A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care

Clare L. Brown, Helen L. Mulcaster, Katherine L. Triffitt, Dean F. Sittig, Joan S. Ash, Katie Reygate, Andrew K. Husband, David W. Bates, Sarah P. Slight

Research output: Contribution to journalReview article

37 Scopus citations

Abstract

Objective: To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems. Materials and Methods: We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken. Results: A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and autopopulation, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users' work processes, and clinical decision support systems. Displaying an incomplete list of a patient's medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users' misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors. Discussion and Conclusions: Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users' workflow expectations.

Original languageEnglish (US)
Article numberocw119
Pages (from-to)432-440
Number of pages9
JournalJournal of the American Medical Informatics Association
Volume24
Issue number2
DOIs
StatePublished - Mar 1 2017

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Keywords

  • Alerts
  • Clinical decision support
  • Computerized provider order entry
  • Decision-making
  • Medication errors
  • Patient safety

ASJC Scopus subject areas

  • Health Informatics

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