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Impact of format and content of visual display of data on comprehension, choice and preference: a systematic review
Zoe Hildon1, Dominique Allwood, Nick Black
1Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK. zoe.hildon@lshtm.ac.uk
Purpose:
Displays comparing the performance of healthcare providers are largely based on commonsense. To review the literature on the impact of compositional format and content of quantitative data displays on people's comprehension, choice and preference.
Data Sources:
Ovid databases, expert recommendations and snowballing techniques.
Study Selection:
Evaluations of the impact of different formats (bar charts, tables and pictographs) and content (ordering, explanatory visual cues, etc.) of quantitative data displays meeting defined quality criteria. Data extraction Type of decision; decision-making domains; audiences; formats; content; methodology; findings.
Results Of Data Synthesis:
Most of the 30 studies used quantitative (n= 26) methods with patients or public groups (n= 28) rather than with professionals (n= 2). Bar charts were the most frequent format, followed by pictographs and tables. As regards format, tables and pictographs appeared better understood than bar charts despite the latter being preferred. Although accessible to less numerate and older populations, pictographs tended to lead to more risk avoidance. Tables appeared accessible to all. Aspects of content enhancing the impact of data displays included giving visual explanatory cues and contextual information while still attempting simplicity ('less is more'); ordering data; consistency. Icons rather than numbers were more user-friendly but could lead to over-estimation of risk. Uncertainty was not widely understood, nor well represented.
Conclusions:
Though heterogeneous and limited in scope, there is sufficient research evidence to inform the presentation of quantitative data that compares the performance of healthcare providers. The impact of new formats, such as funnel plots, needs to be evaluated.
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