ePrints

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

Lookup NU author(s): Dr Clare TolleyORCiD, Professor Andy HusbandORCiD, Professor Sarah Slight

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. 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.

Publication metadata

Author(s): Brown CL, Mulcaster HL, Triffitt KL, Sittig DF, Ash JS, Reygate K, Husband AK, Bates DW, Slight SP

Publication type: Review

Publication status: Published

Journal: Journal of the American Medical Informatics Association

Year: 2017

Volume: 24

Issue: 2

Pages: 432-440

Print publication date: 01/03/2017

Online publication date: 30/08/2016

Acceptance date: 08/07/2016

ISSN (print): 1067-5027

ISSN (electronic): 1527-974X

Publisher: Oxford University Press

URL: https://doi.org/10.1093/jamia/ocw119

DOI: 10.1093/jamia/ocw119

Share