Technologies such as smart pumps, intensive care unit monitoring systems, and computerized physician order entry can be used to identify near misses. There is a need to investigate these systems and how the near misses they identify can be used to improve patient safety.
Automated triggers already allow for the detection of some types of adverse events, such as nosocomial infections (Evans et al., 1986) and ADEs (Classen et al., 1991; Jha et al., 1998), and it appears likely that this general approach could be extended to other types of adverse events (Bates et al., 2003). The approach works through detection of a signal, such as a high serum drug level, use of an antidote, or a laboratory abnormality in the context of use of a specific medication. A program called an event monitor is integrated with the clinical database to detect the presence of such a signal. Once a signal has been identified, it can be sent to the appropriate person or written to a file for later action. Currently, such detection approaches have high false-positive rates (Bates et al., 2003; Jha et al., 1998). Further research is needed to reduce false-positive rates for ADEs and nosocomial infections and to develop and validate computerized clinical trigger detection systems for other high-frequency sources of injury, such as decubitus ulcers, patient falls, complications of blood product transfusions, and complications of central and peripheral venous lines.
Tools such as computerized physician order entry incorporate capabilities to prevent adverse events, for example, by checking to see whether drug interactions with negative side effects could occur. Further research is needed to convert the growing knowledge base on patient safety risks into existing and new point-of-care decision support tools.