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 for increased anxiety might be that participants worry about physicians' automation bias.
“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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Should AI-Enabled Medical Devices be Explainable?
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“…bags when the automated system incorrectly fails to flag them) [29], electronic prescription decisions (e.g., avoiding certain prescriptions based on inappropriate alerts) [30], the reading and interpretation of electrocardiograms [31], the identification of suspected criminals [11], and more.”
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“Lyell et al. ( 2017 ) investigated the phenomenon of automation bias in the context of electronic prescribing, where 120 final-year medical students used a simulated e-prescribing system with clinical decision support (CDS) under varying conditions of task complexity and interruption.”
PDMP causes more than just testimonial injustice
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Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement
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“…non-human elements of the system can be a serious design challenge and, if unsuccessful, can create a judgment gap in the interpretation of the general rules for specific cases. complexity of verification task (Goddard et al. 2012; Lyell and Coiera 2017; Lyell et al. 2017 ; Harbarth et al. 2024).”
Working memory capacity improves checking performance for errors on a simulated rail control task
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Writing a prescription: the law and good practice
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Integration of AI for Clinical Decision Support
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ChatGPT in Medical Education: A Precursor for Automation Bias? (Preprint)Preprint
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The Clinical Considerations for Trustworthy AI in Oncologic Imaging
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A nursing perspective on human-AI collaboration in personalized breast cancer care pathways
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“Within this structure, ultimate decision-making authority remains with licensed clinicians (60).”
Technology and Health Care Improvement
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Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules
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“22,23 This tool has the capability to rapidly assess all drug rules in a corpus in an automated fashion, identify and prioritize candidate rules for editing based on the potential decrease in alert burden or other factors, and pre-sent these rules to a human expert for validation and implementation.”
MethodInfluences on User Trust in Healthcare Artificial Intelligence (HAI): A Systematic Review (Preprint)Preprint
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“However, electronic prescription introduces a new source of risk [4], one of which is called automation bias, which occurs when a physician blindly trusts prescription-helping software, thereby reducing vigilance in information-seeking and validation [5].”
Artificial Intelligence in Dental Education: Opportunities, Challenges, and Implementation
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The Transparency and Liability Issues Associated with AI-Based Medical Systems
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“(15) A systematic review focusing on automation bias and verification complexity found that such bias can occur even in single-task environments and may relate to cognitive load and task verification difficulty.”
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“Alert fatigue increases the risk of automation bias whereby humans in the loop may act more passively, or ‘on the loop’, agreeing blindly with AI outputs and merely ‘pressing go’ without much thought, allowing potential mistakes to slip by [20,21].”
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IMPLEMENTATION OF ELECTRONIC PRESCRIPTIONS: THE PHYSICIANS VIEW
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Ethical and Legal Protection Mechanisms for Security Risks of Medical Artificial Intelligence
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Electronic Prescribing and Medicines Administration
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When Silence Signals Safety: Governance and Responsibility in AI-Enabled Prescription Verification
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- 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 · 2016: 1
- 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 › Health Professions › Emergency Medical Services › 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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