4.6 Article

Evaluation of medication alerts in electronic health records for compliance with human factors principles

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Publisher

BMJ PUBLISHING GROUP
DOI: 10.1136/amiajnl-2013-002279

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  1. Center for Education and Research on Therapeutics on Health Information Technology (CERT-HIT) grant from Agency for Healthcare Research and Quality (AHRQ) [U18HS016970]

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Introduction Increasing the adoption of electronic health records (EHRs) with integrated clinical decision support (CDS) is a key initiative of the current US healthcare administration. High over-ride rates of CDS alerts strongly limit these potential benefits. As a result, EHR designers aspire to improve alert design to achieve better acceptance rates. In this study, we evaluated drug-drug interaction (DDI) alerts generated in EHRs and compared them for compliance with human factors principles. Methods We utilized a previously validated questionnaire, the I-MeDeSA, to assess compliance with nine human factors principles of DDI alerts generated in 14 EHRs. Two reviewers independently assigned scores evaluating the human factors characteristics of each EHR. Rankings were assigned based on these scores and recommendations for appropriate alert design were derived. Results The 14 EHRs evaluated in this study received scores ranging from 8 to 18.33, with a maximum possible score of 26. Cohen's kappa (kappa=0.86) reflected excellent agreement among reviewers. The six vendor products tied for second and third place rankings, while the top system and bottom five systems were home-grown products. The most common weaknesses included the absence of characteristics such as alert prioritization, clear and concise alert messages indicating interacting drugs, actions for clinical management, and a statement indicating the consequences of over-riding the alert. Conclusions We provided detailed analyses of the human factors principles which were assessed and described our recommendations for effective alert design. Future studies should assess whether adherence to these recommendations can improve alert acceptance.

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