Development of a Web-Portal Health Information System for Barangay

In the Philippines, a barangay is the smallest administrative unit serving as suburban neighborhoods' first line of defense. According to Bautista, barangays conduct a manual file-based process of storing the community's health information. Therefore, the need for a single platform enables a small government unit to manage its resources while being transparent to its community. The study aims to develop a web- based barangay health information system portal for Barangay 69 District 1 in Tondo Manila. The system would be a reference tool for barangays as their platform provides inventory management, the barangay's health programs, and a dashboard for data visualization inventory management, tracking of Covid cases, administration of health activities, and a dashboard for data visualization. As a result, the web portal is functional, and different test scenarios show above-average results. The study concludes that the system provided a platform for the barangay and its residents. It also concludes that it is user-friendly and efficiently disseminates the barangay's health programs and activities. © 2023 IEEE.

The development and validation of a dashboard prototype for real-time suicide mortality data.

Introduction/Aim: Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface. Materials and Methods: Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008-2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the "rsatscan" and "shiny" packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface. Results: Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence. Discussion: The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making. Conclusions: The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Pandemic Geopolitics and the Bordering of COVID-19: Academic and Lay Geographies of the Pandemic and Policies to Contain and Mitigate the Novel Coronavirus

Individual and collective strategies to cope with the pandemic are highly geopolitical, revealing the intricacies of the relations between power and space. Attempts to contain and mitigate the spread of COVID-19 can be understood as sociospatial practices. Places where the disease has been identified are isolated. Spaces are disrupted when established connections (such as airlines connections) are severed and mobility is restricted. Networks are disturbed through social distancing (a misnomer for physical distancing) and self-isolation, and reinvented and reconfigured through telecommunications. More specifically scale is a useful lens to examine pandemic geopolitics, both the policy responses and the representations that make sense of these policy responses. Practices aiming at containing the pandemic are multiscalar bordering processes. They range from the delimitation and the separation of specific body parts (through facemasks, gloves, new habits including handwashing to avoid contact of potentially contaminated body parts with mouth and eyes), the seclusion of ill, contaminated and/or (potentially) contagious bodies (through protective suits and quarantine arrangements), the segregation of hospital departments devoted to COVID-19 patients and of accommodations dedicated to potential virus carriers, and even entire cities (through isolation), countries (through closed state borders) and continents (through discontinued intercontinental air traffic). Last but not least the temporality of these bordering practices remain uncertain with no perspective on the temporal closure of the pandemic, of the sanitary measures or of the exceptional political and policing arrangements that enabled them (and their impact on the rule of law). This chapter explores the representations of the pandemic and the measures taken to contain and mitigate it through the lens of critical geopolitics. It analyzes academic and lay geographies of the pandemic by reviewing representations of the pandemic in popular culture and news media (popular geopolitics), in COVID-19 policy communication (practical geopolitics) and in geographical publications (formal geopolitics). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Shared longitudinal health records for clinical and population health

The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.

IOT based Temperature and Oxygen level monitoring and Data visualization

The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.

Best Practices for the Design of COVID-19 Dashboards.

Since 2020, health informaticians have developed and enhanced public-facing COVID-19 dashboards worldwide. The improvement of dashboards implemented by health informaticians will ultimately benefit the public in making better healthcare decisions and improve population-level healthcare outcomes. The authors evaluated 100 US city, county, and state government COVID-19 health dashboards and identified the top 10 best practices to be considered when creating a public health dashboard. These features include 1) easy navigation, 2) high usability, 3) use of adjustable thresholds, 4) use of diverse chart selection, 5) compliance with the Americans with Disabilities Act, 6) use of charts with tabulated data, 7) incorporated user feedback, 8) simplicity of design, 9) adding clear descriptions for charts, and 10) comparison data with other entities. To support their findings, the authors also conducted a survey of 118 randomly selected individuals in six states and the District of Columbia that supports these top 10 best practices for the design of health dashboards.

Digital dashboards visualizing public health data: a systematic review.

