Lusvin Amado

Preventive Medicine 2.0 represents a paradigm shift in public health, driven by the integration of digital technologies, big data analytics, and artificial intelligence. This study provides a comprehensive review of the role of integrated digital health data systems in improving preventive strategies and community-level health outcomes. A structured analysis of high-impact literature published from 2020 onwards was conducted, focusing on key domains such as data integration, interoperability, artificial intelligence, and digital health implementation. The findings indicate that while foundational components such as electronic health records and big data systems have achieved significant adoption, critical gaps remain in interoperability and advanced analytics. The results demonstrate that higher levels of data integration are associated with substantial improvements in early detection, risk stratification, and targeted interventions. However, persistent barriers—including data fragmentation, infrastructure limitations, workforce gaps, and health data poverty—continue to limit the full potential of digital health systems. The study also highlights regional disparities, particularly in Latin America, where countries such as Mexico, Colombia, and Ecuador show progress but remain in transitional stages of digital transformation. The findings emphasize that the success of Preventive Medicine 2.0 depends not only on technological advancements but also on governance, equity, and system-level integration. Ultimately, this work supports the need for coordinated strategies to strengthen data-driven public health systems and enhance community-level health outcomes.

Atrial fibrillation (AF) is a common health issue, not only in developed countries but also in developing ones. AF can lead to strokes, heart failures, and even death if it is not diagnosed and treated on time, therefore automatic detection of AF is an urgent need, particularly using Internet- connected devices that can alert healthcare services. Detection of AF typically involves the analysis of electrocardiogram (ECG) recordings, where P-waves that characterize the atrial activity are substituted with f-waves of variable amplitude and duration. In this paper, we used the discrete wavelet transform to decompose the ECG signal into detail and approximation coefficients with different time-frequency resolutions. Features extracted from ECG signals, RR interval time series and detail and approximation coefficients were used as inputs to an artificial neural network trained to identify four classes of heart rhythms: normal sinus rhythm (NSR), AF, other rhythms (OR) and noisy signals (NS). By performing a Monte Carlo 10- fold cross-validation of 10 iterations approach, average micro F ₁ scores of 83.64%, 61.61%, 56.88% and 53.88% to classify NSR, AF, OR and NS respectively, and average macro F ₁ of 64.00% were obtained on the publicly available training set of PhysioNet/Computing in Cardiology Challenge 2017. In addition, in a one-vs.-the-rest strategy, i.e., AF-vs-the-rest, averages sensitivity and specificity of 95.70% and 72.39% respectively were achieved to classify AF recordings.

This paper presents the design and implementation of a new training exercise to improve technical skills in the surgeons who perform Single Port Laparoscopic Cholecystectomy (SPLC), a technique that requires active improvements to overcome the lack of triangulation and collision of instruments both within and outside the abdominal cavity. The proposed mechanisms were developed based on peg transfer tests, performed by an expert surgeon in SPLC, with straight forceps and SILSTM access in a pelvictrainer afterwards an unstructured interview was showed to the surgeon. These methodological tools provided the characteristics of the type of movement required by the peg transfer test at the time of performing the training task, which was taken as a reference to propose a new protocol to be implemented. The mentioned structure was tested by an expert surgeon, who performed 5 tasks, within an average running time of 170 seconds. At the end of the tests, a semi-structured interview was carried out again to the surgeon, where the improvement of the technique was tested.

The acquisition of laparoscopic technical skills is constrained by the limited training opportunities and the necessity of having staff physicians on site to provide feedback to the trainees. In addition, the assessment tools used to measure trainees performance are not always sensitive enough to detect different levels of expertise. To address this problem, two Apple Watches worn on inexperienced subjects in laparoscopy were used to record their motion signals (attitude, rotation rate and acceleration) during multiple practices of the peg transfer task in a fundamentals of laparoscopic surgery (FLS) trainer box. This training process was carried out through a massed practice methodology (two hours of training), in which subjects were assessed following the guidelines of the FLS program. Subsequently, a series of metrics were estimated from the acquired motion signals and the Spearman's rank correlation coefficient was used to select the most statistically significant attributes. Then, a classification model based on artificial neural networks was trained, using these attributes as model inputs, to classify trainees according to their level of expertise into three classes: low, intermediate and high. Using this approach, an average classification performance of F ₁ = 86.11% was achieved on a test subset. This suggests that new technologies, such as smartwatches, can be used to complement surgical training by including motion-based metrics to improve current clinical education and offering a new source of feedback through objective assessment.