Workflow in Clinical Trial Sites and Data Quality Figure 4. CRF’s to do and CRF’s completed in two situations: simulated with rework and simulated with policies suggested. doi:10.1371/journal.pone.0039671.g004 problems resulting from multiple individuals with different levels of information about the subject handling the data. Previous literature suggests that interruption during task implementation may not only have a negative effect on task performance but also lead to serious errors [42]. For example, CRCs may receive unexpected workload and thus forget a critical portion of their task lists or incur in data entry errors [13]. We also noted that an absence of decision support systems for clinical research coordinators had a significant influence on their task performance and resultant near miss events for data quality. Clinical research coordinators and physicians frequently need to answer queries or consult the study material while evaluating a research subject. Lack of readily available guidelines has implications on time and effective subject management in a clinical trial. In our study we identified difficulties encountered by CRC to quickly locate information while handling long paper- based protocols. This difficulty could be alleviated with, for example, electronic documents with better navigability that could be immediately queried at the time when they are needed. Although the benefits of decision support systems in enhancing the safety and quality of clinical practice [43,44,45], reducing errors [46,47] and ensuring adherence to practice guidelines [48,49] has amply been demonstrated in the past, they are equally useful in clinical research settings. In the latter context, decision support tools have been effectively used to determine subject eligibility [50,51]. Based on our results, the implementation of a decision support tool to guide CRC tasks would similarly result in the optimization of clinical trial tasks. Although our report has made a significant contribution in terms of understanding, representing, and simulating strategic workflow decisions to reduce the incidence of near miss data quality issues in clinical trial sites, our study has limitations. As in any observational study, the presence of observers in the clinical trial site might interfere with the way subjects behave, potentially introducing bias into our observations [52]. However, since most of our findings were confirmed by triangulation [53] to be part of the daily routine of the investigated sites, this effect should be considered minimal. The results obtained from the suggested interventions imple- mented in our model simulation were not validated through real life implementation, since it would require a substantial investment from the site and sponsor’s perspective. Given the highly regulated environment within clinical trials, the level of variability in our UML model is reduced thus enhancing its applicability. Several of the investigators were familiar with the clinical trial process and, therefore, could have unintentionally introduced their opinions into the observational process. Another issue in comparing workflows across trial sites is the absence of a taxonomy listing variables that should be compared. This taxonomy would ideally focus on areas of dissonance between workflow and data quality and requires a specific article describing this approach. Moreover, although the two sites where we collected data represent active Cancer trial sites in Brazil, our sample size was small and may not be judged representative. A larger sample size could increase the generalizability of our results, but qualitative studies are aimed at depth rather than generaliz- PLoS ONE | www.plosone.org 8 June 2012 | Volume 7 | Issue 6 | e39671