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

• Published in: Journal of the American Medical Informatics Association : JAMIA 2017-03, Vol.24 (2), p.432-440
• Main Authors: Brown, Clare L, Mulcaster, Helen L, Triffitt, Katherine L, Sittig, Dean F, Ash, Joan S, Reygate, Katie, Husband, Andrew K, Bates, David W, Slight, Sarah P
• Format: Article
• Language: English
• Subjects: Drug Prescriptions; Ergonomics; Humans; Medical Order Entry Systems; Medication Errors - prevention & control; Reviews
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocw119

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 auto-population, 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.