Automated search methods for identifying wrong patient order entry—a scoping review

Mathew Garrod, Andy Fox, Paul Rutter

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Objective: To investigate: (1) what automated search methods are used to identify wrong-patient order entry (WPOE), (2) what data are being captured and how they are being used, (3) the causes of WPOE, and (4) how providers identify their own errors.

Materials and Methods: A systematic scoping review of the empirical literature was performed using the databases CINAHL, Embase, and MEDLINE, covering the period from database inception until 2021. Search terms were related to the use of automated searches for WPOE when using an electronic prescribing system. Data were extracted and thematic analysis was performed to identify patterns or themes within the data.

Results: Fifteen papers were included in the review. Several automated search methods were identified, with the retract-and-reorder (RAR) method and the Void Alert Tool (VAT) the most prevalent. Included studies used automated search methods to identify background error rates in isolation, or in the context of an intervention. Risk factors for WPOE were identified, with technological factors and interruptions deemed the biggest risks. Minimal data on how providers identify their own errors were identified.

Discussion: RAR is the most widely used method to identify WPOE, with a good positive predictive value (PPV) of 76.2%. However, it will not currently identify other error types. The VAT is nonspecific for WPOE, with a mean PPV of 78%–93.1%, but the voiding reason accuracy varies considerably.

Conclusion: Automated search methods are powerful tools to identify WPOE that would otherwise go unnoticed. Further research is required around self-identification of errors.
Original languageEnglish
Article numberooad057
Number of pages12
JournalJAMIA Open
Issue number3
Early online date2 Aug 2023
Publication statusPublished - 1 Oct 2023


  • computerized provider order entry
  • electronic prescribing
  • medication errors
  • surveillance
  • error detection

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