The NIFty Method; Developing the “Neonatal Intubation Flowchart”

Introduction: When intubation is required during a neonatal emergency, it is imperative there are no delays in collecting equipment. Current practice is for the Intubation Assistant to gather intubation equipment with no memory adjuncts. This study aimed to develop a Neonatal Intubation Flowchart (NIF) and test whether it improves performance in equipment collection. Methodology: Using simulation, a control group (n = 21) was compared to an intervention group (utilizing prototype NIF 1) (n = 24), with both groups consisting of neonatal nurses, midwives, and junior and senior pediatric doctors. The simulation involved a neonate requiring emergency intubation and the participant was tasked to collect the intubation equipment. The outcomes measured were Percentage of Equipment Collected, Time to Collect Equipment, and Amount of Questions Asked. Following post-simulation feedback, the NIF 2 was developed. The same simulation was repeated to an intervention group (n = 28) which utilized NIF 2. Results: The NIF 2 displayed a significantly better Time to Collect Equipment compared to control and NIF 1 (Mean [M] = 64.3-102.0 and 150.8 s, respectively). The NIF 2 had a significantly better Percentage of Equipment Collected compared to the control and NIF 1 (M = 96%-59% and 87%, respectively). The NIF 2 demonstrated significantly less Amount of Questions Asked compared to the control and NIF 1 groups (Median = 0-1 and 2, respectively). Conclusion: The NIF 2 provides a significantly superior method to collect equipment, in a quicker time, with less distracting questions asked, compared to current practice.

A Spatial Grammar Method for the Computational Design Synthesis of Virtual Soft Locomotion Robots

Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. They lack rigid components that provide them with higher flexibility, and they have no joints that need protection from liquids or dirt. However, the hand-design of soft robots is often a lengthy trail-and-error process. This work presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The computational approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization. Here, designs are generated using a spatial grammar to explicitly guide the type of solutions generated and exclude infeasible designs. The soft material simulation method developed and integrated is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the method capabilities. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.