Near-miss analysis

Although near-miss events are much more common than adverse events—as much as 7–100 times more frequent—reporting systems for such events are much less common. As the airline industry has realized, analysis of near-miss data provides an opportunity to design systems that can prevent adverse events. Near-miss data for the health care domain should be analyzed more extensively than is currently the case. The data provide two types of information relevant to patient safety—on weaknesses in the health care system and, equally important, on recovery processes. The latter data are an underutilized source of valuable patient safety information. This chapter examines the functional requirements of near-miss systems and the implications for data standards. With some exceptions, near-miss data (and adverse event data) should be examined in the aggregate to determine priorities for health care improvement. The analysis of aggregate event data requires the use of standardized taxonomies to describe the root causes of failure, recovery processes, and situational contexts uniformly. Since near misses and adverse events are thought to be part of the same causal continuum, there should be identical taxonomies for failure root causes and context variables for both types of events. The development of near-miss systems works best when the systems are initially established and designed for the benefit of those delivering care, for example, a hospital department. Data from this level can be aggregated for higher-level purposes—reports for hospital-wide systems and domain-specific nationwide systems. However, uses of the data require that the same data standards be applicable across all domains and at all levels of aggregation. Near-miss systems should be an integral part of clinical care and quality management information systems. To foster data reuse across all health care applications, the same data standards should be used for all applications