Seventy-one events were confirmed near-miss prescribing errors, resulting in a positive predictive value of 85.0% (95% confidence interval, 75%–91%) and an estimated near-miss prescribing error rate of 259/100,000 medication orders. ResultsĮighty-four RAR events were identified in 27,407 medication orders. We calculated the positive predictive value of the RAR tool by dividing the number of confirmed near-miss prescribing errors by the total number of RAR events identified. Logistic regression with robust clustered standard errors was used to determine predictors for near-miss prescribing errors. Interviews were analyzed with reference to the London Protocol, a process of incident analysis that categorizes causes of errors into various contributory factors including patient factors, task and technology factors, and work environment. Errors were judged to be clinical decisions in chart reviews through review of narrative notes, order components, and patient’s clinical history. Chart reviews were performed if the prescriber was unavailable for interview.

Potential errors flagged by the tool were validated using prescriber interviews.

We used the RAR tool to detect near-miss prescribing errors in audit log data. This is a cross-sectional study between February and March 2021 in an Irish maternity hospital. The aims of this study were to develop and to validate an adapted Retract-and-Reorder (RAR) tool to identify and quantify near-miss/intercepted prescribing errors in an electronic health record.

Tool 10000 days rar