Search

As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.

PROBLEM Family medicine faculty and residents have observed that continuity clinic is often unsatisfying, attributed to a lack of patient and team continuity and erratic clinic schedules pieced together after the prioritization of hospital service and rotation schedules. APPROACH In 2019, a 3-year Clinic First project, called Clinic as Curriculum (CaC), was launched across the 4 family medicine residencies of the Department of Family Medicine and Community Health, University of Minnesota Medical School. The department began publishing quarterly CaC dashboard data. Each clinic completed a baseline assessment of their performance on the 13 Building Blocks of High-Functioning Primary Care Residency Clinics. Using their baseline data, each clinic identified which block or blocks, in addition to the blocks on continuity of care and resident scheduling, to focus on. The plan is to collaboratively implement the overall and local goals using dashboard data and iterative process improvement over 3 years. OUTCOMES At baseline, clinics functioned quite well with respect to the 13 building blocks, but CaC dashboard data varied across the 4 clinics, with large variation between clinics on how frequently faculty were scheduled in the clinic and the proportion of total clinic visits seen by faculty. Resident continuity rates were low (range: 38%-47%). Level loading (consistent physician availability to meet patient demand) rates ranged from 1 to 11 days a month. Regarding resident schedules, 2 programs are moving from 4-week to 2-week inpatient blocks, and 2 programs are exploring longitudinal scheduling. One clinic will assign faculty and residents to specific clinic days. Two clinics are implementing microteams of 1 faculty and 3-4 residents. NEXT STEPS The authors plan to analyze the dashboard data longitudinally; explore microteams, team continuity, and team scheduling adherence; and develop and implement resident scheduling changes over the next 3 years.

We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted chance of passing, their predicted grade and their online intermediate performance compared with the total cohort. The email with dashboard access, as well as dashboard use, has positive effects on student behaviour in the online environment, but no effects are found on student performance in the final exam of the programming course. However, we do find differential effects by specialisation and student characteristics.

Designing a Learning Analytics Dashboard for Twitter-Facilitated Teaching Considering the increasing use of Twitter for both formal and informal learning, the primary goal of this project is to design a Learning Analytics (LA) dashboard to support instructors’ evaluation of Twitter-based teaching. To achieve this goal, we conducted an online survey involving 54 higher education instructors who have used Twitter in their past teaching. The main purpose was to identify why instructors use Twitter and what types of analytics they would consider valuable. The results of the survey evidence that instructors use Twitter to help students engage with class material, promote discussion, and build learning communities. Instructors expressed interest in analytical tools to help them quantitatively and qualitatively interpret Twitter data. Coupled with an in-depth literature review in this area, we relied on the survey data to prototype a Learning Analytics dashboard (https://dashboard.socialmediadata.org/educhat). Our online dashboard uses a simple, easy-to-read interface in accordance with previous successful dashboard implementations. Graphical visualizations allow instructors to monitor discussion patterns, such as the frequency and times of posting. Visual content breakdowns by number of retweets, original posts, and topics in the form of hashtags and named entities reveal the constituents of students’ posts. The dashboard provides additional analysis in the form of sentiment and subjectivity ranking as a way to contextually aid qualitative assessment. To support instructors’ awareness of class participation, we incorporated two visualizations that highlight the most active users and individuals who are most frequently mentioned in others’ tweets. Instructors can use the dashboard to gauge the participation at the individual- or classroom-level, and further discover what topics and links students discuss and share on Twitter. Three instructors piloted the LA dashboard over a 4-month semester in the Fall of 2017. Following their use, we conducted evaluation interviews with these instructors. Instructor evaluations confirmed that the proposed design is aligned with their pedagogical needs; they favored an intuitive interface that combined summative metrics for the entire class and personalized assessment of individual students. Based on instructors’ feedback, our future work will iteratively refine the design by integrating additional interactive features to adjust time scales of the output, investigate source data, collect data from lists of Twitter users (as opposed to a single hashtag), and further integrate the dashboard with other LMS (Learning Management System) data.

Social media sites are increasingly being adopted to support teaching practice in higher education. Learning Analytics (LA) dashboards can be used to reveal how students engage with course material and others in the class. However, research on the best practices of designing, developing, and evaluating such dashboards to support teaching and learning with social media has been limited. Considering the increasing use of Twitter for both formal and informal learning processes, this paper presents our design process and a LA prototype dashboard developed based on a comprehensive literature review and an online survey among 54 higher education instructors who have used Twitter in their teaching. Keywords : Learning analytics, teaching, dashboards, survey

