Protocol Deviations at an Academic Clinical Research Unit: Insights and Strategies for Prevention.

Authors

Maran Subramain, Patrick Ten Eyck, Kristy Mahan, Jackline Wangui, Kimberly Sprenger, Donald G McCloud, John M Buatti, Alejandro P Comellas

Published in

Clinical and translational science. Volume 19. Issue 10. Pages e70739.

Abstract

Protocol deviations threaten data integrity, regulatory compliance, and participant safety in clinical trials. Despite their prevalence, few longitudinal studies have characterized deviation patterns and described quality improvement strategies within clinical research units (CRUs). This descriptive longitudinal quality improvement study analyzed protocol deviations and prevented incidents reported at the University of Iowa CRU from June 17, 2020, through December 31, 2024. Events were captured via online survey and classified using a five-grade severity scale. Pearson's chi-square tests assessed associations between event characteristics and outcomes. During the survey data-collection period, 11,581 CRU visits were recorded. Overall, 261 events were reported, including 100 incidents prevented before becoming protocol deviations and 68 confirmed protocol deviations; 93 events preceded systematic collection of the prevented/resulted classification. Grade 1 was the most frequently selected severity classification (126 of 178 severity-grade selections; 70.8%). The laboratory (60.2%) and study setup (33.7%) were the most frequent event contexts. Among events with a prevented/resulted classification, Grade 1 events were more frequently prevented than Grade 2-5 events (71.8% vs. 25.0%; p < 0.0001). Quality improvement strategies implemented during the observation period included dedicated study liaisons, standardized start-up checklists, increased staffing, and structured communication processes. Lower-severity events were more frequently prevented before resulting in protocol deviations. These findings identify operational contexts and event characteristics that may inform proactive, systems-based approaches to deviation prevention in CRUs.

PMID:
42839354
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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