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Tools And Methods For Quality Improvement Analysis

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[Seminars in Perinatology 3500-4000 words 4-6 figures Comment by Kaplan, Heather: I cut a bit which give us 1000 words to add in descriptions of our examples that we will highlight and then add some additional examples and flesh out some other areas (where I indicated)I really think the most important part of this will be weaving in the examples! Comment by Kaplan, Heather: Do we need an abstract?
Tools and Methods for Quality Improvement and Patient Safety in Perinatal Care
Introduction
The Institute of Medicine (IOM) defines quality as ?the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.? In the relatively recent …show more content…

An integration of the two approaches and a general focus on process innovation regardless of the origin of the tools and approaches would be more productive. Lean and Six Sigma have complementary strengths that are useful for systematically developing healthcare service innovations. An integrated Lean-Six Sigma approach incorporates the organizational infrastructure and the thorough diagnosis and analysis tools of Six Sigma with Lean analysis tools and best-practice solutions for problems dealing with waste and unnecessary time …show more content…

It has its foundation in the theory of variation (understanding common and special causes) and was one of the key tools developed by Walter Shewhart in the 1920s. The primary tools used in SPC are graphical, including Pareto analysis, control charts and run charts. It is crucial for QI teams and researchers to understand variation in data to avoid making errors in interpretation, though in practice, many QI teams do not have sufficient biostatistical training to be able to interpret their data with confidence. The practical power of SPC is that people who are not statisticians can bring significant statistical rigor to their quantitative data in an intuitive format by understanding just a few simple, pattern-based rules to distinguish special-cause variation (i.e., signals) from common-cause variation (i.e., noise). Distinguishing special and common cause variation is critical to understanding whether changes you are making result in improvement (i.e., is there a signal of change present?). Among all of the SPC tools, control charts have the most power to distinguish common from special cause variation. Control charts plot data over time (time on the x-axis) and include a measure of central tendency (centerline, mean) and upper and lower control limits (plus/minus 3 sigma of the mean). The control limits reflect the natural variation in the data or the extent of the

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