Accelerating precision medicine: a proposed framework for large-scale multiomics data integrity, interoperability, analysis, and collaboration in biomedical discovery

Abstract

ABSTRACTObjectiveTo identify and define a process and framework for biomedical discovery research. Our study aim was to characterize the biomedical discovery lifecycle across data modalities and professional stakeholders involved in biomedical research to address the multiomics data challenges of precision medicine.Materials and MethodsWe recruited fifteen professionals from various biomedical roles and industries to participate in 60-minute semi-structured interviews, which involved an assessment of common challenges, needs, tasks, and data management methods and a brainstorm exercise to validate each professional’s biomedical research process. We applied a qualitative analysis of individual interviews using a constant comparative approach for emerging themes.ResultsWe found a general process of biomedical discovery across all participants that consisted of four key stages: data plan, data integrity, data analysis, and data-driven discovery. Within each stage of the process, participants highlighted their challenges and needs and emphasized the importance of data integrity and interoperability, particularly during data hand-offs. The process extends across three general levels of data, including non-human, non-clinical human, and clinical human data, to define an overarching framework for biomedical discovery research.ConclusionsThe proposed framework provides researchers with an opportunity to align workstreams and converge on a single process to conduct biomedical research across stakeholders and data types. Key opportunities were found that can be explored in the health technology space, including generative artificial intelligence, to help tackle multiomics data challenges to advance precision medicine.