Quality health care is a great concern not just for my organization but across health care centers in the U.S. Usually, there is a need to monitor and control performance to minimize adverse events such as high failure rates. Since their introduction into the health care industry, control charts have efficaciously played a critical role of monitoring failure rates such as mortality, the number of infections, and post-surgery complications (Smith et al, 2014). Although they are not an end to health care standards, control charts such as the p and np, divide variation into special and common cause both of which require varying managerial responses.
At its core the p control chart is a technique used in data analysis used to determine whether the measurement process is under or out of statistical control. The p control chart combines both time series analysis with graphical data representation by plotting successive indicator measurements chronologically (Suman & Prajapati, 2018). Usually the indicators to be measured is critical. The precise control limits based on the binomial distribution reveals important contributions for improving quality and performance in health care settings. Notably, in a p-chart subgroup size can vary which reveals the proportion on nonconforming rates other than the actual count.
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The p control chart might be used to monitor the rates of readmissions in a health center. For instance, for each individual discharged from the hospital, the p-chart would be used to monitor readmission, if it was planned or unplanned. The subgroup for such a p-chart is the number of patients discharged daily, weekly, or monthly from the health center.
It is also worth noting that the control limits for this discharged subgroup population is based on binomial distribution. Sequentially, for the binomial distribution to be followed, there are four conditions that need to be met. They include the defective rate must be made up of distinct counts (Emura & Liao, 2018). In this case for instance, the number of people discharged, may be 200. The distinct items have some attributes, for instance, if the readmission was planned or unplanned. Thirdly, the probability that a particular count has the attribute should be same for all counts. Lastly, the likelihood of an item possessing an attribute is not affected by the other discharged person in the group.
Procedure
For the study, the number of readmissions for the organization I work for was examined. The data was as shown in the table 1. In SPSS, the p chart was created by clicking “Analyze” then “Quality Control”. “p, np” and “cases are subgroups” were selected. The data was filled as shown in figure 1. Under the “Control Rules” all control rules were applied. The output was as shown in figure 2.
Figure 1: Process of formulating the P-Chart
Figure 2: P control chart for readmissions.
The control limits on this readmissions p-chart vary because of the monthly variance in subgroups. Two points are out of control, with the last point having a lot of older people because it was a season of flu.
References
Emura, T., & Liao, Y. T. (2018). Critical review and comparison of continuity correction methods: The normal approximation to the binomial distribution. Communications in Statistics-Simulation and Computation , 47 (8), 2266-2285.
Smith, I. R., Garlick, B., Gardner, M. A., Brighouse, R. D., Foster, K. A., & Rivers, J. T. (2014). Use of graphical statistical process control tools to monitor and improve outcomes in cardiac surgery. Heart, Lung and Circulation , 22 (2), 92-99.
Suman, G., & Prajapati, D. (2018). Control chart applications in healthcare: a literature review. International Journal of Metrology and Quality Engineering , 9 , 5.
