Introduction

Two men are sitting in the waiting area of a cardiology clinic. They recognise one another and realise that they are both attending the clinic due to the same problem—they have both been experiencing chest pain during their working day. While they are still laughing at the coincidental nature of their meeting, the first of the two is called in to see the consultant cardiologist. They meet again after the clinic.

‘How did you get on?’

‘Great, the doctor said it was just a muscle strain, how about you?’

‘Not good, I have to go in for an angiogram next week.’

‘That’s strange’ the patient remarks, ‘We are both the same age and it sounds like we were both having similar problems, why are you getting an angiogram and not me?’

Healthcare professionals often make different decisions when faced with similar information, referred to as ‘noise’ or unwanted variability in decision making. It is recognised that both bias and noise are important contributors to overall error in decision making and can exist independently to one another.1 We can think of bias and noise by considering the example of bullets being fired at a target. With a noisy shooter, the bullets will hit various parts of the target depending on largely random factors. With a biased shooter, the bullets will be grouped together but may have systematically tended to miss the target to the left. An accurate shooter will consistently hit the target. These patterns are shown in figure 1. If we were using a weighing scale that consistently gave us a weight reading that was 2 kg too low, we would say that the scale is biased. If the scale gives different readings when used several times in quick succession, some too high, some too low, we would …