Making clinical trials more efficient, and ultimately more successful, would significantly advance patient care. However, fragmentation of the relevant data necessary to implement improvements to translational science is a significant barrier.
While some bioinformatic tools have attempted to address this problem, they often lacked the ability to assess the efficiency of translational science. Researchers at the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) have developed a novel bioinformatic tool called RINS, the Research Integrated Network of Systems, that can be used to evaluate whether improvements to the clinical trial process are making a difference. Their results, published in the Journal of the American Medical Informatics Association, showed that RINS can integrate data about clinical studies across disparate systems and provide metrics about MUSC’s clinical trial efficiency and effectiveness.
“You could call it meta-data—it’s data about how we produce data in studies or how we get studies up and running and accelerate them,” said Leslie A. Lenert, MD, chief research information officer for MUSC and director of the Biomedical Informatics Center. “Those were all in a different place, in a different format, using numbers that didn’t have an agreed-upon definition. But we brought it all into one view to get a comprehensive look at the processes for doing science. And we’ve never had that before because the data lived in disparate systems.”
Making informed decisions about how best to improve and streamline translational research practices requires detailed data, and to accumulate that data, institutions need strong evaluative tools. To that end, MUSC has developed and utilized a research transaction management system, SPARCRequest, that provides a powerful tool to help researchers determine budgets for grant applications, navigate the regulatory process, recruit diverse study participants, and conduct clinical trials. While SPARCRequest was instrumental in streamlining clinical research, it wasn’t originally set up for metric tracking across disparate systems. The new, decentralized RINS research data mart envisioned by Lenert took the research metric-tracking activities of SPARCRequest to the next level and enabled cross-disciplinary group tracking and communication.
“It’s a simple concept, but hard to execute,” said Lenert, who also serves as assistant provost for Data Sciences and Informatics. “No one’s had the opportunity to bring together these diverse systems to tell us how long it takes an institutional review board [IRB] protocol to get approved or whether this funding led to that outcome—those are difficult things to do.”
In order to house and access the data within the warehouse, the RINS team needed a way to identify each study uniquely and link the disparate systems together. To solve the problem on identifying each study, the team created a research master identifier (RMID).
Using RMIDs and application programming interfaces, RINS integrates SPARCRequest with other electronic databases, including MUSC’s electronic health record; electronic IRB; and systems for grants award management, expenditure tracking, and clinical trial management. RINS has been sufficiently flexible to accommodate new programs while also maintaining historical data.
Edited by Gary Cramer
