Development Of Dashboard Research Articles

Supporting rural public health practice to address local-level social determinants of health across Northwest states: Development of an interactive visualization dashboard

BackgroundRural local health departments (LHDs) lack adequate capacity and funding to effectively make data-driven decisions to support their communities that face greater health disparities compared to urban counterparts. The need, therefore, exists for informatics solutions to support rural LHDs. PurposeWe describe the user-centered design (UCD) of SHARE-NW: Solutions in Health Analytics for Rural Equity across the Northwest, a website (sharenw.nwcphp.org) with data visualization dashboards for rural LHD practitioners in Alaska, Idaho, Oregon, and Washington to help them identify health disparities in their jurisdictions. MethodsIn this UCD study guided by Munzner’s Nested Model for Visualization Design and Validation, we (1) completed a needs assessment, (2) created and evaluated mockups, and (3) conducted usability testing of a functional alpha testing website. Potential end-users (rural LHD practitioners) and Equity Advisory Committee members (public health experts from state, rural local, and tribal public health agencies) across our four-state catchment area were engaged throughout the website development and testing. We adapted traditional in-person UCD methods to be remote to reach participants across a large geographic area and in rural/frontier areas of Alaska, Idaho, Oregon, and Washington. ResultsWe recruited participants from all four states to engage in each stage of the project. Needs assessment findings informed the mockup development, and findings from the mockup evaluations informed the development of the functional website. Usability testing of the website overall was positive, with priority usability issues identified. ConclusionsBy applying Munzner’s Nested Model and UCD, we could purposefully and intentionally design evidence-based solutions, specifically for rural LHD practitioners. Adaptations of traditional UCD methods were successful and allowed us to reach end-users across a large geographic area. Future work on SHARE-NW will involve the evaluation of the website. We provide insights on our lessons learned to support future public health informatics solution development.

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard.

Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

Using Participatory Design to Engage Physicians in the Development of a Provider-Level Performance Dashboard and Feedback System.

Performance feedback, in which clinicians are given data on select metrics, is widely used in the context of quality improvement. However, there is a lack of practical guidance describing the process of developing performance feedback systems. This study took place at the University of California, San Francisco (UCSF) with hospitalist physicians. Participatory design methodology was used to develop a performance dashboard and feedback system. Twenty hospitalist physicians participated in a series of six design sessions and two surveys. Each design session and survey systematically addressed key components of the feedback system, including design, metric selection, data delivery, and incentives. The Capability Opportunity Motivation and Behavior (COM-B) model was then used to identify behavior change interventions to facilitate engagement with the dashboard during a pilot implementation. In regard to performance improvement, physicians preferred collaboration over competition and internal motivation over external incentives. Physicians preferred that the dashboard be used as a tool to aid in clinical practice improvement and not punitively by leadership. Metrics that were clinical or patient-centered were perceived as more meaningful and more likely to motivate behavior change. The performance dashboard has been introduced to the entire hospitalist group, and evaluation of implementation continues by monitoring engagement and physician attitudes. This will be followed by targeted feedback interventions to attempt to improve performance.

Development and implementation of dashboard in the Pharmacy Department at University Hospital

The pharmacy must assess its own activity in order to better manage and respond to needs. The purpose of our work was to identify key indicators and set up and validate a dashboard for the management and monitoring of pharmaceutical activities. Our study's methodology is based on a process-based management of hospital pharmaceutical activities. Our target was to achieve the same approach, within the various functional and concerned units, in order to harmonize and simplify the quantitative and qualitative monitoring of pharmaceutical activities. Process mapping was established and the most critical processes were selected on the basis of a matrix. In total, seven key processes have been identified. The identity cards for these processes have been drawn up. Quality indicators have been identified and a first design of dashboards with the elements collected was proposed and validated. The difficulty in choosing indicators lies mainly in finding the compromise between exhaustiveness and feasibility of setting up effective monitoring of these indicators. The commitment and motivation of all who are involved in the process are essential to guarantee the success and sustainability of this project. La pharmacie doit évaluer sa propre activité pour mieux gérer et répondre aux besoins. L’objectif de notre travail était d’identifier les indicateurs clés, de mettre en place et valider un tableau de bord pour le management des activités pharmaceutiques. La base méthodologique de notre étude repose sur une gestion par processus des activités pharmaceutiques. Notre objectif était de réaliser une même approche de ces processus, au sein des différentes unités fonctionnelles concernées, afin d’harmoniser et de simplifier le suivi quantitatif et qualitatif des activités pharmaceutiques. La cartographie macroscopique des processus de la pharmacie a été établie et les processus les plus critiques ont été sélectionnés. Au total, nous avons identifié sept processus clefs. Les fiches d’identité de ces processus ont été élaborées. Des indicateurs qualité ont été identifiés et une première conception de tableaux de bord a été proposée et validée. La difficulté du choix des indicateurs réside principalement dans le compromis à trouver entre exhaustivité et faisabilité pour le suivi efficace de ces indicateurs. L’engagement et la motivation de tous les acteurs impliqués dans la démarche sont essentiels pour garantir la réussite et la pérennité de ce projet.

CovidStats: Development and Implementation of a Daily COVID-19 Clinical Dashboard in an Urban Teaching Hospital.

Background and Objectives:Dashboards have been utilized in health care to improve quality and patient care. The purpose of our project was to create a concise, timely, and accurate dashboard for administrative and clinical leadership during the COVID-19 pandemic.Methods:Two authors collaborated to identify 14 metrics and design a comprehensive dashboard (CovidStats, CS) using Microsoft Excel. The dashboard was updated daily and distributed to leadership between December 2020 and April 2021. The utility of this quality measure was assessed by survey of hospital leadership.Results:The 14 metrics included were as follows: (1) elective surgery census threshold; (2) daily COVID admissions; (3) daily COVID discharges; (4) net COVID admissions; (5) ED (emergency department) bed holds; (6) COVID ED bed holds; (7) hospital census; (8) percent COVID census; (9) active COVID census; (10) COVID ICU (intensive care unit); (11) MICU (medical ICU) census; (12) ventilators in use; (13) high-flow oxygen devices in use; and (14) weekly hospital census. The leadership response survey revealed unanimous approval for CS, with a mean rating of 4.9 ± 0.3 (rated 1-5).Conclusions:Effective clinical dashboards can be created using affordable basic computer software. Implementation of the CS dashboard conveyed relevant and timely information, which influenced the decision making of hospital leadership during the COVID-19 pandemic.

Monitoring continuity of maternal and child health services, Indonesia.

ObjectiveTo implement an online system to evaluate the impact of the coronavirus disease 2019 (COVID-19) pandemic on maternal and child health and nutrition essential health services in Indonesia.MethodsWe developed an electronic monitoring and evaluation system to assist district health offices in making rapid assessments of the impact of COVID-19 on maternal and child health and nutrition programmes in their area and in developing policy and programme responses. This implementation research was conducted from September to December 2020 in 304 districts. The strategies consisted of technical assistance for district offices by 21 partner universities and development of an online dashboard for rapid situation analyses and reporting. We collected qualitative data on feasibility and adherence to the intervention, as well as quantitative data from routine health databases to analyse the impact of COVID-19 on maternal and child health and nutrition indicators.FindingsIn the majority of districts key maternal and child health and nutrition services were moderately or severely affected by the pandemic, particularly child growth monitoring and antenatal care services. Adherence to the protocol of the intervention varied across districts but the system is a feasible approach to be scaled up to other regions and health programmes. High uptake by the health ministry, district office and university partners provided the platform with collaborative efforts for health-systems strengthening.ConclusionThe electronic monitoring and evaluation system could be implemented and completed with several modifications to accommodate district offices and universities. There is a potential to scale up the intervention with better implementation planning and training.

Development of an electronic learning progression dashboard to monitor student clinical experiences.

