The Experts below are selected from a list of 40746 Experts worldwide ranked by ideXlab platform
Frazão, Daniela Filipa Rafael - One of the best experts on this subject based on the ideXlab platform.
-
Análise da fiabilidade Humana nos serviços de prestação de cuidados de saúde
Instituto Superior de Engenharia de Lisboa - Escola Superior de Tecnologia da Saúde de Lisboa, 2021Co-Authors: Frazão, Daniela Filipa RafaelAbstract:Trabalho final de mestrado para obtenção do grau de Mestre em Engenharia BiomédicaNa prática da medicina, num sistema complexo como o sistema de saúde, com a ocorrência de erros Humanos, o risco, não só para o doente, mas especialmente para o doente é elevado. Os serviços de prestação de cuidados de saúde devem ter metodologias para minimizar as consequências dos erros Humanos e se possível preveni-los. Ao longo das últimas décadas tem sido normal analisarem-se os erros Humanos, aplicando-se metodologias de Análise da Fiabilidade Humana (Human Reliability Analysis - HRA), com técnicas como a Análise dos Modos de Falha e seus Efeitos (Failure Mode and Effects Analysis - FMEA). No entanto, este tipo de estudos e análises tem-se focado principalmente na área industrial e muito pouco na área da saúde. O estudo da temática da Análise da Fiabilidade Humana bem como a caracterização do sistema de saúde é fundamental para se desenvolver uma variante à FMEA, a hFMEA2020, direcionada para o sistema de saúde, que tenha em conta na Análise dos Modos de Falha e seus Efeitos, os fatores que afetam o desempenho Humano, de forma a modelar o desempenho dos profissionais de saúde, considerando o contexto em que operam, os fatores de modelação do desempenho Humano (Performance Shaping Factors - PSFs) ou fatores de influência do desempenho Humano (Performance Influencing Factors - PIFs). A partir de uma unidade de prestação de cuidados de saúde, como por exemplo o Bloco Operatório de um dos principais Hospitais existentes em Portugal e recorrendo ao uso de um questionário anónimo de avaliação dos PSFs nas várias equipas de profissionais de saúde da unidade, é possível a determinação do impacto dos PSFs que influenciam cada um dos modos de falha considerados permitindo a sua inclusão na metodologia para o cálculo do risco dos erros Humanos, bem como priorização dos modos de falha segundo a classificação do risco para o doente ou para a qualidade dos procedimentos na prestação de cuidados de saúde. Após a aplicação das várias propostas de medidas corretivas às diferentes equipas de profissionais de saúde é assim possível mitigar o risco de erro Humano para o doente e também para a qualidade dos procedimentos numa unidade de prestação de cuidados de saúde. A hFMEA2020 permite, assim, alcançar melhores níveis de segurança e qualidade em serviços de prestação de cuidados de saúde de um sistema de saúde.In the practice of medicine, in a system as complex as the health system, with the occurrence of Human errors, the risk, is not only for the patient, but especially for the patient is high. Health care services should have methodologies to minimize the consequences of Human errors and if possible, prevent them. Over the past few decades, it has been normal to analyze Human errors, applying some Human Reliability Analysis (HRA) methodologies, with techniques such as Failure Mode and Effects Analysis (FMEA). However, these types of studies and analysis have focused mainly in the industrial area and very little on the health area. The study of the Human Reliability Analysis theme as well as the characterization of the health system is fundamental to develop a variant to FMEA, the hFMEA2020, directed to the health system, which takes into account in the analysis of failure modes and their effects, the factors that Affect Human Performance, in order to shape the Performance of health professionals, considering the context in which they operate, the Performance Shaping Factors (PSFs) or Performance Influencing Factors (PIFs). From a health care unit, such as the operating room of one of the main Portuguese Hospitals and using an anonymous questionnaire to access the PSFs in the various teams of health professionals in the unit, it is possible to determine the impact of PSFs that influences each of the failure modes considered, allowing their inclusion in the methodology for calculating the risk of Human errors, as wells as prioritizing the failure modes according to the risk classification for the patient or quality procedures in health care provision. After applying the various proposals for corrective measures to different teams of health professionals, it is thus possible to mitigate the risk of Human error for the patient and also for the quality of the procedures in a health care unit. The hFMEA2020 thus allows to achieve better level of safety and quality in health care services in a health system.N/
Enlie Wang - One of the best experts on this subject based on the ideXlab platform.
