Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools

VisOpt: a visual interactive optimization tool for P-median problems

This study forms a broader input on ICT and learner integration; it has specific scientific results that not only address access but also go beyond social and economic inequalities that communities face with limitations on social and economic activity that impact and scientific and academic growth of communities. This study aimed to investigate the effects of interactive visualization tools on the mathematical performance of intermediate pupils at Dangguinan Elementary School, Conner, Apayao, Philippines. The problem being studied centers on persistent difficulties in Mathematics performance, particularly learners' struggles in grasping abstract concepts and applying problem-solving skills effectively. Amongst the tools the study investigated included GeoGebra, simulations, and animated presentations could address persistent challenges in Mathematics performance. The research utilized a mixed-methods design, combining both quantitative and qualitative approaches to examine improvements in mathematics learning and explore implementation challenges. The study's mixed-methods approach offers a comprehensive understanding of both the quantitative impact on learning outcomes and the qualitative challenges encountered during implementation. Its findings are crucial for educational stakeholders in developing countries like the Philippines, as they highlight the potential of ICT integration in addressing persistent low math proficiency, while also underscoring the critical need to address infrastructure and digital literacy gaps for successful technology adoption in underserved areas. The study was conducted at Dangguinan Elementary School, Conner, Apayao, during the academic year 2024–2025. Forty-six pupils from Grades 4 to 6 participated in the study. The research applied the DepEd Likert Scale to group learners by their mathematics skills, instead of just using mean scores to assess them. Learners who had 15–17 points out of 20 (equivalent to 71%–85%) received the rating of "Proficient"—signifying they met the expected understanding and skills for their age. By using this method, educators could watch how each learner was improving and decide who might need more help. Rather than presenting average scores, the study used a thorough scale to better show how learning improved when interactive visualizations were used. A pre-test was administered to assess baseline mathematical proficiency. Following this, an instructional intervention was implemented using interactive visualization tools. After the intervention, a post-test was conducted to evaluate performance improvement. Additionally, interviews were carried out to understand the challenges encountered during the intervention. Quantitative results were analyzed using descriptive statistics and a paired sample t-test, while qualitative data were thematically analyzed. The pre-test mean score was 7.6, falling under the "Developing" performance level. Post-test scores improved significantly to a mean of 11.8, aligning with the "Approaching Proficiency" level. Statistical analysis revealed a significant difference between pre-test and post-test results (P = .000). Interviews revealed two major challenges: poor internet connectivity and insufficient orientation in using the tools, both of which affected the learners’ engagement with interactive visualization. To address this, alternative offline versions of instructional materials were provided, ensuring accessibility despite connectivity issues. Additionally, structured training sessions were introduced to familiarize learners with visualization tools before implementation, enhancing their confidence and ability to navigate digital learning environments effectively. The study concludes that interactive visualization tools significantly enhance intermediate pupils' mathematical understanding and performance. The research shows that using these interactive tools greatly improves how intermediate pupils understand and perform in mathematics. Still, overcoming problems with access and training is necessary to use them well. According to the study, upgrading the infrastructure and adding satellite technology for better connectivity, as well as apps that let learners work offline, would help make technology-aided math more effective. In addition, teachers should receive instruction, and learners should participate in orientation to help ICT become an important part of learning mathematics. Schools need support from educational stakeholders towards long-term digital change, helping them select and learn how to use the needed technology.

DA VINCI study: Change in approach to cholesterol management will be needed to reduce the implementation gap between guidelines and clinical practice in Europe

