Evidence map›Paper›PMID 42711070›Full record

ArticleBMJ open quality2026

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

Isabelle Carmen Meulenbroeks, Rachel Urwin, Andrea Timothy, Ben Newell, Tracy Bucknall, Sangita Neupane, Tim Badgery-Parker, Karla Seaman, Johanna I Westbrook

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open quality, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Isabelle Carmen MeulenbroeksAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia isabelle.meulenbroeks@mq.edu.au.ORCID http://orcid.org/0000-0002-9083-7845
Rachel UrwinAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0001-7026-8043
Andrea TimothyAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Ben NewellSchool of Psychology, University of New South Wales, Sydney, New South Wales, Australia.
Tracy BucknallCentre for Quality and Patient Safety Research, Deakin University, Melbourne, Victoria, Australia.
Sangita NeupaneAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Tim Badgery-ParkerAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Karla SeamanAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Johanna I WestbrookAustralian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0003-1083-8192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionData 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.

methodsA 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.

results42 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.

conclusionBar 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.

Indexed as

Health PersonnelQuality Indicators, Health CareAdultAustraliaCross-Sectional StudiesFemaleHomes for the AgedHumansMaleQuality ImprovementSurveys and QuestionnairesContinuous quality improvementNursing homesQuality improvementSurveys

Identifiers

PMID42711070
PMCPMC13583859

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.