Statistical process control for hospitals: methodology, user education, and challenges.

The health care industry is slowly embracing the use of statistical process control (SPC) to monitor and study causes of variation in health care processes. While the statistics and principles underlying the use of SPC are relatively straightforward, there is a need to be cognizant of the perils that await the user who is not well versed in the key concepts of SPC. This article introduces the theory behind SPC methodology, describes successful tactics for educating users, and discusses the challenges associated with encouraging adoption of SPC among health care professionals.

Control limits for p control charts with small subgroup sizes.

The p chart is widely used in health care and other service organizations as well as in manufacturing to monitor the proportion of observations with some particular characteristic for comparing several sources of data or for tracking a single source of data over time. The conventional approach is to use 3sigma limits found by using the normal approximation to the binomial distribution. This article reviews a method for taking into account the fact that 3sigma limits are not always appropriate, and suggests the use of the exact binomial distribution instead of the normal approximation to eliminate the problems associated with small subgroups.

X and s charts for health care comparisons.

In previous articles [M.K. Hart, Qual Manage Health Care.

Application of variables control charts to risk-adjusted time-ordered healthcare data.

In a previous article (M. K. Hart, Qual Manag Health Care.

Application of attribute control charts to risk-adjusted data for monitoring and improving health care performance.

This article proposes a new class of control charts that may be used for monitoring and improving the quality of care. Unlike conventional control charts that rely on observed performance data, these charts use risk-adjusted data in addition to the observed data. The resulting time-ordered charts are capable of reducing time-to-time variation that may stem from uncontrollable changes in patient mix over time.