Clinical experience tracking mechanisms for students at dental schools provide patient assignment, student experience, and learning progression feedback. The purpose of this study was to evaluate dental students' clinical experiences following the implementation of a learning progression dashboard (LPD). After developing and deploying an electronic LPD using PHP, secondary data analysis on dental students' clinical experiences from 2017-2019 was conducted. Student experience differences were compared between the year before continuous use of the LPD and the first year using it. LPD data contained the required clinical procedures dentistry students must perform across all disciplines and the number of planned, in progress, and completed tasks each student has accomplished. Using two time points, the students' experiences were compared. Univariate statistics and independent t-tests were conducted in R for detecting the differences in the number and categories of codes. The number and category of codes showed significant differences between the academic year 2017-2018 and 2018-2019 for both third- and fourth-year dental students after one and two terms. Overall, students recorded a 26% greater number of treatment codes and experienced a 26% greater number of code categories compared to the previous year. Applying information management methods such as dashboards can better inform educators on student clinical experiences and improve clinical learning outcomes for students.

Design and Development of IIoT Dashboard for Overall Equipment Effectiveness Monitoring of CNC Machine Tools

Abstract: Smart Manufacturing systems are regarded as the fourth revolution in the manufacturing industry, which is shaped by widespread deployment of sensors and Internet of Things. The present work constitutes of ‘Development of Industrial Internet of Things (IIoT) Dashboard for ‘Overall Equipment Effectiveness’ (OEE) Monitoring of CNC Machine Tools’ for a legacy CNC machine which is converted to smart machine. Data fetched from the CNC controllers through OPCUA is sent to the connected cloud database which will be imported into PowerBI desktop and the data has been classified and processed according to the requirement to develop a data modelling architecture of OEE, the Working status of the machine is visualized by Creating Monitoring and Performance charts and graphs of different design in Microsoft PowerBI Desktop. The Advanced visualizations constitutes od various features along with different analysing capabilities that results is creating reports which enumerates the state of OEE as a Key Performance Indicator (KPI). As Microsoft Power BI pertains a set of pre-established steps for data processing, the situation designated may constitute a limitation to automatic data refresh, leading to a do-over to verify, the specific interval of time, the conformity of data so they can be imported into the system. Keywords: Industrial Internet of Things (IIoT), Open Platform Communications United Architecture (OPCUA), Computer Numerical Control (CNC), Overall Equipment Effectiveness (OEE), Key Performance Indicator (KPI).

128. Development of an Analytics Dashboard to Monitor Antimicrobial Selection and Duration for Pneumonia

BackgroundAnalytical and visual tools can be used to monitor progress for a variety of ASP key performance indicators, but few data describe the process of building disease-state specific tools to retrospectively monitor antimicrobial choice and duration. We describe process and methods for development of a pneumonia dashboard.MethodsIn late 2019, the Carolina ASP began construction of a dashboard to monitor antimicrobial selection and duration in patients admitted with a diagnosis code (ICD-10) consistent with pneumonia. Data extracted from the medical record after discharge included: admission date and time, admission and discharge ICD-10s, inpatient orders and administrations for agents included in the NHSN Antimicrobial Use (AU) option, and antimicrobials ordered at discharge with associated ICD-10. Extracted data fields were validated using a one-month sample. Displays were constructed to trend selection during the first 48 hours of admission, inpatient days of therapy, and total length of therapy (sum of inpatient + outpatient days) for patients who received a discharge prescription for an antimicrobial included in the AU option that was associated with an ICD-10 consistent with pneumonia. Trends observed between Jan 2020 and Mar 2021 are reported. Results341 admissions were trended. Within the first two days of admission, monthly proportions of patients receiving an antimicrobial by category were: anti-MRSA therapies (vancomycin, linezolid), 0.20 to 0.75; broad spectrum beta-lactams (e.g., cefepime, pip/tazo), 0.40 to 0.81; CAP therapies (e.g., ceftriaxone, levofloxacin), 0.48 to 1.00 (Figure). Median inpatient duration of therapy was 5 days (IQR 3-8; range 1 to 68). Total length of therapy was median 6 days (IQR 4-10; range 1 to 68). Figure. Proportions of Patients Prescribed Antimicrobial Categories of Interest During the First 48 Hours of Admissions for Pneumonia. Legend: Anti-MRSA = vancomycin or linezolid; HAP abx = cefepime, piperacillin/tazobactam, ceftazidime, meropenem; CAP = ceftriaxone, azithromycin, ampicillin/sulbactam, amoxicillin/clavulanate, cefdinir, levofloxacin.ConclusionAutomated reports and visual tools can provide actionable insights for ASP practice. From this dashboard, we identified variable but high rates of anti-MRSA and broad-spectrum beta-lactam use within the first 48 hours of admission. The median inpatient and total length of therapy of 5 and 6 days, respectively, were similar to guideline-recommended durations. The up-front cost for building analytical tools can be substantial, but can be viewed as an investment if the metrics and methods are carefully selected. Disclosures All Authors: No reported disclosures