-
integrating intelligent driver warning systems effects of multiple alarms and distraction on driver Performance
Transportation Research Board 85th Annual MeetingTransportation Research Board, 2006Co-Authors: Louis Tijerina, Angela Ho, M L Cummings, Dev S Kochhar, Enlie WangAbstract:Driver warning systems are under development to improve safety in driving; yet the integration of these systems in cars may increase the complexity of driving, especially in high workload situations. Critical Human factors issues arise, such as how the interaction between alerting schemes, system reliabilities, and distractions combine to Affect Human Performance and situation awareness. An experiment was conducted to study how a single master alert versus multiple individual alerts of different reliabilities Affect drivers’ responses to different imminent collision situations while distracted. The driver warning systems included auditory alerts for an imminent frontal or rear collisions, or for unintentional left and right lane departures. The different warning systems and reliability factors produced significantly different reaction times and response accuracies. The low reliability system caused accuracy rates to fall more than 40% across the four warning systems. For the master versus individual alarms factor, drivers responded statistically the same to the different collision warnings for both reaction times and accuracy of responses. In a subjective post-experiment assessment, subjects preferred distinct alarms for different driver warning systems, even though their objective Performance showed no difference to the different alerting schemes.
Franco Ulloa, Juan Pablo - One of the best experts on this subject based on the ideXlab platform.
-
Computational complexity of decisions: Quantifying computational hardness and its effects on Human computation
2021Co-Authors: Franco Ulloa, Juan PabloAbstract:© 2021 Juan Pablo Franco UlloaHumans are presented daily with decisions that require solving complex problems. In many cases, solving these problems is computationally hard. This raises a tension between the computational capacity of the agent and the computational requirements of a task. Whilst the underlying invariants of this mechanism remain unclear in cognition, they have been widely studied in computer science. I build on theoretical and empirical work in computational complexity, which characterizes the intrinsic computational hardness of problems. I first present an adaptation of this theoretical framework for the study of Human cognition by introducing a set of metrics of hardness of instances of problems. I do this in a way that is independent of any algorithm or computational model and that can be generalized to other problems. Based on this, I explore empirically, in a set of lab experiments, how these task-independent metrics of hardness Affect Human problem-solving. I do this at two levels of analysis. Firstly, I study how these metrics Affect Human Performance at the behavioral level in three canonical computational problems: the knapsack problem, the traveling salesperson problem and the Boolean satisfiability problem. Secondly, I examine the relation between computational hardness and the neural processes associated with problem-solving, employing ultra-high field functional MRI. I find that the metrics of intrinsic hardness put forward here predict Human Performance and time-on-task across the three computational problems in a similar way. Moreover, I identify the neural correlates of computational hardness in the knapsack task, a complex problem-solving task. I show that this framework can be used for the study of the neural underpinnings of problem-solving by providing a generic definition of cognitive demand. The results of these studies provide support for the conceptual premise that the quantification of intrinsic hardness is fundamental in the development of more refined theories of Human decision-making and its neural underpinnings. Critically, they provide a framework to study how Humans adapt to computational complexity and how intrinsic hardness of tasks Affect the reliability of Human decision-making. This could inform public policy by identifying which decisions over products involve solving problems that require computational resources beyond those available to an agent, and how this Affects decisions
Louis Tijerina - One of the best experts on this subject based on the ideXlab platform.
-
integrating intelligent driver warning systems effects of multiple alarms and distraction on driver Performance
Transportation Research Board 85th Annual MeetingTransportation Research Board, 2006Co-Authors: Louis Tijerina, Angela Ho, M L Cummings, Dev S Kochhar, Enlie WangAbstract:Driver warning systems are under development to improve safety in driving; yet the integration of these systems in cars may increase the complexity of driving, especially in high workload situations. Critical Human factors issues arise, such as how the interaction between alerting schemes, system reliabilities, and distractions combine to Affect Human Performance and situation awareness. An experiment was conducted to study how a single master alert versus multiple individual alerts of different reliabilities Affect drivers’ responses to different imminent collision situations while distracted. The driver warning systems included auditory alerts for an imminent frontal or rear collisions, or for unintentional left and right lane departures. The different warning systems and reliability factors produced significantly different reaction times and response accuracies. The low reliability system caused accuracy rates to fall more than 40% across the four warning systems. For the master versus individual alarms factor, drivers responded statistically the same to the different collision warnings for both reaction times and accuracy of responses. In a subjective post-experiment assessment, subjects preferred distinct alarms for different driver warning systems, even though their objective Performance showed no difference to the different alerting schemes.
Elena Novak - One of the best experts on this subject based on the ideXlab platform.
-
toward a mathematical model of motivation volition and Performance
Computers in Education, 2014Co-Authors: Elena NovakAbstract:The goal of this study was to (1) empirically examine factors that Affect Human Performance in a simulation-based learning environment, employing the framework of the integrative theory of Motivation, Volition, and Performance (MVP) (Keller, 2008a) and (2) develop and statistically evaluate a mathematical MVP model that can be applied to other digital learning environments. The development of a mathematical MVP model can provide empirical support for the elements included in the MVP theory and serve as a tool for designing effective digital learning environments. A regression analysis of motivational, volitional, and Performance data of 62 graduate students that interacted with an online simulation revealed a significant model that explained approximately 70% of the variation in student satisfaction through motivational and volitional processing variables suggested by the MVP theory. Students' interest and curiosity toward the learning environment had the highest positive predicting power on students' satisfaction, while the volition processing variable had the lowest predicting power. Implications for the digital learning environments design and directions for future research are discussed.