Automation bias and verification complexity: a systematic review

David Lyell*, Enrico Coiera

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

116 Citations (Scopus)


Introduction: While potentially reducing decision errors, decision support systems can introduce new types of errors. Automation bias (AB) happens when users become overreliant on decision support, which reduces vigilance in information seeking and processing. Most research originates from the human factors literature, where the prevailing view is that AB occurs only in multitasking environments.

Objectives: This review seeks to compare the human factors and health care literature, focusing on the apparent association of AB with multitasking and task complexity.

Data sources: EMBASE, Medline, Compendex, Inspec, IEEE Xplore, Scopus, Web of Science, PsycINFO, and Business Source Premiere from 1983 to 2015.

Study selection: Evaluation studies where task execution was assisted by automation and resulted in errors were included. Participants needed to be able to verify automation correctness and perform the task manually.

Methods: Tasks were identified and grouped. Task and automation type and presence of multitasking were noted. Each task was rated for its verification complexity.

Results: Of 890 papers identified, 40 met the inclusion criteria; 6 were in health care. Contrary to the prevailing human factors view, AB was found in single tasks, typically involving diagnosis rather than monitoring, and with high verification complexity.

Limitations: The literature is fragmented, with large discrepancies in how AB is reported. Few studies reported the statistical significance of AB compared to a control condition.

Conclusion: AB appears to be associated with the degree of cognitive load experienced in decision tasks, and appears to not be uniquely associated with multitasking. Strategies to minimize AB might focus on cognitive load reduction.

Original languageEnglish
Pages (from-to)423-431
Number of pages9
JournalJournal of the American Medical Informatics Association
Issue number2
Early online date11 Aug 2016
Publication statusPublished - 1 Mar 2017


  • Clinical cognitive biases
  • Complexity
  • Decision support systems


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