PHUSE SDE Heidelberg 2024 abstract

Near Real Time Data Flow

by Kimberley Santing       

The Near Real Time Data Flow is set up to automate quality control and trial oversight. It uses the process flow capability of LSAF to achieve daily refresh of data and output of reports to a visualization tool.

Connections are set up with EDC systems and central labs to get raw data to create SDTM domains internally. These domains are then used to run quality control checks on the latest data and output issues to reports.

Custom EDC reports are developed and made available daily. These reports are further adjusted internally to better suit the needs of a data manager. Sample tracking reports are combined with EDC data to assist sample managers with oversight.

The Near Real Time Data Flow consists of multiple process flows working together to get the data into LSAF, create SDTM domains and multiple reports and output this to a visualization tool for the end-users.

Experiences and best practices in re-using real-world data for regulatory purposes

by Caro Sluijter (with Berber Snoeijer, ClinLine)

Real-world data is routinely obtained from regular health care without the intend of doing clinical research. The value of re-using this data for substantiation of evidence or as a synthetic control arm in clinical trials is more and more appreciated. Data from registries, electronic health record systems, claims and even patient generated health data can be used for this purpose. As this data was not collected for research purposes, addressing the provenance, governance and traceability aspects is more challenging than with clinical trial data. To reduce this burden, a consistent mapping approach will aid all users throughout the process of data standardization, fit for purpose assessment, selection, and alignment. In this presentation we show our approach and experiences in mapping real-world data using an extended OMOP data model and finally to make it available for submission in SDTM format.