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Simulation plays an integral role in the Canadian healthcare system with applications in quality improvement, systems development, and medical education. High-quality, simulation-based research will ensure its effective use. This study sought to summarize simulation-based research activity and its facilitators and barriers, as well as establish priorities for simulation-based research in Canadian emergency medicine (EM).
Simulation-leads from Canadian departments or divisions of EM associated with a general FRCP-EM training program surveyed and documented active EM simulation-based research at their institutions and identified the perceived facilitators and barriers. Priorities for simulation-based research were generated by simulation-leads via a second survey; these were grouped into themes and finally endorsed by consensus during an in-person meeting of simulation leads. Priority themes were also reviewed by senior simulation educators.
Twenty simulation-leads representing all 14 invited institutions participated in the study between February and May, 2018. Sixty-two active, simulation-based research projects were identified (median per institution = 4.5, IQR 4), as well as six common facilitators and five barriers. Forty-nine priorities for simulation-based research were reported and summarized into eight themes: simulation in competency-based medical education, simulation for inter-professional learning, simulation for summative assessment, simulation for continuing professional development, national curricular development, best practices in simulation-based education, simulation-based education outcomes, and simulation as an investigative methodology.
This study summarized simulation-based research activity in EM in Canada, identified its perceived facilitators and barriers, and built national consensus on priority research themes. This represents the first step in the development of a simulation-based research agenda specific to Canadian EM.
We present a new method for large scale dynamic simulation of colloidal particles with hydrodynamic interactions and Brownian forces, which we call fast Stokesian dynamics (FSD). The approach for modelling the hydrodynamic interactions between particles is based on the Stokesian dynamics (SD) algorithm (J. Fluid Mech., vol. 448, 2001, pp. 115–146), which decomposes the interactions into near-field (short-ranged, pairwise additive and diverging) and far-field (long-ranged many-body) contributions. In FSD, the standard system of linear equations for SD is reformulated using a single saddle point matrix. We show that this reformulation is generalizable to a host of particular simulation methods enabling the self-consistent inclusion of a wide range of constraints, geometries and physics in the SD simulation scheme. Importantly for fast, large scale simulations, we show that the saddle point equation is solved very efficiently by iterative methods for which novel preconditioners are derived. In contrast to existing approaches to accelerating SD algorithms, the FSD algorithm avoids explicit inversion of ill-conditioned hydrodynamic operators without adequate preconditioning, which drastically reduces computation time. Furthermore, the FSD formulation is combined with advanced sampling techniques in order to rapidly generate the stochastic forces required for Brownian motion. Specifically, we adopt the standard approach of decomposing the stochastic forces into near-field and far-field parts. The near-field Brownian force is readily computed using an iterative Krylov subspace method, for which a novel preconditioner is developed, while the far-field Brownian force is efficiently computed by linearly transforming those forces into a fluctuating velocity field, computed easily using the positively split Ewald approach (J. Chem. Phys., vol. 146, 2017, 124116). The resultant effect of this field on the particle motion is determined through solution of a system of linear equations using the same saddle point matrix used for deterministic calculations. Thus, this calculation is also very efficient. Additionally, application of the saddle point formulation to develop high-resolution hydrodynamic models from constrained collections of particles (similar to the immersed boundary method) is demonstrated and the convergence of such models is discussed in detail. Finally, an optimized graphics processing unit implementation of FSD for mono-disperse spherical particles is used to demonstrated performance and accuracy of dynamic simulations of
particles, and an open source plugin for the HOOMD-blue suite of molecular dynamics software is included in the supplementary material.