Integrating clinical decision support systems for pharmacogenomic testing into clinical routine

• Verfasst von: Hinderer, Marc [VerfasserIn]
• Verfasst von: Lablans, Martin [VerfasserIn]
• Verfasst von: Neumaier, Michael [VerfasserIn]
• Titel: Integrating clinical decision support systems for pharmacogenomic testing into clinical routine
• Titelzusatz: a scoping review of designs of user-system interactions in recent system development
• Verf.angabe: Marc Hinderer, Martin Boeker, Sebastian A. Wagner, Martin Lablans, Stephanie Newe, Jan L. Hülsemann, Michael Neumaier, Harald Binder, Harald Renz, Till Acker, Hans-Ulrich Prokosch and Martin Sedlmayr
• E-Jahr: 2017
• Jahr: 06 June 2017
• Umfang: 14 S.
• Fussnoten: Gesehen am 26.10.2018
• Titel Quelle: Enthalten in: BMC medical informatics and decision making
• Ort Quelle: London : BioMed Central, 2001
• Jahr Quelle: 2017
• Band/Heft Quelle: 17(2017), Artikel-ID 81
• ISSN Quelle: 1472-6947
• DOI: doi:10.1186/s12911-017-0480-y
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1582348022

Abstract

Background: Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. Methods: We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. Results: Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype. Conclusions: Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.