Abstract
The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.
Highlights
The clinical and translational science research spectrum outlines how scientific discoveries in the laboratory progress toward improved global health in distinct stages: basic scientific discovery (T0), translation to humans (T1), translation to patients (T2), translation to practice (T3), and translation to communities (T4) [1,2]
Effective team science requires a diversity of thought and true integration across disciplines [5,6]
The ASK Big Data team demonstrated this by recruiting a highly interdisciplinary team of researchers representing every stage of the translational science research spectrum, and by ensuring that each of these team members had an important seat at the table, with everyone at the table engaged
Summary
The clinical and translational science research spectrum outlines how scientific discoveries in the laboratory progress toward improved global health in distinct stages: basic scientific discovery (T0), translation to humans (T1), translation to patients (T2), translation to practice (T3), and translation to communities (T4) [1,2]. When the spectrum is navigated sequentially, discoveries in the preclinical stage, oftentimes in animal studies, generate hypotheses that are subsequently tested in small, well-controlled studies of humans [1]. These early human research studies aim to establish proof of concept and safety before investing substantial resources in large-scale studies [1]. The data management and analytics tasks required for working with healthcare big data in T4 fall primarily to data scientists [3] It is the successful, integrative collaboration between data scientists and researchers from every stage of the translational science research spectrum that ensures that the data-related tasks in T4 are comprehensive and performed in such a way to ensure that the end discovery is an impactful translation [5,6]. We first provide a brief summary of the ASK Big Data team’s relevant research findings in order to give context to the discussion of lessons learned in the effort to bridge preclinical findings to big data discovery
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