Literature DB >> 33120412

Associations of physicians' prescribing experience, work hours, and workload with prescription errors.

Ilona Leviatan1, Bernice Oberman2, Eyal Zimlichman1,3, Gideon Y Stein1,4.   

Abstract

OBJECTIVE: We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err.
MATERIALS AND METHODS: This large-scale study included physicians who prescribed at least 100 systemic medications at Sheba Medical Center during 2012-2017 in all acute care departments, excluding intensive care units. Presumed medication errors were flagged by a high-accuracy computerized decision support system that uses machine-learning algorithms to detect potential medication prescription errors. Physicians' successive work shifts (first or only shift, second, and third shifts), workload (assessed by the number of prescriptions during a shift) and work-experience, as well as a novel measurement of physicians' prescribing experience with a specific drug, were assessed per prescription. The risk to err was determined for various work conditions.
RESULTS: 1 652 896 medical orders were prescribed by 1066 physicians; The system flagged 3738 (0.23%) prescriptions as erroneous. Physicians were 8.2 times more likely to err during high than normal-low workload shifts (5.19% vs 0.63%, P < .0001). Physicians on their third or second successive shift (compared to a first or single shift) were more likely to err (2.1%, 1.8%, and 0.88%, respectively, P < .001). Lack of experience in prescribing a specific medication was associated with higher error rate (0.37% for the first 5 prescriptions vs 0.13% after over 40, P < .001). DISCUSSION: Longer hours and less experience in prescribing a specific medication increase risk of erroneous prescribing.
CONCLUSION: Restricting successive shifts, reducing workload, increasing training and supervision, and implementing smart clinical decision support systems may help reduce prescription errors.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  adverse drug events; clinical decision support system; physician fatigue; prescription errors

Year:  2021        PMID: 33120412      PMCID: PMC8200263          DOI: 10.1093/jamia/ocaa219

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


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