One of my favorite things to do is go back to something I learned how to do once and relearn it. I actually mean that. I try to keep all of the text books from classes that taught me something that might be useful later on, because quite often, I know I might need to know it in the future, even when I don't have plans for it today. Those books live in my office in different places, from each different era of my career (usually because I arrange by topic, and I generally float between topics over time), so long as I remember where / what I was learning, I can go back and find the book.
Earlier today, I was looking at a graph and was reminded that it clearly showed the effect of an intervention (basically hiring my employer to take something on), and I was trying to remember how to evaluate that effect. Then later today, I was trying to identify when and why measuring days between events was a good process measure. Both of these topics are related to control charts, a method that is used for statistical process control. Over the last week, I was also looking for how to identify cases when a regime change was identified to trigger further activity.
So, I dug up my second edition copy of the Healthcare Quality Book (now in it's third edition), and turned to the chapter on statistical tools for quality improvement (Chapter 7 in the Second edition). And there we find a whole chapter basically talking about the utility of control charts, and how to compute the values associated with them.
This would allow me to take a chart like the one below (a graph counting some item N over time T), and through a series of computations, compute a graph like the one on the right.
Where the rate of growth of N clearly enters a new regime, and to clearly show it. Now, for this graph, it's quite obvious, but that's because we've got a couple of years of sample data, and what I want to know is that the regime is changing sooner, rather than later.
In other words, I don't want to wait a day longer than I have to in order to see the effect. Control charts let me do that. Why is this important? Often, providers want to know when something abnormal has happened. What the control chart does is help to establish a normal range of variation, which makes it possible to detect variation due to special cause, i.e., variation that falls outside of the normal range.
In my particular case, I was looking for a way to signal that a particular event needed special attention, and through control charts, I can readily do that visually, and anything I can visualize, I can generally compute. Unfortunately, I didn't find the chapter that talks about G/H charts yet, but that simply means I have more reading and searching to do.
I'd never be in a position to apply these charts to what we applied them to in class, because I'm not a doctor. But I do get to develop software that doctors use, and I also happen to have my own set of "patients" that often need diagnosis and treatment (software systems). Control charts can work for them just as well as they do for quality management in patient care.
Keith
Earlier today, I was looking at a graph and was reminded that it clearly showed the effect of an intervention (basically hiring my employer to take something on), and I was trying to remember how to evaluate that effect. Then later today, I was trying to identify when and why measuring days between events was a good process measure. Both of these topics are related to control charts, a method that is used for statistical process control. Over the last week, I was also looking for how to identify cases when a regime change was identified to trigger further activity.
So, I dug up my second edition copy of the Healthcare Quality Book (now in it's third edition), and turned to the chapter on statistical tools for quality improvement (Chapter 7 in the Second edition). And there we find a whole chapter basically talking about the utility of control charts, and how to compute the values associated with them.This would allow me to take a chart like the one below (a graph counting some item N over time T), and through a series of computations, compute a graph like the one on the right.
Where the rate of growth of N clearly enters a new regime, and to clearly show it. Now, for this graph, it's quite obvious, but that's because we've got a couple of years of sample data, and what I want to know is that the regime is changing sooner, rather than later.
In other words, I don't want to wait a day longer than I have to in order to see the effect. Control charts let me do that. Why is this important? Often, providers want to know when something abnormal has happened. What the control chart does is help to establish a normal range of variation, which makes it possible to detect variation due to special cause, i.e., variation that falls outside of the normal range.
In my particular case, I was looking for a way to signal that a particular event needed special attention, and through control charts, I can readily do that visually, and anything I can visualize, I can generally compute. Unfortunately, I didn't find the chapter that talks about G/H charts yet, but that simply means I have more reading and searching to do.
I'd never be in a position to apply these charts to what we applied them to in class, because I'm not a doctor. But I do get to develop software that doctors use, and I also happen to have my own set of "patients" that often need diagnosis and treatment (software systems). Control charts can work for them just as well as they do for quality management in patient care.
Keith
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