. "6 Action Agenda for the Pharmaceutical, Medical Device, and Health Information Technology Industries ." Preventing Medication Errors: Quality Chasm Series. Washington, DC: The National Academies Press, 2007.
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Preventing Medication Errors
AHRQ should take the lead in organizing mechanisms forsafety alerts according to severity, frequency, and clinical importance to improve clinical value and acceptance.
AHRQ should take the lead in developing intelligent prompting mechanisms specific to a patient’s unique characteristics andneeds; provider prescribing, ordering, and error patterns; andevidence-based best-practice guidelines.
AHRQ should take the lead in developing user interface designs based on the principles of cognitive and human factors andthe context of the clinical environment.
AHRQ should support additional research to determine specifications for alert mechanisms and intelligent prompting, as well asoptimum designs for user interfaces.
Unresolved problems with data standards inhibit the development and use of drug-related technologies, especially the alert functions described above. Data standards serve as the basis for representing and exchanging information electronically. Uniform data standards act as a common language, allowing communication and interoperability between different technologies. For example, a CPOE application on a handheld personal digital assistant (PDA) must be able to communicate with a pharmacy database system to process an electronic prescription. Although different types of data standards serve different functions, uniformity in the representation of similar data is required to optimize the usefulness and efficiency of technologies among systems and institutions.
Four problems are associated with data standards for drug information. First, there is no complete, standardized set of terms, concepts, and codes to represent drug information. Providers compensate for this lack of standards by piecing together different, incomplete datasets from multiple vendors, standards organizations, and internal sources. Second, there is no standardized method for presenting safety alerts, which should be ranked according to severity and/or clinical importance. Instead, providers are inundated with too many nonrelevant alerts, resulting in alert fatigue and high rates of alert overrides (Glassman et al., 2002; Hsieh et al., 2004). Third, systems lack intelligent or intuitive mechanisms for recognizing patient-specific data and relating those data to allowable overrides, such as those associated with a particular patient and drug allergy alert or duplicate therapy request (Abookire et al., 2000). Fourth, the bar codes stamped on drug packaging labels are designed differently by each vendor. Resolving these problems requires standardization on several levels: drug nomenclature, organization of alerts, intelligent prompting, and bar coding.