In QI data collection is continuous. It is always going on in the background, regardless of what else we are doing in the project. This is one aspect where QI differs from an audit cycle. In an audit cycle a data set is collected before an intervention, and then a further set after. However having only 2 data points makes it very difficult to evaluate the impact of our changes to practice. For example if things appear to go up after the intervention, how do we know it’s down to the change, and not some other unknown factor? How do we know the first data point wasn’t unusually low (and most of the time higher), or the second unusually high (and most of the time lower)?
You can hopefully see why we need the data collection to be continuous, it gives a truer picture of the performance of the system we are looking to improve. This means you should define a time period between data points and always collect at these set points. One data point per week is usually a good target, and gives you nearly 20 data points (which is ideal) over a 4 month period. Your project may allow daily points, or fortnightly or monthly; and this is also fine. It depends on what is available to you. Collecting this much data may seem daunting, but don’t worry – each data set can be very small and we’ll tell you how you actually go about getting this easily in the next step (step 6.2).