NIMS: MagIC - Artigos em revista nacional com arbitragem científica (Peer-Review articles in national journals)
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- Why Public Health Needs Human-Centred InnovationPublication . Victorino, Guilherme; Mendonça, Joana; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); KargerDespite substantial investment in new policy programmes, pilot interventions, digital health tools, cross-sector partnerships, and transformation initiatives, innovation efforts in public health do not consistently consolidate into sustained system-level improvement. The principal cause of this issue is misalignment within the system: the policy direction, organizational incentives, service and operational design, and citizens’ experiences often appear to be driving in different directions, producing divergent behaviour. Research in health services has shown that many of the innovations developed to improve the delivery of healthcare do not become part of standard practice due to the requirement for organizations to be ready to adopt new technologies, the fit between the organization and the innovation, and the amount of work required to establish new behaviours over a period of time. These shortcomings are not primarily technical but come from the absence of a system-level design competence that connects policy intent, service delivery, and lived experience, which is an absence that we suggest Design Thinking (DT) can address.
- Improving nearshore bathymetry mapping through spatially adaptive machine learning [poster]Publication . Dias, Telmo; NOVA Information Management School (NOVA IMS)Bathymetry plays a pivotal role in ocean science and blue economy applications What is the current state of seafloor mapping? The land-sea interface remains significantly under-surveyed. In situ observations: Adverse sea conditions; Low efficiency. Remote sensing: LiDAR (expensive); Satellite (low resolution) Unmanned Aerial Vehicle (UAV): Efficient and flexible; High spatial resolution; Optically derived bathymetry; Statistical/empirical approach; Machine learning (ML) techniques: GRF and RF.
- Strategic Consensus on a Proposed Vaccination Schedule for Adults in PortugalPublication . Moura, Laura; Hermenegildo, Catarina; Rosa, Maria Teresa; Castro, Mariana de; Franco, Diogo; Diogo, João; Fernandes, Adalberto Campos; Mateus, Céu; Froes, Filipe; George, Francisco; Lopes, Henrique; Information Management Research Center (MagIC) - NOVA Information Management School; Escola Nacional de Saúde Pública (ENSP); Biblioteca Nacional de Portugal, Centro de Estudos Históricos, CELOMIncreasing life expectancy underscores the importance of strategies to enhance population health across the lifespan. Vaccination is a proven intervention to reduce morbidity and mortality; however, adult vaccination coverage remains suboptimal. Between April and May 2024, the NOVA Center for Global Health conducted the “+Longevity Think Tank” in Portugal, engaging 19 Portuguese healthcare experts across three structured in-person sessions. The initiative aimed to explore the epidemiological, economic, and social impact of vaccine-preventable diseases in adults and develop recommendations for lifelong vaccination. Each session was facilitated by the NOVA research team and included evidence-based presentations to be used as background information, group discussions, convergence of ideas, and consensus building on specific recommendations. Consensus was reached using majority agreement, and participants subsequently rated the impact and priority of each recommendation through a structured survey. Think Tank participants proposed 21 recommendations; the one addressed in this manuscript focuses on the development of an adult vaccination schedule tailored to Portugal, with an emphasis on integration into the National Vaccination Program, targeted awareness campaigns, and multisectoral collaboration to improve vaccination coverage and adherence. The public health challenges we face today, particularly regarding vaccination, call for decisive action. The agreement among national experts on this issue reflects a clear intent to move forward with its implementation.
- AgronomIAPublication . Neto, Miguel de Castro; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)A agricultura, enquanto atividade essencial e sob pressão para satisfazer necessidades alimentares crescentes e responder à sustentabilidade do planeta, encontra-se hoje no epicentro de uma transformação estrutural impulsionada também pelo avanço exponencial das tecnologias digitais, pela ubiquidade da recolha de dados e pela capacidade de ação No entanto, sendo inquestionável que atravessamos atualmente uma profunda transformação, fortemente impulsionada pela rápida adoção de tecnologias digitais decorrente da crescente oferta de produtos e serviços a custos cada vez mais acessíveis, não é menos verdade que este movimento, conhecido hoje como agricultura inteligente não é novo.
