You write
- in any case, it isn't clear to me that your data is measuring the time between events.
- You still haven't described what is being measured
- I know it would help me understand what is going on if you can say something about what these measurements are.
This is taken from the document that shows the data
(I do not know anything more). So, please, ...
- Data were provided from a large hospital system concerned with a very high rate of hospital-acquired urinary tract infections (UTIs). Specifically, the hospital would like to track the frequency of patients being discharged who had acquired a UTI while in the hospital as a way to quickly identify an increase in infection rate or, conversely, monitor whether forthcoming process or material changes result in fewer infections. Because the root cause often differs based on gender, male and female patients are charted separately and this example focuses on males.
The data, which can be seen in the appendix, appear to satisfy the distributional assumption for use of the t chart, with the mean time between male UTI patients at 0.21 days or about 5 hours. The data were plotted using the proposed t chart method and demonstrate statistical control. - The importance of tracking this and similar metrics in a health care setting is the quick identification of an increase in infection rates, which results in two significant costs to the hospital. The health cost to the patient is significant and made more severe by a higher rate of death among patients who contract infections while hospitalized. In 2002, the most recent year of Centers for Disease Control and Prevention reporting, there were an estimated 424,060 hospital-acquired UTIs among adults not in an intensive care unit in the United States, and 13,088 deaths (Klevens et al. 007). Further, the financial cost to the hospital is significant, with Medicare no longer covering the cost to treat hospital-acquired infections. Nearly 80% of the patients who acquired UTIs in this study were covered by Medicare or Medicaid, resulting in a very large expense to the hospital.
- As can be seen from the chart, the data do not show any signs of process degradation or improve ment but the chart can be continuously monitored to detect such changes as quickly as possible.
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Does this help with the data analysis?
You say also:
- Nor do I understand what you are saying is wrong with the control limits I calculated in the file I attached.
As I said previously
- the data are Exponentially distributed
- therefore, the Ranges are Exponentially distributed, as well, with the same mean
- so, the Control Limits cannot be very different in the two charts
You say, also:
- I can verify that, at least on my JMP Pro 18, the T chart does require integer values.
- Multiply my data by 100000 and you get integer values.
- Try to find the T chart
- and see
faustoG
