Real-time Dashboard Research Articles

Real-Time Dashboard for Identifying Overdose Touchpoints in Indiana.

Indiana state government partnered with researchers to develop a real-time dashboard that brings together multiple data sources to provide state and county-level measures around overdose touchpoints, which are settings that people engaged with prior to fatal overdose. Exploration of the dashboard reveals multiple opportunities for overdose prevention and highlights its use as a tool to monitor strategies in reducing overdose deaths.

Santa María Academy: Shaping the Heroes of Today with Tomorrow's Technology

Santa María Academy has successfully integrated advanced technology into its educational framework to enhance the learning experience of its students, preparing them for future careers in law enforcement and the military. The academy's innovative approach includes personalized coaching, interactive resources, and smart tools that cater to individual student needs. By leveraging artificial intelligence and real-time performance monitoring, Santa María Academy has significantly improved student outcomes, motivation, and engagement. This case study explores the key elements of their success, including the implementation of personalized learning plans, interactive podcasts, a smart syllabus, and a real-time performance dashboard. The academy's model serves as a benchmark for educational institutions aiming to merge traditional values with cutting-edge technology to produce well-rounded and highly skilled graduates.

Designing real-time safety monitoring dashboards for industrial operations: A data-driven approach

Designing real-time safety monitoring dashboards for industrial operations: A data-driven approach

Improving preparation in the emergency trauma room: the development and impact of real-time data transfer and dashboard visualization system.

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival. We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians. The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information. The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

A Real-Time Dashboard Approach to Decision Support: An Exercise in Co-Design.

Laboratory tests play an integral role in the delivery of quality health care. However, evidence indicates variations in diagnostic testing, which can lead to patient safety risks. Electronic decision support systems are often identified as key to reducing diagnostic error. However, such tools are often introduced into a clinical setting with little understanding of clinician workflow and how tools are likely to impact this. This study reports a qualitative co-design methodology and results from the first phase in the design and development of an analytics-driven, dashboard approach to supporting clinician test ordering in the Emergency Department.

Designing a digital-twin based dashboard system for a flexible assembly line

The current dashboard system is limited of coordination, flexibility and scalability. This study proposed a digital-twin based dashboard system. The digital twin components are divided into four, namely the operator, screw machine, industrial robot and automatic optical inspection in a flexible GPU card assembly line. This study designs a protocol enabling digital twins to interact and operate the system collaboratively and flexibly. In addition, this study presents a landscape of digital twin dashboards to demonstrate the relationship between the digital twins and the physical modules, providing a use case for the future digital-twin based dashboard design. The proposed system is implemented in a flexible GPU card assembly line and constructed by using JMobile to build a real-time dashboard system capable of scalability. The proposed dashboard framework for the GPU card assembly line offers several advantages over existing dashboards in terms of productivity, quality, and accident reduction. This study contributes to a scalable real-time dashboard system framework by incorporating human–machine collaboration, laying the foundation for advanced dashboard system development.

Constructing a User-Centric Visual Real-Time Surgical Dashboard.

The existing surgery timetable is very simple, not user-friendly, and has very limited information. This article outlines the creation of a surgical dynamic dashboard for users, using Microsoft Power BI. The dashboard was developed based on in-depth interviews with the surgical team members to determine their requirements. The outcome of dashboard was evaluated by 5-likert scale questionnaire to measure satisfaction of all surgical team members. All team members were very satisfied with the dashboard compared to the existing surgery timetable. In addition, with the function of auto-generated analytic reports through data criteria selection on the dashboard saved time on manually generating reports. This study supports to replace the existing surgery timetable by the user-centric and real time surgical dashboard.

Advancing architectural frameworks for vibration signature classification in rotating machinery

Advancements in data-driven predictive maintenance have significantly improved digital twin applications for rotating machinery, offering robust solutions for smart manufacturing challenges. These improvements are crucial since equipment failures can cause extensive and costly disruptions to both maintenance schedules and operations. As precision and reliability are critical in production processes, undetected fluctuations in operating frequencies can swiftly escalate to complete part failure, leading to prolonged repairs and productivity loss. This study explores an integrated dataflow pipeline, specifically through Siemens’ MindSphere, to enable continuous predictive maintenance and enhance data acquisition and management. Particularly, conditions such as normal operation, mass balance, rotating imbalance, and mechanical looseness are classified using support vector machine (SVM), neural network (NN), and K-Nearest Neighbor (KNN) methods for the purpose of comparing results. Our results highlight the efficacy of ensemble techniques in collecting and diagnosing vibration signatures, thereby enabling proactive maintenance. To classify various failure signatures, we have proposed a framework to interpret time-series and frequency-dependent data for determining failure types. This research exemplifies how merging data-driven methods with digital twin can improve the accuracy and reliability of condition monitoring. Additionally, we introduce a cloud-based architecture for the diagnosis of rotating machinery, utilizing Application Programming Interface (API) configurations, and develop a real-time dashboard for streaming and visualizing classified data, fostering immediate and informed decision-making.

