ArticleBMC medical informatics and decision making2017
Automation bias in electronic prescribing.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Does the accuracy of medication administration documentation improve with electronic medication systems? A stepped-wedge cluster randomised trial.BMJ health & care informatics · 2026Trial
- Trial
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- The Risks to Patients Associated with Using Artificial Intelligence Tools in Pharmacy Practice: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Meaningful oversight of medical AI beyond human in the loop.NPJ digital medicine · 2026Article
- Moral diversity and the challenge of responsibility in AI-CDSS.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Artificial Intelligence Literacy in Intensive Care: From Algorithmic Fluency to Clinical Accountability.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- When Silence Signals Safety: Governance and Responsibility in AI-Enabled Prescription Verification.Cureus · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- A nursing perspective on human-AI collaboration in personalized breast cancer care pathways.Frontiers in oncology · 2026Article
- An AI ethics framework for a trustworthy autonomous drone system to support battlefield casualty triage.AI and ethics · 2026Article
- Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.Frontiers in digital health · 2026Article
- Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks.Journal of medical systems · 2025Article
- An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.Neurology · 2025Article
- Impact of Electronic Transition and Prefilled Templates on Drug Prescription Compliance: Retrospective Study.Journal of medical Internet research · 2025Article
- Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement.Bioengineering (Basel, Switzerland) · 2025Article
- High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.BMC medical ethics · 2025Review
- Assessing clinical medicine students' acceptance of large language model: based on technology acceptance model.BMC medical education · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical 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.
methodsOne 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.
resultsCompared 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.
conclusionsThis 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.