Demo Data from EDBT 2026 / reviewers in the wild / expert
Lynn McVey
dblp:235/5610
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visualization design
dashboard design |
0.5 | 1 | 2021 | QualDash: Adaptable Generation of Visualisation Dashboards for Healthcare Quality Improvement · IEEE Trans. Vis. Comput. Graph. 2021 |
Methods — techniques the papers use, named apart from their topics
task analysis · 0.5design study · 0.5co-design · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Designing health IT to support falls prevention in hospitals: Findings from a realist review
Rebecca Randell, Lynn McVey, Hadar Zaman, Judy M. Wright, V.-Lin Cheong, Dawn Dowding, Peter Gardner 0002, Nicholas R. Hardiker, Frances Healey, Alison Lynch, Natasha Alvarado |
AMIA | 2 |
| 2021 | QualDash: Adaptable Generation of Visualisation Dashboards for Healthcare Quality ImprovementabstractAdapting 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. Mai El-Shehaly, Rebecca Randell, Matthew Brehmer, Lynn McVey, Natasha Alvarado, Chris Gale, Roy A. Ruddle |
IEEE Trans. Vis. Comput. Graph. | 4 |
| 2019 | Requirements for a quality dashboard: Lessons from National Clinical Audits
Rebecca Randell, Natasha Alvarado, Lynn McVey, Roy A. Ruddle, Chris Gale, Mamas Mamas, Dawn Dowding |
AMIA | 3 |