Healthcare in the news media: The privileging of private over public

This article reports on a discourse analysis of the representation of healthcare in the print news media, and the way this representation shapes perspectives of healthcare. We analysed news items from six major Australian newspapers over a three-year time period. We show how various framing devices promote ideas about a crisis in the current public healthcare system, the existence of a precarious balance between the public and private health sectors, and the benefits of private healthcare. We employ Bourdieu’s concepts of field and capital to demonstrate the processes through which these devices are employed to conceal the power relations operating in the healthcare sector, to obscure the identity of those who gain the most from the expansion of private sector medicine, and to indirectly increase health inequalities

Gatekeepers in the healthcare sector: Knowledge and Bourdieu's concept of field

Choice is an imperative for patients in the Australian healthcare system. The complexity of this healthcare ‘maze’, however, means that successfully navigating and making choices depends not only on the decisions of patients, but also other key players in the healthcare sector. Utilising Bourdieu's concepts of capital, habitus and field, we analyse the role of gatekeepers (i.e., those who control access to resources, services and knowledge) in shaping patients' experiences of healthcare, and producing opportunities to enable or constrain their choices. Indepth interviews were conducted with 41 gatekeepers (GPs, specialists, nurses, hospital administrators and policymakers), exploring how they acquire and use knowledge within the healthcare system. Our findings reveal a hierarchy of knowledges and power within the healthcare field which determines the forms of knowledge that are legitimate and can operate as capital within this complex and dynamic arena. As a consequence, forms of knowledge which can operate as capital, are unequally distributed and strategically controlled, ensuring democratic 'reform' remains difficult and 'choices' limited to those beneficial to private medicine

Healthcare choice: Bourdieu’s capital, habitus and field

The promotion of choice is a common theme in both policy discourses and commercial marketing claims about healthcare. However, within the multiple potential pathways of the healthcare ‘maze’, how do healthcare ‘consumers’ or patients understand and experience choice? What is meant by ‘choice’ in the policy context, and, importantly from a sociological perspective, how are such choices socially produced and structured? In this theoretical article, the authors consider the interplay of Bourdieu’s three key, interlinked concepts – capital, habitus and field – in the structuring of healthcare choice. These are offered as an alternative to rational choice theory, where ‘choice’ is regarded uncritically as a fundamental ‘good’ and able to provide a solution to the problems of the healthcare system. The authors argue that sociological analyses of healthcare choice must take greater account of the ‘field’ in which choices are made in order to better explain the structuring of choice

Knowledge matters: producing and using knowledge to navigate healthcare systems

In many contemporary healthcare systems, individuals are expected to be rational actors – weighing up available knowledge and making choices about their healthcare needs. In the policy context, this has been most explicitly applied to the financing of healthcare where there is encouragement for the purchase of private health insurance. However, perceptions of public and private healthcare provision, knowledge about healthcare needs, and the types of services people choose, are far from straightforward. Drawing on Bourdieu’s concepts of habitus, field, and capital, and a study of individual experiences of choice in Australian healthcare, we explore the knowledges used by people as they navigate through the healthcare system. Such navigation takes place in a milieu where authoritative medical knowledge intersects with knowledge from other sources, including the Internet and lived experience. However, our study reveals that navigation of healthcare is assisted most of all by the capacity to draw on ‘system knowledge’. System knowledge takes two, sometimes overlapping, forms. First, acquired system knowledge is produced through drawing on experience, formal knowledge and the capacity to undertake research (primarily cultural capital). Second, assumed system knowledge enables navigation of the healthcare system through accessing and utilising networks of privilege (primarily economic and social capital)

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine