Prototype Dashboard Research Articles

Improving transparency and quality assurance: Operationalising semi-automated data provenance tracking in a Trusted Research Environment

We present a prototype solution for improving transparency and quality assurance of the data linkage process through a data provenance dashboard designed to assist data analysts, researchers and information governance teams in authenticating and auditing data workflows within a trusted research environment (TRE). Building on prior research (Scheliga, et al., 2022), this work describes our first operationalised prototype tested in a real-world setting. The prototype development involved four stages: (1) Co-design of interfaces with end users for provenance data collection and visualisation, producing a low-fidelity design; (2) Extension and refinement of the Safe Haven Provenance (SHP) ontology (https://tre-provenance.github.io/SHP-ontology/releases/v0.2/index-en.html); (3) Design and implementation of mechanisms for semi-automated collection of data linkage provenance using the SHP ontology; (4) Implementation and user evaluation of the dashboard. A participatory design process with data analysts, researchers and information governance teams resulted in a low-fidelity prototype and was validated via public consultations to ensure it met public trust. The resulting prototype dashboard (https://tre-provenance.github.io/) was built as an offline desktop app to match the deployment TRE requirements. The interactive dashboard displays the data linkage information extracted from a knowledge graph described using the SHP ontology (e.g., modifications of datasets, data release) and results of rule-based validation checks (e.g., checking extracted data against researchers' specification). User evaluations confirmed the dashboard would contribute to better quality of data linkage. This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking from ingress to egress in a single tool.

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation. ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.

Risk Mapping of COVID-19 to Create a Common Operating Picture Using Data from Wastewater Monitoring

Monitoring wastewater quality at the inlet of a wastewater treatment plant provides an overview of the health of the community connected to the respective wastewater treatment plant. Wastewater monitoring has been increasingly recognized as a valid tool in pandemic risk management, especially during the coronavirus disease 2019 (COVID-19) crisis. However, the results of wastewater monitoring are not yet fully utilized in the decision-making process of public governance across most of the Asia-Pacific region. This study explored the potential of geographic information systems to map the results of wastewater monitoring and provide a common operating picture for swift and informed decision-making, especially in times of disasters. By creating an online dashboard tool, we found that an additional merit of mapping wastewater monitoring is that it allows overlaying various other information, such as evacuation shelters, onto the same map. As the service areas of wastewater treatment plants, or sewersheds, are not necessarily aligned with geopolitical boundaries, mapping provides opportunities for re-calculating wastewater monitoring results at the city or district levels. The potential for recalculation is also applicable to overlayed information, indicating that information from wastewater monitoring may be used to assess disaster preparedness or multi-hazard risks. The dashboard prototype was discussed with government officials and feedback was obtained. We aim to further develop the findings so that the information from wastewater monitoring can be shared on a platform, such as the shared information platform for disaster management, in the future for effective risk governance.

Perancangan Prototipe Business Intelligence Dashboard Penjualan PT ABC

In a competitive business era, efficient and informative sales data management is essential for company success. The purpose of developing this sales dashboard prototype is to monitor sales data, integrate data from various sources, and make smarter business decisions. PT ABC, as a company engaged in milk sales, realizes the importance of timely monitoring of sales data and accurate analysis. The method used in the development of the dashboard is the SDLC prototyping method. The initial stages include needs identification, then design, followed by the creation of an initial prototype that is used to get feedback from stakeholders. The prototype will be designed using Power BI tools then the prototype results will be improved based on feedback. Through this design, the needs of decision makers will be better understood. The result of the design is sales data visualization based on Key Performance Indicators, namely sales targets and sales growth.

