Control Charts Summary, Coursework Example

Control charts are used to determine how particular behaviors of members of a population impact a certain variable. Statisticians also call these charts Shewhart charts, after the man who created them. These charts are meant to identify whether a trend is within a state of statistical control. For this to be possible, 99.7300% of the data points must fall within the control limits. If the less than 1% or more of these data points fall outside of these limits, it indicates that there is a source of variation that statisticians refer to as special-cause variation. Control charts are created by first selecting a population to study and then determining what the independent and dependent variables are. The independent variable is always put on the x and the dependent variable is placed on the y axis. Limits are typically selected on the basis of the expected probability distribution of the population being studied.

When control charts are contrasted, a solid line in the middle of the graph is used to show the mean value of the population being studied, and the two dotted lines show the reader where the upper and lower limits are stated. Summary statistics are occasionally shown on the bottom of the graph in order to indicate to readers the exact information that they would want to see quickly. Briefly, these statistics can include the mean, median, mode, standard deviation, and limits involved in a particular trend. Different statistical software programs can be used to make control charts, and some are better than others. These charts can also be drawn by hand.

In Control Chart

This control chart indicates that total minutes spent within the emergency department is in control. While most control charts have upper control limits and lower control limits, this chart only has an upper control limit because it assumes that we know that the lower control minute is zero. The lower control limit is zero because ideally, the hospital wants to minimize emergency room waiting time to the lowest number possible. This graph shows that the mean waiting time was 163 minutes for the 25 emergency room patients studied. This chart is considered to be in control because there are no variables outside of the range of limits. This is important because the emergency room does not need to make any changes in order to meet its time goal.

Out of Control Chart

This control chart was made in order to evaluate the amount of time that each emergency room patient would need to spend in their emergency room bed. This is an important because this is a measure of the efficiency of the emergency room, as it allows new beds to free up and the focus of emergency room personnel to be spent on important cases. The independent variable is represented by the date of admission of each patient and the dependent variable was the minutes spent by each individual. In this situation, the upper control limit was 375.2 minutes and the lower control limit was 76.0 minutes. This trend was interesting because the average time of approximately 75 minutes fell below the lower control limit. Despite the amazing efficiency in this hospital as demonstrated by a majority of the results received, this control chart can be considered to be out of control because of the one data point that is above the upper control limit. One patient stayed in the emergency room bed for 450 minutes and there is another data point that appears to be off the chart, above this one. It is therefore essential for the hospital to examine these cases and determine what was unique about them that caused them to go on for so long. They will then modify practices to ensure that this does not happen again in the future.

Another Type of Control Chart

This control chart differs from those discussed above because it presents the upper control limit, the control limit, and the lower control limit values more clearly. In this control chart, researchers attempt to demonstrate the amount of falls that occur in the hospital over 1,000 patient days. The independent variable is the date/time/period and the dependent variable is the number of falls. The upper control limit of 10 denotes that a maximum of 10 falls will be considered acceptable. The hospital may use this chart in order to change practices and determine how it will impact the number of falls experienced by their patients. This study indicated that there were an average of 4.3 falls. This trend is in control, which is indicated by the fact that none of the variables are outside of the predetermined control limits. This means that the amount of falls meet the hospitals expectations and if these expectations are desired to be continually met, then aspects of the hospital operations should remain the same. However, limits should be changed in a manner that reflects a 0 fall policy if the hospital wishes to engage in best practices.

Works Cited

Poots A, Woodcock T. Statistical process control for data without inherent order. BMC Medical  Informatics and Decision Making. London, UK: BioMedCentral, 2012.

Shewart, WA. Economic Control of Quality of Manufactured Product. Van Nordstrom, 1931.

Wheeler, Donald J. Understanding Variation. Knoxville, Tennessee: SPC Press, 2000.