INTRODUCTIONSARS-CoV-2 testing is one of the options to combat COVID-19 in Kenya. In the first COVID-19 year there was limited tapping of the private sectors potential to scale up testing in Kenya. In April 2020, we initiated a unique public-private partnership (PPP) project in Kisumu County connecting the private sector to centralized testing supported by the ministry of health (MoH), COVID-Dx, to accelerate the local response to COVID-19. Within COVID-Dx, we aimed to demonstrate this PPPs performance as a replicable model for effective public-private collaboration in responding to the COVID-19 pandemic in similar settings. METHODSKEMRI, Department of Health Kisumu County, PharmAccess Foundation, and local faith-based and private healthcare facilities collaborated in COVID-Dx. COVID-Dx was implemented from June 01, 2020, to March 31, 2021, in Kisumu County, Kenya. Trained laboratory technologists in participating healthcare facilities collected nasopharyngeal and oropharyngeal samples from patients meeting the MoH COVID-19 case definition. Samples were tested at the central laboratory in KEMRI via SARS-CoV-2 RT-PCR. Healthcare workers in participating facilities collected data using the digitized MoH COVID-19 Case Identification Form. We shared aggregated results from these data via (semi-) live dashboard to all relevant stakeholders. We did descriptive statistical analyses using Stata 16 to inform project processes. RESULTSNine facilities participated in the project. A total of 4,324 PCR tests for SARS-CoV-2 were done, with 425 positives. We noted differences in positivity rates between the facilities. Healthcare workers were the largest group tested in the project, 1009, representing 43% of the Kisumu healthcare workforce. CONCLUSIONCOVID-Dx can serve as a model for PPPs scale-up testing, especially LMICs, and digitizing the MoH case report form improved reporting efficiency, demonstrating that digital is the way forward. The COVID-Dx PPP has led to another collaboration with Kisumu County aimed towards extending the COVID-Dx model to other counties. SUMMARY BOX O_TEXTBOXWhat is already known?O_LIIn sub-Saharan Africa, COVID-19 responses are mainly rolled out through the public healthcare sector, even though the private sector plays a significant role in health service delivery C_LIO_LIThe challenge is to combine private and public efforts in healthcare delivery in a mutually supportive and collaborative manner, especially during large outbreaks such as COVID-19. C_LI What are the new findings?O_LIThe development of a public-private partnership (COVID-Dx) at the start of a global pandemic - COVID-19 - is challenging, but PPPs are essential for epidemic preparedness. C_LIO_LIThe development of a digital app with a corresponding dashboard, ensures transparency and efficiency. It has shown to enable data-driven decision-making and provides insight into how to prioritize funding streams. C_LI What do the new findings imply?O_LIThis PPP model is replicable and can serve as an example of PPPs for epidemic preparedness in similar settings in LMICs. C_LIO_LIDigital is the way going forward when combatting large outbreaks in the future, especially in LMICs C_LI C_TEXTBOX

With digital gaming's increasing popularity, Educational Digital Games (EDG) are being more commonly used to complement children's early education. Controlled EDG provides educators a way to observe progress. Many existing applications fail to generate automatic data collection to provide reliable information for feedback on academic aspects needed. This paper describes the usefulness of MIDI-AM, a series of EDG, to link a dashboard, including informative outcomes about use and Playability. It explains how rules of fuzzy logic and Natural Language (NL) can provide consumable feedback. The research objective provides a new component in a longitudinal study to identify a more efficient process for developing and implementing a module to refine the dashboard metrics and outcomes of MIDI-AM EDG. The initial platform was redundant and created inconsistent results. Using Artificial Intelligence (AI) generates valuable information to refine the process of generating feedback reports using detailed data interpretations in NL.

In this chapter, you will learn how to show multiple real-time charts in a dashboard. To understand better how to easily consume a web API and develop a real-time dashboard, you will create one sample learning application. In this chapter, you will also learn some advanced concepts of Angular routing and the Forms module in order to quickly build your interactive app with the use of Angular and Highcharts.

Research supervision is one of the important aspect in academic quality assurance and the sustainbility of the science itself. However, there is lack of attention based on research literature and evidence of good practice on research supervision from the context of academic integrity in higher education. This study aims to develop a data-driven decision making strategy in supervisor selection for post-graduate program based using research projects data. Apart of that, the researchers reviewed the indicator of academic integrity in research supervisory from program standards in masters and doctoral degree by Malaysia Qualification Agency (MQA), international recommendation by UNESCO and Islamic principles according to the roles of the supervisor, administrator and student in the context of research supervisory. This study adopted data analytics and visualization technique using cloud-based collaborative platform as a research method for data acqusition, processing and analyzing the data. The researchers acquired the research projects profile data registered in the institutional database in Universiti Malaysia Pahang from Department of Research and Innovation as a case study. We categorized and mapped the research profile according to Malaysian Research and Development Classification System (MRDCS) code. The combined data was been analyzed and visualized to specific online dashboard to indicate the research experience in fraction of years as a metric. The researchers evaluate the characteristics of the dashboard based on the academic integrity indicators from MQA, UNESCO and Islamic principles as our measures. The result shows that there is a potential usefulness of the proposed strategy in assuring academic integrity for supervisor selection in post-graduate programmes. This novel approach has a potential impact on academic integrity in higher education which can be adopted at larger scale by higher education institution in Malaysia.

Sistem Nurse Call, sistem yang digunakan pasien rawat inap untuk memanggil perawat jika membutuhkan bantuan. Sistem ini sangat bermanfaat bagi pasien yang memiliki keterbatasan seperti tidak dapat bicara, tidak dapat berjalan, dan tidak ada keluarga yang berupa telepon atau tombol yang melekat pada tembok. Kelemahan sistem dengan lokasi tombol permanen adalah mobilitas, misalnya ketika pasien sedang ke kamar mandi dan terjadi hal-hal yang tidak diinginkan, pasien akan kesulitan meminta pertolongan. Maka dibutuhkan sistem Nurse Call nirkabel yang dapat dibawa kemanapun pasien pergi sehingga pasien dapat memanggil perawat dimanapun ketika dibutuhkan. Dalam penelitian ini dikembangkan sistem nurse call nirkabel berbasis zigbee menggunakan protokol websocket untuk notifikasi real-time. Tahapan pengembangan yang dilakukan: studi literatur dan observasi lapangan, pengembangan Zigbee push button dan gateway, pengembangan middleware untuk membaca data dari gateway, pengembangan aplikasi dashboard dan pelaporan. Tahapan terakhir monitoring dan evaluasi sistem yang sudah dibuat. Berdasarkan hasil pengujian yang dilakukan sistem dapat berjalan dengan baik ketika jarak antara zigbee push button dan gateway kurang dari 15 meter dan mempunyai waktu respons kurang dari 1634 milisecond.

