Beyond the Simulation Laboratory

Ponnuru, Srikala; Dong, Chaoyan; Szyld, Demian; Reed, Trent; McGaghie, William C.

doi:10.1097/acm.0000000000000938

Cited by 130 publications

(51 citation statements)

References 44 publications

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“…Simulation-based surgical and procedural skills training offers the ability to ensure patient safety while providing a standardized learning environment that is useful for both trainees and their clinical teachers. While the benefits of simulation-based training can exceed those of traditional clinical education for the acquisition of specific skills [ 1 ], evidence for inferring clinical competence on the basis of simulation training alone remains limited [ 2 , 3 ]. Though commonly used, time and error-based metrics have shown to be insufficient for predicting transfer from the simulation environment to clinical practice [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Cognitive load predicts point-of-care ultrasound simulator performance

2018

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Introduction The ability to maintain good performance with low cognitive load is an important marker of expertise. Incorporating cognitive load measurements in the context of simulation training may help to inform judgements of competence. This exploratory study investigated relationships between demographic markers of expertise, cognitive load measures, and simulator performance in the context of point-of-care ultrasonography. Methods Twenty-nine medical trainees and clinicians at the University of Toronto with a range of clinical ultrasound experience were recruited. Participants answered a demographic questionnaire then used an ultrasound simulator to perform targeted scanning tasks based on clinical vignettes. Participants were scored on their ability to both acquire and interpret ultrasound images. Cognitive load measures included participant self-report, eye-based physiological indices, and behavioural measures. Data were analyzed using a multilevel linear modelling approach, wherein observations were clustered by participants. Results Experienced participants outperformed novice participants on ultrasound image acquisition. Ultrasound image interpretation was comparable between the two groups. Ultrasound image acquisition performance was predicted by level of training, prior ultrasound training, and cognitive load. There was significant convergence between cognitive load measurement techniques. A marginal model of ultrasound image acquisition performance including prior ultrasound training and cognitive load as fixed effects provided the best overall fit for the observed data. Discussion In this proof-of-principle study, the combination of demographic and cognitive load measures provided more sensitive metrics to predict ultrasound simulator performance. Performance assessments which include cognitive load can help differentiate between levels of expertise in simulation environments, and may serve as better predictors of skill transfer to clinical practice. Keywords What this paper addsHigh cognitive load during simulation training is associated with impaired learning and incomplete skill transfer to clinical practice. Measuring cognitive load can thus help to identify individuals whose skills are not fully consolidated. Using cognitive load measures in this way requires a higher standard of validity evidence than currently exists in the literature. Using point-of-care ultrasonography as a model, this study provides a proof-of-principle for how multiple cognitive load measures can be incorporated to strengthen validity claims. Across the expertise continuum, ultrasound simulator performance was most sensitively predicted by a combination of prior ultrasound training and cognitive load.

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Section: Introductionmentioning

confidence: 99%

Cognitive load predicts point-of-care ultrasound simulator performance

2018

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“…To ensure that trainees attain a certain level of competency and to avoid potential pitfalls, a standardized and structured tool for the assessment of LUS skills is required [14, 20, 21]. Future education should be individualized as mastery learning programmes where all trainees undergoing such a programme reach the same level of competency regardless of their individual learning pace and need for supervision [22]. …”

Section: Introductionmentioning

confidence: 99%

Developing and Gathering Validity Evidence for a Simulation-Based Test of Competencies in Lung Ultrasound

et al. 2018

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Background: Clinical lung ultrasound (LUS) is a fast bedside diagnostic tool which can assist clinicians in decisions regarding the treatment and monitoring of patients with respiratory symptoms. LUS training and education differ widely, and is often done in a clinical setting, with potential risks for patients if decisions are made based on the wrong interpretations. No clear guidelines or recommendations for objective and standardized assessment of LUS skills exist, and those that do are often based on a fixed time-frame or an arbitrary number of examinations performed; this does not ensure adequate competencies. Objectives: The study aimed to develop and gather validity evidence for a practical, simulation-based test in LUS. Methods: Nine cases were developed in collaboration with 3D Systems Healthcare, Littleton, CO, USA, representing the most common diagnosis and sonographic findings in patients with respiratory symptoms. Thirty-six participants with different levels of competence in LUS, completed the test. The participants were divided into groups, i.e., novices, intermediates, and experienced, according to their experience with LUS, the number of examinations they had performed, and any research they had conducted. Their answers were used for item analyses. Results: The intraclass correlation coefficient, Cronbachs’ α, was 0.69 summarized, and there was a statistically significant difference (p < 0.001) between the novices and the trained participants (intermediates and experienced). A pass/fail score of 16 points was calculated according to the contrasting-groups method. Conclusion: We developed a test for the assessment of clinical competencies in LUS. The test proved solid validity evidence, and a pass/fail standard without any false-negatives, and only 2 explained false-positives.

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“…The T1 stage is the preclinical aspect of translational research [ 1 , 5 ]. In this stage, researchers identify a link between a basic science concept and human medicine [ 1 , 6 ].…”

Section: Reviewmentioning

confidence: 99%

“…This allows the scientists to assess the efficacy or safety of an intervention [ 1 ]. Depending on the quality and validity of the evidence retrieved from such studies, the project can receive approval to establish an intervention for patient care [ 1 , 5 - 6 ]. This stage involves Phase 2 and 3 clinical trials [ 6 ].…”

Section: Reviewmentioning

confidence: 99%

“…T4 is the ultimate translation stage [ 1 ]. The positive outcomes stemming from the topic, which were studied in the previous stages, are reproduced at a population level to establish its validity and value [ 1 , 4 - 5 ]. This stage assesses the effects of the newly-established intervention and can lead to the beginning of a new project to study and minimize possible weaknesses in the currently developed intervention [ 1 ].…”

Section: Reviewmentioning

confidence: 99%

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The Current Trend of the Translational Research Paradigm

Choi¹,

Tubbs²,

Oskouian³

2018

Cureus

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The translational research paradigm is a process of discovering basic science concepts and applying the knowledge in clinical practice, aiming to improve patient care. The stages involved in the paradigm form a complex network of shared knowledge amongst research collaborators, including patients. This nature of the paradigm allows those involved to work together effectively. However, the translational research paradigm is often overlooked by many scientists, educators, and research institutions. Hence, a large amount of comprehensive and hugely invested research projects fail to make a scientific impact. We aim to outline and describe this paradigm in order to aid in the successful translation of effective research.

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Beyond the Simulation Laboratory

Abstract: Findings suggest health professions education conducted using SBML methodology can improve patient care processes and outcomes. Further research is needed to understand the translational impact of SBML for nontechnical skills, including teamwork, and skill retention.

Cited by 130 publications

References 44 publications

Cognitive load predicts point-of-care ultrasound simulator performance

Cognitive load predicts point-of-care ultrasound simulator performance

Developing and Gathering Validity Evidence for a Simulation-Based Test of Competencies in Lung Ultrasound

The Current Trend of the Translational Research Paradigm

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