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Risk-Adjusted Control Charts: Theory, Methods, and Applications in Health

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Abstract

Control charts, the most popular tool of statistical process control, appeared in the literature to ensure that an industrial process is operating only with natural variability, i.e., under statistical control. In the last decades, control charts have been also widely used to assess the quality of non-industrial processes, such as medicine and public health. Mainly in the last two decades, a modification of standard and advanced control charts appeared in the bibliography to improve the monitoring mainly of medical processes. This is the risk-adjusted control charts which take into consideration the varying health conditions of the patients. These charts are used to monitor certain medical processes such as surgeries, mortality, and doctors’ experience. In this paper, we have tried to present all the risk-adjusted control charts presented in the literature appropriately categorized. The risk-adjusted charts have been grouped into three categories: control charts for continuous variables, control charts for attributes (non-continuous variables), time-weighted control charts. The application of risk-adjusted control charts in practical medical processes is also discussed. This review paper highlights the value of the risk-adjusted control charts.

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Authors and Affiliations

  1. Department of Statistics & Insurance Science, University of Piraeus, 80 Karaoli & Dimitriou str., 185 34, Piraeus, Greece

    Athanasios Sachlas & Sotirios Bersimis

  2. Department of Statistics and Laboratory of Statistical Methodology, Athens University of Economics and Business, 76 Patission Str., 10434, Athens, Greece

    Athanasios Sachlas & Stelios Psarakis

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  1. Athanasios Sachlas
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  2. Sotirios Bersimis
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Correspondence to Athanasios Sachlas.

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Table 6 lists all the acronyms appearing in the manuscript.

Table 6 Definition of acronyms appearing on the text
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Sachlas, A., Bersimis, S. & Psarakis, S. Risk-Adjusted Control Charts: Theory, Methods, and Applications in Health. Stat Biosci 11, 630–658 (2019). https://doi.org/10.1007/s12561-019-09257-z

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