En la actualidad, en especial en tiempos de pandemia, el comercio electrónico se convierte en la manera predominante de comercializar productos y servicios en el mundo. Un estudio realizado por la Cámara Ecuatoriana de Comercio Electrónico, en el año 2020, demuestra que las compras y ventas a través de canales digitales se han incrementado al menos 15 veces desde el inicio de la pandemia. Por lo tanto, para realizar estudios de mercado las empresas deben buscar nuevas formas de extraer información para luego desarrollar un análisis de la misma y así obtener una ventaja competitiva. La extracción de datos es un proceso complejo y poco escalable, de manera que, esta investigación presenta una metodología para la extracción de información de un sector industrial determinado. La metodología consiste en dos pasos fundamentales, primero se realiza un ranking de las principales fuentes de información disponibles y más utilizadas en un determinado sector de la industria, se consideran varias características y opinión de expertos. Segundo, se propone una plataforma, la cual integra las fuentes de información mejor rankeadas y realiza la extracción de datos. Finalmente, estos datos se presentan en un Dashboard con la disponibilidad de poder descargar y hacer uso en estudios posteriores. Se concluye que las 4 plataformas que mayor beneficio ofrecen para esta investigación son: Google Trends, Facebook, YouTube y Twitter. También existen fuentes de información que al aplicar el análisis propuesto tienen una calificación alta, sin embargo, la extracción de los datos es difícil debido a sus políticas de seguridad.

Recently, automated detection has been a new approach to address the risks posed by prescribing errors. This study focused on prescription errors and utilized real medical data to supplement the Drug Utilization Review (DUR)-based rules, the current prescription error detection method. We developed a new hybrid method through artificial intelligence for prescription error prediction by utilizing actual detection accuracy improvement to reduce ‘warning fatigue’ for doctors and improve medical care quality. This study was conducted in the Department of Pediatrics, targeting children sensitive to drugs to develop a prescription error detection system. Based on the DUR prescription history, 15,281 patient-level observations of children from Konyang University Hospital (KYUH)’s common data model (CDM) and DUR were collected and analyzed retrospectively. Among the CDM data, inspection information was interlocked with DUR and reflected as standard information for model development; this included outpatient prescriptions from January 1 to December 31, 2018. Through consultation with pediatric clinicians, rule definitions and model development were conducted for 35 drugs, with 137,802 normal and 1609 prescription errors. We developed a novel hybrid method of error detection in the form of an advanced rule-based deep neural network (ARDNN), which showed the expected performance (precision: 72.86, recall: 81.01, F1 score: 76.72) and reduced alarm pop-up alert fatigue to below 10%. We also created an ARDNN-based comprehensive dashboard that allows doctors to monitor prescription errors with alarm pop-ups when prescribing medications. These results can advance the existing rule-based model by developing a prescription error detection model using deep learning. This method can improve overall medical efficiency and service quality by reducing doctors’ fatigue.

Introduction Globally, supplemental immunization activities (SIAs) are known to be a major strategy for attainment of the global measles elimination goal of less than one measles case per million population within a geographic area by the year 2020. Adequate planning is critical to the success of a vaccination campaign. To achieve a quality SIA implementation for effective interruption of measles transmission, the World Health Organization introduced the SIA Readiness Assessment Tool, which includes the readiness dashboard. It is a strategic planning tool used to ensure critical activities are completed before SIAs. Nigeria implemented a phased measles SIA in 2017/2018 and used the readiness assessment tool in the planning for the campaign. In this article, we report the use of the readiness assessment dashboard in the 2017/2018 measles SIA, we also reviewed its contributions to the outcome of the campaign looking at the post campaign coverage survey results for the states. Methods We conducted a retrospective review of the readiness assessment dashboard used during the 2017/2018 measles vaccination campaign in Nigeria. The readiness dashboard tool was designed using Microsoft Excel 2016. We reported results in frequencies and proportions using charts and tables. Results The states with 100% readiness a week prior to the campaign scored a post campaign coverage survey result of 84.6 – 96.5% with just one out of the eight states in this category getting a score below 90%. In the same vein, of the eight states that their readiness score at one week to the campaign was below 85%, six had post campaign coverage survey score of less than 90% with the highest score in this category being 92.3%. Some states with good readiness scores also had poor post campaign coverage survey which has been attributed to other factors other than readiness. Conclusion The readiness assessment dashboard for the measles vaccination campaign provided a platform for tracking states readiness. It is our view that a link between readiness assessment and coverage should be examined in future studies.

