After reading the assigned chapters (feel free to take a look at the Week 4 readings, as you may find them helpful for this assignment as well) and watching the recommended videos, consider all that you have reviewed and think about the issues and analysis you completed in assignment 3.1. You have disaggregated the length of stay values and separated them into their appropriate MS-DRG category. But you still havent answered the question that prompted the initial analysis: is your facilitys ALOS for heart failure consistent with that of all Medicare heart failure patients? You want to complete the analysis by testing the hypothesis that your facilitys ALOS is greater than the Medicare benchmark. More formally, you have specified the following null and alternative hypotheses:

• Ho: OUR ALOS FOR DRG = BENCHMARK ALOS FOR DRG
• H1: OUR ALOS FOR DRG > BENCHMARK ALOS FOR DRG

Instead of the benchmark data used in assignment 3.1 that are derived from national estimates using AHRQ H-CUP data, you choose to use the ALOS figures found in the Federal Register for 2010:

CMS Data

The CMS link is to a zipped file containing the MS-DRG description, weights, and ALOS for 2010. Use table 5 of from this link is the file that contains the needed information. These figures are the official values used by CMS to make reimbursement decisions. Use the z-test function to test the hypothesis for each of the three MS-DRGs: 291, 292, 293.

You have some familiarity with the Excel software and you have read about finding probabilities using standardized scores. You decide to answer the probability question posed above using Excels z.test function given as =z.test(range:range,TABLE 5 ALOS).

For a demonstration of how to use Excel's z test function, view the video tutorial below:

Calculating a z test in Excel

Transcript of Calculating a z test in Excel Tutorial

The results of this test will tell you the probability that you will reject the null hypothesis when it is actually true (i.e., that you will say your ALOS is greater than the benchmark when in reality your ALOS is not statistically different than the benchmark). This is known as a "Type I" error, and you want the probability of making this type of error to be as small as possible. The conventional threshold (or comfort level) for making this type of mistake is five percent, i.e., you are willing to accept that in five samples out of 100 you will mistakenly reject the null hypothesis when it is true. Naturally, a probability that is smaller is preferred since the likelihood of having made a mistake is smaller. This is essentially what a p-value represents: the probability of making a Type I error (of rejecting the null hypothesis when it is true, or in practical terms, concluding that a difference exists when in reality none exists).

Run your calculations using the data specified above to determine if your facilitys ALOS for heart failure is consistent with that of all Medicare heart failure patients. Then, prepare a brief so that you can replicate your analysis at some future date. Your brief should be between 250 and 300 words in length and should include the following:

1. Discuss whether, in future analyses, you will collect data from a sample of heart failure patients at your facility or look at all heart failure patients at your facility. Be sure to support this decision.
2. Distinguish between the statistics and parameters that you are using (i.e., what are the statistics and what are the parameters?).
3. Once you have estimated the probabilities for each MS-DRG, explain whether you believe your facility has ALOS that are within an acceptable range compared to the national averages.
4. Paste a screenshot of your Excel calculations into the last page of your assignment submission.