., Xie, X., & Wen, J. (2020). Managing appointments with waiting time targets and random walk-ins. Omega—the International Journal of Management Science, 95, 102062.

    Article  Google Scholar 

  • Patrick, J., Puterman, M. L., & Queyranne, M. (2008). Dynamic multipriority patient scheduling for a diagnostic resource. Operations Research, 56(6), 1507–1525.

    Article  Google Scholar 

  • Pearl, J. (2014). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Elsevier.

    Google Scholar 

  • Rajagopalan, S., Pronovost, P., & Al-Kindi, S. (2023). Implementing a sustainability framework in healthcare: A three-lens framework. Healthcare, 11(13), 1867.

    Article  Google Scholar 

  • RelyMD. (2019). Average wait times to see a doctor. Retrieved 5 October 2022 from https://relymd.com/blog-infographic-average-wait-times-to-see-a-doctor/.

  • Rising, E. J., Baron, R., & Averill, B. (1973). A systems analysis of a university-health-service outpatient clinic. Operations Research, 21(5), 1030–1047.

    Article  Google Scholar 

  • Robinson, L. W., & Chen, R. R. (2010). A comparison of traditional and open-access policies for appointment scheduling. Manufacturing & Service Operations Management, 12(2), 330–346.

    Article  Google Scholar 

  • Saghafian, S., Trichakis, N., Zhu, R., & Shih, H. A. (2023). Joint patient selection and scheduling under no-shows: Theory and application in proton therapy. Production and Operations Management, 32(2), 547–563.

    Article  Google Scholar 

  • Salzarulo, P. A., Mahar, S., & Modi, S. (2016). Beyond patient classification: Using individual patient characteristics in appointment scheduling. Production and Operations Management, 25(6), 1056–1072.

    Article  Google Scholar 

  • Samorani, M., & Ganguly, S. (2016). Optimal sequencing of unpunctual patients in high-service-level clinics. Production and Operations Management, 25(2), 330–346.

    Article  Google Scholar 

  • Samorani, M., & LaGanga, L. R. (2015). Outpatient appointment scheduling given individual day-dependent no-show predictions. European Journal of Operational Research, 240(1), 245–257.

    Article  Google Scholar 

  • Simsek, S., Dag, A., Tiahrt, T., & Oztekin, A. (2021). A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories. Omega—the International Journal of Management Science, 100, 102296.

    Article  Google Scholar 

  • Simsek, S., Tiahrt, T., & Dag, A. (2020). Stratifying no-show patients into multiple risk groups via a holistic data analytics-based framework. Decision Support Systems, 132, 113269.

    Article  Google Scholar 

  • Srinivas, S., & Ravindran, A. R. (2018). Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework. Expert Systems with Applications, 102, 245–261.

    Article  Google Scholar 

  • Stein, C., Troung, V.-A., & Wang, X. (2020). Advance service reservations with heterogeneous customers. Management Science, 66(7), 2929–2950.

    Article  Google Scholar 

  • Sviokla, J., Schroeder, B., & Weakland, T. (2010). How behavioral economics can help cure the health care crisis. Harvard Business Review. Retrieved 12 May 2022 from https://hbr.org/2010/‌03/how-behavioral-economics-can-h.

  • Topuz, K., & Delen, D. (2021). A probabilistic Bayesian inference model to investigate injury severity in automobile crashes. Decision Support Systems, 150, 113557.

    Article  Google Scholar 

  • Topuz, K., Jones, B. D., Sahbaz, S., & Moqbel, M. (2021). Methodology to combine theoretical knowledge with a data-driven probabilistic graphical model. Journal of Business Analytics, 4(2), 125–139.

    Article  Google Scholar 

  • Topuz, K., Uner, H., Oztekin, A., & Yildirim, M. B. (2018). Predicting pediatric clinic no-shows: A decision analytic framework using elastic net and Bayesian belief network. Annals of Operations Research, 263, 479–499.

    Article  Google Scholar 

  • Troung, V.-A. (2015). Optimal advance scheduling. Management Science, 61(7), 1584–1597.

    Article  Google Scholar 

  • Tyler, D. C., Pasquariello, C., & Chen, C. H. (2003). Determining optimum operating room utilization. Anesthesia & Analgesia, 96(4), 1114–1121.

    Article  Google Scholar 

  • Wang, D., Muthuraman, K., & Morrice, D. (2019). Coordinated patient appointment scheduling for a multistation healthcare network. Operations Research, 67(3), 599–618.

    Article  Google Scholar 

  • Wang, S., Liu, N., & Wan, G. (2020). Managing appointment-based services in the presence of walk-in customers. Management Science, 66(2), 667–686.

    Article  Google Scholar 

  • Welch, J. D., & Bailey, N. T. J. (1952). Appointment systems in hospital outpatient departments. The Lancet, 259(6718), 1105–1108.

    Article  Google Scholar 

  • Zacharias, C., & Armony, M. (2017). Joint panel sizing and appointment scheduling in outpatient care. Management Science, 63(11), 3978–3997.

    Article  Google Scholar 

  • Zacharias, C., & Pinedo, M. (2014). Appointment scheduling with no-shows and overbooking. Production and Operations Management, 23(5), 788–801.

    Article  Google Scholar 

  • Zacharias, C., & Yunes, T. (2020). Multimodularity in the stochastic appointment scheduling problem with discrete arrival epochs. Management Science, 66(2), 744–763.

    Article  Google Scholar 

  • 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, 121–144.

    Article  Google Scholar 

  • Zeng, Z., Ma, X., Hu, Y., Li, J., & Bryant, D. (2012). A simulation study to improve quality of care in the emergency department of a community hospital. Journal of Emergency Nursing, 38(4), 322–328.

    Article  Google Scholar 

  • Zhan, Y., & Zhang, Z. (2023). A study on pre-charging strategy for appointment scheduling problem with no-shows. Journal of the Operational Research Society, 74(3), 811–825.

    Article  Google Scholar 

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  1. Collins College of Business, The University of Tulsa, Tulsa, OK, 74104, USA

    Kazim Topuz, Timothy L. Urban & Robert A. Russell

  2. Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS, 67260, USA

    Mehmet B. Yildirim

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Correspondence to Timothy L. Urban.

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Topuz, K., Urban, T.L., Russell, R.A. et al. Decision support system for appointment scheduling and overbooking under patient no-show behavior. Ann Oper Res (2024). https://doi.org/10.1007/s10479-023-05799-0

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