Data analytics is increasingly important to the operations and strategic growth of higher education institutions. In September 2019, three higher education professional associations issued a rare joint statement calling for the accelerated investment and intensified efforts to develop and deploy data analytics in support of campus decision making. COVID-19 disrupted the momentum generated by the joint statement. However, during the pandemic, many universities recognized data analytics as a critical asset to their response to the crisis. In this case study of Ohio State University's COVID-19 dashboard development process, the authors demonstrate that the principles advocated by the joint statement are exactly the reasons that a successful and nationally recognized dashboard effort was possible. The case study shows that senior leadership support, strategic investment in the right technologies, and close collaboration among campus data analytics professionals are among the key factors of success.

The objective of the research is to develop a methodology to analyze a set of data extracted from a Learning Management System (LMS), in order to implement a Dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching-learning processes. The methodology used consisted of the analysis of 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analyzed were: number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show the data analysis in the KNIME data mining analysis platform, the model was implemented in five phases: Requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customized Dashboard for teachers to identify observable behavioral patterns that allow them to make decisions to improve the teaching-learning processes of students. Keywords: Analytics, dashboard, KNIME Learning, personalized, teaching.

El sector público en el Perú tiene el desafío de ser más eficiente en la gestión de los procesos de contrataciones, para lo cual necesita una organización ágil que gestione de manera eficaz los procesos y maximice el valor de los recursos asignados al momento de realizar las contrataciones públicas, las organizaciones tendrán éxito cuando se logre mejores condiciones de precio y calidad, de acuerdo a la finalidad pública. La presente investigación tuvo como objetivo proponer la aplicación de estrategias Kaizen, para optimizar la gestión del proceso de contrataciones para montos iguales o inferiores a los ocho (8) UIT en una entidad pública del Perú. La investigación se desarrolló bajo un enfoque mixto con diseño explicativo secuencial, la cual incluyó instrumentos de investigación estructurados y no estructurados, los mismos que fueron elaborados para la recopilación de la información. Se aplicó encuestas (30 personas) y entrevistas (3 personas) donde participaron usuarios del área de logística. Entre los principales hallazgos se identificaron ineficiencias y carencia en la aplicación de las normas, procedimientos y control de los procesos de contrataciones para los bienes y servicios de montos inferiores a ocho Unidades Impositivas Tributarias. Se concluye que es imperioso aplicar mejoras a los procesos de contrataciones, utilizando la herramienta del ciclo de Deming PHVA, el diseño de los diagramas PEPSU, ASME y la implementación de dashboard para el control de requerimientos, hasta que se logre mayor cantidad de bienes y servicios contratados bajo condiciones idóneas de precio y calidad en las entidades públicas.

With the emergence of knowledge-based economies, the performance evaluation of state budget in the field of Science, Technology, and Innovation (STI) through a comprehensive approach has become ess...

Purpose An analysis to determine the frequency of medication administration timing variances for specific therapeutic classes of high-risk medications using data extracted from a health-system clinical data warehouse (CDW) is presented. Methods This multicenter retrospective, observational analysis of medication administration data from 14 hospitals over 1 year was conducted using a large enterprise health-system CDW. The primary objective was to assess medication administration timing variance for focused therapeutic classes using medication orders and electronic medication administration records data extracted from the electronic health record (EHR). Administration timing variance patterns between standard hospital staffing shifts, within therapeutic drug classes, and for as-needed (PRN) medications were also studied. To assess medication administration timing variance, calculated variables were created for time intervals of 30-59, 60-120, and greater than 120 minutes. Scheduled medications were assessed for delayed administration and PRN medications for early administration. Results A total of 5,690,770 medication administrations (3,418,275 scheduled and 2,272,495 PRN) were included in the normalized data set. Scheduled medications were frequently subject to delays of ≥60 minutes (15% of administrations, n = 275,257) when scheduled for administration between 9-10 AM and between 9-10 PM. By therapeutic drug class, scheduled administrations of insulins, heparin products, and platelet aggregation inhibitors were the most commonly delayed. For PRN medications, medications in the anticoagulant and antiplatelet agent class (most commonly heparin flushes and line-management preparations) were most likely to be administered early, defined as more than 60 minutes from the scheduled time of first administration. Conclusion The findings of this study assist in understanding patterns of delayed medication administration. Medication class, time of day of scheduled administration, and frequency were factors that influenced medication administration timing variance.

Within the natural products field there is an increasing emphasis on the study of compounds from microbial sources. This has been fuelled by interest in the central role that microorganisms play in mediating both interspecies interactions and host-microbe relationships. To support the study of natural products chemistry produced by microorganisms we released the Natural Products Atlas, a database of known microbial natural products structures, in 2019. This paper reports the release of a new version of the database which includes a full RESTful application programming interface (API), a new website framework, and an expanded database that includes 8128 new compounds, bringing the total to 32 552. In addition to these structural and content changes we have added full taxonomic descriptions for all microbial taxa and have added chemical ontology terms from both NP Classifier and ClassyFire. We have also performed manual curation to review all entries with incomplete configurational assignments and have integrated data from external resources, including CyanoMetDB. Finally, we have improved the user experience by updating the Overview dashboard and creating a dashboard for taxonomic origin. The database can be accessed via the new interactive website at https://www.npatlas.org.

