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Exponentially Weighted Moving Average Control Schemes for Assessing Hospital Organizational Performance

Author

Listed:
  • Michele Scagliarini

    (Alma Mater Studiorum Università di Bologna; Dipartimento di Scienze statistiche - Italy)

  • Mariarosaria Apreda

    (Alma Mater Studiorum Università di Bologna; Dipartimento di Scienze statistiche - Italy)

  • Ulrich Wienand

    (Alma Mater Studiorum Università di Bologna; Dipartimento di Scienze statistiche - Italy)

  • Giorgia Valpiani

    (Alma Mater Studiorum Università di Bologna; Dipartimento di Scienze statistiche - Italy)

Abstract

Exponentially weighted moving average (EWMA) control charts have been successfully used in recent years in several areas of healthcare. Most of these applications have concentrated on the problem of detecting shifts in the mean level of a process. The EWMA chart for monitoring the variability has received, in general, less attention than its counterpart for the mean, although equally important and, to the best of our knowledge, it has never been used in the healthcare framework. In this work, EWMA control charts were applied retrospectively for monitoring the mean and variability of a hospital organizational performance indicator. The aim was to determine whether EWMA control charts can be used as a comprehensive approach for assessing the steady-state behaviour of the process and for early detection of changes indicating either improvement or deterioration in the performance of healthcare organizations. The results showed that the EWMA control schemes generate easy-to-read data displays that reflect process performance allowing a continuous monitoring and prompt detection of changes in process performance. Currently, hospital managers are designing an operating room dashboard which also includes the EWMA control charts.

Suggested Citation

  • Michele Scagliarini & Mariarosaria Apreda & Ulrich Wienand & Giorgia Valpiani, 2016. "Exponentially Weighted Moving Average Control Schemes for Assessing Hospital Organizational Performance," Statistica, Department of Statistics, University of Bologna, vol. 76(2), pages 127-139.
    • Handle: RePEc:bot:rivsta:v:76:y:2016:i:2:p:127-139
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    Cited by:

    1. Che-Wei Chang, 2020. "Constructing a Novel Early Warning Algorithm for Global Budget Payments," Mathematics, MDPI, vol. 8(11), pages 1-11, November.

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