Provide a method for monitoring the quality of two-stage thyroid cancer surgery using a logistic risk adjustment model

Document Type : Original Article

Authors

1 Industrial Engineering, Technical and Engineering, Khajeh Nasiruddin Tusi University, Tehran, Iran

2 Industrial Engineering, Technical and Engineering, Tehran University of Medical Sciences, Tehran, Iran

Abstract
Abstract Quality control tools are widely used in monitoring production processes. Quality monitoring of various production processes, from one-step processes to complex multi-stage processes in the first and second phases of control has been the focus of researchers. In recent decades, the use of quality monitoring tools in health care processes has also increased significantly. In contrast to the many efforts that have been made to monitor the quality of single-stage surgeries, researchers have not paid much attention to multi-stage surgeries. In this study, we have tried to enter the medical services space, while using the logistics model to mitigate the risk, monitor the two-stage process of thyroid cancer surgery for a set of 94 data and present our predictive model.

Keywords


[1] Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE transactions, 27(6), 800-810. [2] Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376. [3] Zhou, S., Huang, Q., & Shi, J. (2003). State space modeling of dimensional variation propagation in multistage machining process using differen- tial motion vectors. Robotics and Automation, IEEE Transactions on, 19(2), 296-309. [4] Xiang, L., & Tsung, F. (2008). Statistical monitoring of multi-stage processes based on engineering models. IIE transactions, 40(10), 957-970. [5] Shi, J., & Zhou, S. (2009). Quality control and improvement for multistage systems: A survey. IIE Transactions, 41(9), 744-753. [6] Asadzadeh, S., Aghaie, A., & Yang, S. F. (2008). Monitoring and diagnosing multistage processes: a review of cause selecting control charts. Journal of Industrial and Systems Engineering, 2(3), 215-236. [7] Steiner, S. H., Cook, R. J., & Farewell, V. T. (2001). Risk-adjusted monitoring of binary surgical outcomes. Medical Decision Making, 21(3), 163- 169. [8] Grigg, O., & Spiegelhalter, D. (2007). A simple risk-adjusted exponentially weighted moving average. Journal of the American Statistical Association, 102(477), 140-152. [9] Paynabar, K., Jin, J., & Yeh, A. B. (2012). Phase I risk-adjusted control charts for monitoring surgical performance by considering categorical covariates. Journal of Quality Technology, 44(1), 39-53. [13] Rossi, G., Del Sarto, S., & Marchi, M. (2014). A new risk-adjusted Bernoulli cumulative sum chart for monitoring binary health data. Statistical methods in medical research, 0962280214530883. [14] Tang, X., Gan, F. F., & Zhang, L. (2015). Riskadjusted cumulative sum charting procedure based on multiresponses. Journal of the American Statistical Association, 110(509), 16-26. [15] Biswas, P., & Kalbfleisch, J. D. (2008). A risk‐ adjusted CUSUM in continuous time based on the Cox model. Statistics in medicine, 27(17), 3382- 3406. [16] Sego, L. H., Reynolds, M. R., & Woodall, W. H. (2009). Risk‐adjusted monitoring of survival times. Statistics in medicine, 28(9), 1386-1401. [17] Steiner, S. H., & Jones, M. (2010). Risk‐ adjusted survival time monitoring with an updating exponentially weighted moving average (EWMA) control chart. Statistics in medicine, 29(4), 444-454. [18] Gandy, A., Kvaløy, J. T., Bottle, A., & Zhou, F. (2010). Risk-adjusted monitoring of time to event. Biometrika, 97(2), 375-388. [19] Sun, R. J., & Kalbfleisch, J. D. (2013). A Risk‐ Adjusted O–E CUSUM with Monitoring Bands for Monitoring Medical Outcomes. Biometrics, 69(1), 62-69. [20] Assareh, H., & Mengersen, K. (2011). Bayesian estimation of the time of a decrease in risk-adjusted survival time control charts. IAENG International Journal of Applied Mathematics, 41(4), 360-366. [21] Assareh, H., & Mengersen, K. L. (2014). Estimation of the time of a linear trend in monitoring survival time. Health Services and Outcomes Research Methodology, 14(1-2), 15-33. [22] Goldman, L., & Ausiello, D. (2008). Cecil Textbook of Internal Medicine. [23] Myers, R. H., Montgomery, D. C., Vining, G. G., & Robinson, T. J. (2012). Generalized linear models: with applications in engineering and the sciences (Vol. 791). John Wiley & Sons. [24] Thyroid cancer structured reporting protocol (2011). The Royal College of Pathologists of Australasia. [25] Chadwick, D., Kinsman, R. (2012). National audit report, The British Association of Endocrine & Thyroid Surgeons.