Format

Send to

Choose Destination
BMC Med Inform Decis Mak. 2017 Mar 16;17(1):28. doi: 10.1186/s12911-017-0425-5.

Automation bias in electronic prescribing.

Author information

1
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, 2109, Australia. david.lyell@mq.edu.au.
2
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, 2109, Australia.
3
Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, 2109, Australia.
4
St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.
5
St Vincent's Hospital Clinical School and Pharmacology, School of Medical Sciences, Faculty of Medicine, University of New South Wales, Sydney, Australia.

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

PMID:
28302112
PMCID:
PMC5356416
DOI:
10.1186/s12911-017-0425-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center