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

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

LanguageEnglish
Pages1008-1021
Number of pages14
JournalHuman Factors
Volume60
Issue number7
Early online date25 Jun 2018
DOIs
Publication statusPublished - 1 Nov 2018

Fingerprint

Electronic Prescribing
Clinical Decision Support Systems
Automation
automation
electronics
trend
Resource Allocation
Decision support systems
Clinical Medicine
Medical Students
Automatic Data Processing
medical student
Students
acceptance
medicine
scenario
Safety

Keywords

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

Cite this

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title = "The effect of cognitive load and task complexity on automation bias in electronic prescribing",
abstract = "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.",
keywords = "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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The effect of cognitive load and task complexity on automation bias in electronic prescribing. / Lyell, David; Magrabi, Farah; Coiera, Enrico.

In: Human Factors, Vol. 60, No. 7, 01.11.2018, p. 1008-1021.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - The effect of cognitive load and task complexity on automation bias in electronic prescribing

AU - Lyell, David

AU - Magrabi, Farah

AU - Coiera, Enrico

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N2 - 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.

AB - 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.

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