Automation bias: Empirical results assessing influencing factors
Abstract
Objective To investigate the rate of automation bias - the propensity of people to over rely on automated advice and the factors associated with it. Tested factors were attitudinal - trust and confidence, non-attitudinal - decision support experience and clinical experience, and environmental - task difficulty. The paradigm of simulated decision support advice within a prescribing context was used. Design The study employed within participant before-after design, whereby 26 UK NHS General Practitioners were shown 20 hypothetical prescribing scenarios with prevalidated correct and incorrect answers - advice was incorrect in 6 scenarios. They were asked to prescribe for each case, followed by being shown simulated advice. Participants were then asked whether they wished to change their prescription, and the post-advice prescription was recorded. Measurements Rate of overall decision switching was captured. Automation bias was measured by negative consultations - correct to incorrect prescription switching. Results Participants changed prescriptions in 22.5% of scenarios. The pre-advice accuracy rate of the clinicians was 50.38%, which improved to 58.27% post-advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of automation bias, as measured by decision switches from correct pre-advice, to incorrect post-advice was 5.2% of all cases - a net improvement of 8%. More immediate factors such as trust in the specific CDSS, decision confidence, and t…
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Susceptibility to erroneous advice can be greater among users with lower levels of clinical or task-specific expertise.
“Research on clinical decision support suggests that susceptibility to erroneous advice can be greater among users with lower levels of clinical or task-specific expertise (Goddard et al., 2014; Gaube et al., 2021).”
From Between Co-Intelligence and Cognitive Offloading: Educators’ Perspectives from Dutch Academic Medical Settings on Generative AI, Critical Thinking, and Adult Professional Learning (2026) · cites this paper as Goddard et al., 2014
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- Authors
- Goddard, K., Roudsari, A., & Wyatt, J. C.
- Affiliations
- City, University of London (GB)
- University of Leeds (GB)
- Corresponding author
- Goddard, Kate
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 83
- Issue
- 5
- Pages
- 368-375
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2058315996
- Cited by
- 163
- References
- 7
- Citations by year
- 2026: 38 · 2025: 27 · 2024: 25 · 2023: 18 · 2022: 11 · 2021: 11 · 2020: 7 · 2019: 5 · 2018: 5 · 2017: 3 · 2016: 5 · 2015: 6 · 2014: 2
- FWCI
- 9.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- automation bias, clinical decision support systems, decision confidence, clinical experience, task difficulty, prescribing decisions, general practitioners, prescribing accuracy
- MeSH terms
- Attitude to Computers, Automation, Decision Making, Female, United Kingdom, Humans, Male, Middle Aged, Quality Control, Task Performance and Analysis, Medical Errors, Decision Support Systems, Clinical, Trust, General Practitioners
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