A formal assessment of pharmacogenomics clinical decision support (PGx-CDS) by providers is lacking in the literature. The objective of this study was to evaluate the usability of PGx-CDS tools that have been implemented in a healthcare setting. We enrolled ten prescribing healthcare providers and had them complete a 60-min usability session, which included interacting with two PGx-CDS scenarios using the “Think Aloud” technique, as well as completing the Computer System Usability Questionnaire (CSUQ). Providers reported positive comments, negative comments, and suggestions for the two PGx-CDS during the usability testing. Most provider comments were in favor of the current PGx-CDS design, with the exception of how the genotype and phenotype information is displayed. The mean CSUQ score for the PGx-CDS overall satisfaction was 6.3 ± 0.95, with seven strongly agreeing and one strongly disagreeing for overall satisfaction. The implemented PGx-CDS at our institution was well received by prescribing healthcare providers. The feedback collected from the session will guide future PGx-CDS designs for our healthcare system and provide a framework for other institutions implementing PGx-CDS.

To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS). This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS. The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development. Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications. This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.