Literature DB >> 33048727

QualDash: Adaptable Generation of Visualisation Dashboards for Healthcare Quality Improvement.

Mai Elshehaly, Rebecca Randell, Matthew Brehmer, Lynn McVey, Natasha Alvarado, Chris P Gale, Roy A Ruddle.   

Abstract

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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Year:  2021        PMID: 33048727     DOI: 10.1109/TVCG.2020.3030424

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  4 in total

1.  RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses.

Authors:  M Chen; A Abdul-Rahman; D Archambault; J Dykes; P D Ritsos; A Slingsby; T Torsney-Weir; C Turkay; B Bach; R Borgo; A Brett; H Fang; R Jianu; S Khan; R S Laramee; L Matthews; P H Nguyen; R Reeve; J C Roberts; F P Vidal; Q Wang; J Wood; K Xu
Journal:  Epidemics       Date:  2022-04-28       Impact factor: 5.324

2.  Analysis of a Web-Based Dashboard to Support the Use of National Audit Data in Quality Improvement: Realist Evaluation.

Authors:  Natasha Alvarado; Lynn McVey; Mai Elshehaly; Joanne Greenhalgh; Dawn Dowding; Roy Ruddle; Chris P Gale; Mamas Mamas; Patrick Doherty; Robert West; Richard Feltbower; Rebecca Randell
Journal:  J Med Internet Res       Date:  2021-11-23       Impact factor: 5.428

3.  Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

Authors:  Jason Dykes; Alfie Abdul-Rahman; Daniel Archambault; Benjamin Bach; Rita Borgo; Min Chen; Jessica Enright; Hui Fang; Elif E Firat; Euan Freeman; Tuna Gönen; Claire Harris; Radu Jianu; Nigel W John; Saiful Khan; Andrew Lahiff; Robert S Laramee; Louise Matthews; Sibylle Mohr; Phong H Nguyen; Alma A M Rahat; Richard Reeve; Panagiotis D Ritsos; Jonathan C Roberts; Aidan Slingsby; Ben Swallow; Thomas Torsney-Weir; Cagatay Turkay; Robert Turner; Franck P Vidal; Qiru Wang; Jo Wood; Kai Xu
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2022-08-15       Impact factor: 4.019

4.  Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach.

Authors:  Ekaterina Ignatenko; Manuel Ribeiro; Mónica D Oliveira
Journal:  Int J Environ Res Public Health       Date:  2022-09-02       Impact factor: 4.614

  4 in total

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