The above examples and requirements of adverse event analysis point to the need to enhance existing data standards to support adverse event reporting. The most usable of standards will include clear and unambiguous event definitions, minimum datasets that characterize the population and setting, explicit data collection processes, and methods for integrating data across systems and settings.
An examination of the literature on patient safety raises many questions. Paramount among these is the problem of definitions of terms, with differing definitions of errors, adverse events, and near misses being used from one publication to another. Often, the addition of a single word creates ambiguity across the entire spectrum of reporting. For example, are potential adverse events synonymous with near misses? Do nonpreventable adverse events stem from errors? Will medication errors include actions taken by a family member who, for example, might administer insulin injections in an area with poor absorption of the medication?
As with data collected for clinical trials, strict definitions of terms, including processes by which the data may be obtained, are critical to acquiring information on adverse events in a reproducible fashion. For example, each type of adverse event must be precisely defined, including examples and events that are outside the definition. Unfortunately, few standard terminologies include such definitions. DQIP represents a model for both the use of terms and the standardization of data collection. Each measure encompasses inclusion and exclusion definitions, confounding patient demographic or other data, the rationale for the importance of the measure, and a process by which the measure should be obtained. In contrast, many clinical terminologies contain terms that do not have precise definitions or conditions of use. For example, the ICD code for diabetes without ketoacidosis could refer to a patient with either Type II diabetes or well-controlled Type I diabetes. Moreover, it is not clear for many terms whether they are used to describe a point in time or a chronic condition. The ICD code for diabetes with ketoacidosis, for instance, should be applied only to a single encounter because the ketoacidosis will resolve, while the underlying diabetes will remain. In the case of a patient with Alzheimer’s disease, however, the presence of any encounter with that diagnosis passes forward to all subsequent encounters.