Reporting on the Development of a Web-Based Prototype Dashboard for Construction Design Managers, Achieved through Design Science Research Methodology (DSRM)

Construction Design Management (DM) involves the coordination of design processes and activities to ensure quality project design, yet it involves many challenges. This study reports on a collaborative Knowledge Transfer Partnership (KTP) project with a Case Study Organization (CSO) that tackled several issues faced by construction design managers. Employing a design science research methodology (DSRM), qualitative analysis of semi-structured interviews with purposefully identified design managers uncovered real-world concerns around design co-ordination and performance monitoring. To address these concerns, a web-based design management prototype dashboard was developed using typical project data to aid in the management of design coordination, task prioritization, and reporting functionalities. The web-based Design Management prototype dashboard enhances design management productivity in construction firms by monitoring design production, assessing designer performance trends, and focusing on Technical Queries (TQs) and Requests for Information (RFIs). Digitalizing selected design management processes improves efficiency and productivity. The visual reporting of the dashboard enables design production monitoring at project and portfolio levels, assesses trends in designer performance, and maintains focus on TQs and RFIs. Demonstrating how web-based Design Management dashboards can enhance productivity, this study emphasizes practical solutions derived from employing a design science research methodology. The development and application of the web-based dashboard contribute to the growing evidence that employing design science research methodology in construction can yield tangible solutions to address real-world construction concerns.

Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard

The evaluation of a decision support system integrating assistive technology for people with dementia at home.

With a decreasing workforce of carers and a transition from care homes to home care, people with dementia (PwD) increasingly rely on informal caregivers (ICs) and assistive technologies (ATs). There is growing evidence that ATs in the home environment can reduce workload for formal carers (FCs) and ICs, reduce care costs, and can have a positive influence on quality of life (QoL) for PwD and their caregivers. In practice, using multiple ATs still often implies using different separate point solutions and applications. However, the integral, combined use of the data generated using various applications can potentially enhance the insight into the health and wellbeing status of PwD and can provide decision support for carers. The purpose of the current study was to evaluate the use of a DSS that integrated multiple ATs into one dashboard through a small-scale field study. The current study presents the formative evaluation of a Decision Support System (DSS) connected to multiple ATs. This DSS has been developed by means of co-creation during an international project. The DSS provides an insight into the physical and cognitive status of a PwD, as well as an insight into sleep activity and general wellbeing. Semi-structured interview sessions were held in three countries (Netherlands, Italy, and Taiwan) with 41 participants to gain insight into the experiences of formal and informal carers and PwD with both the ATs and the DSS Alpha prototype dashboard. The results showed that participants using the DSS were satisfied and perceived added value and a fit with certain care demands from the PwD. In general, ICs and FCs have limited insight into the status of PwD living independently at home, and in these moments, the DSS dashboard and AT bundle can provide valuable insights. Participants experienced the DSS dashboard as well-organized and easy to navigate. The accuracy of the data displayed in the dashboard is important, the context, and (perceived) privacy issues should be tackled according to all users. Furthermore, based in the insight gained during the evaluation a set of design improvements was composed which can be used to further improve the DSS for the Beta evaluation. The current paper evaluates a possible solution for excess AT usage and how the use of a DSS which integrated multiple AT into one single technology could support caregivers in providing care for PwD. The formative evaluation scrutinized the integration of the developed DSS and the composed bundle of ATs across diverse cultural contexts. Insights from multi-center observations shed light on user experiences, encompassing overall usability, navigational efficacy, and attitudes toward the system. FCs and ICs expressed positivity toward the DSS dashboard's design and functionalities, highlighting its utility in remote monitoring, tracking changes in the person's abilities, and managing urgent situations. There is a need for personalized solutions and the findings contribute to a nuanced understanding of DSS and AT integration, providing insights for future developments and research in the field of DSS for the care of PwD.

Developing an Audit and Feedback Dashboard for Family Physicians: User-Centered Design Process.

Audit and feedback (A&F), the summary and provision of clinical performance data, is a common quality improvement strategy. Successful design and implementation of A&F-or any quality improvement strategy-should incorporate evidence-informed best practices as well as context-specific end user input. We used A&F theory and user-centered design to inform the development of a web-based primary care A&F dashboard. We describe the design process and how it influenced the design of the dashboard. Our design process included 3 phases: prototype development based on A&F theory and input from clinical improvement leaders; workshop with family physician quality improvement leaders to develop personas (ie, fictional users that represent an archetype character representative of our key users) and application of those personas to design decisions; and user-centered interviews with family physicians to learn about the physician's reactions to the revised dashboard. The team applied A&F best practices to the dashboard prototype. Personas were used to identify target groups with challenges and behaviors as a tool for informed design decision-making. Our workshop produced 3 user personas, Dr Skeptic, Frazzled Physician, and Eager Implementer, representing common users based on the team's experience of A&F. Interviews were conducted to further validate findings from the persona workshop and found that (1) physicians were interested in how they compare with peers; however, if performance was above average, they were not motivated to improve even if gaps compared to other standards in their care remained; (2) burnout levels were high as physicians are trying to catch up on missed care during the pandemic and are therefore less motivated to act on the data; and (3) additional desired features included integration within the electronic medical record, and more up-to-date and accurate data. We found that carefully incorporating data from user interviews helped operationalize generic best practices for A&F to achieve an acceptable dashboard that could meet the needs and goals of physicians. We demonstrate such a design process in this paper. A&F dashboards should address physicians' data skepticism, present data in a way that spurs action, and support physicians to have the time and capacity to engage in quality improvement work; the steps we followed may help those responsible for quality improvement strategy implementation achieve these aims.