The use of clinical dashboards has expanded significantly in healthcare in recent years in a variety of settings. The ability to analyze data related to quality metrics in one screen is highly desirable for cardiac anesthesiologists, as they have considerable influence on important clinical outcomes. Building a robust quality program within cardiac anesthesia relies on consistent access and review of quality outcome measures, process measures, and operational measures through a clinical dashboard. Signals and trends in these measures may be compared to other cardiac surgical programs to analyze gaps and areas for quality improvement efforts. In this article, the authors describe how they designed a clinical cardiac anesthesia dashboard for quality efforts at their institution.

The bacterial metadatabase BacDive (https://bacdive.dsmz.de) has developed into a leading database for standardized prokaryotic data on strain level. With its current release (07/2021) the database offers information for 82 892 bacterial and archaeal strains covering taxonomy, morphology, cultivation, metabolism, origin, and sequence information within 1048 data fields. By integrating high-quality data from additional culture collections as well as detailed information from species descriptions, the amount of data provided has increased by 30% over the past three years. A newly developed query builder tool in the advanced search now allows complex database queries. Thereby bacterial strains can be systematically searched based on combinations of their attributes, e.g. growth and metabolic features for biotechnological applications or to identify gaps in the present knowledge about bacteria. A new interactive dashboard provides a statistic overview over the most important data fields. Additional new features are improved genomic sequence data, integrated NCBI TaxIDs and links to BacMedia, the new sister database on cultivation media. To improve the findability and interpretation of data through search engines, data in BacDive are annotated with bioschemas.org terms.

Background Quality improvement (QI) methods have been widely adopted in health care. Although theoretical frameworks and models for organizing successful QI programs have been described, few reports have provided practical examples to link existing QI theory to building a unit-based QI program. The purpose of this report is to describe the authors’ experience in building QI infrastructure in a large neonatal ICU (NICU). Methods A unit-based QI program was developed with the goal of fostering the growth of high-functioning QI teams. This program was based on six pillars: shared vision for QI, QI team capacity, QI team capability, actionable data for improvement, culture of improvement, and QI team integration with external collaboratives. Multiple interventions were developed, including a QI dashboard to align NICU metrics with unit and hospital quality goals, formal training for QI leaders, QI coaches imbedded in project teams, a day-long QI educational workshop to introduce QI methodology to unit staff, and a secure, Web-based QI data infrastructure. Results Over a five-year period, this QI infrastructure brought organization and support for individual QI project teams and improved patient outcomes in the unit. Two case studies are presented, describing teams that used support from the QI infrastructure. The Infection Prevention team reduced central line–associated bloodstream infections from 0.89 to 0.36 infections per 1,000 central line–days. The Nutrition team decreased the percentage of very low birth weight infants discharged with weights less than the 10th percentile from 51% to 40%. Conclusion The clinicians provide a pragmatic example of incorporating QI organizational and contextual theory into practice to support the development of high-functioning QI teams and build a unit-based QI program.

BackgroundContamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature could help to discover and mitigate contamination.ResultsWe propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyses contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for non-specialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles.ConclusionGRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open-source and available at:https://gitlab.com/dacs-hpi/grimer.

Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data in a very easily understandable format. It also allows non-technical users to create customized dashboards. The purpose of this paper is to visualize and analyse the employee attrition rate using the Tableau visualization tool by considering various important factors that play crucial role in affecting the attrition rate. In order to visualize and predict the attrition rate of employees of an organization, we proposed an intelligent, flexible and effective system that helps the managers to identify the valuable employee and try to retain them. The attrition rate can be analysed based on various factors such as job roles, years since last promotion, gender and number of companies worked. Keywords: Employee Attrition, Tableau, Attrition Analysis, Visualization, Employee Turnover, Dashboards

Purpose: Drug shortages are anticipated to worsen with time as the Coronavirus Disease 2019 (COVID-19) pandemic continues. The aim of this study is to track drug shortages within Illinois and identify causes and trends to this time-sensitive problem. Methods: In order to communicate between health systems within the state, the Illinois Council of Health-System Pharmacists (ICHP) developed a medication shortage dashboard, which collects information from health systems in the state on current drug shortages. Classes of medications inquired about included: anti-infectives, neuromuscular blocking agents (NMBAs), sedatives/analgesics, vasopressors, and “others.” Data was gathered from 6 different medication shortage dashboards, ranging from May 20, 2020 to June 22, 2020 and was used to track drug shortages within Illinois. Additionally, this data was analyzed in conjunction with the number of hospital beds utilized by COVID-19 patients at the time. Results: Illinois’s medication shortage dashboard tracked the supply level of 42 medications used in the treatment of patient’s hospitalized with COVID-19. Data from an average of 75 health systems was analyzed each week (average response rate: 52%). For each medication, health systems identified if they had ample supply, mild shortage, moderate shortage, or critical shortage. Overall the trends of these medications positively correlated with the number of hospital beds utilized by COVID-19 patients in Illinois ( r2 = 0.7). Conclusion: The data from this study supports the conclusion that increased hospital bed utilization by COVID-19 patients is correlated with increased drug shortages of medications used in the treatment of COVID-19. It is imperative that health systems take appropriate action to prevent and manage drug shortages.

