ABC | Volume 114, Nº4, April 2020

Short Editorial Colafranceschi How do we know a change is an improvement? Arq Bras Cardiol. 2020; 114(4):613-615 outcomes that vary throughout time, irrespective of being successful or not. Because humans and poorly designed systems are vulnerable to error, a critical assessment of our systems of care is essential for improvement to continue. 6 Variation in a quality measure may result from  common causes — expected causes that are inherent to the system. It may also derive from  special causes —unnatural causes that are not part of the system but arise due to specific circumstances. There are many ways to present and analyze data. For improvement efforts, a control chart  (Figure 1) helps distinguish between special and common causes of variation. It includes an upper control limit and a lower control limit marked above and below the average line. Variation within these limits is expected and attributed to common causes; variation beyond these limits suggests special causes. 7 In a stable system, only common causes affect the outcomes. Variation is predictable within statistically established limits. By contrast, in an unstable system, outcomes are affected by both common causes and special causes. In this case, variation is unpredictable. If the process is stable and variation is predictable, one can foresee the future outcome for the systembeing observed in real-time, which makes it suitable for improvement efforts. Control charts can also be used to identify early signs of success in an improvement project and to monitor a process to ensure it is holding the gains from a quality improvement effort. Like a run chart  it helps determine whether the changes made are leading to improvement. The point here is that improvement efforts can only be made in stable systems. 7 Data driven from the original manuscript in its tables 1 and 2 (total volume of procedures and total operative death index throughout different periods of time) were used to build a control chart as in Figure 1. Statistical process control (SPC) techniques have played an efficacious role inmonitoring hospital performance, such as mortality rate. 6 According to this analysis, the system being used for improvement efforts in the work by Mejia et al. 2 is an unstable system and the outcome (operative death) is affected by both common and special causes. Since variation is unpredictable in an unstable system, the changes from PMCQ at InCor cannot be attributed to the improvement in total operative death from period 4 to period 5. In fact, using SPC methodology, there is no difference in operative mortality between periods 1,2,3 and 5. The operative mortality at InCor has been varying close to international standards since they started collecting these data. A special cause in period 4 increased the operative mortality beyond the upper control limit, which made it statistically different from period 5, when research statistical methods were used to analyze an improvement effort. Health care organizations use data to understand their performance — although they do not always do so effectively. 4 It is important to note that the quality improvement staff view and seek to use data regarding variation in healthcare processes differently from that of health services researchers. Where practical, real-time quality improvement is the goal, variation itself needs to be examined in real time to answer the questions: 1- Are we getting better? And 2- Where can we improve? 4 Thus, “just-in-time” performance data are essential to the effective use of variation data, and the focus is on creating stable processes and learning from special-cause variation . In contrast, health services researchers pose the question, does A cause B (other things being equal?), often taking the long view to examine several years` worth of data and seeking to eliminate special-cause variation and test for significance . 8 These different perspectives can lead healthcare managers and researchers to look at the same results and reach very different conclusions about their significance and the actions that should be taken in response. 4 Learning fast frommistakes is part of the improvement theories and although there was no proven improvement in operative mortality attributed to actions taken after PMCQ consolidation, the continuous quality improvement effort at InCor is far from being unsuccessful. The PMCQ initiative at InCor should be followed by others. InCor not only pioneered and mastered the open-heart surgical academy in the country. Its leadership in the field continues to change our own perspectives regarding what means to be a contemporary heart surgeon within a system. InCor is helping us reflect on the traditional view that patient outcomes are related only to the surgeon’s technical skill to an evolving and broader framework, wherein health care outcomes are affected by a multitude of factors in highly integrated and complex processes and environment. Since physicians (and surgeons) are involved in almost all-important health care processes, it is wasteful to try to improve health care processes without them. 5 It is still required for a surgeon to learn, master and lead the current and new technology and technical skills to care for patients. Contemporarily, however, this is not sufficient to improve outcomes. It is time for cardiothoracic surgeons (and every physician) to reflect on their own personal purposes of being a healthcare professional and learn, master and lead the (not so) new scientific knowledge to improve patient outcomes. Figure 1 - P chart of operative death. Data obtained from tables 1 and 2 of the original manuscript.Average line is in green. Upper and lower control limits are in red. Dots represents the operative death index for the specified period of time. Dots are connected in a black line that shows variation. Operative death in period 4 is beyond the upper control limit (as marked in a red circle), suggesting a special cause in the process of caring within this period of data collection. 614