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Lookup NU author(s): Professor Sarah SlightORCiD
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© 2015 Elsevier Ireland Ltd.Background: Improving the quality of prescribing and appropriate handling of alerts remains a challenge for design and implementation of clinical decision support (CDS) and comparatively little is known about the effects that provider characteristics have on how providers respond to medication alerts. Objectives: To investigate the relationship between provider characteristics and their response to medication alerts in the outpatient setting. Design and participants: Retrospective observational study using a prescription log from the automated electronic outpatient system for each of 478 providers using the system at primary care practices affiliated with 2 teaching hospitals, from 2009 to 2011 for six types of alerts. Provider characteristics were obtained from the hospital credentialing system and the Massachusetts Board of Registration in Medicine. Main measures: Override rates per 100 prescriptions and 100 alerts. Results: The providers' mean override rates per 100 prescriptions and per 100 alerts were 0.52 (95% confidence interval (CI), 0.46-0.58) and 0.42 (95% CI, 0.38-0.44) respectively. The physicians (n = 422) on average overrode drug alerts with rates of 0.48 per 100 drugs and 0.44 per 100 warnings. Univariate analysis revealed that six physician characteristics (physician type, age, number of encounters, medical school ranking, residency hospital ranking, and acceptance of Medicaid) were significantly related to the override rate. Multiple regression showed that house staff were more likely to override than staff physicians (p < 0.001), physicians with fewer than 13 average daily encounters were more likely to override than others with more than 13 encounters (p (range), < 0.001-0.05), and graduates of the top 5 medical schools were more likely to override than the others (p = 0.04). All six predictors together explained 30% and 50% of the variance in override rates, respectively. Conclusions: Consideration of six specific physician characteristics may help inform interventions to improve prescriber decision-making.
Author(s): Cho I, Slight SP, Nanji KC, Seger DL, Maniam N, Fiskio JM, Dykes PC, Bates DW
Publication type: Article
Publication status: Published
Journal: International Journal of Medical Informatics
Print publication date: 01/09/2015
Online publication date: 09/05/2015
Acceptance date: 29/04/2015
ISSN (print): 1386-5056
ISSN (electronic): 1872-8243
Publisher: Elsevier Ireland Ltd
PubMed id: 26004341
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