ArticleBMJ open2016
Visualising linked health data to explore health events around preventable hospitalisations in NSW Australia.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Adverse health outcomes in violent crime convicted persons: risk factors for somatic inpatient healthcare utilization.BMC public health · 2026Article
- Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.Journal of medical Internet research · 2020Article
- Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review.JMIR research protocols · 2019Article
- A time-duration measure of continuity of care to optimise utilisation of primary health care: a threshold effects approach among people with diabetes.BMC health services research · 2019Observational
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveTo explore patterns of health service use in the lead-up to, and following, admission for a 'preventable' hospitalisation.
setting266 950 participants in the 45 and Up Study, New South Wales (NSW) Australia
methodsLinked data on hospital admissions, general practitioner (GP) visits and other health events were used to create visual representations of health service use. For each participant, health events were plotted against time, with different events juxtaposed using different markers and panels of data. Various visualisations were explored by patient characteristics, and compared with a cohort of non-admitted participants matched on sociodemographic and health characteristics. Health events were displayed over calendar year and in the 90 days surrounding first preventable hospitalisation.
resultsThe visualisations revealed patterns of clustering of GP consultations in the lead-up to, and following, preventable hospitalisation, with 14% of patients having a consultation on the day of admission and 27% in the prior week. There was a clustering of deaths and other hospitalisations following discharge, particularly for patients with a long length of stay, suggesting patients may have been in a state of health deterioration. Specialist consultations were primarily clustered during the period of hospitalisation. Rates of all health events were higher in patients admitted for a preventable hospitalisation than the matched non-admitted cohort.
conclusionsWe did not find evidence of limited use of primary care services in the lead-up to a preventable hospitalisation, rather people with preventable hospitalisations tended to have high levels of engagement with multiple elements of the healthcare system. As such, preventable hospitalisations might be better used as a tool for identifying sicker patients for managed care programmes. Visualising longitudinal health data was found to be a powerful strategy for uncovering patterns of health service use, and such visualisations have potential to be more widely adopted in health services research.
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