Workflow in clinical trial sites & its association with near miss events for data quality: ethnographic, workflow & systems simulation

Abstract

Background: With the exponential expansion of clinical trials conducted in (Brazil, Russia, India, and China) and VISTA (Vietnam, Indonesia, South Africa, Turkey, and Argentina) countries, corresponding gains in cost and enrolment efficiency quickly outpace the consonant metrics in traditional countries in North America and European Union. However, questions still remain regarding the quality of data being collected in these countries. We used ethnographic, mapping and computer simulation studies to identify/address areas of threat to near miss events for data quality in two cancer trial sites in Brazil.

Methodology/principal findings: Two sites in Sao Paolo and Rio Janeiro were evaluated using ethnographic observations of workflow during subject enrolment and data collection. Emerging themes related to threats to near miss events for data quality were derived from observations. They were then transformed into workflows using UML-AD and modeled using System Dynamics. 139 tasks were observed and mapped through the ethnographic study. The UML-AD detected four major activities in the workflow evaluation of potential research subjects prior to signature of informed consent, visit to obtain subject́s informed consent, regular data collection sessions following study protocol and closure of study protocol for a given project. Field observations pointed to three major emerging themes: (a) lack of standardized process for data registration at source document, (b) multiplicity of data repositories and (c) scarcity of decision support systems at the point of research intervention. Simulation with policy model demonstrates a reduction of the rework problem.

Conclusions/significance: Patterns of threats to data quality at the two sites were similar to the threats reported in the literature for American sites. The clinical trial site managers need to reorganize staff workflow by using information technology more efficiently, establish new standard procedures and manage professionals to reduce near miss events and save time/cost. Clinical trial sponsors should improve relevant support systems.

Figure 1. System dynamics model showing the three major emerging themes identified and rework caused by these themes.

Available for best visualization in http://goo.gl/XydTd.

Figure 2. CRF’s to do and CRF’s completed in two situations: planned and simulated with rework.

Figure 3. Dynamic model with the policies suggested (circled).

Available for best visualization in http://goo.gl/XydTd.

Figure 4. CRF’s to do and CRF’s completed in two situations: simulated with rework and simulated with policies suggested.

Figure 5. The Workfow of all process simulated for three situations: Planned, with reword identified and wirh policies suggested.