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Effective cancellation policy to reduce the negative impact of patient no-show

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  • Y Huang

    (Department of Industrial Engineering, New Mexico State University, New Mexico, USA)

  • P Zuniga

    (Department of Industrial Engineering, New Mexico State University, New Mexico, USA)

Abstract

The problem of patient no-show in outpatient delivery systems has been a long recognized issue. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Many clinics have cancellation policies of asking their patients to cancel 24 or 48 h in advance. However, there is no logical or mathematical basis for such a policy. The objective is to develop an effective cancellation policy that accounts for current no-show rates, the clinic's flow, and its fill rates to minimize the cost of patient wait time, physician idle time, and overtime. A simulation approach is presented to determine the hours required for patients to call in advance for cancelling appointments. The findings indicate that when fill rates are low and no-show probabilities are high, the time required for patients to cancel appointments needs to increase in order to achieve the goal of being cost-effective.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:5:p:605-615
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    Citations

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    Cited by:

    1. 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.
    2. Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.

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