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Regional Planning Model for Cancer Screening with Imperfect Patient Adherence

Author

Listed:
  • Aaron Ratcliffe

    (Department of Computer Information Systems and Supply Chain Management, Appalachian State University, Boone, North Carolina 28608-2049;)

  • Ann Marucheck

    (Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599-3490)

  • Sean Xin Xu

    (Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599-3490)

Abstract

The effectiveness of cancer screening and adherence to cancer screening guidelines can be inhibited by long wait times for screening appointments. We develop a queueing network model of screening for a disease within a population of patients with imperfect adherence to screening guidelines to characterize the relationship between the screening request frequency rate and the wait time for screens. We first use our model to derive the capacity needed by a given system or the population size a given system can serve to guarantee a certain service level. We analyze routine screening for average-risk patients as well as the additional capacity required for diagnostic screening and surveillance of high-risk patients. We perform a numerical study using national public health data for colorectal cancer (CRC) screening in the United States and provide insights for public health officials, providers, and policy makers by showing how queueing models can be applied to regional planning decisions for determining CRC screening capacity requirements.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orserv:v:11:y:2019:i:2:p:113-137
    DOI: 10.1287/serv.2018.0232
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    References listed on IDEAS

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    Cited by:

    1. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

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