Automation bias in electronic prescribing

BMC Medical Informatics and Decision Making

Research Article

Enrico Coiera¹  David Lyell¹  Farah Magrabi¹  L.G. Pont²  Magdalena Z. Raban²  Melissa T. Baysari³  Richard O. Day⁴ 

[1] Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, 2109, Sydney, NSW, Australia;Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, 2109, Sydney, NSW, Australia;Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, 2109, Sydney, NSW, Australia;St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia;St Vincent’s Hospital Clinical School and Pharmacology, School of Medical Sciences, Faculty of Medicine, University of New South Wales, Sydney, Australia;

关键词: Decision support systems;    Clinical;    Cognitive biases;    Complexity;    Electronic prescribing;    Medication errors;    Automation bias;    Human-computer interaction;    Human-automation interaction;   

DOI  :  10.1186/s12911-017-0425-5

 received in 2016-11-25, accepted in 2017-03-09,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundClinical 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.MethodsOne 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.ResultsCompared 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s). 2017