Addressing Cognitive Load in Training on Electronic Medical Record Systems

Problems with Health Information Technology (HIT) involve human and technical factors with human factor significantly more likely to harm patients. A human factor contributing to these problems is cognitive load – the load imposed on an individual’s working memory. While the literature explored cognitive load in areas of design and use of HIT, little is discussed about it in the area of training – a prerequisite for competent use of HIT. This study subscribed to Cognitive Load Theory (CLT) and explored cognitive load in training on Electronic Medical Record (EMR) systems as a prevalent form of HIT in intensive care environments. Designers, trainers, and trainees of instructional materials for EMR systems training in a neonatal intensive care unit were interviewed in an interpretive case study. The preliminary results indicated cognitive load as a recognised phenomenon in EMR systems training but pointed to a lack of awareness of CLT techniques for managing cognitive load

Computational modelling: moonlighting on the neuroscience and medicine

Computational modelling has emerged as a powerful tool to study the behaviour of complex systems. Computer simulation may lead to a better understanding of the function of biological systems and the pathophysiological mechanisms underlying various diseases. In neuroscience, modelling techniques have provided knowledge about the electrical properties of neurons, activity of ion channels, synaptic function, information processing, and signalling pathways. Using simulations and analysis in network models has resulted in greater understanding of the behaviour of neural networks and dynamics of synaptic connectivity. Moreover, the correlation between the neurobiological mechanisms and a cluster of physiological, cognitive, and behavioural phenomena may be explored by the computational modelling of the neuronal systems. In this context, a significant progress has been made in understanding of the neural network architectures including those with a high degree of connectivity between the units, information processing, performance of complex cognitive tasks, integration of brain signals, as well as the dynamic mechanisms and computations implemented in the brain for making goal-directed choices. Computational models are able to explore the interactions between the brain areas which are involved in predictive processes and high-level skills. In this review, the significance of computational modelling in the study of neural networks, decision-making procedure, nerve growth factor signalling, and endocannabinoid system along with its medical applications have been highlighted.Biomedical Reviews 2013; 24: 25-31

Nursing Practice as Knowledge Work Within a Clinical Microsystem: A Dissertation

Nurses have a key role in keeping patients safe from medical errors because they work at the point of care where most errors occur. Nursing work at the intersection of patients and health care systems requires high levels of cognitive activity to anticipate potential problems and effectively respond to rapidly evolving and potentially harmful situations. The literature describes nursing work at the intersection of patient and health care system as well as barriers to providing safe patient care. However, little is known about the systems knowledge nurses use to negotiate the health care system on their patients’ behalf, or how this systems information is exchanged between nurses. Using the clinical microsystem as the conceptual framework, this qualitative descriptive investigation identified and described: 1) the components of systems knowledge needed by nurses, 2) how systems information is exchanged between nurses, and 3) systems information exchanged between staff nurses and travel nurses. Data were collected from a stratified maximum variation sample of 18 nurse leaders, staff nurses, and travel nurses working within a high-functioning neonatal intensive care nursery within a large academic medical center in New England. Data collection methods included participant observation, document review, individual interviews, and a focus group session. Data were analyzed through constant comparison for emerging themes and patterns. Findings were compared for commonalities and differences within and across groups. Three components of systems knowledge emerged: structural, operational, and relational. Systems information exchange occurred through direct and indirect means. Direct means included formal and informal mechanisms. The formal mechanism of orientation was identified by each participant. Informal mechanisms such as peer teaching, problem solving, and modeling behaviors were identified by participants from each of the three nurse groups. Travel nurses’ descriptions of the common themes focused on individual efficacy. Staff nurses focused on fostering smooth unit functioning. Nurse leaders described common themes from a perspective of unit development. Four overarching domains of systems information were exchanged between staff nurses and travel nurses: practice patterns; staffing patterns and roles; tips, tricks, tidbits, and techniques; and environmental elements. Communication emerged as a common theme across nurse groups and domains of systems information exchanged. These findings have implications for nursing orientation and staff development, continuous improvement at the local level, and curriculum development

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

The role of user requirements research in medical device development

Aims and Objectives: This research aims to suggest a concise framework to help in the better conceptualisation and integration of users in the medical device development (MDD) process. The current economic, political and social climate concerning the matter of healthcare delivery has resulted in the emergence of numerous users and user groups for whom the healthcare system has not previously catered for. These users have created ambiguity for the designers and manufacturers of medical devices as the boundaries between their needs and requirements have blurred, outdating current methods of MDD to meet consumer needs. Research Design and Methodology: The research methodology begins primarily with conducting a literature search on the theories relating to user requirements and medical device development. The paper outlines these findings through initially describing users and user involvement and relating them to medical devices. The cross-disciplinary nature of healthcare influenced the investigation into multiple disciplines including; IT, Ergonomics – particularly participatory research, Psychology and Design. These disciplines expose various methods and processes, which are useful to user requirements research. These methods were analysed for their compatibility, and then used to construct a conceptual framework for user involvement in MDD. Results: The research insinuates the true significance of user involvement and hence resulted in the formation of a conceptual framework to aid user involvement in the MDD process. The framework is produced by the amalgamation of relevant methods examined across the disciplines, in a complimentary fashion. Conclusion: The originality of this research lies in its use of a multidisciplinary approach. Previous research claiming multi-methods has dealt with combining two disciplines or methods at a time i.e. Computer supported cooperative work (CSCW) with participatory research (Scandurra et al, 2008) for the needs analysis of healthcare professionals only. Collaboration across disciplines has also been investigated (Johnson et al, 2005), but this was for the purpose of redesign rather than initial designs. This framework can help medical device designers to fully access all user requirements through more extensive collaboration right at the start. It reduces the risk of high costs involved in device rejection, usually associated with belated recognition of user needs in the design cycle

What do faculties specializing in brain and neural sciences think about, and how do they approach, brain-friendly teaching-learning in Iran?

Objective: to investigate the perspectives and experiences of the faculties specializing in brain and neural sciences regarding brain-friendly teaching-learning in Iran. Methods: 17 faculties from 5 universities were selected by purposive sampling (2018). In-depth semi-structured interviews with directed content analysis were used. Results: 31 sub-subcategories, 10 subcategories, and 4 categories were formed according to the “General teaching model”. “Mentorship” was a newly added category. Conclusions: A neuro-educational approach that consider the roles of the learner’s brain uniqueness, executive function facilitation, and the valence system are important to learning. Such learning can be facilitated through cognitive load considerations, repetition, deep questioning, visualization, feedback, and reflection. The contextualized, problem-oriented, social, multi-sensory, experiential, spaced learning, and brain-friendly evaluation must be considered. Mentorship is important for coaching and emotional facilitation

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care