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|a 10.1109/TVCG.2020.3030424
|2 doi
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|a eng
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| 100 |
1 |
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|a Elshehaly, Mai
|e verfasserin
|4 aut
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| 245 |
1 |
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|a QualDash
|b Adaptable Generation of Visualisation Dashboards for Healthcare Quality Improvement
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| 264 |
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|c 2021
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|a Text
|b txt
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|a Date Completed 13.01.2022
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|a Date Revised 13.01.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Adapting dashboard design to different contexts of use is an open question in visualisation research. Dashboard designers often seek to strike a balance between dashboard adaptability and ease-of-use, and in hospitals challenges arise from the vast diversity of key metrics, data models and users involved at different organizational levels. In this design study, we present QualDash, a dashboard generation engine that allows for the dynamic configuration and deployment of visualisation dashboards for healthcare quality improvement (QI). We present a rigorous task analysis based on interviews with healthcare professionals, a co-design workshop and a series of one-on-one meetings with front line analysts. From these activities we define a metric card metaphor as a unit of visual analysis in healthcare QI, using this concept as a building block for generating highly adaptable dashboards, and leading to the design of a Metric Specification Structure (MSS). Each MSS is a JSON structure which enables dashboard authors to concisely configure unit-specific variants of a metric card, while offloading common patterns that are shared across cards to be preset by the engine. We reflect on deploying and iterating the design of OualDash in cardiology wards and pediatric intensive care units of five NHS hospitals. Finally, we report evaluation results that demonstrate the adaptability, ease-of-use and usefulness of QualDash in a real-world scenario
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Randell, Rebecca
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Brehmer, Matthew
|e verfasserin
|4 aut
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| 700 |
1 |
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|a McVey, Lynn
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Alvarado, Natasha
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gale, Chris P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ruddle, Roy A
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 27(2021), 2 vom: 13. Feb., Seite 689-699
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|x 1941-0506
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|g volume:27
|g year:2021
|g number:2
|g day:13
|g month:02
|g pages:689-699
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|u http://dx.doi.org/10.1109/TVCG.2020.3030424
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