The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
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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Cites
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Automation bias: a systematic review of frequency, effect mediators, and mitigators
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“There are multiple possible causes of AB, [8, 9] and the literature is currently unclear regarding which, or all, of these are genuinely causal, and under which circumstances.”
BackgroundPerformance Consequences of Automation-Induced 'Complacency'
Parasuraman, R., Molloy, R., & Singh, I. L. (1993) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0301_1 · Cited by 875 · cited 1×
“The prevailing view in the human factors literature is that AB only occurs in a multi-task environment [14, 20, 21].”
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Automation bias and verification complexity: a systematic review
Lyell, D., & Coiera, E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw105 · Cited by 451 · cited 3×
“However AB has been reported in some, but not all, tasks in a single task environment [14].”
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Cited by
Sorted by citations
Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., Rigby, M., Scott, P. J., Vehko, T., Wong, Z. S.-Y., et al. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677903 · Cited by 347
“Previous work has demonstrated the effects of automation bias with a prescribing DSS that was based on simple rules of logic [42].”
BackgroundSelf-citationCurrent challenges in health information technology–related patient safety
Sittig, D. F., Wright, A. T., Coiera, E. W., Magrabi, F., Ratwani, R. M., Bates, D. W., & Singh, H. (2018) · Health Informatics Journal · doi:10.1177/1460458218814893 · Cited by 183
“41 How should useful clinical decision support be developed, implemented, and potentially regulated to have the biggest impact?”
BackgroundSelf-citationHumans inherit artificial intelligence biases
Vicente, L., & Matute, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-42384-8 · Cited by 158
General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
Buck, C., Doctor, E., Hennrich, J., Jöhnk, J., & Eymann, T. (2022) · Journal of Medical Internet Research · doi:10.2196/28916 · Cited by 147
“By blindly relying on the AI-enabled systems’suggestions, physicians would no longer critically review them, which can reduce accuracy [86] and increase medical errors [87].”
MethodHuman Factors Challenges for the Safe Use of Artificial Intelligence in Patient Care
Sujan, M., Furniss, D., Grundy, K., Grundy, H., Nelson, D., Elliott, M., White, S., Habli, I., & Reynolds, N. (2019) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100081 · Cited by 144
“22 The study found that clinical decision support reduced prescribing errors when working correctly but also increased prescribing errors by around one-third in cases where the system either did not alert the clinician to a potential problem or provided the wrong advice.”
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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