SummaryRecently, several healthcare organizations use decision‐making systems based on electronic health record (EHR) data in order to guarantee patient's safety and improve the quality of healthcare. In essence, the evolutions of Internet of Things (IoT) technologies have been of great help for implementing an integrated and interoperable decision‐making system based on EHR and medical devices (MDs). Those IoT‐based systems allow Clinicians collecting real‐time health data and provide accurate patient's monitoring. Nevertheless, several studies have shown that it is hard to improve the quality of healthcare using the current EHR IoT‐based systems since they do not allow to easily express clinician needs. Interactive visualization tools have been proposed to improve the efficacy and utility of these EHR based systems. However, there is no framework that provides a visual summary of patient data to clinician for planning specific clinical tasks, subsequently evaluating clinician responses, visually exploring EHR data and MDs data, gaining insights, supporting dynamic coordination processes care, and forming and validating hypotheses and risks. This article addresses this problem and introduces SIMCard, an aggregation‐based connected EHR visualization framework for patient monitoring, interpreting and predicting with MDs. The proposed framework aims to synthesize patient's clinical data into a single aggregating model for both EHR and MD conforming to health standard and terminologies. It also allows to link the aggregating model to the relevant medical knowledge in order to provide a connected and dynamic care and preventive plan. Last but not least, it provides an aggregated visualization model capable of displaying graphically a patient's personal data from databases, healthcare devices and sensors to reduce cognitive barriers related to the complexity of medical information and interpretation of health data. To demonstrate the refinement and design of our system and to observe user's actual practice of visualizing and analyzing real‐world dataset, we evaluated our system and compare to existing ones.

The object-oriented theory has been widely used in software design and development and Java programming language is a popular object-oriented programming language for computer science related majors in higher education. With the huge demand for intelligent manufacturing employees in the work market, more and more universities have listed Java programming language in their curriculum for non-CS undergraduates. However, learning object-oriented programming languages, such as Java, is tough even for CS students. This paper presents how to improve non-CS undergraduate students’ learning outcomes of java language by adopting industry standard professional integrated development tool Eclipse and interactive visual educational tool BlueJ. Our Java programming language course is an alternative 8-week course including 16 h lecture and 16 h lab. Forty-seven junior students from mechanical engineering major were divided into two groups, 23 in the control group (CG) and 24 in the experimental group (EG). Students in both groups used Eclipse to write and run programs during the course while BlueJ was adopted to demonstrate the object-oriented concepts during the fifth and sixth weeks only for students in the EG. A pre-test, which was taken by students from both groups, shows no statistical differences in programming knowledge between the two groups. Results from the post-test at the end of the sixth week show students in EG achieved better performance than students in CG. Results of the survey for the EG reveal that students thought BlueJ was helpful for mastering the concepts of object-oriented programming.KeywordsJava programmingLearning outputHigher educationInteractiveVisualBlueJEclipse

Outcome of very high-risk patients treated by Sotrovimab for mild-to-moderate COVID-19 Omicron, a prospective cohort study (the ANRS 0003S COCOPREV study)

Clinical Prioritization of Antispike Monoclonal Antibody Treatment of Mild to Moderate COVID-19

Challenges With Colorectal Cancer Family History Assessment—Motivation to Translate Polygenic Risk Scores Into Practice

The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians' understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses' interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations.

Local area network (LAN) access is the top vector for insider threats and misuses according to the Verizon's Data Breach Investigations Report. Address Resolution Protocol (ARP) is often exploited by the attackers who have gained access to the LAN. It is critical for students to learn how attacks on ARP work and know the countermeasures. In an earlier work, authors developed a hands-on lab to help students learn how an ARP spoofing attack works by asking them to create and send attack packets. To enhance learning further, we present an interactive visualization tool that intuitively shows the effects of the ARP spoofing attack in real-time. The Hacker Graphical User Interface (HGUI) is an interactive visualization tool developed to assist students in learning how ARP Spoofing works. By modeling a controlled ARP Spoofing attack using virtual machines, we give students the ability to alter elements of the attack by interacting with the visualization. This tool was developed using Processing, an open source programming language used in many visual art communities. It runs on virtual machines installed with Kali Linux. This tool animates attack packets, normal packets, and the status of ARP cache in real-time. If students have successfully carried out the ARP spoofing attack, they can see the normal packets being routed to the attacker machine and the victim's ARP cache being poisoned. In this paper, we present the design, implementation, and evaluation of the lab. Tests were conducted to measure the performance of students before and after using this tool. We also gave students surveys after they completed the hands-on lab. The result shows that this tool can significantly enhance students' understanding of the concept of ARP spoofing attacks and motivate them in learning more about cyber security.

