TABLE 5-3 ADE and Medication Error Detection Methods: Outpatient Setting

Detection Method

Description

Source of Data

Chart review (Gandhi et al., 2003; Morimoto et al., 2004)

Review of patient’s clinic medical record for evidence of an ADE

Medical record

Computer-generated signals (Field et al., 2004)

Computer screens orders, laboratory values, and other data for indicators that an ADE may have occurred; reviewer follows up on results

Triggers from computerized laboratory data

Evaluation of prescriptions (Flynn et al., 2003)

Contents and labels of filled prescriptions are compared against the original order for discrepancies (detects dispensing errors)

Filled prescriptions

Reports, voluntary

Patients or providers may identify an error and report it to the provider or other organization

Patients (symptoms or filled prescriptions)

Survey of patients (Wertheimer, 1973; Forster et al., 2003; Morimoto, 2004)

Patients are interviewed after care or receipt of a prescription to find evidence of ADEs or dispensing errors

Patients

strategies focused on high-risk medications in community hospitals (Cohen et al., 2005).

Computerized Detection Methods

Electronic detection of ADEs should be included in clinical software programs in all areas of health care by 2010. This capability can support early detection of patient harm, with subsequent intervention to correct the problem and treat the patient. Incorporation of this critical feature is important today, at a time when CPOE and EHRs are being developed and implemented. The IOM’s Patient Safety report describes the functional requirements for electronic ADE detection systems, including rules for detecting possible ADEs using automated surveillance (Evans et al., 1991; Classen et al., 1991; Bates et al., 2001; IOM, 2004).



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