Development of a specialty medication clinical dashboard to improve tumor necrosis factor-α inhibitor safety and adherence monitoring.

To describe the development of a pilot specialty medication clinical dashboard targeting tumor necrosis factor (TNF)-α inhibitor therapy. This was a quality improvement project conducted between August 2019 and April 2020. The dashboard was designed with collaboration between clinical pharmacists and specialty providers in rheumatology, gastroenterology, and dermatology. Data was queried from the Veterans Affairs Corporate Data Warehouse. Patients with an active prescription or intravenous order for a TNF-α inhibitor were included. Dashboard flag criteria focused on TNF-α inhibitor safety and adherence monitoring. Flag results from the dashboard were characterized from data captured at a single time point. For 431 patients on TNF-α inhibitor treatment at the institution, 304 flags corresponding to 223 unique patients (51.7%) were identified on the dashboard: 3% of patients had a new infection, 9% had overdue monitoring laboratory tests, 5% had a critical laboratory result, 2% were on 2 biologic agents, 27% were overdue for a refill, 6% had an emergency department visit, and 2% had an inpatient admission. No patients were flagged for heart failure exacerbation or new malignancy. Seventeen percent of patients were prescribed high-dose etanercept or adalimumab, representing a potential annual cost savings of $302,497 if 50% of these patients had their dose successfully reduced to labeled dosing. Opportunities for pharmacist intervention utilizing the dashboard were identified and characterized through chart review of flagged patients. Pharmacists have the opportunity to improve safety and adherence for TNF-α inhibitor therapy through use of a specialty medication clinical dashboard. The dashboard should be used in conjunction with collaborative practice protocols.

Toward Building a Provincial Diabetes Registry of Children and Youth Living With Diabetes in British Columbia, Canada

ObjectivesPediatric diabetes health-care providers and decision-makers in British Columbia (BC) have prioritized the creation of a provincial pediatric diabetes clinical registry to improve care quality. Our objective is to build the first BC Pediatrics Diabetes Registry (BC-PDR) for quality improvement and coordination of pediatric diabetes care across the province. MethodsPatients <19 years of age and diagnosed with diabetes were invited to participate in our study. Recruitment began in 2017 at the BC Children’s Hospital (BCCH) and expanded to 6 community-based pediatric diabetes clinics in the Interior Health Authority (HA) in 2019. In response to COVID-19, recruitment shifted from in-person to virtual using an electronic consent system. Patient-level (e.g. age at diabetes onset, ethnicity) and visit-level (e.g. glycated hemoglobin [A1C], blood pressure, diabetes regimen, technology use, medications) data were collected in addition to screening for and presence of diabetes complications. ResultsAs of January 2021, 635 patients from the BCCH and Interior HA were included in the BC-PDR. From the BCCH, 94% of 590 patients were diagnosed with type 1 diabetes and the median A1C was 7.8% and increased with age. Just under half of the BCCH patients were using insulin pump technology and/or a continuous glucose monitoring system. ConclusionsOver the last 3 years, we have worked to adapt and operationalize the BC-PDR. The next steps for the BC-PDR include engaging diabetes stakeholders in the development of an electronic benchmarking dashboard along with linkage of the data to patient-reported outcome and experience measures and provincial administrative databases.

