Felipe Fernandes

Purpose: The performance of health innovation in Brazil directly impacts both population access to healthcare services and the sustainability of the Brazilian National Health System (SUS). Recognizing that innovation follows a multidimensional pathway, this study seeks to answer the following research question: “How can key competencies in the research translation process be integrated to support Brazilian researchers in developing innovative medical devices for the national health system?” The objective was to propose a structured pathway enabling Brazilian researchers to anticipate requirements and accelerate the development of safe, effective, and affordable technologies aimed at improving equitable access to healthcare within the SUS. Methods: This study was based on an exploratory review of the scientific literature, Brazilian legislation, applicable technical standards, and official systems involved in research, development, and innovation processes, as well as in the requirements and studies demanded for the incorporation of technologies into the SUS. Results: The incorporation of medical devices into the SUS follows a formalized process grounded in the best available scientific evidence and in the comparative economic assessment of relative benefits and costs. The translational route for innovative medical devices (TRIMD) is anchored in the alignment between regulatory processes, research phases, and health technology assessment (HTA) studies. Conclusion: The TRIMD is embedded within a normative and institutional environment and provides an adaptable framework to guide research groups. It contributes to the design of institutional strategies and interinstitutional arrangements that enhance innovation performance in Brazil’s medical device sector.

Background/Objectives: Health education plays a vital role in training health professionals and caregivers, supporting both prevention and the promotion of self-care. In this context, technology serves as a valuable ally by enabling continuous and flexible learning. Among the various domains of health education, nutrition stands out as a key element in the management of Amyotrophic Lateral Sclerosis (ALS), helping to prevent malnutrition and enhance patient well-being. Accordingly, this study aimed to evaluate the effectiveness of the teaching and learning processes within a learning pathway focused on food and nutrition in the context of ALS. Methods: This study adopted a longitudinal, quantitative design. The learning pathway, titled “Food and Nutrition in ALS,” consisted of four self-paced and self-instructional Massive Open Online Courses (MOOCs), offered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). Participants included health professionals, caregivers, and patients from all five regions of Brazil. Participants had the autonomy to complete the courses in any order, with no prerequisites for enrollment. Results: Out of 14,263 participants enrolled nationwide, 182 were included in this study after signing the Informed Consent Form. Of these, 142 (78%) completed at least one course and participated in the educational intervention. A significant increase in knowledge was observed, with mean pre-test scores rising from 7.3 (SD = 1.8) to 9.6 (SD = 0.9) on the post-test across all courses (p < 0.001). Conclusions: The self-instructional, technology-mediated continuing education model proved effective in improving participants’ knowledge about nutrition in ALS. Future studies should explore knowledge retention, behavior change, and the impact of such interventions on clinical outcomes, especially in multidisciplinary care settings.

Bed regulation within Brazil’s National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.

(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.

Equal access to health initiatives and services under the principles of universal and comprehensive care remains a challenge in Brazil. The realization of public health policies is further intricate when one examines the health situation of people deprived of liberty. This study showcases the “Prison System: Beyond the Walls” educational pathway, available on the Virtual Learning Environment of the Brazilian National Health System (AVASUS). The action research methodological strategy guided the pathway development, emphasizing dialogic learning. The goal was to address the need for massive training on the topic of prison health, with the model focusing on engagement through spontaneous, non-mandatory participation in the pathway courses. The pathway comprised four modules, whose educational offerings were based on the self-learning model. Students were free to choose which courses to take and in what order, as there was no prerequisite for participating in modules. Hence, students could either take all the courses or only those with which they identify their learning needs, regardless of work demands or personal interests. Structuring the pathway through action research facilitated a massive, cohesive, and continuous training process. This approach expanded knowledge and established meaningful relationships among the related topics and the key players involved: health professionals, prison officers, and people deprived of liberty. Notably, the pathway courses have surpassed the 50,000-enrollment mark, spanning all five regions of Brazil. In this context, this article presents and discusses the development of the “Prison System: Beyond the Walls” pathway, emphasizing the massive improvement of health within Brazil’s prison system and highlighting the results achieved.

Virtual Learning Environments have become innovative tools in health professionals education. Through Massive Open Online Courses, they enable different ways of connecting with knowledge, facilitating study autonomy, interaction, and closer alignment with professional practices and the context of course participants. MOOCs comprise an educational strategy for many fields, including health. As they educate health professionals about a variety of practices, MOOCs play a crucial role in interculturality by enabling professionals to approach cultural diversity in work settings. This study provides a review of the literature investigating the element of interculturality in the production of healthcare-related MOOCs intended for a variety of audiences, including practicing healthcare professionals, healthcare professional trainees, and the general public. Based on a systematic review protocol, we searched for peer-reviewed studies published between 2016 and 2023 in Science Direct, PubMed, ERIC, and Scopus. Fifteen studies were selected for final analysis, which highlighted MOOC development, its underlying processes, and its importance in promoting health and social well-being. MOOCs have (1) provided new approaches to technology-mediated learning in distance health education, (2) aided training, (3) disseminated knowledge, and (4) promoted interculturality. Continuous collaboration and innovation in MOOC development are essential to ensure their effectiveness and relevance in the contemporary educational scenario.

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

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.

The improvement of laboratory diagnosis is a critical step for the reduction of syphilis cases around the world. In this paper, we present the development of an impedance-based method for detecting T. pallidum antigens and antibodies as an auxiliary tool for syphilis laboratory diagnosis. We evaluate the voltammetric signal obtained after incubation in carbon or gold nanoparticle-modified carbon electrodes in the presence or absence of Poly-L-Lysine. Our results indicate that the signal obtained from the electrodes was sufficient to distinguish between infected and non-infected samples immediately (T0′) or 15 min (T15′) after incubation, indicating its potential use as a point-of-care method as a screening strategy.