Right Idea, Wrong Time: Focusing on Alert Timing for Effective Decision Support

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Abstract

Background Effective clinical decision support (CDS) interventions improve adherence to care guidelines, reduce prescribing errors, and, in some settings, decrease patient mortality. However, misalignment with the Five Rights framework, particularly regarding CDS timing in clinical workflows, can lead to implementation failures, alert fatigue, and physician burnout. Objectives This case series aimed to evaluate and redesign three interruptive CDS alerts at a large safety net health system to better align with clinician workflows, reduce interruptions, and improve compliance with care guidelines. Methods We analyzed three interruptive alerts using data from Epic's SlicerDicer tool, focusing on alert frequency, contributors to alert triggering, and user responses before and after intervention. Alerts were modified to improve their timing and relevance within the workflow. Results Modifications included retiming a human immunodeficiency virus screening alert to trigger during laboratory test orders, reducing alert firings by 87% while increasing monthly screening orders from 3,561 to 4,547 (p < 0.001). An administrative alert's firing frequency decreased by 86% through the introduction of a 4-hour lockout period, maintaining compliance rates. Finally, restricting a pediatric head circumference discrepancy alert to in-person visits only eliminated interruptions during telehealth encounters, addressing a major source of clinician frustration. Conclusion Aligning CDS tools with clinical workflows through adherence to the Five Rights framework reduces interruptions and improves outcomes. Iterative review, user feedback, and proactive redesign are essential to ensure CDS effectiveness, particularly as health care evolves to include novel care delivery models like telehealth.

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APA

Wilson, A. E., Bain, A. P., Webb, J., Lehmann, C. U., Moran, B., Shah, N., & O’Connell, E. (2025). Right Idea, Wrong Time: Focusing on Alert Timing for Effective Decision Support. Applied Clinical Informatics, 16(4), 1200–1207. https://doi.org/10.1055/a-2605-4510

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