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Improvement of health care requires making changes in processes of care and service delivery. Although process performance is measured to determine if these changes are having the desired beneficial effects, this analysis is complicated by the existence of natural variation-that is, repeated measurements naturally yield different values and, even if nothing was done, a subsequent measurement might seem to indicate a better or worse performance. Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time, which can delay decision making. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC and its primary tool-the control chart-provide researchers and practitioners with a method of better understanding and communicating data from healthcare improvement efforts. This paper provides an overview of SPC and several practical examples of the healthcare applications of control charts.

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Alternate Shewhart-type statistical control charts, called "g" and "h" charts, are developed and evaluated for monitoring the number of cases between hospital-acquired infections and other adverse events, such as heart surgery complications, catheter-related infections, surgical site infections, contaminated needle sticks, and other iatrically induced outcomes. These new charts, based on inverse sampling from geometric and negative binomial distributions, are simple to use and can exhibit significantly greater detection power over conventional binomial-based approaches, particularly for infrequent events and low "defect" rates. A companion article illustrates several interesting properties of these charts and design modifications that significantly can improve their statistical properties, operating characteristics, and sensitivity.

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Alternate Shewhart-type statistical control charts, called "g" and "h" charts, have been developed for monitoring the number of cases between hospital-acquired infections and other adverse events, such as heart surgery complications, catheter-related infections, surgical site infections, contaminated needle sticks, medication errors, and other care-induced concerns. This article investigates the statistical properties of these new charts and illustrates several design considerations that significantly can improve their operating characteristics and sensitivity, including the use of within-limit rules, a new in-control rule, redefined Bernoulli trials, and probability-based limits. These new charts are based on inverse sampling from geometric and negative binomial distributions, are simple for practitioners to use, and in some cases exhibit significantly greater detection power over conventional binomial-based approaches, particularly for infrequent events and low "defect" rates.

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This article describes recent work to develop a Web-based statistical surveillance information system to monitor in real time the status of the U.S. Air Force's worldwide healthcare network. The intent is to incorporate statistical and related methods in order to identify unusual events and patterns of concern in large, highly distributed organizations. The work recently received an award from Vice President Gore for reinventing government.

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This is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

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This article is the first in a two-part series discussing and illustrating the application of statistical process control (SPC) to processes often examined by hospital epidemiologists. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand mutual understanding and cross-fertilization between these two disciplines. Part I provides an overview of quality engineering and SPC, illustrates common types of control charts, and provides references for further information or statistical formulae. Part II discusses statistical properties of control charts, issues of chart design and optimal control limit widths, alternate possible SPC approaches to infection control, some common misunderstandings, and more advanced issues. The focus of both articles is mostly non-mathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

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As healthcare continues to become more competitive, the ability to assess tradeoffs between resource utilization, service, and operating costs grows in importance, such as with respect to appointment access, waiting room delays, and telephone service. This paper discusses the use of simulation analysis for studying and improving these and other health systems. A case study concerning pediatric waiting times illustrates typical steps involved in a simulation study, possible types of analyses, and resources required. Other healthcare uses of stimulation, pitfalls to avoid, and software selection also are discussed briefly.

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OBJECTIVE: To develop mathematical models to assist decision makers with the difficult task of evaluating the use of automated rescreening in the process of screening cervical smears. STUDY DESIGN: Using assumptions about incidence, per smear screening costs, and the sensitivity and specificity of cytotechnologists, pathologists and the rescreening device, basic probability models were developed to describe the overall sensitivity, specificity and cost of the screening process. RESULTS: The optimal screening policy is highly dependent on assumptions, and an automated system can significantly affect the overall system cost and accuracy. CONCLUSION: Mathematical planning models are valuable tools to assist decision makers in the design of a screening process for cervical smears.

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