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Updated: Apr 9, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
Published on: February 16, 2011
Comprehension of and preferences for visualization of patient-reported outcome data to support clinical decision
Sneha Kannan1, Brenda J Bergink-Voorthuis1, Catharina G M Groothuis-Oudshoorn1
1Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands.
Objectives:
As Patient-Reported Outcome Measures (PROMs) become integrated into clinical care, interest grows in identifying effective visualization formats. Evidence on formats supporting user preference and comprehension remains limited. Moreover, communication of uncertainty around predicted PROMs remains underexplored. This review evaluates quantitative studies on PROM visualization across clinical domains, focusing on observed past and current scores as well as predicted scores at individual and group levels.
Methods:
This review followed PRISMA guidelines and was registered in PROSPERO (CRD42024505034). Systematic searches of PubMed, Scopus, and Web of Science were conducted for quantitative studies published between 2014 and 2024, assessing user preferences and comprehension for the visualization of PROMs. Two independent reviewers screened articles, extracted data, and assessed study quality using the Mixed Methods Appraisal Tool (MMAT). Data were synthesized descriptively according to outcome and visualization type.
Results:
Thirteen of the 7973 identified studies met inclusion and quality criteria. Seven studies assessed preferences, and seven studies assessed comprehension of visualization formats. For individual-level data, bar charts were preferred over line graphs in three studies, and heat maps in one study; for group-level data, two studies found pie charts preferred over bar charts and icon arrays. For comprehension, bar charts and line graphs were generally well understood, with accuracy ranging from 49 to 100% among patients and 90-100% among clinicians for individual-level data. For group-level data, pie charts were correctly interpreted by 52-95% of the population. One study assessed visualization of predicted PROMs, which patients found difficult to interpret.
Conclusions:
Preferences and comprehension of PROM visualizations vary by format, data type, and users. While bar charts, line graphs, and pie charts are preferred, no one-size-fits-all approach exists. Visualization of predicted outcomes remains underexplored.
Practice Implications:
Future research should study preferences for and comprehension of predicted PROM visualizations and apply user-centered design to support clinical decision-making.
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