Development of a Big Data Radiation Oncology Dashboard

<h3>Purpose/Objective(s)</h3> Healthcare data often exist in silos and in unstructured formats that limit interoperability and require tedious manual extraction. Our institution has adopted a flexible and scalable big data platform built on Hadoop that integrates data from Epic/Clarity as well as Aria and allows users to leverage modern data science tools to facilitate access. We hypothesize that a data analytics and visualization dashboard can be built using open-source tools that will (1) allow non-technical users to explore de-identified clinical data within our institutional big data platform and (2) connect with repositories of molecular data to demonstrate potential methods of integrating clinical and basic science data. <h3>Materials/Methods</h3> De-identified patient-level radiation oncology data from the institutional big data platform (Hadoop) were extracted with the python packages pyodbc and pandas. For the purposes of this dashboard, radiation oncology specific clinical data elements were queried including the date of first radiation treatment, treatment location, treatment modality (SBRT, external beam, SRS, TBI, LDR/HDR brachytherapy), ICD10 codes, anatomic treatment site, number of fractions, treatment prescription, and dose per fraction. A python client connection with the publicly accessible instance of cBioPortal for Cancer Genomics was established using the Bravado library. Data transformation and cleaning was performed in python using panda's data frames. A web-based dashboard to facilitate user-defined visualizations was implemented using the Dash python library and interactive visualizations of subsets of extracted data were generated in real-time using the plotly plotting library. <h3>Results</h3> We developed a web-based dashboard that gives users without extensive programming expertise the ability to explore de-identified clinical data extracted from Hadoop. As proof of principle, the dashboard was used to visualize the clinical impact of the COVID-19 pandemic on radiation oncology patient volumes, revealing a significant decline in new radiation treatments in April and May of 2020 (-54% and -36% compared to 2019) during the initial COVID-19 surge. Furthermore, the dashboard allows users to interact with the cBioPortal for Cancer Genomics repository, which currently houses clinical and molecular data from 301 publicly available studies spanning 869 different cancer types. This interface with cBioPortal illustrates the potential for future integration of clinically meaningful sequencing results with clinical outcomes data. <h3>Conclusion</h3> We built an interactive web-based dashboard to enable general users' easy access to de-identified clinical data stored within the institutional big data platform. Additional data sources, including external molecular data can be connected to the dashboard allowing for future integration.

Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study.

BackgroundWeb-based public reporting by means of dashboards has become an essential tool for governments worldwide to monitor COVID-19 information and communicate it to the public. The actionability of such dashboards is determined by their fitness for purpose—meeting a specific information need—and fitness for use—placing the right information into the right hands at the right time and in a manner that can be understood.ObjectiveThe aim of this study was to identify specific areas where the actionability of the Dutch government’s COVID-19 dashboard could be improved, with the ultimate goal of enhancing public understanding of the pandemic.MethodsThe study was conducted from February 2020 to April 2021. A mixed methods approach was carried out, using (1) a descriptive checklist over time to monitor changes made to the dashboard, (2) an actionability scoring of the dashboard to pinpoint areas for improvement, and (3) a reflection meeting with the dashboard development team to contextualize findings and discuss areas for improvement.ResultsThe dashboard predominantly showed epidemiological information on COVID-19. It had been developed and adapted by adding more in-depth indicators, more geographic disaggregation options, and new indicator themes. It also changed in target audience from policy makers to the general public; thus, a homepage was added with the most important information, using news-like items to explain the provided indicators and conducting research to enhance public understanding of the dashboard. However, disaggregation options such as sex, socioeconomic status, and ethnicity and indicators on dual-track health system management and social and economic impact that have proven to give important insights in other countries are missing from the Dutch COVID-19 dashboard, limiting its actionability.ConclusionsThe Dutch COVID-19 dashboard developed over time its fitness for purpose and use in terms of providing epidemiological information to the general public as a target audience. However, to strengthen the Dutch health system’s ability to cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, we advise (1) establishing timely indicators relating to health system capacity, (2) including relevant data disaggregation options (eg, sex, socioeconomic status), and (3) enabling interoperability between social, health, and economic data sources.

Development, Implementation, and User Evaluation of COVID-19 Dashboard in a Third-Level Hospital in Iran.

