Automation bias in electronic prescribing

Lyell, D; Magrabi, F; Raban, MZ; Pont, LG; Baysari, MT; Day, RO; Coiera, E

Automation bias in electronic prescribing

Lyell, D Magrabi, F Raban, MZ Pont, LG

Baysari, MT Day, RO Coiera, E

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Publication Type:: Journal Article
Citation:: BMC Medical Informatics and Decision Making, 2017, 17 (1)
Issue Date:: 2017-03-16

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Field	Value	Language
dc.contributor.author	Lyell, D	en_US
dc.contributor.author	Magrabi, F	en_US
dc.contributor.author	Raban, MZ	en_US
dc.contributor.author	Pont, LG https://orcid.org/0000-0002-8545-716X	en_US
dc.contributor.author	Baysari, MT	en_US
dc.contributor.author	Day, RO	en_US
dc.contributor.author	Coiera, E	en_US
dc.date.available	2017-03-09	en_US
dc.date.issued	2017-03-16	en_US
dc.identifier.citation	BMC Medical Informatics and Decision Making, 2017, 17 (1)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123624
dc.description.abstract	© 2017 The Author(s). 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.	en_US
dc.relation.ispartof	BMC Medical Informatics and Decision Making	en_US
dc.relation.isbasedon	10.1186/s12911-017-0425-5	en_US
dc.subject.classification	Medical Informatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Medication Errors	en_US
dc.subject.mesh	Students, Medical	en_US
dc.subject.mesh	Automation	en_US
dc.subject.mesh	Decision Support Systems, Clinical	en_US
dc.subject.mesh	Electronic Prescribing	en_US
dc.title	Automation bias in electronic prescribing	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	17	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1103 Clinical Sciences	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Graduate School of Health
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

© 2017 The Author(s). 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.

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http://hdl.handle.net/10453/123624