Generating and entering test data into electronic data capture (EDC) systems is a critical yet laborious task, essential for validating study designs and numerous downstream systems and programs. We previously leveraged open-source technology to develop a tool to significantly ease the time-intensive effort of manual test data entry and notable efficiency gains have been achieved since its release; however, it falls short in effectively covering the full spectrum of test cases necessary for clinical system set-up and programming. To overcome these challenges, Takeda has sought to build on this innovative software solution to harness the power of generative AI (GenAI) to expand the range of covered testing scenarios. During the development process, strategies and best practices for maximizing GenAI potential in the clinical test data context have been established, along with valuable lessons learned along the way. Exciting potential future directions have been identified to further reduce clinical trial system set-up times.
Rita Pecuch | Varun Jagadeesh
Takeda | Takeda
Harnessing Generative AI to Improve Test Data Quality and Efficiency in Clinical Trial Set-Up
Generating and entering test data into electronic data capture (EDC) systems is a critical yet laborious task, essential for validating study designs and numerous downstream systems and programs. We previously leveraged open-source technology to develop a tool to significantly ease the time-intensive effort of manual test data entry and notable efficiency gains have been achieved since its release; however, it falls short in effectively covering the full spectrum of test cases necessary for clinical system set-up and programming. To overcome these challenges, Takeda has sought to build on this innovative software solution to harness the power of generative AI (GenAI) to expand the range of covered testing scenarios. During the development process, strategies and best practices for maximizing GenAI potential in the clinical test data context have been established, along with valuable lessons learned along the way. Exciting potential future directions have been identified to further reduce clinical trial system set-up times.