Thinking of the many variables tracked by hospitals and doctors’ offices, confidence intervals could be created for population parameters (such as means or proportions) that were calculated from many of them. Choose a topic of study that is tracked (or that you would like to see tracked) from your place of work.
Discuss the variable and parameter (mean or proportion) you chose, and explain why you would use these to create an interval that captures the true value of the parameter of patients with 95% confidence.
Consider the following:
How would changing the confidence interval to 90% or 99% affect the study? Which of these values (90%, 95%, or 99%) would best suit the confidence level according to the type of study chosen? How might the study findings be presented to those in charge in an attempt to affect change at the workplace?
Expert Solution Preview
Confidence intervals are statistical tools used to estimate the true value of a population parameter based on a sample. In the context of healthcare, there are numerous variables that are tracked and monitored by hospitals and doctors’ offices. One such variable that I would like to see tracked is the average wait time for patients in the emergency department.
The parameter of interest in this case would be the population mean wait time for all patients visiting the emergency department. By calculating a confidence interval for this mean, we can provide an estimate with a specified level of confidence (e.g., 95%) that captures the true average wait time for patients.
To create a confidence interval, we would need to collect a representative sample of wait times from the emergency department. This sample could be obtained over a specific time period, such as one month, and should include a sufficient number of observations to ensure statistical reliability.
Changing the confidence interval level would impact the width of the interval and the precision of the estimate. A higher confidence level, such as 99%, would result in a wider interval, while a lower confidence level, like 90%, would yield a narrower interval. It is important to strike a balance between precision and reliability when choosing the confidence level.
In this case, a 95% confidence level would be appropriate for the study of average wait times in the emergency department. It provides a reasonable trade-off between precision and reliability while still allowing for a sufficient level of confidence in the findings.
To present the study findings effectively to those in charge, it would be important to emphasize the potential impact of reducing wait times on patient outcomes, satisfaction, and overall efficiency. This could be done through a comprehensive report that highlights the current wait times, the estimated true average wait time based on the confidence interval, and the potential benefits of implementing interventions to reduce wait times. Providing concrete examples and evidence from other healthcare settings where similar interventions have been successful could further support the case for change.
In conclusion, by tracking and studying variables such as the average wait time in the emergency department, we can use confidence intervals to estimate the true population parameter with a specified level of confidence. Choosing an appropriate confidence level, such as 95%, allows for a balance between precision and reliability. Effectively presenting the study findings to those in charge can help drive change and improvements in patient care.