Interactive dashboard development for Irish general practices to visualise and compare patient data

Abstract Background Health information technology has increased worldwide, transforming health and healthcare programs in which information visualisation and visual analytics processes are central. However, aggregating and comparing patient data remains challenging. Therefore, a dashboard was developed with and for Irish general practitioners (GPs) to improve their understanding of their patient population, disease management and prescribing. This paper describes the design and development process of the CARA dashboard. Methods The design and development of the CARA dashboard used an Action Design Research approach, which consists of (1) problem formulation, (2) building, intervention and evaluation, (3) reflection and learning and (4) formalisation of learning. The process included interviews, to explore what type of information GPs need, as well as iterative testing of the CARA dashboard prototype. Results Key challenges identified from the interviews (Context, Sense-making, Audits, Relevancy, Action, Engagement and Ease of Use) formed the basis for the development of the CARA dashboard prototype. The first exemplar dashboard focused on antibiotic prescribing to develop and showcase the proposed platform, including automated audit reports, filters (within practice) and between-practice comparisons as well as a visual overview of practice demographics. Conclusions User involvement in the design and development of the CARA dashboard is crucial to ensure GPs are enabled and supported to visually access and monitor their patients’ data. Next steps will include an exploratory study on dashboard engagement and the development of additional dashboards for topics such as chronic disease and mental health. Key messages • Good knowledge of operating systems, effective use of data, user interaction and context factors are required to design and implement an interactive dashboard. • Crucial components to make sense of the information provided in dashboards include the context, transparency about the data transformation processes and adherence to design principles.

Designing a Cycling Dashboard as a Way of Communicating Local Sustainability

This paper conceptualizes the use of interactive urban dashboards in collecting and visualizing sustainability indicators at local scales through a cycling dashboard prototype for Münster. Urban dashboards are integrated platforms that bring various data types and sources together and automatize the visualization of information in real time. They can function as information hubs that work with mobile applications, sensor-based data, and crowdsourced platforms. Visualization of information can present both map-based data, text, and graphical information. In this study, a cycling dashboard is introduced that is developed as a prototype. The dashboard is based on the sustainability indicators related to cycling infrastructure defined by the German Sustainable Building Council (DGNB). It retrieves crowdsourced open data from Open Street Map (OSM) and automatically updates the information when new data are entered into the platform. This enables detailed exploration of the geo-referenced information up to street level and comparisons between different districts. In order to achieve a comprehensive framework, four main steps were included into the design and development process “determining a content by integrating future users, creating and evaluating a data inventory, designing the architecture of the dashboard, and implementing the prototype ”. As a result, this study holistically draws a comprehensive framework for the urban cycling dashboard around three main modules focusing on the sustainability of cycling infrastructure “infrastructure guide, cyclists’ voice, and idea center”. Additionally, the first prototype of an open local cycling dashboard has been implemented. The prototype enables the automation of collection, analysis, visualisation, and deep exploration of sustainability-related data at local scales. The results of this study contribute to the status quo by supporting the design and development process of local urban dashboards through a participatory approach.

A framework for precision “dosing” of mental healthcare services: algorithm development and clinical pilot

BackgroundOne in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.MethodsHistorical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as “session dosing”: 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.ResultsThe developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.ConclusionsIt is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State.

The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.

Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research. This study aimed to transform primary care data into the OMOP CDM format. We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard. Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data. Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

Design of an adaptable dashboard for smart cities

Today there are smart cities that, through the use of information technologies, sensors, and specialized infrastructure, focus their efforts on improving the quality of life of their inhabitants. From these efforts arose the need to analyze and represent data within a system to make it useful and understandable to people, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and preferences. However, the analysis of adaptability through user interaction and its benefits is a topic still under exploration. This paper analyzes the literature on information visualization in adaptable dashboards for smart cities. Based on the elements of adaptable dashboards identified in the literature review, we propose an adaptable dashboard architecture, identify the main characteristics of the users of a smart city dashboard, and build an adaptable dashboard prototype using user-centered techniques.

PERANCANGAN PURWARUPA DASHBOARD DIGITAL UNTUK PENGUKURAN PRODUKTIVITAS KERJA DENGAN METODE DESIGN THINIKING

Employee productivity is one of the important factors in the progress of the company, to measure productivity time and motion methods are used and a dashboard is needed to review the level of employee productivity. Making dashboards manually takes a long time, while stakeholders need progressive and fast information. So a digital dashboard prototype design was made to meet user needs, then a research method focused on user needs, namely Design Thinking. The stages of this method are empathize, define, ideate, prototype, and test. The implementation of this method begins with interviewing potential users with questions that can explore user needs and direct observation, the answers of potential users will be analyzed so that they are properly defined, and problems that are defined will be given solution ideas that meet user needs, solution ideas will be applied as a digital dashboard prototype design and will be tested to test the prototype using usability testing to determine the effectiveness and efficiency of the prototype made. From the steps carried out to produce conclusions such as defined problems, solution ideas are obtained that are applied to the prototype, with the previously designed image making it easier to work on the prototype design and the test results proving that the prototype that has been designed is effective and efficient, the level of completion of the task carried out. the tester got a perfect score with a 100% success rate and a relatively fast task completion speed.

Electronic Co-design (ECO-design) Workshop for Increasing Clinician Participation in the Design of Health Services Interventions: Participatory Design Approach.

BackgroundParticipation from clinician stakeholders can improve the design and implementation of health care interventions. Participatory design methods, especially co-design methods, comprise stakeholder-led design activities that are time-consuming. Competing work demands and increasing workloads make clinicians’ commitments to typical participatory methods even harder. The COVID-19 pandemic further exacerbated barriers to clinician participation in such interventions.ObjectiveThe aim of this study was to explore a web-based participatory design approach to conduct economical, electronic co-design (ECO-design) workshops with primary care clinicians.MethodsWe adapted traditional in-person co-design workshops to web-based delivery and adapted co-design workshop series to fit within a single 1-hour session. We applied the ECO-design workshop approach to codevelop feedback interventions regarding abnormal test result follow-up in primary care. We conducted ECO-design workshops with primary care clinicians at a medical center in Southern Texas, using videoconferencing software. Each workshop focused on one of three types of feedback interventions: conversation guide, email template, and dashboard prototype. We paired electronic materials and software features to facilitate participant interactions, prototyping, and data collection. The workshop protocol included four main activities: problem identification, solution generation, prototyping, and debriefing. Two facilitators were assigned to each workshop and one researcher resolved technical problems. After the workshops, our research team met to debrief and evaluate workshops.ResultsA total of 28 primary care clinicians participated in our ECO-design workshops. We completed 4 parallel workshops, each with 5-10 participants. We conducted traditional analyses and generated a clinician persona (ie, representative description) and user interface prototypes. We also formulated recommendations for future ECO-design workshop recruitment, technology, facilitation, and data collection. Overall, our adapted workshops successfully enabled primary care clinicians to participate without increasing their workload, even during a pandemic.ConclusionsECO-design workshops are viable, economical alternatives to traditional approaches. This approach fills a need for efficient methods to involve busy clinicians in the design of health care interventions.

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

Introduction/AimData 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 MethodsIndividual-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.ResultsUsing 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.DiscussionThe 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.ConclusionsThe 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.

