Lung simulation & lung physiology with real time artificial intelligence- Neosim

  This article describes a framework that has been developed to monetize the real value of simulation-based training in health care. A significant consideration has been given to the incorporation of the intangible and qualitative benefits, not only the tangible and quantitative benefits of lung simulation -based training in health care. The framework builds from three works: the value measurement methodology (VMM) used by several departments of the US Government, a methodology documented in several books by Dr Jack Phillips to monetize various training approaches, and a traditional return on investment methodology put forth by Frost and Sullivan, and Immersion Medical. All 3 source materials were adapted to create an integrated methodology that can be readily implemented. This article presents details on each of these methods and how they can be integrated and presents a framework that integrates the previous methods. In addition to that, it describes the concept and the application o

  Josef X. Brunner, Neosim AG, Chur, Switzerland Optimizing respiratory therapy in intensive care is a challenge for clinicians at all levels of expertise. The difficulties rise with the degree of illness and along rises the associated morbidity and mortality. Simulation based training is a promising avenue to improve the learning curve and to explore the respiratory management of such difficult and rare cases. In simulation session, patient decompensation can be simulated and therapy options can be practiced without risk for patient - and without risk for clinicians! In contrast to other simulators, autonomous simulators do not need an operator to "fake" vital signs because they create vital signs in real-time and based on physiological parameters that were set to define patient cases. Unfortunately, it is not easy to find values for those parameters in the literature. This white paper is intended to fill the void. Fundamental set of parameters Simulation of respiratory fail