Background Real-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them. Objective The aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose. Methods We conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review. Results Overall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources. Conclusions There are many ongoing data standardization efforts around human health data–related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science.

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Cloud computing carries the promise of providing powerful new models and abstractions that could transform the way IT services are delivered today. In order to establish the readiness of clouds to deliver meaningful enterprise-class IT services, we identify three key issues that ought to be addressed as first priority from the perspective of potential cloud users: how to deploy large-scale distributed services, how to deliver high availability services, and how to perform problem resolution on the cloud. We analyze multiple sources of publicly available data to establish cloud user expectations and compare against the current state of cloud offerings, with a focus on contrasting the different requirements from two classes of users -the individual and the enterprise. Through this process, our initial findings indicate that while clouds are ready to support usage scenarios for individual users, there are still rich areas of future research to be explored to enable clouds to support large distributed applications such as those found in enterprises.

This paper examines the performance of simultaneous multithreading (SMT) for network servers using actual hardware, multiple network server applications, and several workloads. Using three versions of the Intel Xeon processor with Hyper-Threading, we perform macroscopic analysis as well as microarchitectural measurements to understand the origins of the performance bottlenecks for SMT processors in these environments. The results of our evaluation suggest that the current SMT support in the Xeon is application and workload sensitive, and may not yield significant benefits for network servers.In general, we find that enabling SMT on real hardware usually produces only slight performance gains, and can sometimes lead to performance loss. In the uniprocessor case, previous studies appear to have neglected the OS overhead in switching from a uniprocessor kernel to an SMT-enabled kernel. The performance loss associated with such support is comparable to the gains provided by SMT. In the 2-way multiprocessor case, the higher number of memory references from SMT often causes the memory system to become the bottleneck, offsetting any processor utilization gains. This effect is compounded by the growing gap between processor speeds and memory latency. In trying to understand the large gains shown by simulation studies, we find that while the general trends for microarchitectural behavior agree with real hardware, differences in sizing assumptions and performance models yield much more optimistic benefits for SMT than we observe.

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