The implementation of a dashboard enables managers to make informed and evidence-based decisions through data visualization and graphical presentation of information. This study aimed to design and implement a COVID-19 management dashboard in a third-level hospital in Mashhad, Iran. This descriptive developmental applied study was conducted in the second half of 2020 in three stages, using user-centered design methodology in four phases: (1) specification of the application context, (2) specification of requirements, (3) creation of design solutions, and (4) evaluation of designs. Data collection in each phase was performed through holding group discussions with the main users, nominal group techniques, interviews, and questioners. The dashboard prototype for the data display was designed using the Power BI Desktop software. Subsequently, users' comments were obtained using the focus group method and included in the dashboard. In total, 25 indicators related to input, process, and output areas were identified based on the findings of the first stage. Moreover, eight items were introduced by participants as dashboard requirements. The dashboard was developed based on users' feedback and suggestions, such as the use of colors, reception of periodic and specific reports based on key performance indicators, and rearrangement of the components visible on the page. The result of the user satisfaction survey indicated their satisfaction with the developed dashboard. The selection of proper criteria for the implementation of an effective dashboard is critical for the health care organization since they are designed with a high-tech and content-based environment. The dashboard in the present study was a successful combination of clinical and managerial indicators. Future studies should focus on the design and development of dashboards, as well as benchmarking by using data from several hospitals.

28 Geriatric Emergency Department Accreditation Accelerates Integration of Geriatric Care at the Veterans Health Administration

Geriatric emergency department (GED) accreditation (GEDA) through the American College of Emergency Physicians requires developing a strategic plan for education, equipment, policies, environment, staffing, and quality improvement. To support Veterans Affairs (VA) EDs in achieving accreditation, an interdisciplinary group of leaders in the VA National Offices of Emergency Medicine and Geriatrics and Extended Care and subject matter experts created a council that hosted a bootcamp, mentored support, created a Web site housing sample policies, staffing descriptions, workflows, and order sets. Additional individualized support was provided to assist with implementation of GED processes during the COVID-19 crisis. We sought to identify feedback on the accreditation process to inform onboarding of future cohorts. This was a dissemination plan for GED implementation at 20 VA EDs. VA medical centers were invited to submit an application for GEDA mentored support and fee waiver. An in-person kickoff bootcamp was held February 2020 for 10 VA EDs applying for Level 1 or Level 2 GEDA. As part of the planned quality review and to prepare for future VA GEDA, we performed follow-up surveys on the accreditation process, new interdisciplinary services relationships established, outcomes, and feedback on the process. A total of 26/110 VA facilities with EDs applied for VA GEDA support; 10 were accepted for the in person bootcamp and virtual support and 10 additional for virtual support only. Nineteen supported sites have applied for accreditation and 14 have received accreditation: one level 1, four level 2, and nine level 3. Mean ED visit volume in 2019 for accredited sites was 25, 475 (range 8, 212-42, 589 visits/year) with 47% of visits made by veterans 65 years or older (range 33-60%). Sixteen sites responded to the survey. Ninety-four percent (15/16) described new interdisciplinary services (figure 1). When asked what accomplishment associated with the accreditation process they were proud of, 6 reported ED outcome improvements, 12/16 reported connecting patients to VA services, and 13/16 reported improved patient care team engagement. Feedback on the process suggested the need for standardization of data collection, workflow, and documentation, which has led to the development of a central dashboard and patient care note with standardized geriatric screens and assessments. A central supported system allowed 14 VA EDs to achieve geriatric ED accreditation despite the COVID pandemic. Almost all facilities reported they formed new associations with interdisciplinary services as a result of the accreditation process. More than half reported improved patient care team engagement and connection to VA services as additional accomplishments. Feedback has led to increased standardization of geriatric emergency care assessments across the healthcare system.

Exploring Associations Between Children's Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard.

BackgroundObesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project “BigO: Big Data Against Childhood Obesity” aims to tackle childhood obesity by creating new sources of evidence based on big data.ObjectiveThis paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app.MethodsThe OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children’s obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard’s functionality was evaluated during a focus group discussion with public health experts.ResultsThe preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children’s eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children’s physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard’s potential to aid in a better understanding of the interplay between children’s obesogenic behaviors and the environment, and improvements were suggested.ConclusionsOur analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.