Introduction: Public health is not only threatened by diseases, pandemics, or epidemics. It is also challenged by deficits in the communication of health information. The current COVID-19 pandemic demonstrates that impressively. One way to deliver scientific data such as epidemiological findings and forecasts on disease spread are dashboards. Considering the current relevance of dashboards for public risk and crisis communication, this systematic review examines the state of research on dashboards in the context of public health risks and diseases. Method: Nine electronic databases where searched for peer-reviewed journal articles and conference proceedings. Included articles (n = 65) were screened and assessed by three independent reviewers. Through a methodological informed differentiation between descriptive studies and user studies, the review also assessed the quality of included user studies (n = 18) by use of the Mixed Methods Appraisal Tool (MMAT). Results: 65 articles were assessed in regards to the public health issues addressed by the respective dashboards, as well as the data sources, functions and information visualizations employed by the different dashboards. Furthermore, the literature review sheds light on public health challenges and objectives and analyzes the extent to which user needs play a role in the development and evaluation of a dashboard. Overall, the literature review shows that studies that do not only describe the construction of a specific dashboard, but also evaluate its content in terms of different risk communication models or constructs (e.g., risk perception or health literacy) are comparatively rare. Furthermore, while some of the studies evaluate usability and corresponding metrics from the perspective of potential users, many of the studies are limited to a purely functionalistic evaluation of the dashboard by the respective development teams. Conclusion: The results suggest that applied research on public health intervention tools like dashboards would gain in complexity through a theory-based integration of user-specific risk information needs. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=200178, identifier: CRD42020200178.

Effectiveness of Health Communication Technology on Compliance Disposition of Covid-19 Guidelines

Recent research has mostly examined the role of health communication technology (HCT) in patient empowerment and in producing patient-focused outcomes. This study examines HCT in a larger context where it is used as a tool to improve public health. The objective is to examine how HCT is used to monitor Covid-19's spread, and how social factors affect individual assessment of HCT and individual compliance disposition of Covid-19 guidelines. Analyzing data collected from 360 HCT users suggests that the information and system quality of HCT indeed impact users' assessment of its effectiveness and their compliance disposition. However, such effects are strongly mediated by social factors: Peer influence and government trust can sway an individual's cognitive judgments of Covid-19 regardless of HCT's impacts. The findings highlight the importance of social factors in pandemic management and the need to socialize health informatics to make them more effective.

Development of a Dashboard in the Emergency Department during the COVID-19

Background: Information dashboards are useful tools for up-to-date decision-making by visualizing data. Objectives: This study aimed to report the development of a dashboard in the emergency department (ED) during COVID-19 in a big hospital in Iran. Methods: The authors developed a dashboard by user-centered design (UCD) methodology in four phases, namely specification of the context of use, specification of the requirements, creation of design solutions, and evaluation. Indicators were determined by reviewing previous studies and interviewing focus groups with an expert panel. The Power BI Desktop software was used for the development of the dashboard. Users' comments about the dashboard were collected. The dashboard was then developed and revised according to the users' feedback and suggestions. Finally, user satisfaction was evaluated. Results: The authors identified 30 indicators for COVID-19 ED, classified as input, output, and process indicators. The final version of the dashboard was implemented in 2021, and then 28 ED and managerial staff participated in the evaluation of the dashboard. The average score of the system usability scale of the dashboard was 84.10 points, and the situation awareness index was 3.97, which indicates "good" usability and situation awareness. Conclusion: This dashboard presented key managerial and clinical indicators for decision-making in ED. Future studies can be designed to develop dashboards for accidents and burns EDs and create emergency information dashboards for several hospitals for better management in times of crisis.

The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm - RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same. © 2023 IEEE.

Learning Analytics Powered Teacher Facing Dashboard to Visualize, Analyze Students' Academic Performance and give Key DL(Deep Learning) Supported Key Recommendations for Performance Improvement

COVID-19 has forced the government to close educational institutes to reduce the spread of the virus. As a result of this decision, students lose contact with teachers and a communication gap also arises. This survey attempts to bridge the gap between students and teachers. Through this survey, we sought to understand where the students are lacking and what are the different steps that can be taken by the teacher to improve the performance of the student and whether this concept should be reviewed or not. We found that most of the researchers who have published papers that we have read did the same mistake in their research, therefore we realized that the concept of AI should be studied again, and we should try not to repeat the same mistake in our research.The main aim of our project is to build 'Teacher facing dashboard' which can help the teacher to summarize,visualize and analyze the data of the education field(academics) and also understanding the students performance using Machine Learning(ML) and Deep Learning (DL). © 2023 IEEE.