- Implications for Innovation of Portuguese Added Therapeutic Value Assessment GuidelinesPublication . Costa, João; Silva, Luís Miguel; Victorino, Guilherme; Costa, Leonor Beja da; Pedrosa, Hugo; Maia-Lopes, Susana; André, Sofia; Fonseca, Maria do Carmo; Alves, Eurico Castro; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); FormifarmaIntroduction: The growth of innovation in health technologies continues to pose new challenges for pharmaceutical companies and authorities. It requires increasingly complex assessment processes and greater expertise. Recently, the Portuguese authorities updated the Health Technology Assessment framework with new guidelines for evaluating added therapeutic value. The goal is to compare the differences between the National guidelines for Health Technology Assessment that evaluate Added Therapeutic Value from 2016 and 2022, and subsequently, analyze these identified differences through an expert panel organized functionally as a Think Tank, to discuss the implications of the most recent guidelines compared to the previous ones in assessing health technologies considered innovative. Methodology: This reflection compared the new guidelines with earlier versions and was followed by a Think Tank discussion with an expert panel. Results: The new guidelines introduced relevant changes regarding the population(s), comparators, out-comes, meta-analysis, indirect comparisons, research and data sources and final assessment decision(s). Conclusion: Balancing speed and accuracy in health technology assessments will be key for patients to have faster access to innovative medicines. These recent updates should result in timely and more robust assessments, and ultimately pave the way for the future of health technologies. --- Introdução: A crescente inovação em tecnologias da saúde continua a trazer novos desafios para as em-presas farmacêuticas e autoridades. Esta requer processos de avaliação cada vez mais complexos e uma maior especialização do conhecimento. Recentemente, as autoridades portuguesas atualizaram o processo da Avaliação das Tecnologias de Saúde com novas orientações metodológicas para a avaliação do valor terapêutico. Pretende-se comparar diferenças entre as normas de orientação nacionais para a avaliação fármaco-terapêutica de valor terapêutico acrescentado, de 2016 e de 2022, e analisar, subsequentemente, as diferenças identificadas através de um painel de peritos funcionalmente organizados num Think Tank, com vista a discutir as implicações da norma mais recente face à norma anterior na Avaliação de Tecnologias de Saúde tidas por inovadoras. Metodologia: Esta reflexão resultou da comparação entre as novas orientações metodológicas e as anteriores, seguida da discussão em Think Tank por um painel de peritos. Resultados: As novas orientações introduziram alterações importantes no que diz respeito à população, comparadores, resultados clínicos, meta-análises, comparações indiretas, investigação e fontes de dados e decisão final da avaliação. Conclusão: Equilibrar a rapidez e o rigor nas avaliações de tecnologias de saúde será fundamental para garantir um acesso célere dos doentes às novas terapêuticas. As recentes atualizações deverão resultar em avaliações num tempo útil, mais robustas e, em última instância, preparar o caminho para futuras tecnologias de saúde.
- Envelhecimento demográfico, equidade intergeracional e estabilizadores automáticos nos sistemas de protecção socialPublication . Bravo, Jorge Miguel; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)A necessidade de restabelecer a sustentabilidade económica, financeira e demográfica dos sistemas públicos e privados de protecção social em face de uma alteração estrutural nos equilíbrios demográficos, níveis de crescimento económico e da produtividade insuficientes, mercados de trabalho em rápida transformação, uma pressão crescente sobre as finanças públicas, baixa rentabilidade dos investimentos financeiros, reformas no enquadramento regulatório, e uma crescente desconfiança em relação ao contrato social intergeracional despoletaram, nas últimas duas décadas, um intenso debate e a adopção de reformas de vária ordem na protecção social pública e complementar privada.
