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Automation bias in electronic prescribing
Open accessAbstract
BACKGROUND: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. METHODS: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. RESULTS: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. CONCLUSIONS: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.
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“Another explanation might be that participants worry about physicians’ automation bias, 48 that is, physicians might blindly rely on the AI-enabled system's suggestions without critically reviewing them resulting in reduced accuracy 49 and increased medical errors. 50”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [50]
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Metadata
- Authors
- Lyell, D., Magrabi, F., Raban, M. Z., Pont, L. G., Baysari, M. T., Day, R. O., & Coiera, E.
- Affiliations
- Macquarie University (AU)
- UNSW Sydney (AU)
- Corresponding author
- Lyell, David
- ORCID
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2017-03-16
- Volume
- 17
- Issue
- 1
- Pages
- 28-28
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC5356416
- OpenAlex
- W2595764213
- Cited by
- 103
- References
- 39
- Citations by year
- 2026: 25 · 2025: 12 · 2024: 9 · 2023: 12 · 2022: 13 · 2021: 4 · 2020: 9 · 2019: 13 · 2018: 5
- FWCI
- 10.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by)
- Funders
- HCF Research Foundation
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- automation bias, electronic prescribing, clinical decision support, medication errors, omission errors, commission errors, false positive alerts, task complexity, interruptions, medical students, prescribing safety
- MeSH terms
- Automation, Humans, Medication Errors, Students, Medical, Decision Support Systems, Clinical, Electronic Prescribing
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