825. An Academic-Information Technology Partnership to Create an Infectious Diseases Translational Science Database

• Published in: Open forum infectious diseases Vol. 7; no. Supplement_1; pp. S454 - S455
• Main Authors: Lancaster, Christopher K, Gonzales-Luna, Anne J, Beairsto, Jeffrey, Neptune, Randal, Killen, Joanna, Dugas, Corey, Webster, Beth, Rutter, John N, Rutter, Tristan, Garey, Kevin W
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 31.12.2020
• Subjects: Poster Abstracts
• ISSN: 2328-8957; 2328-8957
• DOI: 10.1093/ofid/ofaa439.1014

Abstract Background Translational science is the process of turning observations in the laboratory, clinic, and community into interventions that improve human health. The coordinated effort to maintain integrated, validated laboratory and clinical data is often a rate-limiting step for research laboratories, especially for multi-site studies. Previous research shows a rate of error between 2.3 and 5.2% for basic data collection in clinical databases, up to 26.9% for more complex data points. The purpose of this project was to create a translational science database prototype that would be responsive to the unmet needs of the translational research community. Methods Translational scientists, IT experts, and lab technicians mapped the workflow of a high-throughput research laboratory including clinical and laboratory data. Database goals were to develop processes that would minimize data entry time, avoid redundancies, and validate data in a secure environment (HIPAA-compliant). Unique to this platform was the ability to map creation of new samples (for example, PCR products) from parent samples (biologic samples). The platform was developed in an iterative process utilizing interviews, workflow study, analysis of supporting artifacts, and mock-ups. Results The current prototype allows for electronic upload or manual data entry of clinical data. In a small controlled study we found the rate of error for basic data entry to be below 1% within it. Pre-populated data entry screens map laboratory work-flow with custom data entry fields produced based on laboratory results earlier in the work flow. Work-flow mapping includes microbiology, phenotypic descriptions (MIC), molecular biology (PCR), and customized experiments. Sequence data, housed separately, has data linkers stored in the database. The launch screen and data entry forms are populated based on specific criteria entered for each user. Conclusion The Translational Science Database allows for efficient capture of high-quality data with baseline validation enabling seamless linking of translational data for single or multi-site laboratories. Future development work will expand the number of experiments and also incorporate stored biobank information into the database. Disclosures Jeffrey Beairsto, BSc Eng (ME), Populus (Employee, Shareholder) Randal Neptune, BSc., MSc., Populus Global Solutions (Employee) Beth Webster, BSc, MBA, Populus Global Solutions Inc (Employee, Shareholder) John N. Rutter, BscEng, Populus Global Solutions (Board Member, Employee, Shareholder) Tristan Rutter, BA, Populus (Employee) Kevin W. Garey, PharMD, MS, FASHP, Merck & Co. (Grant/Research Support, Scientific Research Study Investigator)