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Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Objective: To retrospectively analyse the key factors associated with listing decisions by the Pharmaceutical Benefits Advisory Committee (PBAC) for medicines for diabetes and its complications on the Pharmaceutical Benefits Scheme. Methods: The clinical and economic evidence were retrieved from public summary documents (PSD) of all major submissions between July 2005 and March 2020. A multivariate binary logit regression analysis was conducted to assess the relationship between the categorical explanatory variables and PBAC recommendations. Results: We identified a total of 211 PSD of which 118 (56%) were recommended for listing. Clinical and economic uncertainty were significantly and inversely associated with the PBAC recommendation. Submissions with high clinical and economic uncertainty were less likely to be recommended. Conclusion: Our findings will enhance the understanding of medical professionals, pharmaceutical companies, and other stakeholders about the rationale of PBAC reimbursement decisions for these medicines and assist prospective applicant sponsor companies in preparing their submissions. What is known about the topic? Most studies on Pharmaceutical Benefits Advisory Committee (PBAC) decisions focused mainly on the cost-effectiveness, while some assessed only the financial impact of medicines. A few PBAC studies analysed the reimbursement decisions for medicines targeting specific disease groups, e.g. oncology, orphan disease, and multiple sclerosis. What does this paper add? This study presents an in-depth retrospective analysis of the PBAC decisions for listing medicines related to diabetes and its complications on the Pharmaceutical Benefits Scheme and contributes to the existing evidence-based knowledge of health technology assessment. What are the implications for practitioners? Our findings will enhance the understanding of medical professionals, pharmaceutical companies, and other stakeholders about the rationale of PBAC reimbursement decisions for these medicines and assist prospective applicant sponsor companies in preparing their submissions. [ABSTRACT FROM AUTHOR]

Objectives: The primary objective of this study was to establish whether clinical depression and increased suicide risk differed between Australian fly-in, fly-out (FIFO) workers and their residential counterparts in the resources sector. We also sought to identify whether bullying and social support were associated with depression and suicide risk in this cohort. Methods: A cross-sectional survey design was used. Completed questionnaires were received from 751 respondents who were employed in the Australian resources sector. Primary outcomes were assessed with the Beck Depression Inventory, Beck Hopelessness Scale and Negative Acts Questionnaire – Revised. A general linear model was used to examine the association between depression, hopelessness and predictive factors. Results: The results of a general linear model analysis demonstrated that depression (partial η2 = 0.02; P = 0.01) and hopelessness (partial η2 = 0.02; P = 0.02) were significantly higher in residential than FIFO workers. In addition, bullying was significantly associated with higher levels of depression (partial η2 = 0.11; P = 0.001) and hopelessness (partial η2 = 0.04; P = 0.001). Finally, increased social support was significantly associated with lower depression rates (partial η2 = 0.13; P = 0.001) and hopelessness (partial η2 = 0.14; P = 0.001). Conclusions: Interventions should be delivered to reduce depression and hopelessness in Australian resource workers, particularly through addressing workplace bullying and enhancing social support. What is known about the topic?: One-third of Australian resource workers experience psychosocial distress. Research examining factors that influence such distress is lacking. What does this paper add?: The results of this study demonstrate that clinical depression and suicide risk were significantly higher in residential resource workers than in FIFO workers. Depression and suicide risk were directly associated with workplace bullying. In addition, social support was an important protective factor against depression and bullying in Australian FIFO and residential resource workers. What are the implications for practitioners?: There is an urgent need to introduce interventions that reduce psychosocial distress in the resource sector, particularly through the mitigation of bullying [ABSTRACT FROM AUTHOR]