Les indicateurs d'alerte et de surveillance de la Covid-19

Face à la crise sanitaire provoquée par la pandémie de Covid-19 en France, Santé publique France a mis en place un système de surveillance évolutif fondé sur des définitions de cas possible, probable et confirmé. Le décompte quotidien se limite cependant aux cas confirmés par reverse transcriptase polymerase chain reaction ou sérologie SARS-CoV-2 (actuellement via la plateforme SI-DEP), aux cas hospitalisés (via le Système d'information pour le suivi des victimes d'attentats) et aux décès hospitaliers par Covid-19. Ce suivi de la circulation virale est forcément non exhaustif, et l'estimation de l'incidence est complétée par d'autres indicateurs comme les appels au 15, les recours à SOS Médecins, les passages dans les services d'accueil des urgences, les consultations de médecine de ville via le réseau Sentinelle. Le suivi de la mortalité non hospitalière s'est heurté aux délais de transmission des certificats de décès et au manque de diagnostic fiable. Seule la létalité hospitalière a pu être mesurée de manière fiable. Moyennant un certain nombre de précautions statistiques et d'hypothèses de travail, les modèles ont permis d'anticiper l'évolution de l'épidémie à partir de deux indicateurs essentiels : le ratio de reproduction R et le temps de doublement épidémique. En Île-de-France, l'Assistance publique– Hôpitaux de Paris a complété ce tableau de bord grâce à son entrepôt de données de santé et a ainsi pu modéliser de manière fine le parcours de soins des patients. L'ensemble de ces indicateurs a été essentiel pour assurer une planification de la réponse à la crise.Alternate abstract: Facing the arrival of the COVID-19 pandemic in France, Santé Publique France has set up an evolutionary surveillance system based on definitions of possible, probable and confirmed cases. But only cases confirmed by SARSCoV-2, RT-PCR (reverse transcriptase polymerase chain reaction) or serology, hospitalized cases and in-hospital deaths have been recorded on a daily basis. COVID-19 actual incidence has thus been estimated through additional indicators such as specific calls to emergency services (Samu) and SOS doctors, emergency rooms visits, or consultations in a sentinel network of general practitioners. Surveillance of non-hospital mortality has been impaired by delays and diagnostic inaccuracies of death certificates. Only in-hospital lethality could be reliably monitored.With a few essential statistical precautions and working hypotheses, models made it possible to anticipate the evolution of the epidemic based on two essential indicators: the reproduction ratio R, and the epidemic doubling time. In Ile-de-France region, the Greater Paris University Hospitals Group has used its data warehouse to complete this epidemic dashboard, including a fine modeling of patients' care pathways. All these indicators have proved essential to plan the response to this unprecedented crisis.

Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

Explaining variation in teachers' use of ict: A learning analytics approach

Significant attention has been paid to the use of ICT by teachers, especially during the COVID-19 health crisis. This usage has mostly been captured through self-reported survey measurements. Learning analytics can complement such findings, by using log data to document precisely how long teachers use ICT, and what ICT behaviors they perform online. Using log data of 800 teachers, the present study documents their use of ICT in mathematics on a digital learning platform used across Luxembourg during COVID-19 remote education. Our findings confirm the large differences between teachers' use of ICT found in previous research, measured here through the time spent active on the platform. The types of ICT behaviors teachers engage with online, measured via the SAMR model, explain most of this variation. Specifically, more time on the platform is associated with activities that create a meaningful learning experience, and redefined tasks that could engage students as active learners. Experience with the technology, and participation in incentive events and teacher training, explain another significant part of this variation. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Learning analytics dashboards: What do students actually ask for?

Learning analytics (LA) has been opening new opportunities to support learning in higher education (HE). LA dashboards are an important tool in providing students with insights into their learning progress, and predictions, leading to reflection and adaptation of learning plans and habits. Based on a human-centered approach, we present a perspective of students, as essential stakeholders, on LA dashboards. We describe a longitudinal study, based on survey methodology. The study included two iterations of a survey, conducted with second-year ICT students in 2017 (N = 222) and 2022 (N = 196). The study provided insights into the LA dashboard features the students find the most useful to support their learning. The students highly appreciated features related to short-term planning and organization of learning, while they were cautious about comparison and competition with other students, finding such features possibly demotivating. We compared the 2017 and 2022 results to establish possible changes in the students' perspectives with the COVID-19 pandemic. The students' awareness of the benefits of LA has increased, which may be related to the strong focus on online learning during the pandemic. Finally, a factor analysis yielded a dashboard model with five underlying factors: comparison, planning, predictions, extracurricular, and teachers. © 2023 ACM.

