able and automated detection becomes increasingly feasible (Bates et al., 2003). The result will be the detection of a much higher proportion of adverse events than are found today. Events may be detected through sending signals to quality personnel who can evaluate them, yet increasingly, electronic records will prompt providers to assess in real time whether signals represent an adverse event. For example, when one medication is discontinued and a prescription for diphenhydramine is written, the clinician should be asked whether the patient is allergic to the first medication. Note that it will be important to determine how much data point-of-care providers can handle, since warnings and messages may be ignored if they are too numerous, especially if their relevance is not immediately apparent. Therefore, although automated triggers have enormous potential and have been shown to be highly valuable, the committee recognizes that in the end they will be suitable for certain types of problems but not others.
As noted above, a fundamental and nettlesome issue has been defining the key concepts relating to patient safety—adverse events and near misses. The failure to use standard definitions for these core concepts has made comparisons among institutions challenging at best. Broad adoption of the patient data safety standards recommended by this committee (and, where necessary, further refinement of the individual constructs) would represent a major step forward in enabling meaningful aggregation and comparison of rates of such incidents from different settings.
Another approach to detecting adverse events involves using claims data (Iezzoni et al., 1994). While this approach has been fairly effective for surgical patients, it has not worked well for medical patients. However, a recent tool developed by the Agency for Healthcare Research and Quality has demonstrated excellent specificity, although its sensitivity is still quite low (Zhan and Miller, 2003).
Improving the coding sets for patient safety–related conditions and events used in claims data (i.e., ICD-9) and employing incentives more broadly could represent an extremely attractive approach, especially if combined with the collection of clinical data (Classen, 2003). For example, codes to distinguish between preexisting conditions prior to a hospital admission and those predating the performance of a procedure would assist in auto-