A Geospatial Dashboard Prototype for Evaluating Spatial Datasets by using Semantic Data Concepts and Open Source Libraries

Abstract. In today’s research data management, experts discuss datasets to be FAIR, as they should become findable, accessible, interoperable and reusable (Lacagnia et al. 2021). In recent years, quality information and provenance information as well as dataset’s general metadata have become important aspects to evaluate a dataset's fitness for use. In order to capture and process this meta-information in a systematic way, users need frameworks and meaningful user interfaces that allow them to interact with the information and to visualize them. Therefore, we provide a user-friendly and interactive geodashboard implementation as first prototype that supports the evaluation of spatial datasets with linked widgets by applying semantic concepts and using open source libraries.

Learning Loss Recovery Dashboard: A Proposed Design to Mitigate Learning Loss Post Schools Closure

Research has shown the effectiveness of designing a Learning Analytics Dashboard (LAD) for learners and instructors, including everyone’s levels of progress and performance. An intertwined relationship exists between learning analytics (LA) and the learning process. Understanding information or data about learners and their learning journey can contribute to a deeper understanding of learners and the learning process. The design of an effective learning dashboard relies heavily on LA, including assessment of the learning process, i.e., gains and losses. A Learning Loss Recovery Dashboard (LLRD) can be designed as an instructional tool, to support the learning process as well as learners’ performance and their academic achievement. The current project proposes a LLRD prototype model to deal with potential learning loss; increase the achievement of learning outcomes; and provide a single, comprehensive learning process, where schools can evaluate and remedy any potential learning loss resulting from the distance-learning period that was caused by the COVID-19 pandemic. This systematic dashboard prototype functions to determine learning gains by K–12 learners. It is expected that the implementation of the proposed dashboard would provide students, teachers, and educational administrators with an integrated portal, for a holistic and unified remedial experience for addressing learning loss.

Co-design of an Electronic Dashboard to Support the Coproduction of Care in Pediatric Rheumatic Disease: Human-Centered Design and Usability Testing.

BackgroundThe coproduction of care involves patients and families partnering with their clinicians and care teams, with the premise that each brings their own perspective, knowledge, and expertise, as well as their own values, goals, and preferences, to the partnership. Dashboards can display meaningful patient and clinical data to assess how a patient is doing and inform shared decision-making. Increasing communication between patients and care teams is particularly important for children with chronic conditions. Juvenile idiopathic arthritis (JIA), the most common chronic pediatric rheumatic condition, is associated with increased pain, decreased function, and decreased quality of life.ObjectiveThe aim of this study is to design a dashboard prototype for use in coproducing care in patients with JIA. We evaluated the use and needs of end users, obtained a consensus on the necessary dashboard data elements, and constructed display prototypes to inform meaningful discussions for coproduction.MethodsA human-centered design approach involving parents, patients, clinicians, and care team members was used to develop a dashboard to support the coproduction of care in 4 ambulatory pediatric rheumatology clinics. We engaged a multidisciplinary team (n=18) of patients, parents, clinicians, nurses, and staff during an in-person kick-off meeting followed by biweekly meetings. We also leveraged advisory panels. Teams mapped workflows and patient journeys, created personas, and developed dashboard sketches. The final dashboard components were determined via Delphi consensus voting. Low-tech dashboard testing was completed during clinic visits, and visual display prototypes were iterated by using the Plan-Do-Study-Act methodology. Patients and clinicians were surveyed regarding their experiences.ResultsTeams achieved consensus on what data mattered most at the point of care to support patients with JIA, families, and clinicians collaborating to make the best possible health care decisions. Notable themes included the right data in the right place at the right time, data in once for multiple purposes, patient and family self-management components, and the opportunity for education and increased transparency. A final set of 11 dashboard data elements was identified, including patient-reported outcomes, clinical data, and medications. Important design considerations featured the incorporation of real-time data, clearly labeled graphs, and vertical orientation to facilitate review and discussion. Prototype paper-testing with 36 patients and families yielded positive feedback, with 89% (8/9) to 100% (9/9) of parents (n=9) and 80% (8/10) to 90% (9/10) of clinicians (n=10) strongly agreeing or agreeing that the dashboard was useful during clinic discussions, helped to talk about what mattered most, and informed health care decision-making.ConclusionsWe developed a dashboard prototype that displays patient-reported and clinical data over time, along with medications that can be used during a clinic visit to support meaningful conversations and shared decision-making among patients with JIA, their families, and their clinicians and care teams.