|P.Mean >> Category >> Quality control (created 2007-06-18).|
These pages discuss some of the organizational and pragmatic issues associated with developing a quality control program. Also see Category: Analysis of means, Category: Control charts. Other entries about quality control can be found in the quality control page at the StATS website.
18. Some quasi-experimental alternatives to randomization (January/February 2012)
17. What is a fishbone diagram? (July 2010) and P.Mean: Examples of a fishbone diagram (created 2006-03-24, revised 2010-06-29). This is an update of a webpage originally published at http://www.childrens-mercy.org/stats/weblog2006/FishboneDiagram.asp. 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.
16. What is a control chart? (February/March 2010)
US Navy Total Quality Leadership Office. Basic Tools for Process Improvement: Cause-and-Effect Diagram [PDF]. This website is cited in Category: QualityControl. Description: This website offers simple explanations of the cause and effect diagram, a classic tool used in quality improvement. This same guide is also found at www.management-tools.org/files/c-ediag.pdf and www.saferpak.com/cause_effect_articles/howto_cause_effect.pdf. Other guides are available at www.hq.navy.mil/RBA/text/tools.html. This website was last verified on 2006-03-24. URL: www.hq.navy.mil/RBA/c-ediag.pdf.
Gary Wolf. The Data-Driven Life. The New York Times. 2010. Excerpt: "And yet, almost imperceptibly, numbers are infiltrating the last redoubts of the personal. Sleep, exercise, sex, food, mood, location, alertness, productivity, even spiritual well-being are being tracked and measured, shared and displayed. On MedHelp, one of the largest Internet forums for health information, more than 30,000 new personal tracking projects are started by users every month. Foursquare, a geo-tracking application with about one million users, keeps a running tally of how many times players �check in� at every locale, automatically building a detailed diary of movements and habits; many users publish these data widely. Nintendo�s Wii Fit, a device that allows players to stand on a platform, play physical games, measure their body weight and compare their stats, has sold more than 28 million units." [Accessed May 1, 2010]. Available at: http://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html.
Brian L. Joiner, Sue Reynard, Yukihiro Ando. Fourth generation management. McGraw-Hill Professional; 1994. Excerpt: "I knew that it was important to find better ways to do things and to eliminate waste and inefficiencies; that data could shed light on murky situations; that people needed to work together. But it took another 20 years working with large companies and small, with government, service, and manufacturing organizations, with top managers, with operators on the shop floor, before I had a good understanding of how all these pieces fit into a system of management that brings rapid learning and rapid improvement. It's a system I've come to call 4th Generation Management." Available at: http://books.google.com/books?id=E99OVbYUmhEC.
Gaye P, Nelson D. Effective scale-up: avoiding the same old traps. Human Resources for Health. 2009;7(1):2. Available at: www.human-resources-health.com/content/7/1/2. [Accessed February 24, 2009].
Julie Weed. Factory Efficiency Comes to the Hospital. The New York Times. 2010. Excerpt: "The program, called �continuous performance improvement,� or C.P.I., examines every aspect of patients� stays at the hospital, from the time they arrive in the parking lot until they are discharged, to see what could work better for them and their families. Last year, amid rising health care expenses nationally, C.P.I. helped cut Seattle Children�s costs per patient by 3.7 percent, for a total savings of $23 million, Mr. Hagan says. And as patient demand has grown in the last six years, he estimates that the hospital avoided spending $180 million on capital projects by using its facilities more efficiently. It served 38,000 patients last year, up from 27,000 in 2004, without expansion or adding beds." [Accessed July 13, 2010]. Available at: http://www.nytimes.com/2010/07/11/business/11seattle.html.
David Leonhardt. Making Health Care Better. The New York Times. November 8, 2009. Description: This article profiles Brent James. chief quality officer at Intermountain Health Care, and his pioneering efforts to rigorously apply evidence based medicine principles. It highlights some of the quality improvement initiatives at Intermountain and documents the resistance to change among many doctors at Intermountain. [Accessed January 14, 2010]. Available at: http://www.nytimes.com/2009/11/08/magazine/08Healthcare-t.html.
Office for Human Research Protections, U.S. Department of Health & Human Services. Quality Improvement Activities Frequently Asked Questions. Excerpt: "Protecting human subjects during research activities is critical and has been at the forefront of HHS activities for decades. In addition, HHS is committed to taking every appropriate opportunity to measure and improve the quality of care for patients. These two important goals typically do not intersect, since most quality improvement efforts are not research subject to the HHS protection of human subjects regulations. However, in some cases quality improvement activities are designed to accomplish a research purpose as well as the purpose of improving the quality of care, and in these cases the regulations for the protection of subjects in research (45 CFR part 46) may apply." [Accessed September 24, 2010]. Available at: http://www.hhs.gov/ohrp/qualityfaq.html.
Frank Davidoff, Paul Batalden, David Stevens, Greg Ogrinc, Sue Mooney, Joy McAvoy, Leslie Walker. SQUIRE. Standards for Quality Improvement Reporting Excellence. Excerpt. The SQUIRE Guidelines help authors write excellent, usable articles about quality improvement in healthcare so that their findings can be easily discovered and widely disseminated, thus spreading improvement work to a broader population. URL: http://squire-statement.org/index.php.
