scholarly journals Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards

10.2196/25682 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25682
Author(s):  
Damir Ivanković ◽  
Erica Barbazza ◽  
Véronique Bos ◽  
Óscar Brito Fernandes ◽  
Kendall Jamieson Gilmore ◽  
...  
Keyword(s):  
Decision Making ◽  
Time Trends ◽  
Information Needs ◽  
Assessment Tool ◽  
Data Sources ◽  
Nominal Group ◽  
Full Potential ◽  
Web Based ◽  
Descriptive Assessment ◽  
Common Features

Background Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. Objective The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (“why”), content and data (“what”), and analyses and displays (“how” they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. Methods We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. Results A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are “close to home”; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. Conclusions COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified.

scholarly journals Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards (Preprint)

2020 ◽  
Author(s):  
Damir Ivanković ◽  
Erica Barbazza ◽  
Véronique Bos ◽  
Óscar Brito Fernandes ◽  
Kendall Jamieson Gilmore ◽  
...  
Keyword(s):  
Decision Making ◽  
Time Trends ◽  
Information Needs ◽  
Assessment Tool ◽  
Data Sources ◽  
Nominal Group ◽  
Full Potential ◽  
Web Based ◽  
Descriptive Assessment ◽  
Common Features

BACKGROUND Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. OBJECTIVE The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (“why”), content and data (“what”), and analyses and displays (“how” they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. METHODS We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. RESULTS A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are “close to home”; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. CONCLUSIONS COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified.

Implementing Information Technology to Improve Workplace Health: A Web-Based Information Needs Assessment of Managers in Fraser Health, British Columbia

2005 ◽  
Vol 18 (4) ◽  
pp. 6-10 ◽  
Author(s):  
Jag S. Sandhu ◽  
Keith Anderson ◽  
Dave Keen ◽  
Annalee Yassi
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
British Columbia ◽  
Questionnaire Survey ◽  
Information Needs ◽  
Health And Safety ◽  
Workplace Health ◽  
Historical Trends ◽  
Web Based ◽  
Support Decision Making

A web-based questionnaire-survey was administered primarily to determine what information is useful to managers in Fraser Health, of British Columbia to support decision-making for workplace health and safety. The results indicated that managers prefer electronic quarterly reports, with targets, goals, and historical trends rated as “very important.” Over 85.7% “agree” that if information was readily available in the “most beneficial” format, they would be able to improve workplace health. Recommendations include that managers be presented with clear and concise workplace health reports that facilitate analysis for decision-making.

Measuring intra-operative decision-making during laparoscopic cholecystectomy: validity evidence for a novel interactive Web-based assessment tool

2016 ◽  
Vol 31 (3) ◽  
pp. 1203-1212 ◽  
Author(s):  
Amin Madani ◽  
Yusuke Watanabe ◽  
Elif Bilgic ◽  
Philip H. Pucher ◽  
Melina C. Vassiliou ◽  
...  
Keyword(s):  
Decision Making ◽  
Laparoscopic Cholecystectomy ◽  
Assessment Tool ◽  
Validity Evidence ◽  
Web Based

Measuring Decision-Making During Thyroidectomy: Validity Evidence for a Web-Based Assessment Tool

2017 ◽  
Vol 42 (2) ◽  
pp. 376-383 ◽  
Author(s):  
Amin Madani ◽  
Jordan Gornitsky ◽  
Yusuke Watanabe ◽  
Cassandre Benay ◽  
Maria S. Altieri ◽  
...  
Keyword(s):  
Decision Making ◽  
Assessment Tool ◽  
Validity Evidence ◽  
Web Based

scholarly journals Usage and impact metrics for Parliamentary libraries

IFLA Journal ◽  
2019 ◽  
Vol 45 (2) ◽  
pp. 104-113
Author(s):  
Tarek Al Baghal
Keyword(s):  
Decision Making ◽  
Data Collection ◽  
Significant Relationship ◽  
Information Needs ◽  
Data Science ◽  
Informed Decision ◽  
Data Sources ◽  
Informed Decision Making ◽  
House Of Lords

Parliamentary libraries are important in supporting informed decision-making in democracies. Understanding Members’ information needs is important, but the usage and impact of these libraries have been less explored. A particular example of the United Kingdom’s House of Lords Library is studied, collecting and analysing data using techniques from the field of data science. These techniques are useful in extracting information from existing sources that may not have been designed for the purpose of data collection. A number of data sources available at the Lords Library are outlined and an example of how these data can be used to understand Library usage and impact is presented. Results suggest that Member usage varies significantly and that there is a small but significant relationship between usage and making speeches in the chamber. Further work should explore other indicators of impact, but these methods show promise in creating library metrics, particularly in Parliamentary settings.

Structuring group decision making in a web-based environment by using the nominal group technique

2007 ◽  
Vol 52 (2) ◽  
pp. 277-295 ◽  
Author(s):  
Pilar Pazos Lago ◽  
Mario G. Beruvides ◽  
Jiun-Yin Jian ◽  
Ana Maria Canto ◽  
Angela Sandoval ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Nominal Group Technique ◽  
Nominal Group ◽  
Web Based ◽  
Group Technique

scholarly journals Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3296
Author(s):  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Taqdir Ali ◽  
Syed Imran Ali ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Machine Learning Algorithms ◽  
Nominal Group ◽  
Specific Information ◽  
Matching Techniques ◽  
Disease Specific

Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns “Heuristic Patterns” using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinusitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians’ performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation.

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