The Big Book of Dashboards

Wexler, Steve; Shaffer, Jeffrey; Cotgreave, Andy

doi:10.1002/9781119283089

Cited by 86 publications

(23 citation statements)

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“…6. Descriptive analytics often includes building a KPI-tailored dashboard that allows user interaction to gain useful KPI insights (Wexler et al 2017). Tool 3 facilitates clear dashboards to be circulated among stakeholders to ensure that every stakeholder is informed on the KPIs related to the important AM subject areas.…”

Section: Adjacency Matrixmentioning

confidence: 99%

Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Goforth

El‐Dakhakhni

Wiebe

2022

J. Infrastruct. Syst.

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The ever-increasing investment gap for deteriorating infrastructure has necessitated the development of more effective asset management (AM) strategies. However, information asymmetry among AM stakeholder silos has been recognized as a key challenge in implementing effective AM strategies. The connectivity within the AM system introduces systemic risks (possibility of dependenceinduced cascade failure) to the entire AM system operation when information asymmetry occurs. This study describes a toolbox to enable asset management stakeholders to assess such systemic risks through a network analytics approach. The network, representing the AM system, is examined through its centrality measures to identify the most critical subject areas within the AM system. These subject areas are subsequently paired with assets' key performance indicators (KPIs). Within the developed toolbox, descriptive analytics provide transferrable KPI insights between stakeholders to reduce key asset information asymmetry. In parallel, predictive analytics forecast KPIs, ensuring stakeholder awareness of future asset performance to allow for appropriate preparation. Subsequently, prescriptive analytics employ heuristic-based optimization for optimal configuration of the AM network. The five tools presented are as follows: (1) dependence identification and network modeling; (2) network centrality analysis; (3) descriptive analytics of critical subject area paired KPI; (4) KPI-based predictive analytics; and (5) prescriptive analytics for optimal network configuration. The utility of the developed toolbox is demonstrated for Tools 1-3 using a real AM system network and KPIs associated with power transmission infrastructure outages. Based on the analyses, managerial insights are drawn to illustrate the usefulness of the developed approach in improving information asymmetry within the AM system, subsequently mitigating dependence-induced systemic risks.

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Section: Adjacency Matrixmentioning

confidence: 99%

Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Goforth

El‐Dakhakhni

Wiebe

2022

J. Infrastruct. Syst.

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“…Healthcare dashboards are a type of health information technology (HIT) (Dowding et al, 2015 ) that present information relevant to the quality of patient care in a timely manner (Daley et al, 2013 ). Dashboards allow raw tabulated data to be organised into clear visuals that provide important information relevant to the user (Wexler et al, 2017 ). Within healthcare there are two main types of dashboards: clinical dashboards that focus on the performance of individual clinicians or patients (Dowding et al, 2018 ) and quality dashboards for evaluating the performance of a unit such as a ward (Kroch et al, 2006 ; Keen et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

Requirements for a Dashboard to Support Quality Improvement Teams in Pain Management

et al. 2021

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Pain management is often considered lower priority than many other aspects of health management in hospitals. However, there is potential for Quality Improvement (QI) teams to improve pain management by visualising and exploring pain data sets. Although dashboards are already used by QI teams in hospitals, there is limited evidence of teams accessing visualisations to support their decision making. This study aims to identify the needs of the QI team in a UK Critical Care Unit (CCU) and develop dashboards that visualise longitudinal data on the efficacy of patient pain management to assist the team in making informed decisions to improve pain management within the CCU. This research is based on an analysis of transcripts of interviews with healthcare professionals with a variety of roles in the CCU and their evaluation of probes. We identified two key uses of pain data: direct patient care (focusing on individual patient data) and QI (aggregating data across the CCU and over time); in this paper, we focus on the QI role. We have identified how CCU staff currently interpret information and determine what supplementary information can better inform their decision making and support sensemaking. From these, a set of data visualisations has been proposed, for integration with the hospital electronic health record. These visualisations are being iteratively refined in collaboration with CCU staff and technical staff responsible for maintaining the electronic health record. The paper presents user requirements for QI in pain management and a set of visualisations, including the design rationale behind the various methods proposed for visualising and exploring pain data using dashboards.

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“…9 The best practices in creating statistical graphics are discussed extensively in the books by Cleveland and Tufte. 9,10 There is also rich literature on developing effective real-world dashboards, 11 and interactive visualization for the web. 12 We check for the presence of visuals in the form of trend graphics while assessing the quality of data reporting.…”

Section: Introductionmentioning

confidence: 99%

Disparity in the quality of COVID-19 data reporting across India

Vasudevan¹,

Gnanasekaran²,

Sankar³

et al. 2020

Preprint

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Background. Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each state and union territory (UT) of India has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state and union territory governments. This assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting by other governments. Methods. We designed a semi-quantitative framework to assess the quality of COVID-19 data reporting done by the states and union territories of India. This framework captures four key aspects of public health data reporting - availability, accessibility, granularity, and privacy. We then used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0 to 1) for 29 states based on the quality of COVID-19 data reporting done by the state during the two-week period from 19 May to 1 June, 2020. States that reported less than 10 total confirmed cases as of May 18 were excluded from the study. Findings. Our results indicate a strong disparity in the quality of COVID-19 data reporting done by the state governments in India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Only ten states provide a visual representation of the trend in COVID-19 data. Ten states do not report any data stratified by age, gender, comorbidities or districts. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by releasing their personally identifiable information on the official websites. Across the states, the CDRS is positively associated with the state's sustainable development index for good health and well-being (Pearson correlation: r=0.630, p=0.0003). Interpretation. The disparity in CDRS across states highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights from them, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states in India. Coordination among states is particularly important as more people start moving across states in the coming months. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence. Funding. J.Z. is supported by NSF CCF 1763191, NIH R21 MD012867-01, NIH P30AG059307, NIH U01MH098953 and grants from the Silicon Valley Foundation and the Chan-Zuckerberg Initiative.

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The Big Book of Dashboards

Cited by 86 publications

References 0 publications

Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Network Analytics for Infrastructure Asset Management Systemic Risk Assessment

Requirements for a Dashboard to Support Quality Improvement Teams in Pain Management

Disparity in the quality of COVID-19 data reporting across India

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