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Monitoring of high-yield and periodical processes in health care

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  • Nataliya Chukhrova

    (University of Hamburg)

  • Arne Johannssen

    (University of Hamburg)

Abstract

Statistical control charts have found valuable applications in health care, having been largely adopted from operations research in manufacturing. However, the most common types are not best-suited to monitor high-yield processes (outcomes comprising true/false fractions, ‘near-zero’) and periodical processes (characterized by sequences of single populations of finite sizes), but rather to monitor variable vital signs levels and, to a lesser degree, service performance indicators. We discuss control charts that are most suitable for fraction non-conforming measurements. We focus particularly on high-yield and periodical processes, i.e. range in which out-of-control conditions are expected and should be identified. For these conditions, we discuss control charts based on the family of hypergeometric distributions, explaining and comparing their application to more traditional alternatives with two health care case studies. We demonstrate that hypergeometric-type control charts provide higher sensitivity in timely identification of changing rare event fractions and are well-suited for monitoring of periodical processes, while remaining more resistant to false alarms, versus their alternatives.

Suggested Citation

  • Nataliya Chukhrova & Arne Johannssen, 2020. "Monitoring of high-yield and periodical processes in health care," Health Care Management Science, Springer, vol. 23(4), pages 619-639, December.
    • Handle: RePEc:kap:hcarem:v:23:y:2020:i:4:d:10.1007_s10729-020-09514-4
    DOI: 10.1007/s10729-020-09514-4
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    References listed on IDEAS

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    1. Charalambos Sismanidis & Martin Bland & Jan Poloniecki, 2003. "Properties of the Cumulative Risk-Adjusted Mortality (CRAM) Chart, Including the Number of Deaths Before a Doubling of the Death Rate is Detected," Medical Decision Making, , vol. 23(3), pages 242-251, May.
    2. O. Grigg & V. Farewell, 2004. "An overview of risk‐adjusted charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 523-539, August.
    3. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21, February.
    4. Chan, L. Y. & Lai, C. D. & Xie, M. & Goh, T. N., 2003. "A two-stage decision procedure for monitoring processes with low fraction nonconforming," European Journal of Operational Research, Elsevier, vol. 150(2), pages 420-436, October.
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