Literature DB >> 30699458

Reduced Verification of Medication Alerts Increases Prescribing Errors.

David Lyell1, Farah Magrabi1, Enrico Coiera1.   

Abstract

OBJECTIVE: Clinicians using clinical decision support (CDS) to prescribe medications have an obligation to ensure that prescriptions are safe. One option is to verify the safety of prescriptions if there is uncertainty, for example, by using drug references. Supervisory control experiments in aviation and process control have associated errors, with reduced verification arising from overreliance on decision support. However, it is unknown whether this relationship extends to clinical decision-making. Therefore, we examine whether there is a relationship between verification behaviors and prescribing errors, with and without CDS medication alerts, and whether task complexity mediates this.
METHODS: A total of 120 students in the final 2 years of a medical degree prescribed medicines for patient scenarios using a simulated electronic prescribing system. CDS (correct, incorrect, and no CDS) and task complexity (low and high) were varied. Outcomes were omission (missed prescribing errors) and commission errors (accepted false-positive alerts). Verification measures were access of drug references and view time percentage of task time.
RESULTS: Failure to access references for medicines with prescribing errors increased omission errors with no CDS (high-complexity: χ 2(1) = 12.716; p < 0.001) and incorrect CDS (Fisher's exact; low-complexity: p = 0.002; high-complexity: p = 0.001). Failure to access references for false-positive alerts increased commission errors (low-complexity: χ 2(1) = 16.673, p < 0.001; high-complexity: χ 2(1) = 18.690, p < 0.001). Fewer participants accessed relevant references with incorrect CDS compared with no CDS (McNemar; low-complexity: p < 0.001; high-complexity: p < 0.001). Lower view time percentages increased omission (F(3, 361.914) = 4.498; p = 0.035) and commission errors (F(1, 346.223) = 2.712; p = 0.045). View time percentages were lower in CDS-assisted conditions compared with unassisted conditions (F(2, 335.743) = 10.443; p < 0.001). DISCUSSION: The presence of CDS reduced verification of prescription safety. When CDS was incorrect, reduced verification was associated with increased prescribing errors.
CONCLUSION: CDS can be incorrect, and verification provides one mechanism to detect errors. System designers need to facilitate verification without increasing workload or eliminating the benefits of correct CDS. Georg Thieme Verlag KG Stuttgart · New York.

Entities:  

Mesh:

Year:  2019        PMID: 30699458      PMCID: PMC6353646          DOI: 10.1055/s-0038-1677009

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  21 in total

1.  Overriding of drug safety alerts in computerized physician order entry.

Authors:  Heleen van der Sijs; Jos Aarts; Arnold Vulto; Marc Berg
Journal:  J Am Med Inform Assoc       Date:  2005-12-15       Impact factor: 4.497

Review 2.  The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review.

Authors:  Jesse I Wolfstadt; Jerry H Gurwitz; Terry S Field; Monica Lee; Sunila Kalkar; Wei Wu; Paula A Rochon
Journal:  J Gen Intern Med       Date:  2008-04       Impact factor: 5.128

Review 3.  Automation bias: a systematic review of frequency, effect mediators, and mitigators.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  J Am Med Inform Assoc       Date:  2011-06-16       Impact factor: 4.497

Review 4.  Where to find information about drugs.

Authors:  Richard O Day; Leone Snowden
Journal:  Aust Prescr       Date:  2016-06-01

5.  Multiple significance tests: the Bonferroni method.

Authors:  J M Bland; D G Altman
Journal:  BMJ       Date:  1995-01-21

6.  Automation bias: decision making and performance in high-tech cockpits.

Authors:  K L Mosier; L J Skitka; S Heers; M Burdick
Journal:  Int J Aviat Psychol       Date:  1997

7.  Overrides of medication-related clinical decision support alerts in outpatients.

Authors:  Karen C Nanji; Sarah P Slight; Diane L Seger; Insook Cho; Julie M Fiskio; Lisa M Redden; Lynn A Volk; David W Bates
Journal:  J Am Med Inform Assoc       Date:  2013-10-28       Impact factor: 4.497

8.  How to discriminate between computer-aided and computer-hindered decisions: a case study in mammography.

Authors:  Andrey A Povyakalo; Eugenio Alberdi; Lorenzo Strigini; Peter Ayton
Journal:  Med Decis Making       Date:  2013-01       Impact factor: 2.583

9.  The Effect of Cognitive Load and Task Complexity on Automation Bias in Electronic Prescribing.

Authors:  David Lyell; Farah Magrabi; Enrico Coiera
Journal:  Hum Factors       Date:  2018-06-25       Impact factor: 2.888

Review 10.  Automation bias and verification complexity: a systematic review.

Authors:  David Lyell; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

View more
  5 in total

Review 1.  A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists.

Authors:  Liang Yan; Thomas Reese; Scott D Nelson
Journal:  Appl Clin Inform       Date:  2021-03-17       Impact factor: 2.342

2.  Drug Alert Experience and Salience during Medical Residency at Two Healthcare Institutions.

Authors:  Kinjal Gadhiya; Edgar Zamora; Salim M Saiyed; David Friedlander; David C Kaelber
Journal:  Appl Clin Inform       Date:  2021-04-28       Impact factor: 2.342

3.  Reducing Inappropriate Outpatient Medication Prescribing in Older Adults across Electronic Health Record Systems.

Authors:  Michael P Friebe; Joseph R LeGrand; Bryan E Shepherd; Elizabeth A Breeden; Scott D Nelson
Journal:  Appl Clin Inform       Date:  2020-12-30       Impact factor: 2.342

4.  A qualitative study of prescribing errors among multi-professional prescribers within an e-prescribing system.

Authors:  Fahad Alshahrani; John F Marriott; Anthony R Cox
Journal:  Int J Clin Pharm       Date:  2020-11-09

5.  A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.

Authors:  Jeonghwan Hwang; Taeheon Lee; Honggu Lee; Seonjeong Byun
Journal:  J Med Internet Res       Date:  2022-01-19       Impact factor: 5.428

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.