Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization
Abstract
The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.
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Visualizing health data is one of the most effective methods for providing insight and facilitating patient-provider communication.
“Visualizing health data is one of the most effective methods for providing insight and facilitating patient-provider communication [12, 19].”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [19]
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References 12 · Cited by 120
- Authors
- Gotz, D., & Borland, D.
- Affiliations
- University of North Carolina at Asheville (US)
- Venue
- IEEE Computer Graphics and Applications
- Published
- 2016-05
- Volume
- 36
- Issue
- 3
- Pages
- 90-96
- ISSN
- 0272-1716 (ISSN-L)
- OpenAlex
- W2347052952
- Cited by
- 120
- References
- 12
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 22 · 2023: 18 · 2022: 15 · 2021: 10 · 2020: 7 · 2019: 8 · 2018: 14 · 2017: 9 · 2016: 3
- FWCI
- 7.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Graphics and Applications (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- interactive visualization, data-driven healthcare, health data, personalized treatment, high-risk patient identification, clinical decision support, digitalization of healthcare
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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