CONTROL CHART. We have what it takes! Continuous quality improvement - our tool for delivering... service of greater worth.

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1 TMC Quality Improvement Program Self-Instructional Modules We have what it takes! THE PDCA CYCLE SERIES Continuous quality improvement - our tool for delivering... service of greater worth. CONTROL CHART Join TMC s Quality Improvement Program now! 20 Published by the Medical Quality Improvement Office

2 Resources: 1. Benchbook on Performance Improvement of Health Services, PhilHealth, 2. A Toolbox for Quality Improvement and Problem Solving, by David Staker, 3. Continuous Improvement Tools Vol 2, by Richard Chang and Matthew Niedzwiecki, 1993: Wheeler Publishing.. (website of the American Society for Quality 2 19

3 7. After you have interpreted your chart, decide on next steps. If your data points are within the control limits- continue process with no changes recreate the chart periodically to doublecheck process control make improvements to reduce common variations review to ensure any changes have had posi tive effect If your data points are outside the control limits- investigate and take steps to eliminate the causes review to ensure changes have had a positive effect (i.e., special causes have been elimi nated) take new samples and create a new chart using limits based on the new information. What is a control chart? A control chart is a type of line graph which shows variations in a process or service. The line graph can tell you if the variations occurring in your process or service are within acceptable limits or boundaries. The control chart shows whether the process is in control or out of control. When to use a control chart? A control chart is a monitoring tool that you can use over time. Use it to - Measure health of a process to help spot problems before they become significant; Differentiate between special and common causes of variation so that the special ones can be addressed first Demonstrate trends to see how much improvement occurred in the process when changes are implemented 18 3

4 How does a control chart look like? Interpreting Control Charts Y or vertical axis shows the value of the variable you are measuring Upper line on the graph shows the Upper Control Limit or UCL The center line of the graph indicates the average of all your observations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec This chart shows a shift. There are eight or more consecutive data points on one side of the average line. It may indicate that the overall average has changed. Depending on the process being measured, it may indicate a process improvement. X or horizontal axis shows the period of observation The bottom line shows the Lower Control Limit or LCL. Your observed values are plotted against the average, UCL and LCL REMINDERS If data fluctuates within the limits, it is caused by common causes within the process flaws inherent in the process and can only be changed if the system is improved. If the data falls outside of the limits, it is the result of special causes. In service processes, these can include lack of training, wrong instructions, or ineffective processes. Such special causes should first be eliminated before the control chart can be used as a monitoring tool. For instance, in a health care setting, staff may need to be given better instructions or further training before the process can be under control. 17

5 Interpreting Control Charts TIP Why do we need to detect variations? In the chart below, a trend is seen. Six or more consecutive data points steadily decrease or increase. There is a gradual change in the process and indicates the process to be out of control. A control chart will show the variations in a process. We need to determine such variations to further study their cause. IT IS IMPORTANT TO KNOW THE CAUSES BECAUSE There are two main causes of variation in a process- Common causes are flaws inherent in the design of the process; they can be measured and monitored but not entirely eliminated. Special causes are due to employee errors or unusual circumstances or events; they affect quality and should be monitored, analyzed and eliminated B irt hweig ht s o f 2 0 b ab ies d elivered o n Jan 1, W t t W Lying - in DBM CDA FGH T JI JUN DLA DM A CAM FPG HJG EPR JM A GM A ARB VBA M CS BJM M RG LAB T RY Distinction between the two causes is important. Most variations are caused by system or process errors rather than employee errors. Once identified, appropriate changes in the system can be instituted by management. By so doing, the culture of blame is thus prevented. 16 5

6 There are two kinds of control charts: variable control charts and attribute control charts. Interpreting Control Charts Variable Control Chart It is used to present measurable data (such as weight, length, time, temperature) The following are critical in a variable control chart: precise measurement accurate and reliable measuring devices The following are the types of variable control charts: X chart is used to present individual measurements. Example: birth weights of babies born in our delivery room, temperature of food when delivered to patient s room X-bar chart is used to present average. Example: the daily average of birth weights born (values obtained in a day is considered as one subgroup), weekly average electric consumption of the different departments (consumption of the departments in one week is considered as one subgroup) Above chart shows process out of control. There are points above the UCL and below the LCL. There is a special cause of variation; something unusual happened. R chart is used to present the range which is the difference between the largest and the smallest values observed S chart is used for standard deviation 6 15

