(1) Background: With the increasing digitalization of healthcare systems, data security and privacy have become crucial issues. In parallel, blockchain technology has gradually proven to be an innovative solution to address this challenge, as its ability to provide an immutable and secure record of transactions offers significant promise for healthcare information management. This systematic review aims to explore the applications of blockchain in health information systems, highlighting its advantages and challenges. (2) Methods: The publications chosen to compose this review were collected from six databases, resulting in the initial identification of 4864 studies. Of these, 73 were selected for in-depth analysis. (3) Results: The main results show that blockchain has been used mainly in electronic health records (63%). Furthermore, it was used in the Internet of Medical Things (8.2%) and for data sharing during the COVID-19 pandemic (6.8%). As advantages, greater security, privacy, and data integrity were identified, while the challenges point to the need for standardization and regulatory issues. (4) Conclusions: Despite the difficulties encountered, blockchain has significant potential to improve healthcare data management. However, more research and continued collaboration between those involved are needed to maximize its benefits.

Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human–Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human–Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human–Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.

O Transtorno de Déficit de Atenção/Hiperatividade (TDAH) é um distúrbio neurobiológico que se caracteriza por um padrão persistente de desatenção/hiperatividade-impulsividade. Em crianças na fase escolar a influência desse transtorno pode implicar em baixo rendimento acadêmico, no entanto, o principal fator é a interferência na vida social, acadêmica e profissional do indivíduo. Assim, este trabalho propõe desenvolver um sistema de análise baseado no sinal de Eletroencefalograma (EEG) para incentivo de desenvolvimento de ferramentas de identificação de sinais sugestivos relacionado ao TDAH em crianças na fase escolar. Para isso, o classificador é baseado na técnica de Threshold utilizando Transformada Wavelet Discreta Redundante para extração de características do sinal. O ambiente de simulação usando foi o sotware MATLAB (2015a). O conjunto de dados analisados foi do banco de dados do IEEE Dataport. Para alcançar o objetivo do trabalho, as faixas de frequência delta e teta dos coeficientes wavelet foram utilizadas como parâmetros para o método threshold, além do mais, os eletrodos analisados foram da região frontal do cérebro. O modelo proposto apresentou um desempenho com sensibilidade de 88,58 % e preditividade positiva de 73,26 % para um conjunto de 40 dados analisados. Dentre os aspectos identificados, verifica-se que a performance do algoritmo foi satisfatória, no entanto, para um volume de dados pequeno.