Low local recurrence rate without postmastectomy radiation in node-negative breast cancer patients with tumors 5 cm and larger

Regulatory T cells promote corneal endothelial cell survival following transplantation via interleukin-10.

Gonadotropin-releasing hormone agonist to induce final oocyte maturation prevents the development of ovarian hyperstimulation syndrome in high-risk patients and leads to improved clinical outcomes compared with coasting

In Nigeria, the healthcare sector faces big challenges. Limited access to quality services and not enough resources are major issues. Using Artificial Intelligence (AI) could help improve healthcare. But understanding AI predictions is hard, especially in healthcare where transparency is crucial. This article looks at Explainable AI (XAI) to help with this problem in Nigeria. It talks about XAI techniques like feature importance examination, model-agnostic methods (e.g., LIME, SHAP), and interactive visualization tools. These tools can make AI models easier to understand and help with decision-making. A literature review was done to see how XAI can help healthcare in Nigeria. The review included scholarly articles, books, and reports on AI in Nigerian healthcare. We looked at methods from past XAI studies to find common approaches and best practices. XAI offers techniques that make AI models easier to understand in healthcare systems. These techniques include feature importance examination, model-agnostic methods, and interactive visualization tools. Case studies from Nigeria show how XAI is used in areas like disease diagnosis, treatment recommendations, and public health interventions. The findings show the importance of XAI in solving interpretability issues in healthcare AI, especially in places with limited resources like Nigeria. By explaining why AI makes certain predictions, XAI helps healthcare workers make better decisions for Nigerian patients. However, more research is needed to improve XAI techniques for Nigeria’s healthcare system. Policymakers and healthcare leaders should focus on using XAI-enabled systems to drive innovation and improve healthcare outcomes in Nigeria.

Rationale: Existing data processing applications for SP-ICP-ToF-MS, while valuable, have some limitations including restricted multi-sample analysis capabilities, vendor-specific constraints and the lack of efficient visualization approaches. Researchers need flexible, interactive visualization tools that can reveal compositional patterns and enable side-by-side sample comparisons for rapid trend identification and comprehensive data exploration across large datasets. Methodology: we developed IsotopeTrack, an open-source Python-based platform designed specifically for SP-ICP-ToF-MS data processing with cross-platform compatibility (Windows and MacOS). The application implements complete calibration methodologies including transport rate and sensitivity calibrations, three distinct peak detection algorithms (Currie method, Formula C, and compound Poisson log-normal) and element specific parameter optimization. Performance was validated using diverse engineered nanoparticles including TiO₂, CeO₂, metallic alloys (Ni-Fe-Co, Ni-Fe-Cr-Mn, Ni-Fe-Mo), and Au/Ag core-shell particles. The platform features an interactive results canvas with drag and drop capabilities for constructing customized analysis pipelines and it supports multiple file formats. Results: IsotopeTrack successfully analyzed multi-element alloy compositions with a high accuracy. For Ni-Fe-Co alloys, measured mass compositions of ⁶⁰Ni (29.3%), ⁵⁷Fe (55.1%), and ⁵⁹Co (17.7%) closely matched known values of 28% Ni, 55% Fe, and 17% Co. Ultra-uniform gold nanoparticles yielded mean diameters of 52.0±5.0 nm, 29.5±4.4 nm, and 21.1±4.3 nm for nominal 50, 30, and 20 nm particles. The platform generated comprehensive visualizations including elemental correlations, isotopic ratio distributions, ternary diagrams, and composition heatmaps. Processing time was reduced from hours to minutes through parallel processing. The comparison of environmental samples consisting of hundreds of thousands of particles was greatly facilitated. Discussion: IsotopeTrack addressed critical limitations in SP-ICP-ToF-MS data analysis by providing batch processing and interactive visualization tools. Element specific optimization ensured analytical rigor, while dramatically reducing processing time. This open-source framework represents a significant advance in single particle analysis, enabling efficient processing of large datasets essential for nanoparticle characterization in complex systems.