Deploying an asthma dashboard to support quality improvement across a nationally representative sentinel network of 7.6 million people in England

Every year, there are ~100,000 hospital admissions for asthma in the UK, many of which are potentially preventable. Evidence suggests that carefully conceptualised and implemented audit and feedback (A&F) cycles have the potential to improve clinical outcomes for those with chronic conditions. We wanted to investigate the technical feasibility of developing a near-real time asthma dashboard to support A&F interventions for asthma management in primary care. We extracted cross-sectional data on asthma from 756 participating GP practices in the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) database in England comprising 7.6 million registered people. Summary indicators for a GP practice were compared to all participating RCGP RSC practices using practice-level data, for the week 6–12th-Mar-2023. A weekly, automated asthma dashboard with features that can support electronic-A&F cycles that compared key asthma indicators for a GP practice to RCGP RSC could be created (https://tinyurl.com/3ydtrt85): 12-weeks-incidence 0.4% vs 0.4%, annual prevalence 6.1% vs 6.7%, inhaled relievers to preventer 1.2 vs 1.1, self-management plan given 83.4% vs 60.8%, annual reviews 36.8% vs 57.3%, prednisolone prescriptions 2.0% vs 3.2%, influenza vaccination 56.6% vs 55.5%, pneumococcal vaccination ever (aged ≥65 years) 90.2% vs 84.1% and current smokers 14.9% vs 14.8%. Across the RCGP RSC, the rate of hospitalisations was 0.024%; comparative data had to be suppressed for the study practice because of small numbers. We have successfully created an automated near real-time asthma dashboard that can be used to support A&F initiatives to improve asthma care and outcomes in primary care.

Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach.

Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities. We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.

A near real-time interactive dashboard for monitoring and anticipating demands in emergency care in the Île-de-France region (France)

Abstract Objective To allow health professionals to monitor and anticipate demands for emergency care in the Île-de-France region of France. Materials and Methods Data from emergency departments and emergency medical services are automatically processed on a daily basis and visualized through an interactive online dashboard. Forecasting methods are used to provide 7 days predictions. Results The dashboard displays data at regional and departmental levels or for 5 different age categories. It features summary statistics, historical values, predictions, comparisons to previous years, and monitoring of common reasons for care and outcomes. Discussion A large number of health professionals have already requested access to the dashboard (n = 606). Although the quality of data transmitted may vary slightly, the dashboard has already helped improve health situational awareness and anticipation. Conclusions The high access demand to the dashboard demonstrates the operational usefulness of real-time visualization of multisource data coupled with advanced analytics.

ITS 90 - Advancing Patient Safety: Integration of Real-Time Hospital-Acquired Infection Dashboard in Acute Care Quality Improvement

ITS 90 - Advancing Patient Safety: Integration of Real-Time Hospital-Acquired Infection Dashboard in Acute Care Quality Improvement

Enhancing Business Intelligence with Real-Time Sales Dashboards: Integrating SQL Databases and Power BI

Abstract: In today's fast-paced business environment, the ability to make informed decisions swiftly is crucial for maintaining a competitive edge. This project proposes the integration of SQL databases and Power BI to develop a real-time sales dashboard, aimed at transforming how sales data is visualized and analysed within the organization. By harnessing the robust capabilities of SQL databases for structured data management and Power BI for data visualization and analytics, the project seeks to provide comprehensive insights into sales trends, performance metrics, and customer behaviours. The integration will enable the automatic synchronization of sales data from SQL databases to Power BI, facilitating the creation of interactive and dynamic dashboards. These dashboards will not only display key sales indicators and metrics but also allow users to drill down into specific data points for deeper analysis. The real-time nature of the data presented will empower sales managers and executives to make data-driven decisions rapidly, adjust strategies on the fly, and identify opportunities for growth and improvement with unprecedented speed and accuracy.

Enhancing online learning environments through real-time social presence dashboards

Online learning is promoted to play a pivotal role in the future of education, necessitating a focus on student engagement and interactivity as essential components for successful online educational environments. Social presence, as a construct capturing these social aspects, is extensively recognized, and investigated within the realm of education and widely shaping the online learning experience. However, a crucial gap remains in the absence of a real-time measurement of social presence.Drawing from prior research, this study identifies indicators derived from social network analysis as promising metrics for assessing social presence in online learning environments. What remains unclear is the means to effectively convey this information to instructors, enabling them to enhance the educational online experience. To address this gap, we conducted focus groups involving seven experts in online education and pedagogical expertise.Our findings reveal that the development of a social presence dashboard holds immense potential for enriching the online teaching experiences through the utilization of these identified indicators. Furthermore, incorporating additional visualizations and graphical representations with such dashboards can equip online instructors with the necessary insights to foster social interactions and capture real-time social dynamics within online courses.However, the implementation of such dashboards necessitates a careful consideration of important ethical and practical considerations, including informed consent procedures, student monitoring practices, and adherence to ethical standards. This research contributes valuable insights into the ongoing discourse on improving online education by leveraging technology to enhance social presence and pedagogical effectiveness.

Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.

Learning analytics for student homework activities during a long break: Evidence from K-12 education in Japan

Learning Analytics (LA) is an emergent field that aims to better understand students and provide intelligence to learners, teachers, and administrators using learning log data. Although the use of technology in class is increasing in the K-12 sector and tertiary education, cases of effective implementation of LA in secondary schools have rarely been reported. This study offers an example of LA implemented in a junior high Math class during long vacations in Japan. This paper comprises two studies: first, we analyzed 121 students’ answer logs and their exam performance after vacation by the K-means clustering method. We found that students’ progress patterns were categorized into four types of engagement—early, late, high, and low—and the early and high-engagement groups obtained significantly higher scores than the low-engagement group. In the second study, we implemented a real-time dashboard that visualizes students’ progress patterns and gives students insights about their progress during the vacation period. We found that the dashboard significantly increased students’ interactions with the assignment, and the questionnaire survey determined that the LA dashboard motivated students to learn during the long vacation period. Considering the previous studies of LA, we estimate that LA-based interventions enhance students’ self-regulation skills, which is crucial for learning during long vacation periods. Our study offers a novel approach to implementing LA in K-12 education.

Technical Report: Development and validation of continuous monitoring system for calves based on commercially available sensor for humans

In the last decade, development of continuous sensor based monitoring systems for farm animals (Precision Livestock Farming (PLF)) has markedly increased with the aim to automate and improve disease detection. In calves, multiple technologies exist, but most detect only a single parameter, and none are available for continuous heart rate and electrocardiogram (ECG). The objective of this paper was to develop and validate a sensor system, derived from a commercially available sensor for humans, for heart (ECG, heart rate), movement (inertial measurement unit with nine degrees of freedom (IMU9)) and temperature data. Data is sent over Bluetooth Low Energy (BLE) to a gateway equipped with a built-in camera for monitoring individual calves. The gateway transmits the data using the Message Queuing Telemetry Transport (MQTT) protocol to a server for long term storage. The data can also be shown on a real-time dashboard. Results indicate a very high correlation for heart rate compared with a widely used veterinary sensor (Pearson correlation coefficient (r) = 0.98), and a high level of accuracy for the movement data for lying versus standing detection (94.3 %). However, a lower correlation between sensor temperature and rectal temperature measurements was observed (r = 0.56). In conclusion, the adapted Movesense system exhibits the capability to accurately measure heart rate and detect the position (lying – standing) of calves but was insufficient as such for temperature monitoring. This sensor system has potential not only for disease detection, but equally for continuous, non-invasive heart rate monitoring, which can be used as an indicator (besides others) for animal welfare.

Unscheduled Emergency Department Revisits Within 48 Hours of Discharge.

This study aimed to analyze early revisits (within 48 hours of discharge) in an Emergency Department. Among the 178,295 visits, 11,686 were revisits, resulting in a rate of 6.55% (95%CI 6.43-6.67). A total of 1,410 revisits required hospitalization, and 252 were due to preventable errors (17.87%). These errors were mainly related to an inadequate therapeutic plan at discharge (47.22%), an incomplete diagnostic process (29.37%), and misdiagnoses (13.10%). These findings represent a technology-enabled clinical audit tool. Electronic Healthcare Records have the potential to: provide quality metrics of hospital performance, help to keep revisit rates updated (assessment through a real-time dashboard), and improve clinical management (by transparency initiatives about errors, and a supportive learning environment regarding lessons learned).

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes

Implementation of an enhanced fiber optic sensing network for structural integrity monitoring at the Brenner Base Tunnel

The reliable assessment of deformation characteristics along tunnel linings is essential to understand ongoing structural processes during construction and operation. Conventional monitoring techniques may involve limitations, either in the spatial or the temporal resolution and do not deliver the overall deformation behavior along the entire lining. Distributed fiber optic sensing (DFOS) has significantly evolved in recent years to monitor large-scale civil infrastructure, with scientific sensing designs being realized within various research projects. The technology can be advantageous for in-situ tunnel monitoring since the distributed strain and temperature sensing feature delivers a more complete picture of the linings’ structural deformation behavior.This paper introduces the design and realization of an enhanced distributed fiber optic sensing network inside concrete tunnel lining segments, currently being implemented at the Brenner Base Tunnel. The construction is one of the largest civil infrastructure projects world-wide and will be globally the longest underground railway connection with a total length of about 64 km once completed. The designed DFOS system, consisting of more than 35 km sensing cable overall, provides distributed strain and temperature information along numerous tunnel cross-sections, spread over more than 30 km tunnel drive and two different construction lots. After installation, measurements are being continuously performed, autonomously evaluated, and transferred to the ACI online visualization dashboard in real time. It can be shown that fiber optic sensors have considerably developed from research into innovative practice and are capable to extend or even to replace conventional, geotechnical sensors.