Step Change Point Estimation in g and h Control Charts in Healthcare

Document Type : Research Paper

Authors

Department of Industrial Engineering, Shahed University, Tehran, Iran

Abstract

Estimating the real time of a change point in process is one of the main purposes in the statistical process control. On the other hand, it is important to estimate the change point in healthcare processes regarding the relation between quality engineering and hospital epidemiology. Hence, in this paper, maximum likelihood estimators are proposed in g and h control charts for healthcare systems. We applied Monte Carlo simulation to assess the proposed approaches in terms of accuracy and precision. In addition, there are provided the corresponding cardinality sets and coverage probabilities based on logarithm of the likelihood function. Results indicate that the proposed estimators have a satisfactory performance under different shifts.

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References

1. Hajahmadi, F., and Noorossana, R. (2015). “Profiles non-parametric monitroring and make a dicision on quality process using mix effects models”, Journal Industrial Engineering , Vol. 50, No. 1, PP. 13-22.
2. aminnayeri, M., Mohammadi, B., and Ayoubi, M. (2013). “Change point estimation on multiple profiles under drift shifts under mean process”, Journal Industrial Engineering , Vol. 48, Special Issue, PP. 63-71.
3. Taghizadeh, A. and Noorossana, R. (2011). “The necessity of re-conceptualizing the European foundation for quality management’s (EFQM) excellence model for the health care sector and its use in the Iranian national productivity and excellence award”, Hospital , Vol. 10, No. 2, PP. 46-61.
4.Mohammed, M.A., Worthington, P., and Woodall W.H. (2008). “Plotting basic control charts: tutorial notes for healthcare practitioners”,Quality and Safety Health Care, Vol. 17, No. 1, PP. 137-145.
5. Noyes, L. (2009). “Cusum techniques and funnel plots. A review of methods for monitoring performance in healthcare”, Interactive cardiovascular and thoracic surgery, Vol. 9, No. 3, PP. 494-499.
6. Tennant, R., and et al., (2013). “Monitoring patients using control charts: a systematic review”, International journal for quality in healthcare, Vol. 19, No. 4, PP. 187-194.
7. Fatahi, A.A., et al., (2011). “A review on statistical monitoring of rare events and their necessity in healthcare area”, Payesh, Vol. 10, No. 4, PP. 429- 437.
8. Assareh, H., Smith, I., and Mengersen, K. (2011). “Bayesian change point detection in monitoring cardiac surgery outcomes”, Quality Management in Healthcare, Vol. 20, No. 3, PP. 207-222.
9. Grigg, O., and Farewell, V. (2004). “An overview of risk-adjusted charts”, Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 167, No. 3, PP. 523-539.
10. Kaminsky, F.C., Benneyan J.C., and Davis R.D. (1992). “Statistical control charts based on Geometric distribution”, Journal of Quality Technology, Vol. 24, No. 2, PP. 63-69.
11. Benneyan, J.C. (1998). “Statistical quality control methods in infection control and hospital epidemiology, Part 2: chart use, Statistical Properties and Research Issues”, Statistics for Hospital Epidemiology, Vol. 19, No. 4, PP. 265-283.
12. Benneyan, J.C., (1998). “Statistical quality control methods in infection control and hospital epidemiology, Part 1: Introduction and Basic Theory”, Statistics for Hospital Epidemiology, Vol. 19, No. 3, pp. 194-214.
13. ____________, (2001). “Number-between g-type statistical quality control charts for monitoring adverse events”, Health Care Management Science, Vol. 4, No. 4, PP. 305-318.
14. Benneyan, J.C., Lloyd, R.C., and Plsek, P.E. (2003). “Statistical process control as a tool for research and healthcare improvement”, Quality Safety in Health Care, Vol. 12, No. 6, PP. 458-464.
15. Woodall, W.H. (2006). “The use of control charts in health-care and Public-Health surveil- lance”, Journal of Quality Technology, Vol. 38, No. 2, PP. 89-104.
16. Thor, J., et al., (2007). “Application of statistical process control in healthcare improvement: systematic review”, Quality Safety in Health Care, Vol. 16, No. 5, PP. 387-399.
17. Fatahi, A., et al., (2011). “Zib-EWMA control chart for monitorring rare health”,Journal of Mechanics in Medicine and Biology, Vol. 11, No. 4, PP. 881-895,
18. Fatahi, A., et al., (2012). “Zero inflated Poisson EWMA control chart for monitoring health-related events”, Journal of Mechanics in Medicine and Biology, Vol. 12, No. 4, PP. 43-57.
19. Fatahi, A.A., et al., (2010). “Truncated zero inflated binomial control chart for monitoring rare health events”, International Journal of Research and Reviews in Applied Sciences, Vol. 4, No. 4, PP. 380-387.
20. Fatahi, A.A., Noorossana, R., and Dokouhaki, P., (2012). “Copula-Based Bivariate ZIP Control Chart for Monitoring Rare Events”, Communications in Statistics - Theory and Methods, Vol. 41, No. 15, PP. 2699-2716.
21. Duclos, A., and Voirin, N. (2010). “The p-control chart: a tool for care improvement”, International Journal for Quality in Health Care, Vol.4, No, 2. PP. 1-6.
22. Noorossana, R. and Shadman, A, (2009). “Estimating the change point of a normal process mean with a monotonic change”, Quality and Reliability Engineering International, Vol. 25, No. 1, PP. 79-90,
23. Box, G. and Cox, D., (1964). “An analysis of transformations”, Journal of the Royal Statistical Society B, Vol. 26, No. 2, PP. 211–243.
Advances in Industrial Engineering
Volume 52, Issue 1
April 2018
Pages 1-12

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