Teacher dashboards in mathematics classrooms tend to provide teachers with information on student performance that are often linked to classroom management systems, online course systems, or peer-tutoring software. Teacher dashboards also tend to emphasize features that support teachers using a “transition” or “direct instruction” model. In our approach, we iteratively designed, developed, tested, and refined a teacher dashboard that is linked to a student digital collaborative environment with an embedded problem-based mathematics curriculum. In this study, we investigate teacher dashboard features that support teacher enactment of problem-based mathematics curriculum embedded in a digital collaborative platform. We report on design principles that guided the development of three teacher dashboard features: (1) monitoring evidence of student thinking in real-time or after class, (2) accessing workspace for whole-class discussions of the problem, and (3) creating and sending “just-in-time” supports. The pedagogical advantages and challenges teachers face throughout the iterative development process are also discussed. Evidence from observational data and teacher interviews suggests that the organic synergism generated from the student and teacher digital platform offers several ways that teachers are provided with new and timely information from teacher dashboards that supports problem-based mathematics teaching.

Rapid increase in stroke incidence coupled with the high cost and limited availability of healthcare professionals has made stroke rehabilitation process inaccessible to many patients in developing countries. Stroke rehabilitation exercises are of critical importance in ensuring quick and lasting recovery. A machine learning-based system that can automatically identify the completion status of upper limb rehabilitation exercises is presented in this study. Proposed system can detect completion status of twelve hand and arm rehabilitation exercises: six hand, four forearm and two shoulder exercises. Accelerometers record the movement of the upper limb part being exercised in the form of data sequences. Six curve-fitting-based machine learning model types are trained and evaluated for their effectiveness in modelling these sequences. This resulted in a total of 216 models (i.e. 12 exercises × 3 axes of motion × 6 model types) being evaluated on three performance measures: SSE, RMSE and R2. Best-performing model for each exercise is plugged into the proposed exercise completion status identification algorithm. System effectiveness is validated using four performance measures: accuracy, precision, recall and F1-score. Proposed system is demonstrated to be capable of detecting the exercise completion status with up to 90% accuracy. The proposed system is connected to cloud via an IoT interface with a dashboard type visualization system that allows for the healthcare professionals to remotely monitor the progress for each patient as well as carry out offline analysis. A game interaction interface is also provided to enable interaction with video games, helping in higher patient engagement while performing exercises.

Educational technologies, such as teacher dashboards, are being developed to support teachers’ instruction and students’ learning. Specifically, dashboards support teachers in providing the just-in-time instruction needed by students in complex contexts such as science inquiry. In this study, we used the Inq-Blotter teacher-alerting dashboard to investigate whether teacher support elicited by the technology influenced students’ inquiry performance in a science intelligent tutoring system, Inq-ITS. Results indicated that students’ inquiry improved after receiving teachers’ help, elicited by the Inq-Blotter alerts. This inquiry improvement was significantly greater than for matched students who did not receive help from the teacher in response to alerts. Epistemic network analyses were then used to investigate the patterns in the discursive supports provided to students by teachers. These analyses revealed significant differences in the types of support that fostered (versus did not foster) student improvement; differences across teachers were also found. Overall, this study used innovative tools and analyses to understand how teachers use this technological genre of alerting dashboards to dynamically support students in science inquiry.

Equitable distribution of vaccines is necessary to ensure those at highest risk of illness are protected from COVID-19 (coronavirus disease 2019). Unfortunately, there is significant evidence that vaccines have not been reaching the most vulnerable. At our large hospital system, we created interactive online tools to measure and visualize equitability of vaccine administrations and to help stakeholders identify populations at highest risk within state-designated eligible vaccine groups. Using race, ethnicity, gender, and social vulnerability, we are able to measure and reflect our vaccine administration performance against the communities that we serve. With our visualization tools, stakeholders have been able to target interventions to improve equity in vaccine administrations, including improvements in race, ethnicity, and social vulnerability. We plan to use the data elements incorporated in our electronic health record and data warehouse due to the COVID-19 pandemic to guide further population health efforts at decreasing disparities.

The multidisciplinary team is the primary means for delivery of complex cancer care in the United States. Considerable variability exists in how multidisciplinary teams operate across the landscape of oncology, including variation in represented specialties and specifics of the shared medical decision-making process. Here, we describe operations of a multidisciplinary clinic focused on the management of nonmelanoma skin cancer, formed as a joint effort between departments at the Massachusetts General Hospital and Massachusetts Eye and Ear Infirmary. We describe deployment of a flexible Web-based operational tool created on the Research Electronic Data Capture platform to facilitate provider coordination and tracking and visualization of the patient census, offering a new perspective on optimization of the multidisciplinary workflow. To help promote further discussion, we have made the data dictionary for the operational tool and R code for the accompanying data visualization dashboard freely available online for download and customization.

Computer vision has the potential to play a significant role in capacity building for classroom instructors via automated feedback. This paper describes the implementation of an automated sensing and feedback system, TEACHActive. The results of this paper can enable other campuses to replicate a similar system using open-source software and consumer-grade hardware. Some of the challenges discussed include faculty recruitment, IRB procedures, camera-based classroom footage privacy, hardware setup, software setup, and IT support. The design and implementation of the TEACHActive system is being carried out at Iowa State University and is being tested with faculty in classrooms pilots. Preliminary interviews with instructors show a desire to include more active learning methods in their classrooms and overall interest in a system that can perform automated feedback. The primary results of this paper include lessons learned from the institutional implementation process.

Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.

