CBC Laboratory Turnaround Time - Speech or Presentation Example

Case Study # 3: Primary Cesarian Sections

• C-section data are counted. Counts are classified as attribute data.
• The p-chart (see chart below) should be constructed for the C-section data, as C-section has been represented as a percentage of total cases.

n=27; UCL=0.3629; Center=0.1543; LCL=0

The year 2005

n=12; UCL=0.4726; Center=0.1573; LCL=0

The year 2006

n=12; UCL=0.4675; Center=0.1545; LCL=0

The year 2007

n=3; UCL=0.74; Center=0.1416; LCL=0

• As none of the data points are outside the control limits, there has been no shift in the process.
• The future C-section rate would be approximately 15.43 percent if no changes were made in current practices.
• Control limits look like steps if constructed separately for specific periods (see figures for years 2005; 2006; 2007). However, if they are constructed as a single chart over time as a continuous chart, it is a straight line. There is no problem with the computer software.

Case Study # 4: Patient Falls

• Patient falls are counted. Counts have been classified as attribute data.
• C-charts have been considered appropriate for monitoring the count of patient falls, relative to a constant sample size.
• There has been a steady decline in counts within the chart after the implementation of the program. From the chart, the number of counts beyond September 2005 had the program not been implemented would have remained above the average of 51.88. After the intervention, it was approximately 37.8.

n=24; UCL=62.775; Center=43.083; LCL=0. 

• If the data is segregated into two parts one before the intervention, and one after the intervention the program could be considered adequate. However, if all the data is plotted on one chart one of the points lies above the UCL. Also, there are three consecutive points above and below the center line indicating that the process is not in control.

Before intervention

n=9; UCL=73.499; Center=51.889; LCL=30.28

After intervention

n=15;  UCL=56.245; Center=37.80; LCL=19.35

• The mean and control limits were frozen to determine the state of the process control after the implementation of the intervention program. This allowed the determination of process control effectiveness over time without any changes in the intervention program.
• The second control chart allows a comparison of the program before and after the intervention. The new chart displays a lower control line indicating a lower number of incidents.

CASE-Study # 5:

8. Sample size = 4; Number of Samples = 20; X-bar UCL=8.69; Center=6.16; LCL = 3.63

R UCL = 7.92; Center = 3.47; LCL = 0.0 

1. The process is not in control as there is one point above the X-bar UCL, and there is another below the LCL.
2. The process of waiting times for customers at the teller during lunch hour has been studied. The process is not in control, and there is huge variability. The process can be standardized for completion within a specified servicing time. Special cases that require deviation from the standard process should be referred to a specific teller for unique cases. This will allow for accomplishing servicing standards.

CASE-Study # 6:

1. n=20; p-chart UCL=0.1563; Center=0.0343; LCL=0

1. The process is out of control as two points lie below the lower control limits. A process in control would have points fluctuating above and below the center line, without going outside the control limits.
2. Possible reasons for out-of-control signals include accounting clerical errors as a count and expressing it as a proportion. The nature of clerical errors being an attribute, there could be days when the clerical errors do not occur on specific days, and repeat after intervals. In this case, no errors were observed in intervals of 10 days.