High Fidelity Simulation
High Fidelity Simulation - Related Videos
18:10
Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
0 Views •
Cited by 40 •
2011
The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.
13:44
Simulation, Fabrication and Characterization of THz Metamaterial Absorbers
0 Views •
Cited by 6 •
2012
This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.
07:08
A High-Fidelity Porcine Model of Orthotopic Heart Transplantation Following Donation after Circulatory Death
0 Views •
Cited by 2 •
2025
The protocol here describes a high-fidelity porcine model of heart transplantation following donation after circulatory death utilizing ex vivo perfusion of the allograft.
09:46
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
0 Views •
Cited by 48 •
2012
An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.
08:45
Study of Siphon Breaker Experiment and Simulation for a Research Reactor
0 Views •
Cited by 2 •
2017
The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.
FAQs
Programmed scenarios determine how a clinical situation unfolds, while responsive physiological models allow the simulated patient state to change as learners assess, diagnose, communicate, or intervene. This connection makes performance consequential within the exercise: learners must interpret the available response, choose an action, and observe how the scenario develops without exposing an actual patient to risk.
Adjustable conditions let instructors vary the demands placed on individuals or teams during the same educational exercise. They can use those changes to examine decision-making, procedures, and responses to complex care situations, then use the findings to identify safety gaps that may remain hidden during routine practice.
Debriefing converts performance into targeted learning by connecting observed actions with feedback. Instructors can review how learners assessed the situation, communicated, made decisions, and carried out interventions, rather than treating the scenario outcome alone as the assessment. The discussion therefore supports deliberate practice and helps identify specific competence or safety gaps for improvement.
An educational session can progress from a programmed clinical scenario to learner assessment, diagnosis, communication, and intervention, followed by instructor feedback and debriefing. The scenario should present a defined care situation and use responsive patient or system behavior so that actions can be examined. This sequence links practice with evaluation and subsequent improvement.
Depending on the learning goal, a session may use realistic clinical equipment, manikins, or virtual systems, together with programmed scenarios and responsive physiological models. These components recreate selected environments, patient responses, and healthcare workflows. The choice supports practice of assessment, diagnosis, communication, interventions, or team performance within the simulated setting.
Medical educators can apply High Fidelity Simulation for deliberate practice, team training, emergency preparedness, and evaluation of clinical procedures or decision-making. It is especially relevant when learners must respond to complex care situations or when an activity could expose patients to risk. The approach supports both individual competence and coordinated team performance.
Performance observations from a session can show how individuals and teams respond to complex care situations, including their assessment, decisions, communication, and interventions. Instructor feedback and debriefing add context, helping learners recognize competence and safety gaps and guiding subsequent practice or evaluation within the clinical learning process.