Automated surveillance of data and monitoring of patient progress, referred to as concurrent surveillance methods, are prospective in that they start with a clinical care process and seek to identify critical points in that process at which failures are likely to occur (e.g., when medication is prescribed). These approaches aim to prevent adverse events from happening in the first place or to quickly identify an adverse event once it has happened. For example, a concurrent surveillance system might monitor pharmacy orders for the use of antidote medications, then quickly send a trained professional to review any such case detected. The reviewer determines whether an injury or near miss has occurred and then investigates and classifies the event. More important, because such a review occurs in real time, a clinician can often intervene to prevent or ameliorate resulting harm. While prospective surveillance systems can be created and operated effectively using solely manual methods, automated methods offer more cost-effective and elegant solutions when automated clinical data systems are available. The committee believes increased attention should be devoted to concurrent surveillance methods since many common causes of adverse events are already known (Agency for Healthcare Research and Quality, 2001).
Broad-based studies of the relative effectiveness of the detection methods outlined above have not yet been carried out. However, a number of epidemiological studies have examined the relative strengths and weaknesses of voluntary reporting, retrospective chart review, and automated surveillance for detection of adverse drug events (ADEs).
Using inpatient data, Classen et al. (1991) established that automated surveillance could effectively detect ADEs at a much higher rate than voluntary reporting. Cullen et al. (1995), again using inpatient data, demonstrated that voluntary reporting uncovered only a small fraction of the ADEs identified by a nurse investigator reviewing charts daily.
Jha et al. (1998) compared automated surveillance with chart review and voluntary reporting using inpatient data. They found that automated surveillance and chart review each identified many more ADEs than did voluntary reporting. They also found that automated surveillance and chart review identified different types of events. Automated surveillance was more effective at identifying events associated with changes in laboratory results, such as renal failure. Chart review was more effective at identifying events manifested primarily through symptoms, such as changes in mental state.