Table 1. Terminology standardization. Term Protocol Actor Principal Investigator (PI) Sub-Investigator CRC (Clinical Research Coordinator) Research Subject CRF (Case Report Form) Source Document doi:10.1371/journal.pone.0039671.t001 Workflow in Clinical Trial Sites and Data Quality Description Clinical trial documentation that outlines the objective, methodology, design, analysis plan. It also describes the background and reason for the conduct of the study and puts forth a standard method for the conduct of the clinical trial [22] A person who is a part of the trial workflow and interacts with tasks or other actors [21] An individual who is directly responsible for the conduct and completion of a funded clinical trial. He/She directs the research project and reports study results directly to the sponsors [22] Physicians designated and supervised by the PI to perform procedures and monitor subjects in the clinical trial [22] Individuals responsible for operational tasks and for providing support to PI/Subinvestigator in a clinical trial study. They could be involved in the process of inclusion, recruitment and still maintain the registry of participants, provide the signature of informed consent forms and schedule procedures and lab tests, ensuring accuracy of source documentation, dispensing study medications and maintaining databases with clinical research data, filling the CRF [22] Once the patient agrees to be a part of a clinical trial and signed the informed consent document, he/she becomes a research subject [22] It is a paper-based or electronic record of subject data specifically used in clinical trial research [22] a document where collected data is first recorded for a clinical trial and later entered in the CRF [22] the clinical trial, therefore attempting to minimize the Hawthorne effect that is characteristic of every observational study [32]. Observers did not have access to protected health information (PHI) or any specific data related to the ongoing clinical trials to ensure confidentiality. Since observers did not have access to raw data for privacy and security reasons, the identification of data quality issues was made following the concept of near miss events (the happening of an unexpected event that did not generate important problems, but had big potential to) [33]. Following is an example of a near miss event: a CRC schedules an appointment in his/her personal electronic calendar, notifying other staff person- nel about the same. On the day of appointment, in the event of the CRC’s absence, the patient would still be received and assisted by other staff personnel at the site. Yet some important detail/ document, available only to the CRC may be missed during this process. Consequently, it may generate a greater problem at the end of the study. When we observed a task that had a higher potential of near miss events, we highlighted it as a potential problem. Information on tasks was later submitted back to the staff at each of the two sites to ensure that information was not misrepresented, thus ensuring appropriate data triangulation [34]. All data collected through the ethnographic study and interviews were transcribed in a Google Docs Word file. After analysis, we synthesized emerging themes using atlas.ti [35], a software for qualitative data analysis. Each interview was coded by two researchers using Grounded Theory methods [36] where emerging themes were extracted from patterns evolving from the text and by avoiding pre-conceived hypothesis. Ambiguities if any were resolved by discussion. Categories were reduced to major themes through discussion amongst the study team while re- reading the transcripts. Observation Categories Observers documented CRC tasks by selecting tasks descrip- tions from a list of tasks compiled from the emerging themes obtained from the previously described ethnographic study and by findings from previous studies [13]. A large group of major and minor categories were then used to facilitate the classification of any possible CRC tasks. For the purpose of analysis, this large group of tasks was summarized into a smaller number of categories. Workflow Mapping Information from the ethnographic study was condensed using a modified version of UML (Unified Modeling Language) Activity Diagrams (UML-AD), version 2.0 [37]. All modeling was conducted using Astah community version 6.1 [38]. The variation of activities between two sites was represented by the workflow (S6) pattern ‘‘exclusive choice’’ [39]. Exclusive choice represents a situation where there are two or more exclusive alternate paths to a workflow and only one of them can be chosen. The terminology for workflow and UML- AD diagram are present in Supporting information S6. Preliminary Simulation Model Next, we formed an external panel with five clinical researchers, affiliated to the Research on Research group (RoR), Duke University, to discuss the findings and summarize it as a simulation model. The simulation was performed by taking the UML activity diagram as the basis for a System Dynamics model [21,22,23]. All modeling was conducted using Vensim DSS [40]. This process involved two steps. First, a baseline model was created to reproduce the current workflow of each clinical trial site along with the reported problems related to near miss events for data quality (Table 2). A system analysis of each major loop in the system identified points where workflow could be potentially improved in terms of a better execution of tasks. Second, the model was presented to each participant for feedback, who suggested modifications. Interventions were then tested in subsequent models, where we simulated an improvement in the system. Finally, the model was fed with qualitative data and simulated using different scenarios. Results Overall, the two sites evaluated in this study demonstrated consistent data collection system, with only a few discrepancies between them. The inconsistencies were related to specific infrastructure aspects. In addition, a group of areas with higher PLoS ONE | www.plosone.org 3 June 2012 | Volume 7 | Issue 6 | e39671