a frequently used tool in quality improvement is something that is traced back to a fellow by the name of vredo predo it's a great name to say actually vredo predo and predo was a sociologist and Economist in Italy who developed what is classically known as the prao concept or the old 8020 rule that is when he looked at the economics of Italy he discovered that 80% of the wealth of Italy was owned by 20% of the people now the Poo concept was turned later on into a poo chart a diagram that is able to show you essentially what are called the vital few and separate those from what are classically known as the trivial many and so I want to show you what that tool looks like it is again grounded in that principle that prao established back in the late 1800s now that we've briefly explained the prao concept let's show what a prao chart actually looks like I've laid one out here to save a little time interestingly enough when you make these by hand it's actually uh rather interesting task the computer does them in the blink of an eye but let me show you the pieces that we had to set out in order to make this particular graph we have two ya AES on a poo chart the first one is dealing with the number or frequency of a reason why something didn't happen or happen properly on the right side the Y AIS is the cumulative percentage of of these individual reasons so let's see how that plays out first of all let's think of looking at Med errors as our topic what are the reasons for a medication error it could be wrong dose wrong time wrong patient it could be dose repeated or dose omitted now we have a bunch of Med orders all stacked up and we're going to review them and so this one is okay this one is not okay this one is not okay and this one's okay and so let's say that we have a whole pile of orders and now we're going to take those that were not okay and look at our reasons wrong dose wrong time patient repeated omitted out of this stack of orders let's say that we find that there are about 200 errors that we've detected so what we do is we go through all the charts and then we start to look at the reasons and then we put little tick marks to figure out the frequency and which one is the highest number of frequency becomes the first box this is a rank order decreasing array of the data and let's say that our first box here contains the wrong dose that was the major reason for why there were medication errors and then it's the wrong time let's say that this was because the dose was repeated this is the wrong patient category 4 and finally the dose omitted as the last one when we work out the numbers let's first of all look at the frequency there were 90 reasons that the dose was detected as being the wrong dose we had 70 in terms of the frequency of wrong time we had 20 repeated 16 the wrong patient and finally four in terms of being a med that was omitted now if we compute the absolute percentage that each of these is of the total of 200 errors we see that the First Column is 45% the second is 35% this next column is 10% then 8% and finally 2% so now we have the absolute the number in dark the frequency and the percent that each of those is of the total of 200 errors that we detected now what we do is we start accumulating these so 45 + 35 + 10 8 2 eventually all add up to 100 we start to make a curve coming off this first bar and then we add the next one to it it starts to rise quickly but then it starts tapering off because the percent es get smaller then to apply the Poo principle we look at the spot where 80% on the cumulative percentage this is the point at which 80% of all the errors are detected and if we want to apply that prao principle of 8020 will come across and find the point at which that horizontal line strikes the curved line the cumulative percentage and then we drop a line down now we find very quickly that these two categories classically known as the vital few are the two areas that if we want to work on improving this procedure medication detection and errors these are the two things wrong dose and wrong time these things while interesting are called the trivial many it's not that not important but they are less important than these two main categories so the Poo principle two axes the categories or the reasons why something didn't work properly are laid out according to the frequency and then the absolute percentage and then we accumulate those to find where this 8020 split occurs the vital Fe versus the trivial many