The goal of patient safety efforts is to reduce the harm we do to our patients while providing them with the care they need, and recognizing the true nature and sources of harm is critical to this endeavor. The paper by Szekendi et al¹ in this issue of QSHC describes a return to automated methods for detecting adverse events, and provides an opportunity to review the evolution of adverse event detection as well as the challenges associated with different models.

First, however, we must emphasize why some form of surveillance for detection of harm to patients is indispensable to modern patient safety practices: it allows us to overcome the serious defects associated with dependence upon spontaneous reporting as a method for detecting adverse events. While such reporting can play an important role in supporting a culture of safety—for example, encouraging the candid discussion of errors—it is by its nature anecdotal and superficial. In addition to the obvious barriers to reporting (time constraints, fear of retribution, liability concerns), we know that most events causing harm to patients are not even recognized as such by clinicians at the time they occur.² Thus, voluntary reporting describes a small—and by no means representative—minority of the universe of harm to our patients. It is useless for the quantitative study of adverse …