- Justin Guinney, Director and EHR Challenge Project Lead (Sage Bionetworks)
- Philip Payne, Co-Director and Cloud Architecture Project Lead (Wash U)
- Fires Wehbe, Competitions Project Lead (Northwestern University)
- Adam Wilcox, Leaf Project Lead (University of Washington)
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- Create common cloud computing architecture that can enable the rapid deployment and sharing of reusable software components by CTSA hubs;
- Demonstrate the use of shared tools and platforms for the collaborative analysis of clinical data in a manner that transcends individual CTSA hub “boundaries”
- Disseminate a common set of tools that can be employed for the both local and collaborative query of common data warehousing platforms and underlying data models
- Pilot the “cloudification” of software artifacts that can be shared across CTSA hubs to address common and recurring information needs.
Much has been written in the contemporary scientific literature and general media concerning the promise of leveraging advanced computational technologies and methods to enable new paradigms for clinical and translational research. Ultimately, such research can and should generate health benefits at both the patient and population levels, informed by the knowledge generated and disseminated via said efforts. We believe that these types of emergent clinical and translational research paradigms can and should be predicated on the collection, analysis, and dissemination of relevant, timely, and comprehensive data and knowledge by a variety of end-users in a highly liquid and democratic manner. Recent reports have show the substantial benefits when employing these types of approaches to clinical and translational research, which can be summarized as the ability to conduct investigations in a timely and resource efficient manner, often working across and between traditional organizational boundaries (Jones, Rudin et al. 2014, King, Patel et al. 2014). The pursuit of clinical and translational research at a national level, emblematic of the NCATS-funded CTSA consortium, represent an exciting inflection point in the history of the health and life sciences. It is our perspective that capitalizing on this opportunity requires the democratization and wide-spread use of computational technologies by a broad spectrum of researchers with variable degrees of technical capability and training. Such democratization of research-relevant computing will require us to:
- enable effective end-user adoption and utilization of computational platforms and tools in a broad variety of settings (Bates, Kuperman et al. 2003, Garg, Adhikari et al. 2005, Kawamoto, Houlihan et al. 2005, Goldspiel, Flegel et al. 2014);
- ensure that technology deployment and user experience are compatible with “real world” workflows and environments (Kawamoto, Houlihan et al. 2005, King, Patel et al. 2014, Kinvey 2014);
- overcome limitations in vendor-specific technologies that may make it difficult to leverage such systems for the purposes of integrating and interacting with diverse and complex data types across and between traditional organizational boundaries (Mandl and Kohane 2012, Masys, Jarvik et al. 2012, Bender and Sartipi 2013);
- ensure that such platforms are elastic, scalable, and sustainable from both a technology and resource perspective. In response to these challenges and opportunities, the CD2H Tool and Cloud Architecture Core (TCA) will focus upon the following specific aim, as was found in our original application for funding.
We believe that the aim and approach associated with the TCA are innovative as a result of the following factors:
- To-date, CTSA hubs have developed, deployed, and managed computational technologies and tools in support of clinical and translational research in a localized and highly heterogeneous manner. This approach does not allow for economies-of-scale in terms of technology infrastructure and/or operations, nor does it facilitate the rapid and efficient sharing of software components as a result of substantial local variation and dependencies therein. By establishing a common cloud computing architecture, along with reproducible technology deployment workflows and tools, CD2H will provide CTSA hubs with an easy to use, elastic, and scalable alternative to local technology deployment paradigms. We envision that such an alternative approach will serve as the basis for creating a robust and efficient software sharing ecosystem that involves collaborative development, deployment, and use of such technologies across and between CTSA hubs.
- In a similar manner, a critical area of activity for many clinical and translational researchers is the collection and analysis of patient-derived data, such as that which can be extracted from Electronic Health Record (EHR) systems. However, mirroring the challenges described above concerning localized technology deployment, to-date, most CTSA hubs have conducted such analyses at a local level, due to concerns over data sharing, privacy, confidentiality, and the ability to ensure the reproducibility and rigor of the analytical methods being used. This “silo” based approach to data analysis often precludes the aggregation and interrogation of large-scale data sets, as are frequently needed to assess state-of-the-art research hypotheses, especially with the advent of precision medicine based approaches to the diagnosis and treatment of conditions with relatively low population-level prevalence. By demonstrating a secure, shared, cloud-based environment in which shared analysis of such patient-derived data can be conducted, and where critical issues of data “ownership”, privacy, confidentiality, and methodological reproducibility/rigor are addressed, CD2H will introduce a new model for shared data analytics that fully leverages the breadth and depth of the national CTSA consortium.
- Finally, by showing how common software tools and artifacts that meet shared information needs across and between CTSA hubs, can be readily shared and deployed both locally and in a common cloud environment, CD2H will demonstrate the economies-of-scale that a software sharing ecosystem can achieve for the national CTSA consortium. Ideally, these economies-of-scale would include: 1) reduced development costs; 2) sustainable technology deployment models and associated resourcing needs; and 3) improved ability to share and integrate data, information, and knowledge products amongst CTSA hubs.
When viewed collectively, we believe that these innovative dimensions of our work will serve as the foundation for the creation of software ecosystem, consisting of a combination of people, technologies, and methods, which can collectively predispose and enable successful and collaborative clinical and translational research projects.