Optimising data visualisation formats for reporting medication-related care quality indicators: residential aged care staff preferences, interpretations and actions.

Abstract

Data visualisations are widely used to communicate complex performance information within healthcare quality improvement feedback cycles. When tailored to their intended audience, these visualisations have the potential to stimulate change to improve care quality. This study assessed which data visualisation formats Australian residential aged care (RAC) staff interpret correctly, prefer and find informative enough to prompt action to improve care quality. A 37-item survey was administered via REDCap to Australian RAC staff between September and November 2024. The survey included demographic and data visualisation questions. Participants evaluated five cross-sectional data visualisation formats-table, star rating, bar chart, funnel plot and caterpillar plot-and two longitudinal data visualisation formats-box plot and line graph-based on interpretability, preference and actionability. Quantitative data were analysed descriptively, while qualitative responses underwent content analysis. 42 RAC staff participated in the survey. Respondents favoured bar charts for reporting cross-sectional data (n=23, 55%) with 79% (n=33) interpreting them correctly. For longitudinal data, 30 respondents (91%) favoured line graphs, with 71% (n=24) interpreting them accurately. Star ratings were the least preferred cross-sectional data visualisation format due to insufficient contextual information. In open-ended responses, respondents suggested that data visualisations would prompt them to review outliers, conduct medication reviews and educate staff. However, some respondents indicated that more contextual information, such as facility size and resident characteristics, was required in visualisations before they would take action to improve care quality. Bar charts and line graphs appeared to be the most suitable visualisation formats to convey medication-related care quality information to RAC staff for cross-sectional and longitudinal data respectively, as they were generally interpreted correctly with relative ease, were perceived as actionable and were preferred by most participants. In contrast, complex visualisations, such as funnel and box plots, were not preferred and were misinterpreted by half or more of the participants.