Journal article: G Ogrinc, S E Mooney, C Estrada, T Foster, D Goldmann, L W Hall, M M Huizinga, S K Liu, P Mills, et al. The SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for quality improvement reporting: explanation and elaboration Quality and Safety in Health Care. 2008;17(Suppl 1):i13-i32. Abstract: "As the science of quality improvement in health care advances, the importance of sharing its accomplishments through the published literature increases. Current reporting of improvement work in health care varies widely in both content and quality. It is against this backdrop that a group of stakeholders from a variety of disciplines has created the Standards for QUality Improvement Reporting Excellence, which we refer to as the SQUIRE publication guidelines or SQUIRE statement. The SQUIRE statement consists of a checklist of 19 items that authors need to consider when writing articles that describe formal studies of quality improvement. Most of the items in the checklist are common to all scientific reporting, but virtually all of them have been modified to reflect the unique nature of medical improvement work. This 'Explanation and Elaboration' document (E & E) is a companion to the SQUIRE statement. For each item in the SQUIRE guidelines the E & E document provides one or two examples from the published improvement literature, followed by an analysis of the ways in which the example expresses the intent of the guideline item. As with the E & E documents created to accompany other biomedical publication guidelines, the purpose of the SQUIRE E & E document is to assist authors along the path from completion of a quality improvement project to its publication. The SQUIRE statement itself, this E & E document, and additional information about reporting improvement work can be found at http://www.squire-statement.org." [Accessed on December 10, 2011]. http://qualitysafety.bmj.com/content/17/Suppl_1/i13.full.
Peter R Scholtes, Brian L. Joiner, Barbara J Streibel. The Team Handbook Third Edition. 3rd ed. Joiner/Oriel Inc; 2003. Product description: "This updated best-selling, comprehensive resource book provides everything you need to create high performing teams. In addition, book purchasers will be able to download electronic versions of forms and templates found in the book for use within their organization! The third edition provides information on the context teams need to be successful. Organizations using teams to improve efficiency and better serve customers will find information on how to start quality initiatives such as Six Sigma or Lean. New information on different types of teams, and new tools and strategies for leading change are covered as well. Several new tools have been added to help teams work well together: affinity diagrams, prioritization matrixes, effort/impact grids, new planning tools, and additional information on effective presentations. The Team Handbook Third Edition contains a brief description of the Six Sigma improvement method DMAIC, and highlights the methods and strategies that are useful in Lean. Also included is a new strategy for using designed experiments to identify and control sources of process variation. The book includes tools and techniques that go beyond the basics such as creativity tools, force-field analysis, and information to help leaders manage project pipelines."
Denise Dougherty, Patrick H. Conway. The "3T's" Road Map to Transform US Health Care: The "How" of High-Quality Care. 2008. Excerpt: "The ongoing significant investment in basic science and clinical discovery in the United States continues to produce impressive results. However, the United States struggles to deliver high-quality care and improved health outcomes due to the systematic failure of discoveries to reach patients in a timely fashion.1-2 Despite expenditures that reached $2 trillion or more than $6000 per capita in 2005,3 the United States will continue to fail to fully leverage new clinical discoveries into improved health outcomes unless there is an accelerated transformation of the health care system.4 The research enterprise cannot achieve this alone. We propose a model to transform the US health care system (Figure), intended to accelerate the pace at which innovations are implemented in clinical settings by addressing the "how" of health care delivery." [Accessed July 13, 2010]. Available at: http://jama.ama-assn.org/cgi/content/extract/299/19/2319.
Journal article: F Davidoff, P Batalden. Toward stronger evidence on quality improvement. Draft publication guidelines: the beginning of a consensus project Quality and Safety in Health Care. 2005;14(5):319 -325. Abstract: "In contrast with the primary goals of science, which are to discover and disseminate new knowledge, the primary goal of improvement is to change performance. Unfortunately, scholarly accounts of the methods, experiences, and results of most medical quality improvement work are not published, either in print or electronic form. In our view this failure to publish is a serious deficiency: it limits the available evidence on efficacy, prevents critical scrutiny, deprives staff of the opportunity and incentive to clarify thinking, slows dissemination of established improvements, inhibits discovery of innovations, and compromises the ethical obligation to return valuable information to the public.The reasons for this failure are many: competing service responsibilities of and lack of academic rewards for improvement staff; editors' and peer reviewers' unfamiliarity with improvement goals and methods; and lack of publication guidelines that are appropriate for rigorous, scholarly improvement work. We propose here a draft set of guidelines designed to help with writing, reviewing, editing, interpreting, and using such reports. We envisage this draft as the starting point for collaborative development of more definitive guidelines. We suggest that medical quality improvement will not reach its full potential unless accurate and transparent reports of improvement work are published frequently and widely." [Accessed on September 14, 2011]. http://qualitysafety.bmj.com/content/14/5/319.abstract.
All of the material above this paragraph is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2017-06-15. The material below this paragraph links to my old website, StATS. Although I wrote all of the material listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright ownership of this material. The brief excerpts shown here are included under the fair use provisions of U.S. Copyright laws.
15. 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.
14. 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).
13. 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:
12. 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.
11. 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 (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.
10. 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.
9. 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.
8. 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.
7. 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.
6. 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.
5. 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.
4. 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.
3. 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.
2. StATS: Steps for establishing a quality control program (March 12, 2004). So you've decided to implement a quality control program in your laboratory. What will it take to make that program successful? There are three steps, and you must follow these steps in order. Step 1. Establish management support. Step 2. Measure your process. Step 3. Experiment. Every work situation is different, of course, but these steps are a useful guideline.
1. 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.
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