The HITECH (Health Information Technology for Economic and Clinical Health) Act of 2009 galvanized the universal adoption of electronic health record (EHR) systems to improve the quality, delivery, and coordination of patient care.¹ Initial results demonstrated improvement in population health outcomes and increased transparency.^2-3 Through the HITECH Act’s Meaningful Use (MU) incentives, EHR adoption also promised shorter hospital stays, reduced costs and improved access to healthcare data.⁴ These promises, however, never materialized; studies have demonstrated that EHR adoption causes decreased rates of patients seen per hour, highly variable documentation times, and increased order entry times.⁵

The unintended consequences of the HITECH Act are exacerbated in the emergency department (ED). While the few studies examining practical limitations of ED EHR use are limited to single-site studies with variable, non-validated outcomes, they suggest that MU obstructs ED best customs and practices and is potentially dangerous. ^6-7 For instance, real-time computerized charting is difficult because it requires a bedside computer and Internet access, but installing the required hardware is limited by cost and regulations governing the use and renovation of hospital facilities.⁸ MU requirements also stipulate a transition to computerized physician order entry (CPOE); however, prior studies have demonstrated that CPOE increases order entry times, exacerbating the well-documented issue of ED crowding and boarding.^1,5,9 In emergent situations, CPOE forces physicians to leave the deteriorating patient’s bedside to access a computer before treatment can be rendered.

 

The global nature of the ITER project along with its projected approximately petabyte-per-day data generation presents not only a unique challenge but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks to enable global near-real-time analysis of fusion data. This would broaden the available computational resources for analysis/simulation and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea, and Princeton Plasma Physics Laboratory (PPPL) in Princeton, New Jersey. Streaming large data transfer, with near-real-time movie creation and analysis of the KSTAR electron cyclotron emission imaging data, was performed using the Adaptable Input Output (I/O) System (ADIOS) framework, and comparisons were made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from the National Fusion Research Institute to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.