Inter-professional in-situ simulated team and resuscitation training for patient safety: Description and impact of a programmatic approach

© 2015 Zimmermann et al.Background: Inter-professional teamwork is key for patient safety and team training is an effective strategy to improve patient outcome. In-situ simulation is a relatively new strategy with emerging efficacy, but best practices for the design, delivery and implementation have yet to be evaluated. Our aim is to describe and evaluate the implementation of an inter-professional in-situ simulated team and resuscitation training in a teaching hospital with a programmatic approach. Methods: We designed and implemented a team and resuscitation training program according to Kerns six steps approach for curriculum development. General and specific needs assessments were conducted as independent cross-sectional surveys. Teamwork, technical skills and detection of latent safety threats were defined as specific objectives. Inter-professional in-situ simulation was used as educational strategy. The training was embedded within the workdays of participants and implemented in our highest acuity wards (emergency department, intensive care unit, intermediate care unit). Self-perceived impact and self-efficacy were sampled with an anonymous evaluation questionnaire after every simulated training session. Assessment of team performance was done with the team-based self-assessment tool TeamMonitor applying Van der Vleutens conceptual framework of longitudinal evaluation after experienced real events. Latent safety threats were reported during training sessions and after experienced real events. Results: The general and specific needs assessments clearly identified the problems, revealed specific training needs and assisted with stakeholder engagement. Ninety-five interdisciplinary staff members of the Childrens Hospital participated in 20 in-situ simulated training sessions within 2 years. Participant feedback showed a high effect and acceptance of training with reference to self-perceived impact and self-efficacy. Thirty-five team members experiencing 8 real critical events assessed team performance with TeamMonitor. Team performance assessment with TeamMonitor was feasible and identified specific areas to target future team training sessions. Training sessions as well as experienced real events revealed important latent safety threats that directed system changes. Conclusions: The programmatic approach of Kerns six steps for curriculum development helped to overcome barriers of design, implementation and assessment of an in-situ team and resuscitation training program. This approach may help improve effectiveness and impact of an in-situ simulated training program

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the state-action space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.Comment: 8 pages, 9 figures, AAAI 201

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Perceptual adaptation by normally hearing listeners to a simulated "hole" in hearing

Simulations of cochlear implants have demonstrated that the deleterious effects of a frequency misalignment between analysis bands and characteristic frequencies at basally shifted simulated electrode locations are significantly reduced with training. However, a distortion of frequency-to-place mapping may also arise due to a region of dysfunctional neurons that creates a "hole" in the tonotopic representation. This study simulated a 10 mm hole in the mid-frequency region. Noise-band processors were created with six output bands (three apical and three basal to the hole). The spectral information that would have been represented in the hole was either dropped or reassigned to bands on either side. Such reassignment preserves information but warps the place code, which may in itself impair performance. Normally hearing subjects received three hours of training in two reassignment conditions. Speech recognition improved considerably with training. Scores were much lower in a baseline (untrained) condition where information from the hole region was dropped. A second group of subjects trained in this dropped condition did show some improvement; however, scores after training were significantly lower than in the reassignment conditions. These results are consistent with the view that speech processors should present the most informative frequency range irrespective of frequency misalignment. 0 2006 Acoustical Society of America

Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

In this paper, we describe how to efficiently implement an acoustic room simulator to generate large-scale simulated data for training deep neural networks. Even though Google Room Simulator in [1] was shown to be quite effective in reducing the Word Error Rates (WERs) for far-field applications by generating simulated far-field training sets, it requires a very large number of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used approximately 80 percent of Central Processing Unit (CPU) usage in our CPU + Graphics Processing Unit (GPU) training architecture [2]. In this work, we implement an efficient OverLap Addition (OLA) based filtering using the open-source FFTW3 library. Further, we investigate the effects of the Room Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the tail portions of RIRs whose power is less than 20 dB below the maximum power without sacrificing the speech recognition accuracy. However, we observe that cutting RIR tail more than this threshold harms the speech recognition accuracy for rerecorded test sets. Using these approaches, we were able to reduce CPU usage for the room simulator portion down to 9.69 percent in CPU/GPU training architecture. Profiling result shows that we obtain 22.4 times speed-up on a single machine and 37.3 times speed up on Google's distributed training infrastructure.Comment: Published at INTERSPEECH 2018. (https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2566.html