ABSTRACT
Low-accruing clinical trials delay translation of research breakthroughs into the clinic, expose participants to risk without providing meaningful clinical insight, increase the cost of therapies, and waste limited resources. By tracking patient accrual, Clinical and Translational Science Awards hubs can identify at-risk studies and provide them the support needed to reach recruitment goals and maintain financial solvency. However, tracking accrual has proved challenging because relevant patient- and protocol-level data often reside in siloed systems. To address this fragmentation, in September 2020 the South Carolina Clinical and Translational Research Institute, with an academic home at the Medical University of South Carolina, implemented a clinical trial management system (CTMS), with its access to patient-level data, and incorporated it into its Research Integrated Network of Systems (RINS), which links study-level data across disparate systems relevant to clinical research. Within the first year of CTMS implementation, 324 protocols were funneled through CTMS/RINS, with more than 2600 participants enrolled. Integrated data from CTMS/RINS have enabled near-real-time assessment of patient accrual and accelerated reimbursement from industry sponsors. For institutions with bioinformatics or programming capacity, the CTMS/RINS integration provides a powerful model for tracking and improving clinical trial efficiency, compliance, and cost-effectiveness.
ABSTRACT
SPARCRequest© (Services, Pricing, & Application for Research Centers) is a web-based research management system that provides a modular and adaptable "electronic storefront" for research-related services. Developed by the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, it was released as open source (OS) code in 2014. The adoption of SPARCRequest© accelerated in 2016, when, to ensure responsiveness to the needs of partners, its governance also became open. This governance model enables OS partners to suggest and prioritize features for new releases. As a result, the software code has become more modularized and can be easily customized to meet the diverse needs of adopting hubs. This article describes innovative aspects of the OS governance model, including a multi-institutional committee structure to set strategic vision, make operational decisions, and develop technical solutions; a virtual roadmap that ensures transparency and aligns adopters with release-based goals; and a business process model that provides a robust voting mechanism for prioritizing new features while also enabling fast-paced bug fixes. OS software evolves best in open governance environments. OS governance has made SPARCRequest© more responsive to user needs, attracted more adopters, and increased the proportion of code contributed by adopters.
ABSTRACT
Most HPC clusters are purchased with a large quantity of identical hardware, which is maintained through its lifecycle and then another HPC cluster takes its place. However, some clusters, like ours, are maintained by frequently adding new hardware, which is then integrated into the system. Over the years, the cluster has grown to include 300+ compute nodes with 8000+ cores from 6 vendors, spanning 5 generations of CPUs; 7 network technologies from 6 switch vendors (1Gbps-100Gbps, including Ethernet, Infiniband, and OmniPath); 102 GPUs (3 different GPU models); 28 storage nodes (3+ PB raw storage); and 7 virtualization nodes hosting 65 VMs. Having such a diverse system has significant advantages, although the management is more difficult. This paper outlines our strategy of managing this very heterogeneous and complex system. Topics covered include software optimization, consistency of operating system updates, identity management, resource prioritization, network infrastructure, storage, and management of non-compute-intensive resources. Our combination of open source and internally developed software used to manage this cluster are a model to other heterogeneous systems and to smaller clusters which have not expanded because of management worries.
