OHSU

Sample Manager at the OHSU Brenden-Colson Center for Pancreatic Care

Sydney Owen, Research Analyst, Oregon Health Science University Brenden-Colson Center for Pancreatic Care

This presentation will describe how Sample Manager is used to manage samples and related data at the Oregon Pancreatic Tissue Registry (OPTR), where clinical, research, and specimen data for OPTR participants is managed within a LabKey database.

 

 

The presentation provides a detailed insight into the utilization of LabKey by the Brendan Coulson Center for Pain Care at Oregon Health & Science University (OHSU) for managing blood and tissue samples for the Oregon Pancreatic Tissue Registry (OPTR). Initially, data management was challenged by reliance on Excel spreadsheets for tracking freezer inventory, sample data, and assay results, leading to duplicated entries, inconsistencies, and a lack of audit capabilities. The introduction of Sample Manager, part of the LabKey platform, offered a solution with features such as customizable freezer inventory, check-in/check-out functionality, audit logs, and the ability to customize fields for assays and sample data.

The transition to LabKey allowed for a seamless integration of existing sample IDs, ensuring data integrity and consistency through controlled fields and lookup lists. The freezer storage box view, with its color coding and space management capabilities, improved sample management efficiency. Moreover, the platform’s ability to link assay data with sample data in a central database enhanced the research workflow. The implementation also included workflow tracking and plans to link LabKey server-side data with Sample Manager through ETL, streamlining data management processes.

Despite initial resistance and the dual use of Excel and Sample Manager during the transition phase, the lab eventually fully adopted LabKey, aided by training sessions and responsive adjustments to the system based on lab feedback. This move to LabKey represented a significant step forward in data management, ensuring data integrity, efficiency, and the potential for more sophisticated data analysis in the future.

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