Automation bias in electronic prescribing

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Clinical 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.

Methods: One 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.

Results: Compared 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.

Conclusions: This 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.

LanguageEnglish
Article number28
Pages1-10
Number of pages10
JournalBMC Medical Informatics and Decision Making
Volume17
Issue number1
DOIs
Publication statusPublished - 16 Mar 2017

Fingerprint

Electronic Prescribing
Clinical Decision Support Systems
Automation
Clinical Medicine
Automatic Data Processing

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Automation bias
  • Clinical
  • Cognitive biases
  • Complexity
  • Decision support systems
  • Electronic prescribing
  • Human-automation interaction
  • Human-computer interaction
  • Medication errors

Cite this

@article{b5382c8977834b5f9bafaaecd7fe870b,
title = "Automation bias in electronic prescribing",
abstract = "Background: Clinical 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. Methods: One 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. Results: Compared 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. Conclusions: This 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.",
keywords = "Automation bias, Clinical, Cognitive biases, Complexity, Decision support systems, Electronic prescribing, Human-automation interaction, Human-computer interaction, Medication errors",
author = "David Lyell and Farah Magrabi and Raban, {Magdalena Z.} and Pont, {L. G.} and Baysari, {Melissa T.} and Day, {Richard O.} and Enrico Coiera",
note = "Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",
year = "2017",
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Automation bias in electronic prescribing. / Lyell, David; Magrabi, Farah; Raban, Magdalena Z.; Pont, L. G.; Baysari, Melissa T.; Day, Richard O.; Coiera, Enrico.

In: BMC Medical Informatics and Decision Making, Vol. 17, No. 1, 28, 16.03.2017, p. 1-10.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Automation bias in electronic prescribing

AU - Lyell, David

AU - Magrabi, Farah

AU - Raban, Magdalena Z.

AU - Pont, L. G.

AU - Baysari, Melissa T.

AU - Day, Richard O.

AU - Coiera, Enrico

N1 - Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2017/3/16

Y1 - 2017/3/16

N2 - Background: Clinical 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. Methods: One 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. Results: Compared 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. Conclusions: This 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.

AB - Background: Clinical 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. Methods: One 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. Results: Compared 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. Conclusions: This 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.

KW - Automation bias

KW - Clinical

KW - Cognitive biases

KW - Complexity

KW - Decision support systems

KW - Electronic prescribing

KW - Human-automation interaction

KW - Human-computer interaction

KW - Medication errors

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DO - 10.1186/s12911-017-0425-5

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EP - 10

JO - BMC Medical Informatics and Decision Making

T2 - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

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