Concept Mapping to Develop a Framework for Characterizing Electronic Data Capture (EDC) Systems

CTSAs have brought about a push to find better EDC systems, which facilitate translational research. Based on the data management needs of a specific clinical/translational research lab, concept mapping was used to create a framework to evaluate EDCs. After refinement based on a spiral model, including consultations with the UW CTSA and a survey of other CTSAs, the tool was used to characterize EDCs used at CTSA sites across the country

A Partnership Approach for Electronic Data Capture in Small-Scale Clinical Trials

The data collection process for clinical trials can be a tedious and error-prone process, and even a barrier to initiating small-scale studies. Electronic Data Capture (EDC) software can meet the need for faster and more reliable collection of data, but these informatics solutions can also be difficult to for researchers to set up. Establishing a full-featured commercial Clinical Trials Management System (CTMS) ecosystem is not realistic due to current institutional resource constraints. As an alternative solution, our Biomedical Informatics core (BMI) provided the technical expertise to pilot each EDC system in partnership with research teams and performed a qualitative evaluation using criteria we had established with prior research.1 When we began our pilot process, we assumed that each system’s EDC functionality would be the most important aspect and we produced a whitepaper focused on functionality.2 However, as we worked with various study teams it became clear they were willing to work around limitations since any web-based EDC software was a step up from paper forms. In our evaluation we found that the design of the Catalyst Web Tools3 made it difficult to use for clinical trials. OpenClinica4 has the most advanced functionality, for example in site management and complex CRF design, but what documentation is available is written in less user-friendly technical language. REDCap5 had a very clear advantage due to its ease of use extensive tutorials, and online training materials. In early 2010, BMI decided on REDCap as the preferred EDC software to support for small-scale studies. Since then usage has steadily increased. As of August 2010 there were 98 active REDCap users and 16 production studies at the University of Washington, Seattle Children’s, Fred Hutchinson Cancer Research Center, and Bastyr University, with collaborators from many other institutions. Post-evaluation, in addition to maintaining our installation of REDCap we are concentrating on future work in two areas: partnerships with investigators to enhance the local usage of REDCap, and informatics research to solve problems in data integration and interoperability. BMI members have contributed to the Ontology of Clinical Research.7 Additionally through our i2b2 Cross-Institutional Clinical Translational Research (CICTR) project we have identified use cases for moving data between REDCap and i2b2.8 Lastly, in keeping with our “bottom up” philosophy we are applying lightweight data integration techniques to query across REDCap and other systems, such as freezer inventory

Ontology-Based Data Integration of Open Source Electronic Medical Record and Electronic Data Capture Systems

Thesis (Ph.D.)--University of Washington, 2013In low-resource settings, the prioritization of clinical care funding is often determined by immediate health priorities. As a result, investment directed towards the development of standards for clinical data representation and exchange are rare and accordingly, data management systems are often redundant. Open-source systems such as OpenMRS and OpenClinica provide an opportunity to leverage available systems to improve standards and increase interoperability. Nevertheless, continuity of care and data sharing between these systems remains a challenge, particularly in populations with changing health needs, and inconsistent access to health resources. The overarching goal of this project is to enable sharing of data across low cost systems like OpenMRS and OpenClinica using ontologies. The project consists of three aims: 1) describing clinical research and visit data related to the treatment and care of HIV/AIDS patients, 2) developing a prototype data integration system between electronic medical record and electronic data capture systems, and 3) evaluating the utility of the prototype system using simulated and real-world data. In the first aim, I developed a patient identifier and a HIV/AIDS treatment and care ontology to represent the types of data and information created and used by clinicians. This was achieved by gathering data forms used in HIV/AIDS clinics in low-resource settings. From these forms, the patient identifier and HIV/AIDS variables were extracted and used to create the ontologies. In aim 2, the ontologies from aim 1, along with simulated data, were used to develop a prototype data integration system that improves the ability of developers to implement integration systems that meet the needs of users, based on previously created use cases. In the third aim, I evaluated whether the matching algorithm used in the prototype can correctly identify matching patients, and whether the prototype is generalizable to clinical care and research data collected in a real world setting. This work contributes two ontologies to the medical and public health fields that are useful in providing standardization of data elements. Additionally, I provide a prototype data integration system that is useful in facilitating access to previously siloed data and helps reduce the burden of integrating future systems