collect data and who passes the task of data entry to another CRC who might enter data that he/she does not understand. While previous studies involving clinicians, radiology interpre- tation, healthcare management systems, have evaluated the relationship between workflow and data quality [4,5,6,7], most have been conducted in a clinical rather than a research environment, frequently using workflow data that had been automatically captured. Although this approach is appealing, it does not apply to a clinical research setting where each step of a Principle Investigator (PI), Clinical research coordinator (CRC), nurse, receptionist and pharmacist is not automatically captured in an electronic system. Moreover, previous studies [5,8,9,10,11,12] investigated methods of standardizing and improving the quality of clinical trial procedures. Between them, only a few workflow studies were conducted in research settings using a pure ethnographic approach [13]. Recent studies have used Unified Modeling Language (UML) [14] - a method that helps assess workplace efficiency, to assess and suggest improvements in clinical research workflow [15,16,17]. UML is composed of a set of graphical notations initially created to help understand software processes. It allows us to model a system and help visualize and understand its operation. It is currently used to model processes in diverse areas such as web applications, business processes and health care [14]. Despite their benefits, UML models are static and hence are limited by their description of a problem in a specific instance in time. Dynamic models can be simulated to understand their behaviour and analyze a range of ‘‘what if scenarios’’ thus providing crucial information before the practical implementa- tion of a system level change/policy. Previous studies have [15,18,19,20] used simulation techniques to represent diverse aspects of the real world in an interactive way. Systems Dynamics (SD) a modeling and simulation technique has been widely used to evaluate the behaviour of systems [21,22,23]. It includes a set of principles for modeling and simulation that are used to conceptualize and evaluate complex systems. Through a SD model, we can represent the relationship between elements, their activities as well as the flow and accumulation of information between them. We can also feed the model with qualitative and quantitative data and then create multiple models representing a variety of ‘‘what if’’ scenarios that help in decision making. Given the complex nature of health systems and clinical research, SD has previously been used to model decision support systems in this area [24,25,26,27]. Although there is a wide variety of publications in each of these areas, to the best of our knowledge, no previous study has evaluated the workflow [28] of clinical trials in depth through a mix of techniques such as ethnographic studies to capture information about work routine, used UML [14] to graphically represent workflow in the form of activity diagrams [29] and SD modeling [4,13,30] to evaluate the resulting behaviour over time. Modeling and simulation performed before initiation of a clinical trial may help trial planners and managers to improve the quality of data and save time. Accordingly, the objective of this study was to 1. Map the workflow of PIs, CRC’s, nurses, receptionists and pharmacists of two oncology clinical sites in Brazil, documenting ethnographic observations using a standard task diagram language [14] and 2. Introduce workflow modeling using UML and propose the use of simulation modeling to understand the underlying behaviour of the workflow model and 3. Conducting computer simulation experiments [31] using a system dynamics approach [21] to estimate work efficiency gains when solutions are applied. Workflow in Clinical Trial Sites and Data Quality Methods Ethics We obtained ethics approval from Comiteˆ de E´tica em Pesquisa do Hospital Pro Card´ıaco, at Rua Dona Mariana, Rio de Janeiro, Brazil and Comiteˆ de E´tica em Pesquisa (CEPesq) of Hospital S´ırio Libaneˆs, Sa˜o Paulo, Brazil for conducting the study at the trial sites in Rio de Janeiro and Sa˜o Paulo respectively. Inclusion and Exclusion Criteria We evaluated two oncology trial sites working with Qualidoc (consultant and training support at all levels of personnel in ISO9001: 2008) recommended by Ageˆncia Nacional de Vigilaˆncia Sanita´ria (ANVISA) - a National Agency of Sanitary Surveillance in Brazil. Both sites were located in Brazil: the first being a clinic- based environment in the city of Rio de Janeiro with one PI oncologist, two nurses CRCs, one pharmacist, one clinical research administrator and fourteen studies in phase III and the second being a hospital-based environment in Sa˜o Paulo, with two PIs oncologists, two nurses CRCs, four pharmacists, one clinical research administrator and three studies in phase III. Both sites present a high volume of trial recruitment. We considered sites with an active phase III or IV trials as high volume sites. We excluded Phase I and II trials since they do not represent the majority of trials conducted in BRIC countries, also presenting a very diverse workflow when compared to phase III/IV trials. Operational Definitions Although some technical terminology might encounter different definitions across clinical trials, workflow, and qualitative research literature, for the purposes of this manuscript, we have standard- ized the most common concepts to facilitate understanding (Table 1). Ethnographic Study Two researchers from our team [AB, EC] made ethnographic field observations for approximately 40 hours and conducted qualitative interviews at two sites in Rio de Janeiro and Sa˜o Paulo (Brazil), where they shadowed PIs, CRCs, nurses, receptionists and pharmacists. We recorded information on tasks (S1), interactions between other actors (defined in table 1), communication patterns, information needs, and other aspects related to their workflow. We conducted semi-structured interviews using open-ended questions to complement and fill gaps in our ethnographic observations, being broadly directed to (1) understand the average order of tasks in different stages of each protocol, (2) main causes that affect the average order of tasks, including the most common interruptions; (3) main communication contacts for each actor, how frequently communication occurs, how frequently the workflow is affected by the patterns of communication and what type of noise affects the performance of communication flow; (4) documentation models and migration from paper to computer interface used for documentation; (5) How documentation templates are affected when trial site staff are confronted with any query about events, for example, a subject enrolled sometime ago; (6) information necessary that affect the workflow, causing the CRC to search out information from different sources and need to contact other personnel; (7) How clinical trial workflow has been integrated with information technology and if there are in any area where some disconnection between them exists causing a disruption in the regular workflow. These seven concepts were extracted from the study of Khan et. al. in [13]. Observations were made in a non-participatory manner, in that observers were not directly involved in actively collecting data for PLoS ONE | www.plosone.org 2 June 2012 | Volume 7 | Issue 6 | e39671