7 Interpreting Control Charts Determine if the process mean (center line) is where it should be relative to your organizational specifications. Analyze your data relative to the control limits. This determines if your process is in control. Two ways to say your process is in control - The process is consistent: all data points are within the upper and lower control limits Any variation is due to some random cause: the data points are randomly distributed without any pattern on either side of the process average or center line. Here is an example of an X-chart. It shows the birthweights (in kg) of 20 infants delivered on January 1 in Welcome to the World Lying-in Center. Each point in the chart represents the birth weight of an infant born on that day. The middle line is the average of the 20 birth weights observed during that day..5 Birthweights of 20 babies delivered on Jan 1, WttW Lying-in DBM CDA FGH TJI JUN DLA DMA CAM FPG HJG EPR JMA GMA ARB VBA MCS BJM MRG LAB TRY Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 7

8 This is another type of a variable control chart. It is an X-bar chart showing the average daily birth weights of babies delivered at the lying-in over a two-week period (from Jan 1 to Jan 1). Each point represents the average of the birth weights computed for each day. The average of the measures shown in the previous chart is plotted here as the point for Jan 1. The middle line represents the average of the averages computed from Jan 1 to Jan 1. Average daily birth w eights of babies delivered at WttW Lying-in, Jan 1-Jan 1 5. Construct the control charts For attribute control charts, construct one chart with proportion/number defective, number of defects, or defects per unit, for each subgroup. For variable control charts, construct two charts: on the top chart plot each subgroup s mean, median, or individual measures; on the bottom chart, plot each subgroup s range and standard deviation /1 1/2 1/3 1/ 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12 1/13 1/1 6. Interpret the charts. Determine if your process is under control or out of control. If we take the babies born on each day as one subgroup, the chart above compares the variation on the average birth weight between the different subgroups. On the other hand, if a chart shows the standard deviation (s chart) or the range (r chart) of the daily birth weights, it compares variation within a subgroup. 8 13

9 3. Collect the data. Attribute Control Chart You should run the process untouched (without any intervention) and collect sampled data. Data should always be collected randomly. It is used to present countable data or the number of times a certain characteristic (or attribute) is present or absent in items or process being studied. Calculate the data mean, standard deviation, and the upper and lower control limits. Calculating mean, standard deviation and control limits may scare the wits out of many of us but the good news is these can be computed using commonly available software (e.g. Excel). The following are the types of attribute control charts: np chart shows the number of attributes, for instance, the number of lunch plates >30C when delivered to patients room p chart shows the proportion of items with attribute, for example, the proportion of lunch plates >30C when delivered to patients room out of the total lunch plates sampled c chart shows the number of attributes per item. For example, you want to show how many of the following attributes are found per lunch tray delivered: a) with temperature >30C, b) with serving size <250gm, and c) delivered after 12:30pm u chart shows the average number of items with attribute, for example, you want to keep track of the weekly average number of lunch trays that are delivered beyond 12:30pm. 12 9

10 How is a control chart constructed? 1. Determine what to measure. It should be a critical and important process that needs to be controlled. It should be a key measure that needs to be tracked over time. For example, the temperature of food at the time it is delivered to the patient s room is a critical indicator of the quality of our dietary services. You need to control it and ensure that it is at the right temperature when the patient receives it not too cold, not too hot. It is something that you can measure over time. Another example: patients need not stay at the Emergency Room for more than two hours. It reflects the quality of care being provided at the ER. The number or proportion of patients who stayed for more than two hours at the ER can be measured and presented in a control chart. 2. Determine the sampling method and plan. Select the kind of chart to use (depends on the data you will be collecting) See table below to determine your sample size Your observations should be under the same technical conditions (same measuring instrument, same method, same evaluator, etc. Type of chart Number of subgroups (i.e. No. of means) Number of samples per subgroup Variable control chart consecutive samples Attribute control chart At least 50 individual samples EXAMPLE 1. Variable to be measured: Discharge turn-around time Control chart: Variable control chart Sampling: 5 consecutive discharges per day (you may randomize starting case); collect on a Monday, Wednesday, Friday and Sunday for Week1, then on Monday, Tuesday, Thursday, and Saturday on Week2. Collect for 20 weeks alternating the schedule (one day=one subgroup) EXAMPLE 2. Indicator: Proportion of discharges with turn-around time more than 60 minutes Control chart: Attribute control chart Sampling: Randomize 50 discharges per day; collect for one month. (One day=one subgroup) 10 11