Q1
One statistical method that can be utilized for detecting changes in quality or performance is called a t-test. A t-test is a form of statistical hypothesis testing. If an administrator were looking to compare the statistical average of the number of patients infected with covid at their organization versus the state percentage, a t-test could be used to determine if there is a significant difference between the means of two groups, which may be related in certain features (Hayes, 2021). This would show the organization if its case percentages are statistically different from the state average of covid cases. A graphical method that would show changes would be a control chart that visualizes the approach for administrators. A control chart would plot the average of the state covid cases versus the average of the organization so that the differences could be seen and recognized by individuals concerned with quality and performance associated with covid cases by the organization. Lastly “putting is together” emphasizes the idea that these two principles of utilizing both statistical and graphical data can influence the trajectory of quality and performance for an organization. In order to be successful, it is important to distribute the knowledge and data to the right sources aka people. Without widespread understanding and performance improvement running these types of methods to find differences in quality or performance are a waste of time (Strome, 2013). Putting together these pieces of information in a dashboard setting can give the audience a lot of information in one place. Instead of being separate, these data indicators are vital to the performance of the health care organization.
Q2
here are multiple factors involved in improving the quality of care in health care organizations. However, qualitative evaluation through statistical data seems authentic and inevitable for future decision-making in different health practices (Bing M; Abel RL; Pendergrass P; Sabharwal K; McCauley C; 2000). For instance, in acute patients, measuring the mortality rate through statistical data help to find out the significant factors of morbidity and mortality during hospital stay of patients. Measuring performance and comparing with other quality systems is critical; it is only possible through deep statistics.
In 2019, I started the study in my hospital to evaluate the effectiveness of passive chest physiotherapy in preventing ventilator-associated pneumonia in sepsis. It was a quasi-experimental study. My coworkers and I considered sixty intubated patients who were diagnosed with sepsis. This study aimed to observe most patients admitted to the medical intensive care unit after diagnosed sepsis. Unfortunately, the ratio of stay and deaths was increasing. As a data tool, we considered the APACHE II scale and CPIS. Furthermore, descriptive statistics, including mean and standard deviation, were taken for the patients’ age included in the study. Moreover, a paired-sample T-test was applied to assess chest physiotherapy pre- and post-effective effect of chest physiotherapy in ventilated sepsis patients (Nida Rizvi, SmFahad, et al., 2020).
Secondly, we considered different graphical presentations that represent the quality improvement in the critical care unit. According to this bar graph, a Total of sixty patients fulfilled inclusion criteria. APACHE II was measured during the first 24 hours in ICU stay for predicting mortality. Predicted mortality has been shown CPIS the primary outcome variable was measured before initiation of passive chest physiotherapy and after the tenth day of passive chest physiotherapy, which showed that chances of ventilator-associated pneumonia are 53.3 % before initiation of treatment while on tenth-day chances of ventilator-associated pneumonia was decreased to 3.3%.
Overall, quality treatment, the effectiveness of services is measured through statistical data and its implementation. Therefore, data analytics is necessary for the health care industry to assess different challenges and its solution.