Nowadays, security and safety are big concerns in this modern and cyberwar era. Many countries invest some safety infrastructure to ensure their inhabitants for keeping their lives safely. Indonesia is the country with many problems because of urbanization and other challenges. This problem should be solved with smart city solution and it must be able to face the challenge of ensuring the safety and improving the quality of life regarding network centric warfare era. This problem also should be tackled with CCTV analytics with the ability to implement an automatic weapon detection system. It also can provide the early detection of potentially violent situations that is of paramount importance for citizens security. This paper is using deep Learning techniques based on Convolutional Neural Networks (CNN) can be trained to detect this type of object with YOLOv4 model and it proposes to implement CCTV analytics as a platform to process real-time data for monitoring weapon detection into knowledge displayed in a dashboard with accuracy 0.89, precision 0.82, recall 0.96 dan F1 Score 0.90 result on weapon detection with a real time speed of processing with NVIDIA 2080 Ti around of 35 FPS. It will send an early warning notification if the system detects the weapon detection such as a knife, gun etc.

Purpose This paper aims to discover why such a public partnership project had been successful with a non-profit third-party alliance such as a smart city consortium (SCC) promoting smart city development. Design/methodology/approach This descriptive case study is primarily based on analysing data collected from various texts, public statements, media interviews and three semi-structured interviews with key members involved in the Covid-19 dashboard project. Findings The data and analysis reviews that both interpersonal and interorganisational trust, dedication and proactiveness of the leaders at SCC were major contributing factors to why SCC was able to partner with the Hong Kong Government in the Covid-19 dashboard in the first place and that the success was also a direct outcome of effective mass collaborative knowledge management activities. Research limitations/implications The research in leadership attributes and activities in the non-profit alliance has been few and this collaborative partnership between the alliance and the government is an example of the importance of further research in smart city leadership. Practical implications In deploying projects for mass collaboration and knowledge sharing in smart city development (which is multi-disciplinary in nature). there are still many new and evolving organisational practices and leadership matters that many business leaders and city managers can learn from. Social implications Smart city development projects involve the notion of sharing data in an open environment enabled by software and mediating tools. Successful projects such as this Hong Kong Covid-19 dashboard which serves a diverse audience can further promote the importance of an open data policy regime for the benefit of the public. Originality/value This case study covers a highly original and unique case study with the leaders at the SCC and representatives from the Hong Kong Government.

Objective To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers. Methods Guided by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a user-centered design process involving English- and Spanish-speaking patients and providers affiliated with an academic medical center. We conducted a secondary analysis of interview transcripts from our prior study, new design sessions with patients and primary care providers (PCPs), and a survey of PCPs. We determined EHR integration requirements as part of the asthma app design and development process. Results Analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs (71% of 78) identified requirements. Patient-facing requirements included: 1- or 5-item symptom questionnaires each week, depending on asthma control; option to request a callback; ability to enter notes, triggers, and peak flows; and tips pushed via the app prior to a clinic visit. PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. PCP preferences diverged regarding graphical presentations of patient-reported outcomes (PROs). Nurse-facing requirements included callback requests sent as an EHR inbox message. Requirements were consistent for English- and Spanish-speaking patients. EHR integration required use of custom application programming interfaces (APIs). Conclusion Using the NASSS framework to guide our user-centered design process, we identified patient and provider requirements for scaling an EHR-integrated remote symptom monitoring intervention in primary care. These requirements met the needs of patients and providers. Additional standards for PRO displays and EHR inbox APIs are needed to facilitate spread.

The research presents precision education that aims to regulate students’ behaviors through the learning analytics dashboard (LAD) in the AI-supported smart learning environment (SLE). The LAD basically tracks and visualizes traces of learning actions to make students aware of their learning behaviors and reflect these against the agreed goals. This research aims to realize the digital transformation of the learning space, thereby improving students’ learning outcomes with the assistance of the learning dashboard. To examine whether there was a close relationship between the frequency of using the whole platform and academic results, the data was collected from 50 first-year university students who registered with the innovative thinking course. Based on the data, we constructed the Technology Acceptance Model (TAM) questionnaire and interview guide to realize the students’ acceptance and feedback towards the SLE. Students were clustered into high-mark and low-mark groups based on their final results. The Wilcoxon rank-sum test is used to identify a significant difference between the two groups using the precision education platform. Subsequently, the partial least squares structural equation modeling (PLS-SEM) is further utilized to analyze the relationship between system quality, perceived ease of use, and perceived usefulness on behavioral intention and learning transfer.

Internal audit is a crucial constituent in supporting the management of the prosperous entity in the circumstances of the competitive economy. The significant structural variations that have taken place in recent years both in the Republic of Moldova and in the world, the determination of new priorities in economic development, the formation of a new management system within the economic entity have contributed to the development of internal audit in several countries. A keen interest in the implementation of internal audit also belongs to the owners, who are interested in monitoring the effectiveness of material, financial and human resources within the entity. The internal audit is actively implemented in the entity’s activity, such as in the preparation of economic and financial information, its analysis and evaluation, control over the quality and efficiency of the entity’s subdivisions, also on the safety of assets and efficiency of their use, etc. On these grounds, the role of quality assurance of the internal audit activity is currently on the rise, which is in line with its performance. In this context, the author approaches the study of the basic aspects of the internal audit evaluation process in terms of identifying the evaluation method, establishing and interpreting the indicators used in the evaluation, as well as determining the evaluation principles. As a method of evaluation of the quality of the internal audit activity, the author uses the dashboard, determines the basic steps in its use and suggests a list of indicators, which can be applied in the evaluation process. At the same time, the basic functions of using the dashboard in the process of evaluating the work of internal audit are identified.

