Thomas H Payne1, Lisa E Hines2, Raymond C Chan3, Seth Hartman4, Joan Kapusnik-Uner5, Alissa L Russ6, Bruce W Chaffee7, Christian Hartman8, Victoria Tamis9, Brian Galbreth10, Peter A Glassman11, Shobha Phansalkar12, Heleen van der Sijs13, Sheila M Gephart14, Gordon Mann15, Howard R Strasberg12, Amy J Grizzle16, Mary Brown2, Gilad J Kuperman17, Chris Steiner18, Amanda Sullins19, Hugh Ryan19, Michael A Wittie20, Daniel C Malone21. 1. Department of Medicine, University of Washington, Seattle, WA, USA. 2. Department of Pharmacy Practice & Science, The University of Arizona College of Pharmacy, Tucson, AZ, USA. 3. Information Technology, Sentara Healthcare, Virginia Beach, VA, USA. 4. Department of Pharmacy Services, Oregon Health & Science University, Portland, OR, USA. 5. Clinical Editorial, FDB (First Databank, Inc.), San Francisco, CA, USA. 6. Center for Health Information and Communication, Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service, Richard L. Roudebush VA Medical Center; Regenstrief Institute, Inc. Indianapolis, IN USA; and Purdue University College of Pharmacy, West Lafayette, IN USA. 7. Department of Pharmacy, The University of Michigan Health System, Ann Arbor, MI, USA. 8. Clinical Solutions, Pharmacy OneSource, Boston, MA, USA. 9. PeaceHealth St. John Medical Center, Longview WA, USA. 10. At the time of this study was with Pharmacy Services, PeaceHealth Southwest Medical Center, Vancouver, WA, USA. 11. Internal Medicine, Department of Veterans Affairs (VA), Greater Los Angeles Healthcare System, Los Angeles, CA, USA. 12. Medical Informatics, Wolters Kluwer Health - Clinical Solutions, Newton, MA, USA/San Diego, CA, USA. 13. Department of Hospital Pharmacy, Erasmus Medical Center, Rotterdam, Netherlands. 14. College of Nursing, The University of Arizona, Tucson, AZ, USA. 15. Pharmacy, Epic, Verona, WI, USA. 16. Center for Health Outcomes & PharmacoEconomic Research, The University of Arizona College of Pharmacy, Tucson, AZ, USA. 17. Interoperability Informatics, New York-Presbyterian Hospital, New York, NY, USA. 18. Editorial Systems, Gold Standard Drug Databases/Elsevier, Tampa, FL, USA. 19. Information Technology and Services, Cerner Corporation, Kansas City, MO, USA. 20. Office of Clinical Quality and Safety, Office of the National Coordinator for Health Information Technology, Department of Health and Human Services, Washington, DC, USA. 21. Department of Pharmacy Practice & Science, The University of Arizona College of Pharmacy, Tucson, AZ, USA malone@pharmacy.arizona.edu.
Abstract
OBJECTIVE: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. MATERIALS AND METHODS: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to improve decision support for DDIs. We addressed three key questions: (1) what, how, where, and when do we display DDI decision support? (2) should presentation of DDI decision support vary by clinicians? and (3) how should effectiveness of DDI decision support be measured? RESULTS: Our recommendations include the consistent use of terminology, visual cues, minimal text, formatting, content, and reporting standards to facilitate usability. All clinicians involved in the medication use process should be able to view DDI alerts and actions by other clinicians. Override rates are common but may not be a good measure of effectiveness. DISCUSSION: Seven core elements should be included with DDI decision support. DDI information should be presented to all clinicians. Finally, in their current form, override rates have limited capability to evaluate alert effectiveness. CONCLUSION: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.
OBJECTIVE: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. MATERIALS AND METHODS: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to improve decision support for DDIs. We addressed three key questions: (1) what, how, where, and when do we display DDI decision support? (2) should presentation of DDI decision support vary by clinicians? and (3) how should effectiveness of DDI decision support be measured? RESULTS: Our recommendations include the consistent use of terminology, visual cues, minimal text, formatting, content, and reporting standards to facilitate usability. All clinicians involved in the medication use process should be able to view DDI alerts and actions by other clinicians. Override rates are common but may not be a good measure of effectiveness. DISCUSSION: Seven core elements should be included with DDI decision support. DDI information should be presented to all clinicians. Finally, in their current form, override rates have limited capability to evaluate alert effectiveness. CONCLUSION: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.
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