Development and Implementation of a Real-time Bundle-adherence Dashboard for Central Line-associated Bloodstream Infections.

Introduction:Central line-associated bloodstream infections (CLABSIs) are the most common hospital-acquired infection in pediatric patients. High adherence to the CLABSI bundle mitigates CLABSIs. At our institution, there did not exist a hospital-wide system to measure bundle-adherence. We developed an electronic dashboard to monitor CLABSI bundle-adherence across the hospital and in real time.Methods:Institutional stakeholders and areas of opportunity were identified through interviews and data analyses. We created a data pipeline to pull adherence data from twice-daily bundle checks and populate a dashboard in the electronic health record. The dashboard was developed to allow visualization of overall and individual element bundle-adherence across units. Monthly dashboard accesses and element-level bundle-adherence were recorded, and the nursing staff’s feedback about the dashboard was obtained.Results:Following deployment in September 2018, the dashboard was primarily accessed by quality improvement, clinical effectiveness and analytics, and infection prevention and control. Quality improvement and infection prevention and control specialists presented dashboard data at improvement meetings to inform unit-level accountability initiatives. All-element adherence across the hospital increased from 25% in September 2018 to 44% in December 2019, and average adherence to each bundle element increased between 2018 and 2019.Conclusions:CLABSI bundle-adherence, overall and by element, increased across the hospital following the deployment of a real-time electronic data dashboard. The dashboard enabled population-level surveillance of CLABSI bundle-adherence that informed bundle accountability initiatives. Data transparency enabled by electronic dashboards promises to be a useful tool for infectious disease control.

A scalable platform for investigating the space-specific features of the temporal energy usage pattern and saving potential with real-time bigdata

The Korean government has initiated a “Green New Deal” policy, in which a smart energy platform was proposed for more efficient building energy management that could monitor energy consumption in real time and control electrical installation and appliance in a timely manner. With the energy bigdata collected via IoT-based smart energy meter, this study aimed to develop a scalable platform for investigating the space-specific features of the temporal energy usage pattern and the relevant energy saving potential by electrical installation (i.e., lighting, electrical appliances, heating & cooling systems) in a building. This study was conducted in four stages: (i) real-time bigdata collection; (ii) development of dashboard; (iii) scalable analysis of energy usage pattern; and (iv) scalable analysis of energy saving potential. Main finding can be summarized as follow. First, more than 50% of the energy usage in main spaces (i.e., classrooms) was consumed by the heating & cooling systems. Second, the total energy saving potential was estimated at 11.50% when applying the developed actuator to the standby power and electrical radiator. Third, the annual cost saving potential as economic feasibility was estimated at 11.13%. The proposed scalable platform can contribute to investigating the energy usage pattern by electrical installation from a spatio-temporal perspective and to changing the occupants’ behaviors for energy savings by considering space-specific features and occupancy patterns. The proposed novel approach can be extended to various types of buildings.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

BackgroundThe COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States.ObjectiveWe aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events?MethodsWe analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states.ResultsThe data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others.ConclusionsCOVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Anov: A Framework for Rapid Construction and Development of Web-Based Large Screen Visual Dashboard

With the advent of the era of big data, extract and display effective information from massive amount of data has become a hot research field. Visualization is the most direct way for users to perceive and understand data. That’s why data dashboard with its cool dynamic visual effect has become so popular. In this paper, we propose a framework for rapid construction and development of web-based large screen visual dashboard—Anov. Anov dedicates to three goals: high efficiency, innovative AI user interaction and 3D scene. First, this framework provides a “30% configuration, 70% customization” development scheme which shortens the development cycle by at least one third. Second, Anov adopts a hierarchical architecture model to achieve high reusability, easy extensibility, high reliability, and easy maintenance. Finally, this paper introduces Anov’s wide range of application field and rich user experience through multiple cases to demonstrate the practicality of the framework.