Network analysis and practice
We analysed data drawn from publicly released datasets of all MBS claims during the year period — for a random sample of Australian residents downloaded from www. We constructed bipartite provider—patient networks, including two node types providers and patients , with edges indicating at least one consultation of a patient with a provider. PPCs were identified after network construction by graph partitioning or cluster identification.
In comparison with the traditional method of modularity maximisation, this technique is more robust for a very large graph and can overcome the problem of resolution limit, allowing small blocks to be detected. We calculated descriptive statistics for claims, patients, providers, and PPCs for each calendar year, but present full results only for the years , , , and Statistics related to claims and patients per provider or PPC were inflated by a factor of 10 to account for the sampling of the claims data.
Short items 3, 52 , standard items 23, 53 , and long and prolonged consultations items 36, 44, 54, 57 were classified by MBS item numbers. During —, an average 9. The numbers of claims and patients increased from 9 million claims for 1. The mean age of patients increased from 35 years standard deviation [SD], 22 years in to 39 years SD, 23 years in The number of providers increased from 24 in to 34 in Box 2 , and the numbers of claims and patients per provider were lower in than in Box 1.
Throughout the study period, the median number of practice locations per provider was one.
The proportion of providers who did not share patients with other providers declined from 0. While the median number of shared patients per provider was stable, the median number of peers with whom a provider shared patients decreased from interquartile range [IQR], 56— to 92 IQR, 50— Box 1.
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The estimated median number of claims for in-hours consultations per PPC increased from in to in ; the estimated number of patients per PPC rose from patients to Box 1. Box 4 depicts examples of PPC structures and corresponding metrics related to continuity of care and patient sharing. The proportions of claims for brief consultations less than 5 minutes: 1. There were corresponding changes in the proportions of patients whose consultations were all bulk-billed and of providers who bulk-billed all patients. As expected, the proportions of young under 13 years of age and older patients 75 years or older whose claims were bulk-billed were larger than for the overall population Box 5.
Our study is the first to analyse Australian Medicare claims data — and internationally the first to analyse a whole-of-population national sample — to construct real world GP provider—patient networks, to identify communities of GP providers, and to explore the characteristics of these communities, including novel metrics for describing patient sharing that can be generated only by network analysis.
Ours is also the first study of medical provider networks to apply a novel hierarchical block modelling algorithm that can effectively overcome the structural and computational hurdles associated with large scale data. No contemporary data for the number of general practices in Australia have been published with which we could compare our findings.
The total number of PPCs for generated by our analysis was similar to the estimated number of practices reported by the ASD for , based on unique locations.
Similarly, there are no recent comparable data on the sizes of general practices in Australia. For example, locums who see substantial numbers of patients may be included in a PPC but not be included in a practice survey.
2 Social Network Analysis and the Practice of History
Our data indicate that GP PPCs in Australia have grown in size, while the median number of claims per patient per year has remained stable over the past 20 years. These findings probably reflect a growing preference for flexible working hours, a higher proportion of part-time medical providers, and increasing corporatisation of practice ownership.
Usual provider continuity of care and patient loyalty were both stable during —; earlier population-level data for Australia are not available. We found that the density of patient sharing within a PPC was positively correlated with patient loyalty. The relationships between such metrics and patient outcomes could be investigated with the aim of informing practice design; for example, for developing team-based GP care models. Moreover, there were no external standard data with which we could compare our findings.
Including some specialists who claimed GP-like consultation items may have inflated the number of practitioners included in our analysis. PPCs may not be identical with bricks-and-mortar medical practices or business entities, as they are constructed solely according to patterns of patient sharing. Comparison of our PPCs with provider and practice locations in the Department of Health database would be enlightening. However, we analysed real world data, and our study can be readily replicated and was not subject to selection and non-response bias, in contrast to Australian surveys of general practice activity.
The novel data-driven methods we have developed provide a new toolkit for primary care research in Australia that maximises the power of big data analysis. These methods will allow ongoing exploration of the structure and characteristics of general practice, and of medical provider communities more broadly, as well as of how both PPC- and provider-level factors influence the care that patients receive.
Priorities for future analyses include exploring how PPC characteristics are related to the quality and quantity of care received by the PPC patient population, and to measures of unnecessary clinical variation. Received 12 December , accepted 15 May Publication of your online response is subject to the Medical Journal of Australia 's editorial discretion.
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Identifying communities of practice through ontology network analysis - Open Research Online
Short reports. Guidelines and statements. Narrative reviews. Ethics and law. Medical education. Volume Issue 2. Overcoming the data drought: exploring general practice in Australia by network analysis of big data. Med J Aust ; 2 : Topics General medicine. Health services administration. Information science. Abstract Objectives: To investigate the organisation and characteristics of general practice in Australia by applying novel network analysis methods to national Medicare claims data.
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View this article on Wiley Online Library. Australian Government Productivity Commission. Report on Government Services We publish our research mostly at DB and IR-related journals and conferences. Many of our publications are available as downloads. If you cannot find one, please contact one of the authors. In this seminar, students will study various aspects of Social Network Analysis. In the second phase, students will form small groups and implement their own idea or pcik one from a selection of ideas.
Students will put their proposals into practice using real world social network data in the form of corporate emails. Therefore, we will provide a pre-processed Version of the Enron corpus. Results from these projects can be very interesting for journalists or auditors, who have to dig through huge datasets to find interesting facts or uncover inherent structures manually. A list of project ideas will be presented in our introduction meeting see schedule. The admins are working to fix this issue.
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