The effect of cognitive load and task complexity on automation bias in electronic prescribing

David Lyell*, Farah Magrabi, Enrico Coiera

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Objective: Determine the relationship between cognitive load (CL) and automation bias (AB). Background: Clinical decision support (CDS) for electronic prescribing can improve safety but introduces the risk of AB, where reliance on CDS replaces vigilance in information seeking and processing. We hypothesized high CL generated by high task complexity would increase AB errors. Method: One hundred twenty medical students prescribed medicines for clinical scenarios using a simulated e-prescribing system in a randomized controlled experiment. Quality of CDS (correct, incorrect, and no CDS) and task complexity (low and high) were varied. CL, omission errors (failure to detect prescribing errors), and commission errors (acceptance of false positive alerts) were measured. Results: Increasing complexity from low to high significantly increased CL, F(1, 118) = 71.6, p <.001. CDS reduced CL in high-complexity conditions compared to no CDS, F(2, 117) = 4.72, p =.015. Participants who made omission errors in incorrect and no CDS conditions exhibited lower CL compared to those who did not, F(1, 636.49) = 3.79, p =.023. Conclusion: Results challenge the notion that AB is triggered by increasing task complexity and associated increases in CL. Omission errors were associated with lower CL, suggesting errors may stem from an insufficient allocation of cognitive resources. Application: This is the first research to examine the relationship between CL and AB. Findings suggest designers and users of CDS systems need to be aware of the risks of AB. Interventions that increase user vigilance and engagement may be beneficial and deserve further investigation.

Original languageEnglish
Pages (from-to)1008-1021
Number of pages14
JournalHuman Factors
Issue number7
Early online date25 Jun 2018
Publication statusPublished - 1 Nov 2018


  • automation bias
  • cognitive load
  • compliance
  • health information technology
  • human-computer interaction
  • medication alerts
  • medication management and safety
  • patient safety
  • reliance
  • task complexity
  • working memory


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