Evidence map›Paper›PMID 30840080›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2019

Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation.

Danny T Y Wu, Annie T Chen, John D Manning, Gal Levy-Fix, Uba Backonja, David Borland, Jesus J Caban, Dawn W Dowding, Harry Hochheiser, Vadim Kagan and 5 more

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Systematic review and narrative synthesis of computerized audit and feedback systems in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2022
    Pooled it
  2. Pooled it
  3. KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
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  9. Food and Nutrition Systems Dashboards: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2022
    Article
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  12. Review
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  20. Advancing biomedical and health informatics knowledge through reviews of existing research.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Danny T Y WuDepartment of Biomedical Informatics, University of Cincinnati, Cincinnati, Ohio, USA.
Annie T ChenDepartment of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington, USA.
John D ManningDepartment of Emergency Medicine, Atrium Health's Carolinas Medical Center, Charlotte, North Carolina, USA.
Gal Levy-FixDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Uba BackonjaDepartment of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington, USA.
David BorlandRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Jesus J CabanNational Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.
Dawn W DowdingDivision of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, United Kingdom.
Harry HochheiserDepartment of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Vadim KaganSentiMetrix, Inc, Bethesda, Maryland, USA.
Swaminathan KandaswamyDepartment of Mechanical and Industrial Engineering, University of Massachusetts at Amherst, Amherst, Massachusetts, USA.
Manish KumarMEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Alexis Nunez
Eric PanHealthcare Delivery Research and Evaluation, Westat, Rockville, Maryland, USA.
David GotzCarolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices.

methodsA systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design.

resultsAfter screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. DISCUSSION: When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings.

conclusionEvaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.

Indexed as

Data VisualizationEvaluation Studies as TopicMedical Informatics ApplicationsInformation Storage and Retrievalevaluation studies (V03.400)MeSH termsreview (V02.600.500)

Identifiers

PMID30840080
PMCPMC7647177

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.