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Monitoring of high-yield and periodical processes in health care

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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.

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Abbreviations

ALI:

average length of inspection

ARL:

average run length

B:

Binomial

CCC:

cumulative count of conforming

CL:

center line

G:

Geometric

H:

Hypergeometric

LCL:

lower control limit

NB:

Negative binomial

NH:

Negative hypergeometric

P:

probability control limits

S:

Shewhart-type control limits

TBE:

time between events

UCL:

upper control limit

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Acknowledgments

The authors would like to thank Gregory Zaric and three anonymous reviewers for their valuable feedback and suggestions, which were very important and helpful to significantly improve the paper.

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  1. University of Hamburg, 20146, Hamburg, Germany

    Nataliya Chukhrova & Arne Johannssen

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  1. Nataliya Chukhrova
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Correspondence to Arne Johannssen.

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Chukhrova, N., Johannssen, A. Monitoring of high-yield and periodical processes in health care. Health Care Manag Sci 23, 619–639 (2020). https://doi.org/10.1007/s10729-020-09514-4

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