Abstract TP298: Code Stroke Simulation Training Benefits Junior Neurology Residents

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Linda C Wendell ◽  
Bradford B Thompson ◽  
Mahesh Jayaraman ◽  
Muhib Khan ◽  
David Lindquist ◽  
...  

Introduction: Junior neurology residents frequently receive the first call for emergency neurological conditions, including acute ischemic stroke and intracerebral hemorrhage (ICH) (Code Stroke). Code Stroke simulations allow residents to gain experience in the evaluation and treatment of a potential stroke patient without compromising patient care. Simulations also give residents the opportunity to improve their skills through direct observation and feedback. We hypothesized that simulation training would increase junior neurology residents’ confidence, comfort level and preparedness in leading a Code Stroke. Methodology: Ten neurology residents in their first months of training each took turns leading a Code Stroke simulation – either assessment of an ischemic stroke patient for intravenous thrombolytics, coordination of an ischemic stroke patient for embolectomy, or management of an ICH patient. Standardized patients were used in each case. Emergency medicine, vascular neurology and neurointerventional radiology attendings were active participants in the cases and gave feedback. Residents completed a survey before and after the simulation. Results: On a 5-point Likert scale (1 – least true and 5 – most true), confidence in leading a Code Stroke significantly increased from 2.80 to 3.95 (p=0.01) and perceived preparedness for the next Code Stroke significantly improved from 2.80 to 4.30 (p<0.01). Residents reported significantly improved comfort levels in rapidly assessing the National Institutes of Health Stroke Scale score (3.35 vs. 4.25, p=0.03) and rapidly assessing a Code Stroke patient for thrombolytics (3.15 vs. 4.25, p=0.02), making the decision to give thrombolytics (2.80 vs. 4.00, p=0.02) and assessing a patient for embolectomy (3.33 vs. 4.67, p=0.03). There was a perception of enhanced mutli-disciplinary collaboration with emergency medicine providers (3.55 vs. 4.40, p=0.04) and neurointerventional radiologists (3.00 vs. 4.50, p=0.07). Conclusion: Simulation training is a beneficial part of medical education for junior neurology residents and should be considered in addition to traditional didactics and clinical training.

2018 ◽  
Vol 8 (2) ◽  
pp. 116-119 ◽  
Author(s):  
Muhib Khan ◽  
Grayson L. Baird ◽  
Theresa Price ◽  
Tricia Tubergen ◽  
Omran Kaskar ◽  
...  

BackgroundAdvanced practice providers (APPs) are important members of stroke teams. Stroke code simulations offer valuable experience in the evaluation and treatment of stroke patients without compromising patient care. We hypothesized that simulation training would increase APP confidence, comfort level, and preparedness in leading a stroke code similar to neurology residents.MethodsThis is a prospective quasi-experimental, pretest/posttest study. Nine APPs and 9 neurology residents participated in 3 standardized simulated cases to determine need for IV thrombolysis, thrombectomy, and blood pressure management for intracerebral hemorrhage. Emergency medicine physicians and neurologists were preceptors. APPs and residents completed a survey before and after the simulation. Generalized mixed modeling assuming a binomial distribution was used to evaluate change.ResultsOn a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree), confidence in leading a stroke code increased from 2.4 to 4.2 (p < 0.05) among APPs. APPs reported improved comfort level in rapidly assessing a stroke patient for thrombolytics (3.1–4.2; p < 0.05), making the decision to give thrombolytics (2.8 vs 4.2; p < 0.05), and assessing a patient for embolectomy (2.4–4.0; p < 0.05). There was no difference in the improvement observed in all the survey questions as compared to neurology residents.ConclusionSimulation training is a beneficial part of medical education for APPs and should be considered in addition to traditional didactics and clinical training. Further research is needed to determine whether simulation education of APPs results in improved treatment times and outcomes of acute stroke patients.


2019 ◽  
Vol 7 (3) ◽  
pp. 132-135
Author(s):  
Yoshiaki Takahashi ◽  
Kota Sato ◽  
Namiko Matsumoto ◽  
Yuko Kawahara ◽  
Taijun Yunoki ◽  
...  

2021 ◽  
Vol 21 (2) ◽  
pp. 317-320
Author(s):  
Burak Emre Gilik ◽  
Çağdaş Yıldırım ◽  
Talat Cem Özdemir

2014 ◽  
Vol 20 (2) ◽  
pp. 65-68
Author(s):  
Ilay Hilal Kilic ◽  
Ilknur Donmez ◽  
Cihat Uzunkopru ◽  
Ayse Guler ◽  
Hadiye Sirin

2017 ◽  
Vol 139 (2) ◽  
Author(s):  
Shruthi Bezawada ◽  
Qianyu Hu ◽  
Allison Gray ◽  
Timothy Brick ◽  
Conrad Tucker

Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.


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