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. 2017 Mar 16;17(1):28.
doi: 10.1186/s12911-017-0425-5.

Automation bias in electronic prescribing

David Lyell  1 , Farah Magrabi  2 , Magdalena Z Raban  3 , L G Pont  3 , Melissa T Baysari  3   4 , Richard O Day  5 , Enrico Coiera  2
Affiliations

Automation bias in electronic prescribing

David Lyell et al. BMC Med Inform Decis Mak. .

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.

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Figures

Fig. 1
Fig. 1
Experimental design with the number of participants in each condition. All participants completed all conditions. However, some were excluded from the analysis of interruption conditions as they did not trigger the interruption task
Fig. 2
Fig. 2
The e-prescribing system interface and scenario
Fig. 3
Fig. 3
CDS Alert
See this image and copyright information in PMC

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