Studies in cognitive psychology inform us that the recall of information and its application are best when it is taught and rehearsed in environments similar to workplace. The healthcare profession...

Simulation in medical education

A high variety of team interventions aims to improve team performance outcomes. In 2008, we conducted a systematic review to provide an overview of the scientific studies focused on these interventions. However, over the past decade, the literature on team interventions has rapidly evolved. An updated overview is therefore required, and it will focus on all possible team interventions without restrictions to a type of intervention, setting, or research design. To review the literature from the past decade on interventions with the goal of improving team effectiveness within healthcare organizations and identify the “evidence base” levels of the research. Seven major databases were systematically searched for relevant articles published between 2008 and July 2018. Of the original search yield of 6025 studies, 297 studies met the inclusion criteria according to three independent authors and were subsequently included for analysis. The Grading of Recommendations, Assessment, Development, and Evaluation Scale was used to assess the level of empirical evidence. Three types of interventions were distinguished: (1) Training, which is sub-divided into training that is based on predefined principles (i.e. CRM: crew resource management and TeamSTEPPS: Team Strategies and Tools to Enhance Performance and Patient Safety), on a specific method (i.e. simulation), or on general team training. (2) Tools covers tools that structure (i.e. SBAR: Situation, Background, Assessment, and Recommendation, (de)briefing checklists, and rounds), facilitate (through communication technology), or trigger (through monitoring and feedback) teamwork. (3) Organizational (re)design is about (re)designing structures to stimulate team processes and team functioning. (4) A programme is a combination of the previous types. The majority of studies evaluated a training focused on the (acute) hospital care setting. Most of the evaluated interventions focused on improving non-technical skills and provided evidence of improvements. Over the last decade, the number of studies on team interventions has increased exponentially. At the same time, research tends to focus on certain interventions, settings, and/or outcomes. Principle-based training (i.e. CRM and TeamSTEPPS) and simulation-based training seem to provide the greatest opportunities for reaching the improvement goals in team functioning.

/pdf/interventions-to-improve-team-effectiveness-within-health-3yn9dvno5m.pdf

Interventions to improve team effectiveness within health care: a systematic review of the past decade

With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge. We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels. We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved competitive accuracies of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within a 1–3 second window, without needing an observation of entire training trial. This study highlights the potential of deep architectures for efficient online skill assessment in modern surgical training.

Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery

Cardiac anatomy can be challenging to grasp because of its complex three-dimensional nature and remains one of the most challenging topics to teach. In light of some exciting technological advances in the field of virtual reality (VR), we sought to test the viability and the assess efficacy of this computer-generated model for the purposes of teaching cardiac anatomy. Before learning cardiac anatomy, first-year undergraduate medical students participated in an anatomically correct VR simulation of the heart. Students were randomly distributed into control and variable groups. Each student completed a pre-intervention quiz, consisting of 10 multiple choice questions with 5 conventional cardiac anatomy questions and 5 visual-spatial (VS) questions. The control group continued to independent study, whereas the variable group subjects were exposed to a 30-min immersive cardiac VR experience. At the end of the intervention, both the groups underwent a separate post-intervention 10-question quiz. Forty-two students participated in the cardiac VR experiment, separated into 14 control and 28 variable subjects. They scored 50.9% on average on the pre-intervention quiz (SD = 16.5) and 70.2% on the post-intervention quiz (SD = 18.7). Compared to the control group, the students exposed to VR scored 21.4% higher in conventional content (P = 0.004), 26.4% higher in VS content (P < 0.001), and 23.9% higher overall (P < 0.001). VR offers an anatomically correct and immersive VS environment that permits learner to interact three-dimensionally with the heart's anatomy. This study demonstrates the viability and the effectiveness of VR in teaching cardiac anatomy. Clin. Anat. 32:238-243, 2019. © 2018 Wiley Periodicals, Inc.

Virtual reality and cardiac anatomy: Exploring immersive three-dimensional cardiac imaging, a pilot study in undergraduate medical anatomy education.

Background. Fidelity - an intrinsic property of simulation is crucial to simulation design and to educational effectiveness. Yet the term fidelity is inconsistently used, which makes it difficult to draw inferences from current literature and translate research into practice.

Aim. In this article, we attempt to bring some clarity to the term simulation fidelity in healthcare education.

Method. We are opposed to the notion that high-fidelity simulation requires complete and faithful replication of reality, and instead argue for an accurate representation of real-world cues and stimuli. We address a number of issues surrounding the term fidelity and how it is currently used in the literature.

Result. In recognising the limitations of current methods of describing fidelity in the literature, we propose an alternative 3-dimensional framework for fidelity along the axes of the patient, clinical scenario, and healthcare facilities as a means for more precise and practical positioning of current healthcare simulation activities.

Conclusion. All aspects of fidelity significantly hinge on the learners' perceived realism of the context of the learning episode as opposed to any one particular element such as the technology used.

/pdf/redefining-simulation-fidelity-for-healthcare-education-20qkn98nau.pdf

Redefining Simulation Fidelity for Healthcare Education

Background Simulation-based medical education enables knowledge, skills and attitudes to be acquired for all healthcare professionals in a safe, educationally orientated and efficient manner. Procedure-based skills, communication, leadership and team working can be learnt, be measured and have the potential to be used as a mode of certification to become an independent practitioner.

Results Simulation-based training initially began with life-like manikins and now encompasses an entire range of systems, from synthetic models through to high fidelity simulation suites. These models can also be used for training in new technologies, for the application of existing technologies to new environments and in prototype testing. The level of simulation must be appropriate to the learners' needs and can range from focused tuition to mass trauma scenarios. The development of simulation centres is a global phenomenon which should be encouraged, although the facilities should be used within appropriate curricula that are methodologically sound and cost-effective.

Discussion A review of current techniques reveals that simulation can successfully promote the competencies of medical expert, communicator and collaborator. Further work is required to develop the exact role of simulation as a training mechanism for scholarly skills, professionalism, management and health advocacy.

https://qualitysafety.bmj.com/content/qhc/19/Suppl_2/i34.full.pdf

Barry Issenberg

Papers

Training and simulation for patient safety