- Advancing Vascular SurgeryPublication . Pias, Ana Daniela; Pereira-Macedo, Juliana; Marreiros, Ana; António, Nuno; Rocha-Neves, João; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Introduction: Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclerotic plaque accumulation, significantly contributes to this, posing a risk for cerebrovascular events and ischemic strokes. With carotid stenosis (CS) being a primary concern, accurate diagnosis, clinical staging, and timely surgical interventions, such as carotid endarterectomy (CEA), are crucial. This review explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) in improving diagnosis, risk stratification, and management of CS. Methods: A comprehensive literature review was conducted using PubMed and SCOPUS, focusing on AI and ML applications in diagnosing and managing extracranial CS. English language publications from the past two decades were reviewed, including cross-referenced scientific articles. Results: Recent advancements in AI-enhanced imaging techniques, particularly in deep learning, have significantly improved diagnostic accuracy in identifying carotid plaque vulnerability and symptomatic plaques. Integration of clinical risk factors with AI systems has further enhanced precision. Additionally, ML models have shown promising results in identifying culprit arteries in patients with previous cerebrovascular events. These advancements hold immense potential for improving CS diagnosis and classification, leading to better patient management. Conclusion: Integrating AI and ML into vascular surgery, particularly in managing CS, marks a transformative advancement. These technologies have significantly improved diagnostic accuracy and risk assessment, paving the way for more personalized and safer patient care. Despite clinical validation and data privacy challenges, AI and ML have immense potential for enhancing clinical decision-making in vascular surgery, marking a pivotal phase in the field's evolution.
- Application of Artificial Intelligence in HealthcarePublication . Tavares, Jorge; Information Management Research Center (MagIC) - NOVA Information Management School; Biblioteca Nacional de Portugal, Centro de Estudos Históricos, CELOMUnderstanding artificial intelligence (AI) and its different types is of the utmost importance for the application of this technology in healthcare. Artificial intelligence is a field of knowledge which combines computer science and advanced statistics to support problem-solving. It is divided in two sub-fields: machine learning (ML) and deep learning. The ML concept resides in the ability of using computer algorithms that have the capability to recognize patterns and efficiently learn to train the model to predict, make recommendations or find data patterns. After a sufficient number of repetitions and algorithm adjustments, the machine becomes capable to accurately predict an output. Deep learning is a newer and more complex approach of AI that uses deep neural networks. The neural network starts with an input layer that then progresses to a variable number of hidden layers. Since the algorithm uses multiple layers with deep neural networks, it can successively refine itself, without explicitly programmed directions. It is a fact that, by using deep learning, the models usually achieve higher accuracy compared with ML. Still, when using ML, it is frequently possible to better understand which are the input variables that have more influence on the output variables. In both medical and clinical practices, it is often particularly relevant to understand why an AI technique is suggesting a certain classification or direction for a certain action. Not only in healthcare but also in other fields of knowledge, explain-able AI (also called XAI) is growing its influence. The current European legal regulation, specifically the General Data Protection Regulation (GDPR), requires that automated models provide meaningful information about the rationale on how the algorithm operates. The goal of this article is not to provide an exhaustive view about all existing AI models and explainable AI, but instead to provide a summarized and easy to understand view of what should be considered when implementing AI in healthcare and in clinical practice.
- Qualidade 4.0Publication . Saraiva, Pedro; Cruz-Jesus, Frederico; Coelho, Pedro; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School: The year 2019 is symbolic for Quality in Portugal, as a moment of celebration of a decade of activities of TMQ magazine and the RIQUAL network, and five decades of APQ’s existence, reason why it is important to take advantage of these just moments of celebration to point out and construct paths for the future. From this point of view, it is particularly gratifying to be able to participate in this special issue, and with great pride we associate. The review of some of our many reflections over 25 years on quality, combined with the current monitoring of it at multiple levels, has helped us to clarify standards and prospect an evolution, or even revolution of data-based quality. As always has been the case, but now in the context of the immense diversity and variety of data in which we live and we will live, thus giving origin to what has been dubbed as Quality 4.0 (Zairi, 2018). This is undoubtedly one of the most determining issues in the present and future of quality and quality professionals. Without exhausting the subject at all, we will characterize the contexts from which Quality 4.0 emerges, following the analyses of the corresponding implications in terms of: approaches to the definition and measurement of satisfaction of customers or other stakeholders of organizations; methodologies for analysis and improvement of processes; models of adherence to new technologies and paradigms, ensuring that new opportunities are inclusive and capable of reaching as many people, entities and territories as possible.
- A informação geográfica, a geografia e a internetPublication . Painho, Marco; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)