title: Patient Experience of Australian General Practices.
abstract: The number of data-based research articles focusing on patient sociodemographic profiling and experience with healthcare practices is still relatively small. One of the reasons for this relative lack of research is that categorizing patients into different demographic groups can lead to significant reductions in sample numbers for homogeneous subgroups. The aim of this article is to identify problems and issues when dealing with big data that contains information at two levels: patient experience of their general practice, and scores received by practices. The Practice Accreditation and Improvement Survey (PAIS) consisting of 27 five-point Likert items and 11 sociodemographic questions is a Royal Australian College of General Practitioners (RACGP)-endorsed instrument for seeking patient views as part of the accreditation of Australian general practices. The data were collected during the 3-year period May 2011-July 2014, during which time PAIS was completed for 3734 individual general practices throughout Australia involving 312,334 anonymous patients. This represents over 60% of practices in Australia, and ∼75% of practices that undergo voluntary accreditation. The sampling method for each general practice was convenience sampling. The results of our analysis show how sociodemographic profiles of Australian patients can affect their ratings of practices and also how the location of the practice (State/Territory, remote access area) can affect patient experience. These preliminary findings can act as an initial set of results against which future studies in patient experience trends can be developed and measured in Australia. Also, the methods used in this article provide a methodological framework for future patient experience researchers to use when dealing with data that contain information at two levels, such as the patient and practice. Finally, the outcomes demonstrate that different subgroups can experience healthcare provision differently, especially indigenous patients and young patients. The implications of these findings for healthcare policy and priorities will need to be further investigated.
authors.affiliation: 1 School of Computer and Mathematical Sciences, Auckland University of Technology , Auckland, New Zealand .
authors.forename: Ajit
authors.initials: A
authors.lastname: Narayanan
authors.affiliation: 2 School of Medicine, Griffith University , Brisbane, Australia .
authors.forename: Michael
authors.initials: M
authors.lastname: Greco
mesh_terms: D000328:Adult; D000368:Aged; D001315:Australia; D057225:Data Mining; D005260:Female; D058006:General Practice / Q000592:standards*; D019538:Health Care Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D017060:Patient Satisfaction; D055815:Young Adult
keywords: big data analytics; data acquisition and cleaning; data mining