9 November 2021
47 active QI projects showing improvement across CNWL
Photo: Cyclamen Hederifolium, Peter Smith
As we move towards the end of Autumn, this month we are looking at how we interpret QI run charts. This is a subject that all our QI project need to consider as the project moves into maturity and we have gathered data to support our improvement work. So, understanding what to look out for in our charts is a crucial aspect of our QI knowledge and our lead article is aimed at de-mystifying the rules that tell us that special cause variation is happening in our data. If that sounds intriguing, please read on to the article.
Also this month we have reminders about applications for the exciting new CNWL QI Coach Development Programme, which close very soon, and the QI Clinic where you can book a slot to talk to one of the QI Team about any aspect of your improvement work.
The regular report on news from the QI Practicum highlights the ‘all teach all learn’ approach in action and how we can share our QI work and help colleagues with their improvement.
We also meet the newest member of the QI Team, Vernanda Julien, who has joined us as an Improvement Coach and we very much look forward to working with her.
The Virtual Bitesize QI training took a break in November and we are pleased to see the December date is fully booked already, but there are dates from January which are available for any staff to book via LDZ, so do encourage anyone who wants to start a QI journey to make a booking.
We welcome your feedback and if there is anything you would like to see in future editions of the newsletter, do please get in touch by e-mailing email@example.com.
Interpreting QI Run Charts
Imagine that we have got a SMART aim, worked up our driver diagram, developed our set of measures (outcome, process and balance) and collected some data to plot on a series of charts on Life QI. So what next? We want to answer the question ’How will we know that a change is an improvement?’ in the Model for Improvement, so we need to deploy our knowledge of how to interpret or ‘read’ our data when it is plotted out on a run chart.
To do this, we need to know how to spot when special cause variation is occurring in our data. All data will randomly vary over time. This is a naturally occurring phenomenon where we see a ‘saw tooth’ pattern to data when we plot it on a chart over time. Special cause variation occurs when we have made some effect on the data, such as when we have introduced a change idea and hence changed our system. It is the special cause variation that we need to look out for. Indeed, if we are trying to improve something, we want to see some change in our data to show that our changes are having an effect!
There are four common ‘rules’ that tell us that we are seeing special cause variation; the good news is that when plotted on Life QI, the system provides us with alerts to show when a rule has triggered and special cause variation is present. But it is necessary to appreciate what the rules are and why each is significant, so let us look at each of the rules in turn.
Why is the median value so important?
To interpret our run charts we need to draw a median line on the chart. The median is the middle value of all our data; importantly it is not the average (known as the mean). This is because the average value of our data is more sensitive to ‘outliers’ or unusually big or small data points and this would skew our interpretation of the chart and what the data is doing. The median value is less sensitive and therefore we get more reliable identification of that special cause variation that we are looking for in our data. The median is important because the rules are based on statistical probability that our data is or is not behaving in a random way. There are lots of statistics behind this based on normal distributions of data, but thankfully we don’t actually need to know what all that statistical calculation is because it is done for us in Life QI. We do not have to become statisticians to be good at using data in our QI work!
Rule 1: A Trend
5 or more consecutive points increasing or decreasing
Probably the easiest to think about, if the data is ‘going in one direction’, then it is definitely being affected. We need to see a minimum of 5 data points in an ascending or descending order for this to be statistically indicating special cause variation, but we then need to ask ourselves which is the ‘direction of good’. If the data is going in the right direction, then our changes are helpful and we should do more or continue with them, but if the data is moving against the ‘direction of good’, we need to stop or abandon our changes.
Rule 2: A Shift
6 or more consecutive points above or below the median
A shift in your data is when the data points consistently move or ‘shift’ above or below the median line. We need to see at least 6 data points that are all in sequence on one side of the median, so the data is not crossing the median during a run that is classed as a shift. If a shift happens in our data after we have started a PDSA test of a change, we can get very excited because it is highly likely that the changes we make are causing the shift.
Rule 3: Astronomical Point
Data points that are obviously, or even blatantly different from all or most of the other data values
Astronomical points are data points that do not seem to fit with all the other data we have plotted on a run chart. They are literally astronomically or greatly different (higher or lower) than the other data points. In this sense, astronomical points are most easily spotted by just looking at your run chart; they can ‘stand out like a sore thumb’! Of course, there is a statistical reason that we can identify a data point as an astronomical one and Life QI identifies them for us. When we see one, we need to think about what might have happened to cause it and we can usually explain something that happened ‘out of the ordinary’, for example, ‘we had three staff off sick that week’ or ‘we had 10 extra referrals to deal with on that day’. Most often, astronomical points should not concern us unless they keep happening, which would indicate that there is instability in our system and we might want to investigate the reason behind that.
One question that is asked frequently is “should we replot our data without the astronomical point(s)?’ The answer to this is definitely ‘No’; the astronomical points are part of your data, and are part of the description of how your data and system are behaving, so you need to keep them in your run chart.
Rule 4: Too few or too many runs
A non-random pattern or signal of change is indicated by too few runs* or too many runs
*number of times the data line crosses the median and add one
A run is when the data points cross the median line, so data that is acting in a normal random way can be expected to cross the median frequently and for a certain total number of data points, there are statistical ‘norms’ of how many runs you should expect to see. Too few or too many runs are therefore another indication that your data is behaving in a non-random way and you are seeing special cause variation. So, once again, if your run chart is showing this rule of the number of runs being triggered, something is acting on your data to make it abnormal and you need to think about what that is causing the change. We would hope that the changes we are making in our PDSA cycles are the explanation to seeing too few or too many runs in our data.
So, we have four rules that when triggered show us that we are seeing special cause variation in our data. And any of the rules triggered should prompt us to think about what we have done or what has happened in our system to make the data behave differently. Bearing in mind what is the ‘direction of good’ for our data, we can see the effects of our change ideas as we plot our data.
Working with these rules can be tricky or befuddling at first, but the more you look for them and think about them, the easier and more comfortable they get. If you have data that you are wanting to understand and interpret, don’t forget that one of the quickest ways to get help and support is to either talk to your QI Coach or to book into a QI Clinic and talk through your run chart with one of the QI Team. The team will be more than happy to help you. Contact us at firstname.lastname@example.org to book in to a QI Clinic slot.
News from the Practicum 2021
On Thursday 21 October we held our second full Practicum Learning Session, where 24 teams across the four Practicum workstreams (safety, violence reduction, pressure ulcers and flow) shared learning and worked together on their change ideas and how to implement these using PDSA cycles. Teams also learned about how to interpret their run chart data.
Thank you to the two teams who shared their improvement journey so far. We heard from two teams from Milton Keynes, one team from Community Services who are on the Pressure Ulcer improvement workstream and the other team was from Hazel ward at the Campbell centre who are on the Violence Reduction workstreams.