Advances in HealthCare teaching: a patent mapping about the models simulators or not used

The objective in this article, the realization of a patent mapping on medical education, especially using simulators, since we know that in the teaching of medicine, the development of technical and motor skills in practice is essential, and historically, this training happens with the use of corpses, small animals and later monitoring procedures. With all the advent of technological innovation, financial, cultural and social changes demanded the emergence of new teaching technologies, and through this study, one of the utilities of the technological mapping of the activity can be confirmed through patent documents, which is to obtain the technological evolution of a given subject, in this case: use of models, including simulators in medical education. The temporal evolution of patent documents referring to medical education peaked from 2009 to 2018, and above all, it can be concluded that the latest technologies are models of simulators and there are indications from the United States in exporting this technology to Brazil. recent. From this mapping, it is possible to subsidize technological innovation strategies and assist in the promotion of policies and legislation aimed at stimulating national entrepreneurship and the generation of business opportunities

Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

Global sensitivity analysis aims at quantifying the impact of input variability onto the variation of the response of a computational model. It has been widely applied to deterministic simulators, for which a set of input parameters has a unique corresponding output value. Stochastic simulators, however, have intrinsic randomness due to their use of (pseudo)random numbers, so they give different results when run twice with the same input parameters but non-common random numbers. Due to this random nature, conventional Sobol' indices, used in global sensitivity analysis, can be extended to stochastic simulators in different ways. In this paper, we discuss three possible extensions and focus on those that depend only on the statistical dependence between input and output. This choice ignores the detailed data generating process involving the internal randomness, and can thus be applied to a wider class of problems. We propose to use the generalized lambda model to emulate the response distribution of stochastic simulators. Such a surrogate can be constructed without the need for replications. The proposed method is applied to three examples including two case studies in finance and epidemiology. The results confirm the convergence of the approach for estimating the sensitivity indices even with the presence of strong heteroskedasticity and small signal-to-noise ratio

Virtual reality simulation for the optimization of endovascular procedures : current perspectives

Endovascular technologies are rapidly evolving, often - requiring coordination and cooperation between clinicians and technicians from diverse specialties. These multidisciplinary interactions lead to challenges that are reflected in the high rate of errors occurring during endovascular procedures. Endovascular virtual reality (VR) simulation has evolved from simple benchtop devices to full physic simulators with advanced haptics and dynamic imaging and physiological controls. The latest developments in this field include the use of fully immersive simulated hybrid angiosuites to train whole endovascular teams in crisis resource management and novel technologies that enable practitioners to build VR simulations based on patient-specific anatomy. As our understanding of the skills, both technical and nontechnical, required for optimal endovascular performance improves, the requisite tools for objective assessment of these skills are being developed and will further enable the use of VR simulation in the training and assessment of endovascular interventionalists and their entire teams. Simulation training that allows deliberate practice without danger to patients may be key to bridging the gap between new endovascular technology and improved patient outcomes

Equation-Free Multiscale Computational Analysis of Individual-Based Epidemic Dynamics on Networks

The surveillance, analysis and ultimately the efficient long-term prediction and control of epidemic dynamics appear to be one of the major challenges nowadays. Detailed atomistic mathematical models play an important role towards this aim. In this work it is shown how one can exploit the Equation Free approach and optimization methods such as Simulated Annealing to bridge detailed individual-based epidemic simulation with coarse-grained, systems-level, analysis. The methodology provides a systematic approach for analyzing the parametric behavior of complex/ multi-scale epidemic simulators much more efficiently than simply simulating forward in time. It is shown how steady state and (if required) time-dependent computations, stability computations, as well as continuation and numerical bifurcation analysis can be performed in a straightforward manner. The approach is illustrated through a simple individual-based epidemic model deploying on a random regular connected graph. Using the individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of Simulated Annealing I compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level under a pairwise representation perspective

Simulation-Based Inference for Global Health Decisions

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies. Work in this setting involves solving challenging inference and control problems in individual-based models of ever increasing complexity. Here we discuss recent breakthroughs in machine learning, specifically in simulation-based inference, and explore its potential as a novel venue for model calibration to support the design and evaluation of public health interventions. To further stimulate research, we are developing software interfaces that turn two cornerstone COVID-19 and malaria epidemiology models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria (https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling efficient interpretable Bayesian inference within those simulators

In-situ simulation: A different approach to patient safety through immersive training

Simulation is becoming more and more popular in the field of healthcare education. The main concern for some faculty is knowing how to organise simulation training sessions when there is no simulation centre as they are not yet widely available and their cost is often prohibitive. In medical education, the pedagogic objectives are mainly aimed at improving the quality of care as well as patient safety. To that effect, a mobile training approach whereby simulation-based education is done at the point of care, outside simulation centres, is particularly appropriate. It is usually called “in-situ simulation”. This is an approach that allows training of care providers as a team in their normal working environment. It is particularly useful to observe human factors and train team members in a context that is their real working environment. This immersive training approach can be relatively low cost and enables to identify strengths and weaknesses of a healthcare system. This article reminds readers of the principle of « context specific learning » that is needed for the good implementation of simulation-based education in healthcare while highlighting the advantages, obstacles, and challenges to the development of in-situ simulation in hospitals. The objective is to make clinical simulation accessible to all clinicians for the best interests of the patient.Peer reviewe

mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks

Knowledge of the battery lifetime of the wireless sensor network is important for many situations, such as in evaluation of the location of nodes or the estimation of the connectivity, along time, between devices. However, experimental evaluation is a very time-consuming task. It depends on many factors, such as the use of the radio transceiver or the distance between nodes. Simulations reduce considerably this time. They allow the evaluation of the network behavior before its deployment. This article presents a simulation tool which helps developers to obtain information about battery state. This simulator extends the well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications using an accurate model of the battery consumption and its relation to the radio power transmission. Although an specific indoor scenario is used in testing of simulation, the simulator is not limited to this environment. It is possible to work in outdoor scenarios too. Experimental results validate the proposed model.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules