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Category: Quality control. These pages discuss some of the organizational and pragmatic issues associated with developing a quality control program. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.
Stats: A plea for open mindedness (November 2, 2006). Most people that I work with are quite open minded, but I do encounter, from time to time, someone who is resistant to ideas that originate from outside the sphere of medicine. In particular, some individuals are almost cynical about the application of quality control in health care. The attitude seems to be something like this: Quality control is an approach that works on assembly lines. I'm a doctor not a factory worker, and my patients are not products on an assembly line. That's a fair statement. Patients are not widgets, and it is a mistake to treat them the same way. But it's also a mistake to think that we can't learn from how other people have approached problems that do indeed bear some semblance of similarity to the problems that you face.
Stats: Davis Balestracci seminar (January 19, 2006). A couple of people I work with are very interested in applying quality control in various processes at Children's Mercy Hospital. We already have a quality improvement program in place, but these folks want to incorporate some ideas they learned after attending a seminar by Davis Balestracci at the Institute for Healthcare Improvement annual forum. I was unfamiliar with Mr. Balestracci's work, but he has a very nice website (www.dbharmony.com) that discusses many of the left brain (analytic/rational) and right brain (emotional/intuitive) issues associated with implementing a quality program.
Stats: Examples of a fishbone diagram (March 24, 2006). The fishbone diagram (also called the Ishikawa diagram, or the case and effect diagram) is a tool for identifying the root causes of quality problems. It was named after Kaoru Ishikawa, the man who pioneered the use of this chart in quality improvement in the 1960's. Surprisingly, I have had to hunt very hard to find any good examples of a fishbone diagram.
Stats: Examples of Pareto charts (April 5, 2006). The Pareto chart is a graphical display of categorical data that is intended to show the relative frequency of different events that all impact the quality of a process. The graph is typically drawn to examine the Pareto principle, also known as the 80-20 principle. The Pareto principle, which does not always work in the real world, but occurs often enough to merit its own name, says that 80% of the problems in a system can be attributed to 20% of the causes. There are applications in other areas as well (80% of the wealth in a country might be held by the richest 20% of the population, for example). The 80-20 split might actually be closer to 90-10 in some situations, or perhaps closer to 70-30 in other situations. Still it is worth remembering the a very few things in your workplace are responsible for most of your quality problems.
Stats: Handouts for quality control workshop (March 2, 2007). I am in charge of a workshop for the American Society for Andrology for their 32nd Annual Conference in Tampa Florida. I am putting together some handouts for this workshop. These handouts are consolidated in a single web page and an abbreviated version will be included in the packet that students receive: Stats #18: Quality Control: A Hands-On Workshop, and Stats #18: Quality Control: A Hands-On Workshop (condensed version).
Stats: Quality control exercises, Part 2 (October 5, 2005). I tried a pilot experiment of a quality control exercise. It seemed to go fairly well. The goal of the exercise was to flip a coin from a table onto a target on the floor below.
Stats: Quality control exercises (September 1, 2005). I've taught several courses on Quality Control, and the best part is the practice exercises. At the American Society of Andrology's lab workshop in 2005, I used a blind paper cutting exercise described in Stone, Richard A. (1998) The blind paper cutter: Teaching about variation, bias, stability, and process control. The American Statistician, 52, 244-247. It worked very well, and I wanted to use it again for the 2006 workshop. But unfortunately, many of the people attending the new workshop will have attended the previous workshop. So I have to find a new practice exercise.
Stats: Quality control humor (August 20, 2006). It is important in any quality improvement process to define precisely what it is that you are trying to improve. Sloppy and imprecise definitions will make it hard for you to measure your process, much less improve it. But sometimes this effort to define things can go to far, as illustrated in this cute story on rec.humor.funny.
Stats: Quality control in the laboratory (March 9, 2004). I'll be giving a talk at the American Society for Andrology in April about the use of quality control for sperm morphology assessments. I'll put some of my notes up on the web when I get the chance.
Stats: Resources for the use of Statistical Process Control in Healthcare (September 15, 2006). Someone on the MedStats email discussion group asked for resources that "explain the use of SPC (statistical process control) to analyze quality indicators in a healthcare organization." I'm working on some research grants to use control charts to provide guidance to continuing review and monitoring of clinical trials. The most recent page that discusses this is at: Stats: My second grant, part 2 (Model, Quality, September 13, 2006). I also may end up giving a talk for PharmaIQ, a division of the International Quality & Productivity Center (IQPC), and they look to have a lot of interesting conferences on healthcare and quality. Of course, my opinion is probably biased by the belief that any group that invites me to talk must have a good appreciation of talent. The Healthcare IQ section actually looks to be quite interesting. There's a lot out there, and this is only a partial list. I tried to include only those resources that had a direct link to health care, with the exception of Donald Wheeler's book, which is a worthwhile read for anyone in any discipline.
Stats: Taguchi methods (February 22, 2005). Genichi Taguchi was a Japanese engineer and statistician who developed a wide range of statistical tools for improving the quality of industrial manufacturing. These tools are collectively known as Taguchi methods.
Stats: Three simple rules to establish quality (February 15, 2007). I received a recommendation to purchase a book (Process Quality Control, by Ellis Ott) and while searching for reviews of this book, found something called Ott's Rules. These are three simple rules advocated by Dr. Ott in any process control problem:
Stats: Tolerance limits (April 15, 2005). Someone asked me about the difference between control limits and tolerance limits. I have a web page about quality control models that talks a bit about control limits for a control chart. The word "tolerance" is ambiguous and could mean several things. There is a formal tolerance interval which is a confidence interval for percentile limits of a distribution. In another context, tolerance limit might represent an engineering specification, where values inside the limit represent parts that will work reliably in the machine or product.
Stats: What I'm working on right now (March 18, 2007). There are several research projects where I am actively looking for collaborators. I thought I'd outline these topics briefly here.
Theme and closely related categories:
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This webpage was written by Steve Simon on 2007-06-18, edited by Steve Simon, and was last modified on 2008-07-14. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.