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Estimating the Cost of No-Shows and Evaluating the Effects of Mitigation Strategies

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  • Bjorn P. Berg
  • Michael Murr
  • David Chermak
  • Jonathan Woodall
  • Michael Pignone
  • Robert S. Sandler
  • Brian T. Denton

Abstract

Objective. To measure the cost of nonattendance (“no-shows†) and benefit of overbooking and interventions to reduce no-shows for an outpatient endoscopy suite. Methods. We used a discrete-event simulation model to determine improved overbooking scheduling policies and examine the effect of no-shows on procedure utilization and expected net gain, defined as the difference in expected revenue based on Centers for Medicare & Medicaid Services reimbursement rates and variable costs based on the sum of patient waiting time and provider and staff overtime. No-show rates were estimated from historical attendance (18% on average, with a sensitivity range of 12%–24%). We then evaluated the effectiveness of scheduling additional patients and the effect of no-show reduction interventions on the expected net gain. Results. The base schedule booked 24 patients per day. The daily expected net gain with perfect attendance is $4433.32. The daily loss attributed to the base case no-show rate of 18% is $725.42 (16.4% of net gain), ranging from $472.14 to $1019.29 (10.7%–23.0% of net gain). Implementing no-show interventions reduced net loss by $166.61 to $463.09 (3.8%–10.5% of net gain). The overbooking policy of 9 additional patients per day resulted in no loss in expected net gain when compared with the reference scenario. Conclusions. No-shows can significantly decrease the expected net gain of outpatient procedure centers. Overbooking can help mitigate the impact of no-shows on a suite’s expected net gain and has a lower expected cost of implementation to the provider than intervention strategies.

Suggested Citation

  • Bjorn P. Berg & Michael Murr & David Chermak & Jonathan Woodall & Michael Pignone & Robert S. Sandler & Brian T. Denton, 2013. "Estimating the Cost of No-Shows and Evaluating the Effects of Mitigation Strategies," Medical Decision Making, , vol. 33(8), pages 976-985, November.
    • Handle: RePEc:sae:medema:v:33:y:2013:i:8:p:976-985
    DOI: 10.1177/0272989X13478194
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    References listed on IDEAS

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    1. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
    2. Lawrence R. Weatherford & Samuel E. Bodily, 1992. "A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing," Operations Research, INFORMS, vol. 40(5), pages 831-844, October.
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    Cited by:

    1. 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.
    2. 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).
    3. Aaron Ratcliffe & Ann Marucheck & Sean Xin Xu, 2019. "Regional Planning Model for Cancer Screening with Imperfect Patient Adherence," Service Science, INFORMS, vol. 11(2), pages 113-137, June.
    4. 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.

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