nesses, automated surveillance is likely to become the most important source of adverse event data. More research is needed to improve the effectiveness of these detection systems and to broaden the types of adverse events that can be detected through automated triggers. Ultimately, an integrated approach, using patient safety data standards, will evolve, with electronic health record systems providing decision support at the point of care, preventing adverse events to the extent possible and facilitating the collection of reporting data when adverse events do occur.

Use of adverse event systems is also aimed at identifying improved health care processes through the analysis of adverse event data. This process involves selecting and defining the adverse events to survey, defining the analysis population, collecting surveillance data, analyzing surveillance findings (identifying causal factors), and using the findings to develop interventions. The process requires standard definitions of adverse events, minimum datasets for describing the events, standard definitions of dataset variables, and standard approaches for collecting and integrating the data.

INTRODUCTION

An adverse event is defined as an event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient. The understanding that adverse events are common and often result from the poor design of health care delivery systems (Institute of Medicine, 2000) has led to the development of institutional adverse event systems. These systems are used to collect data on adverse events that make it possible to learn from such events and identify trends that may reveal organizational, systemic, and environmental problems.

Despite these developments, most adverse events are undetected. The reason is that most health care organizations rely on voluntary reporting for the detection of adverse events (Bates et al., 2003; Cullen et al., 1995), and spontaneous reporting has been demonstrated to be a minimally effective way of detecting such events (Classen et al., 1991; Cullen et al., 1995; Jha et al., 1998).

Even if larger numbers of adverse events were detected, the value of the information would be limited because existing adverse event systems use widely differing definitions, characterizations, and classification approaches.



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