Longitudinal analysis of the preterm cortex using multi-modal spectral matching

Extremely preterm birth (less than 32 weeks completed gestation) overlaps with a period of rapid brain growth and development. Investigating longitudinal brain changes over the preterm period in these infants may allow the development of biomarkers for predicting neurological outcome. In this paper we investigate longitudinal changes in cortical thickness,cortical fractional anisotropy and cortical mean diffusivity in a groupwise space obtained using a novel multi-modal spectral matching technique. The novelty of this method consists in its ability to register surfaces with very little shape complexity,like in the case of the early developmental stages of preterm infants,by also taking into account their underlying biology. A multi-modal method also allows us to investigate interdependencies between the parameters. Such tools have great potential in investigating in depth the regions affected by preterm birth and how they relate to each other

Patient Safety in the Cardiac Operating Room: Human Factors and Teamwork: A Scientific Study from the American Heart Association

The cardiac surgical operating room (OR) is a complex environment in which highly trained subspecialists interact with each other using sophisticated equipment to care for patients with severe cardiac disease and significant comorbidities. Thousands of patient lives have been saved or significantly improved with the advent of modern cardiac surgery. Indeed, both mortality and morbidity for coronary artery bypass surgery have decreased during the past decade. Nonetheless, the highly skilled and dedicated personnel in cardiac ORs are human and will make errors. Refined techniques, advanced technologies, and enhanced coordination of care have led to significant improvements in cardiac surgery outcomes

Developing cognitive task analysis and the importance of sociocognitive competence/insight for professional practice

Accelerating the cognitive expertise of professionals is a critical challenge for many organizations. This paper reports a collaborative, longitudinal, academic practitioner project which aimed to elicit, document, and accelerate the cognitive expertise of engineering professionals working with the manufacture and management of petroleum additives. Twenty-five engineering experts were trained by three academic psychologists to use applied cognitive task analysis (ACTA) interview techniques to document the cognition of their expert peers. Results had high face validity for practitioners who elicited hot/ sensory-based cognition, a number of perceptual skills and mental models, highlighting undocumented context specific expertise. We conclude from a peer review of findings, combined with experienced CTA analysts that ACTA technique

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases ‘uniformly’ distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course

Non-technical Skills in Healthcare

AbstractNon-technical Skills (NTS) are a set of generic cognitive and social skills, exhibited by individuals and teams, that support technical skills when performing complex tasks. Typical NTS training topics include performance shaping factors, planning and preparation for complex tasks, situation awareness, perception of risk, decision-making, communication, teamwork and leadership. This chapter provides a framework for understanding these skills in theory and practice, how they interact, and how they have been applied in healthcare, as well as avenues for future research