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-021-00674-6.

The article published on 16 May 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as the presentations in Table 1 and Fig. 1 that should be improved further for providing more valuable information to readers. After viewing Table 1, measuring the strength of quantity (= 0.84) referred to the next two counterparts for the top one acronym (e.g., COVID) is demonstrated using the absolute advantage coefficient (AAC). Similarly, Traditional line charts on top-eight acronyms provide us with messages, including (i) DNA and RNA are popular over three decades; (ii) CT, MRI, HIV, SARS, and CoV start in 1972, 1985, 1986, 2003, and 2003, respectively; (iii) the number of COVID substantially surpasses over other seven acronyms in 2020 though the seven acronyms are almost equal in quantity in 2020. We are interested in producing similar Table 1 and Fig. 1 with a video MP4 provided to readers who can click on the link to manipulate the scenarios on their own. We found that the AAC and the traditional line charts on a dashboard make data clear for a better understanding of demonstrating the ascendancy of COVID-19 research using acronyms. The line charts are easily examined on Google Maps.

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

A data warehouse is essential for an organization to process and analyze data coming from the organization. Hence, a data warehouse together with a dashboard to visualize the processed data are built to accommodate the need of the church administrator to analyze a large set of church congregation data. The data warehouse is built using the Kimball principle. This Kimball principle emphasizes the implementation of a dimensional model in the data warehouse, not a relational model used in a regular transactional database. An ETL process that contains extract, transform and load processes is used to retrieve all data from the regular transactional database and transform the data so the data can be loaded into the data warehouse. A dashboard is then built to visualize the data from the data warehouse so the users can view the processed data easily. Users can also export the processed data into an excel file that can be downloaded from the dashboard. A web service is built to get data from the data warehouse and return it to the dashboard.

With the spread of learning analytics (LA) dashboards in K--12 schools, educators are increasingly expected to make sense of data to inform instruction. However, numerous features of school settings, such as specialized vantage points of educators, may lead to different ways of looking at data. This observation motivates the need to carefully observe and account for the ways data sensemaking occurs, and how it may differ across K--12 professional roles. Our mixed-methods study reports on interviews and think-aloud sessions with middle-school mathematics teachers and instructional coaches from four districts in the United States. By exposing educators to an LA dashboard, we map their varied reactions to visual data and reveal prevalent sensemaking patterns. We find that emotional, analytical, and intentional responses inform educators’ sensemaking and that different roles at the school afford unique vantage points toward data. Based on these findings, we offer a typology for representing sensemaking in a K--12 school context and reflect on how to expand visual LA process models.

Learning activities are one of the processes of delivering information or messages from teachers to students. SMPN 4 Sidoarjo is a State Junior High School (JHS) located in Sidoarjo Regency. During the learning process, the collected academic score data were still not well organized by teachers and school principals in monitoring student learning performance. The score data is from Bahasa Indonesia subject from a teacher with 222 data included at 2019/2020 school year. The method used in student clustering is K-Means. The number of clusters are determined using the elbow method and displayed in graphic form. Clustering result can be used as a reference for teachers in determining study groups and determining the best treatment for each cluster. The best clustering results are proven by validation score using Davies-Bouldin Index, Silhouette Width, and Calinski-Harabasz Index. Three clusters were obtained for each class level of data, while the cluster ranges from two to five for the data for each study group. The dashboard is used in order to visualize the clustering result. Usability testing using System Usability Scale (SUS) has a score value of 87.5, which means that the dashboard can be accepted by SMPN 4 Sidoarjo.

The three contiguous National Estuary Programs of southwest Florida, along with partners from six coastal counties, have developed a tidal creek water quality assessment framework to help prioritize natural resource investigations across a large population of tidal creeks between Tampa Bay and Estero Bay, Florida. The assessment framework is based on outcomes of a multidisciplinary study and includes a nutrient based report card that characterizes nutrient conditions relative to regional numeric nutrient criteria developed for contributing freshwater streams, identification of site-specific water quality indicators of tidal creek condition, and an online open science dashboard to display the assessment framework and provide access to all information relevant to its implementation. Application of the assessment framework has provided an actionable list of southwest Florida tidal creeks prioritized for further research and potential management action along with a host of site-specific indicator results that provide insights into drivers of tidal creek condition. The open science dashboard provides a platform for dissemination of this information in a readily accessible and reproducible format and a means to incorporate new data and indicators as they become available. Local resource managers are in need of tools to help prioritize natural resource investigations and management actions that achieve the greatest resource benefit with limited available resources. This assessment framework informs these efforts and builds capacity for future research to identify and refine management tools for these creeks where management resources, data, and sentinel biological response endpoints are limited.

The purpose of the article is to use the latest information technologies to clearly reflect the impact of various factors on the spread of the COVID-19 pandemic. The creation of a dashboard (PoltP, 2017) to visualize the statistics of the COVID-19 epidemic in the spring of 2021 is considered in detail. The research methodology lies in the use of a systematic analysis of statistical data. The novelty of the study is the combination of diverse statistical information for preliminary qualitative analysis of a complex epidemiological situation. Conclusions. The material presented in the article gives a clear example of the use of various data to visualize the epidemiological situation. The possibility of loading statistical data of any format into a common source for the analysis of the influence on the general level of disease distribution is shown.