Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate.
 Chuang, S., & Howley, P. P. (2017). Strategies for integrating clinical indicator and accreditation systems to improve healthcare management. International Journal of Healthcare Management, 10(4), 265-274.
 Chuang, S., Howley, P. P., & Hancock, S. (2013). Using clinical indicators to facilitate quality improvement via the accreditation process: an adaptive study into the control relationship. International journal for quality in health care, 25(3), 277-283.
 Loeb, J. M. (2004). The current state of performance measurement in health care. International journal for quality in health care, 16(suppl_1), i5-i9.
 Nuru, N., Zewdu, F., Amsalu, S., & Mehretie, Y. (2015). Knowledge and practice of nurses towards prevention of pressure ulcer and associated factors in Gondar University Hospital, Northwest Ethiopia. BMC nursing, 14(1), 34.
 Dever, G. A. (1997). Improving outcomes in public health practice: strategy and methods. Jones & Bartlett Learning.
 Evans, S. M., Lowinger, J. S., Sprivulis, P. C., Copnell, B., & Cameron, P. A. (2009). Prioritizing quality indicator development across the healthcare system: identifying what to measure. Internal medicine journal, 39(10), 648-654.
 Gibberd, R., Hancock, S., Howley, P., & Richards, K. (2004). Using indicators to quantify the potential to improve the quality of health care. International Journal for Quality in Health Care, 16(suppl_1), i37-i43.
 Howley, P. P., & Gibberd, R. (2003). Using hierarchical models to analyse clinical indicators: a comparison of the gamma-Poisson and beta-binomial models. International Journal for Quality in Health Care, 15(4), 319-329.
 Howley, P. P., Hancock, S. J., Gibberd, R. W., Chuang, S., & Tuyl, F. A. (2015). Bayesian methods in reporting and managing Australian clinical indicators. World Journal of Clinical Cases: WJCC, 3(7), 625.