I have been in quality for over 30 years, and in my lifetime, only a few people have been able to explain it as nicely as you did. Every time I mentored my colleagues, I tried to explain Control Chart methodology similarly to how you explained it. Kudos!
Hello, nice overvieuw video. In the example of the XBarChart you take 25 data points (each containing 5 samples a day) so in fact the UCL/LCL are based on 5x 25 samples = 125 samples Yes? But suppose in a laboratory certain analysis runs on one daily run with control standards/samples ... so 1 datapoint/day 1. How many samples (days) would you take to calculate the Mean, StdDev and UCL/LCL ? Should you take 30 as a minimum (30 is the border between T-distr. vs. Normal distr.) or do you take another statistical limit ? 2. When, in time, there is a clear indication of a shift of the mean (up or down...) how to react in a correct way for recalculation of Mean, StdDev, UCL/LCL etc... ?
Hi, many thanks for your feedback! Yes, Control charts help track and monitor the performance of healthcare processes over time. This could include patient wait times, surgical outcomes, infection rates, or medication errors. By identifying variations in these processes, healthcare providers can detect and correct issues, leading to improved quality of care. Regards Hannah
Control charts are also used in biochemical laboratories for analyzing biological samples (blood, urine, etc.). Every day, in parallel with the patients tests, the same tests are performed on 'standard samples'(usually provided by the manufacturer of the test kit with a known level of measured substance). The results of daily tests of these 'standard samples' are also plotted on the charts and ideally for each test result should fall within the 2 sigma interval. If there is a consistent tendency for results to be over- or under-estimated within 2 sigma, either the measurement device or the performance of the reagents should be checked/calibrated. If any standard sample result is outside 2 sigma, all patient results for that test (say total protein test or glucose test, etc.) become invalid because they are either overestimated when the standard sample result is above 2 sigma or underestimated when the standard sample result is below 2 sigma. Typically on such a day, all processes that may have gone wrong for that particular test/reagents are checked, the standard sample result is re-measured and ensured to be within the 2 sigma interval, and then all patient samples are re-measured.
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That is so nice. Within 10 minutes, had an idea about various Control Charts. Thank you.
Glad it was helpful!
I have been in quality for over 30 years, and in my lifetime, only a few people have been able to explain it as nicely as you did.
Every time I mentored my colleagues, I tried to explain Control Chart methodology similarly to how you explained it.
Kudos!
Thank you so much for explaining this concept better ....
Splendid 🎉🎉🎉
Thanks : )
Please explain about Six Sigma concept also with reference to statistical measures.
Many thanks! I will put it on my to do list!
Hello, nice overvieuw video.
In the example of the XBarChart you take 25 data points (each containing 5 samples a day) so in fact the UCL/LCL are based on 5x 25 samples = 125 samples Yes?
But suppose in a laboratory certain analysis runs on one daily run with control standards/samples
... so 1 datapoint/day
1. How many samples (days) would you take to calculate the Mean, StdDev and UCL/LCL ?
Should you take 30 as a minimum (30 is the border between T-distr. vs. Normal distr.) or do you take another statistical limit ?
2. When, in time, there is a clear indication of a shift of the mean (up or down...) how to react in a correct way for recalculation of Mean, StdDev, UCL/LCL etc... ?
Thank you very much...Are these tests important in medical statistics?!
Hi, many thanks for your feedback! Yes, Control charts help track and monitor the performance of healthcare processes over time. This could include patient wait times, surgical outcomes, infection rates, or medication errors. By identifying variations in these processes, healthcare providers can detect and correct issues, leading to improved quality of care. Regards Hannah
Control charts are also used in biochemical laboratories for analyzing biological samples (blood, urine, etc.). Every day, in parallel with the patients tests, the same tests are performed on 'standard samples'(usually provided by the manufacturer of the test kit with a known level of measured substance). The results of daily tests of these 'standard samples' are also plotted on the charts and ideally for each test result should fall within the 2 sigma interval. If there is a consistent tendency for results to be over- or under-estimated within 2 sigma, either the measurement device or the performance of the reagents should be checked/calibrated. If any standard sample result is outside 2 sigma, all patient results for that test (say total protein test or glucose test, etc.) become invalid because they are either overestimated when the standard sample result is above 2 sigma or underestimated when the standard sample result is below 2 sigma. Typically on such a day, all processes that may have gone wrong for that particular test/reagents are checked, the standard sample result is re-measured and ensured to be within the 2 sigma interval, and then all patient samples are re-measured.
@@datatab Thank you very much for your illustration.💐
@@tais51534 Thank you very much for your illustration.💐💐
By the way, that software is nice.