User Perceptions of Actionability in Data Dashboards

This article reports on a multiphase study designed to understand how nonexpert users interact with COVID-19 data dashboards, particularly in terms of the dashboards' actionability, or ability to support decision making. Analysis of the videos and transcriptions of user interviews shows the variable relevance of proposed criteria for dashboard actionability and suggests additional criteria for users' emotional responses to data and for the presentation of data at degrees of personal and local granularity. These findings advance an understanding of how nonexpert audiences interact with and derive value from complex visualized data. © The Author(s) 2023.

Data stories and dashboard development: a case study of an aviation schedule and delay causes

In this case study, five key processes in modelling a data story of aviation data patterns during COVID-19 have been executed. It started with the collection of secondary data from relevant sources. Data inspection, transformation, and preparation activities, including data cleaning, filtering, and sampling, are all included in this work. Iterative exploratory data analysis (EDA) has been conducted to determine the pattern of each independent attribute, followed by an assessment after the data story is modelled and integrated on a dashboard. The questionnaire has been distributed and the visuals were assessed by giving respondents a few tasks to interpret stories based on their comprehension. The result shows that the data stories have been interpreted in a similar narrative by all the respondents. The overall mean score is 4.71, and this significantly shows that the respondents agree and strongly agree that the visual objects help in communicating patterns and stories. The overall process gives researchers experience and guidelines for future work. Overall, the objectives of the study have been met. Nevertheless, it gives researchers a lot of experience in interpreting data, cleansing and transformation, analysis, modelling the visualisation by selecting suitable charts, and integrating the objects together into a dashboard.

COVID-19 Vaccination Data Visualization: Issues and Challenges

Data Visualization plays an important role for patterns and trends analysis in trillion of data rows Big Data analysis, where the data can be represented in some graphical forms. Hence, the data could be more comprehensible in its visual summary in dashboards and storyboards. This study aims to discuss some issues and challenges in visualizing COVID-19 vaccination datasets. There are some possible issues in data visualization, as it is not easy and may be challenging to produce a good dashboard that are interesting and easy for viewers to understand. Therefore, this study focuses on some issues that may arise during performing a data visualization on the COVID-19 dataset. In this study, there are three dashboards have been studied, which are the COVID-19 tracker, its effectiveness, and its acceptance. The first two dataset are derived from Ministry of Health Malaysia bank data, whereas the third dataset is from a survey to support this analysis. The selected attributes are states, the number of people who have received the vaccine as adults, children, and teenagers, and the number of people who already received boosters, and reasons to not get a booster. The visualization issues found within the dashboard are mis-choice of colors, mis-choice of visual object type, lack of interactivity, and plotting too much data. As a result, this proposed alternative solutions for those issues such as color deliberately, pick a suitable visual object, create an interactive dashboard, and reduce the information overload in visualizing the data. © 2022 IEEE.

Web-Based COVID-19 Dashboards and Trackers in the United States: Survey Study.

BACKGROUND: The SARS-CoV-2 pandemic provided an opportunity to use public-facing web data visualization tools to help citizens understand the evolving status of the outbreak. Given the heterogeneity of data sources, developers, tools, and designs used in this effort, it raised questions about how visualizations were constructed during a time when daily batches of data were available, but issues of data quality and standardization were unresolved. OBJECTIVE: This paper surveyed web-based COVID-19 dashboards and trackers that are likely to be used by the residents of the United States to monitor the spread of infection on a local, national, and global scale. This study is intended to provide insights that will help application developers increase the usefulness, transparency, and trustworthiness of dashboards and trackers for public health data in the future. METHODS: Websites of coronavirus dashboards and trackers were identified in August 2020 using the Google search engine. They were examined to determine the data sources used, types of data presented, types of data visualizations, characteristics of the visualizations, and issues with messy data. The websites were surveyed 3 more times for changes in design and data sources with the final survey conducted in June 2022. Themes were developed to highlight the issues concerning challenges in presenting COVID-19 data and techniques of effective visualization. RESULTS: In total, 111 websites were identified and examined (84 state focused, 11 nationwide, and 16 with global data), and this study found an additional 17 websites providing access to the state vaccination data. This study documents how data aggregators have played a central role in making data accessible to visualization developers. The designs of dashboards and tracker visualizations vary in type and quality, with some well-designed displays supporting the interpretation of the data and others obscuring the meaning of the data and potentially misleading the viewers. Five themes were identified to describe challenges in presenting COVID-19 data and techniques of effective visualization. CONCLUSIONS: This analysis reveals the extent to which dashboards and trackers informing the American public about the COVID-19 pandemic relied on an ad hoc pipeline of data sources and data aggregators. The dashboards and trackers identified in this survey offer an opportunity to compare different approaches for the display of similar data.