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The Australian government, through Medicare, defines the type of medical specialist services it covers and subsidizes, but it does not regulate prices. Specialists in private practice can charge more than the fee listed by Medicare depending on what they feel 'the market will bear'. This can sometimes result in high and unexpected out-of-pocket (OOP) payments for patients. To reduce pricing uncertainty and 'bill shock' faced by consumers, the government introduced a price transparency website in December 2019. It is not clear how effective such a website will be and whether specialists and patients will use it. The aim of this qualitative study was to explore factors influencing how specialists set their fees, and their views on and participation in price transparency initiatives. We conducted 27 semi-structured interviews with surgical specialists. We analysed the data using thematic analysis and responses were mapped to the Theoretical Domains Framework and the Capability, Opportunity, Motivation and Behavior model. We identified several patient, specialist and system-level factors influencing fee setting. Patient-level factors included patient characteristics, circumstance, complexity, and assumptions regarding perceived value of care. Specialist-level factors included perceived experience and skills, ethical considerations, and gendered-behavior. System-level factors included the Australian Medical Association recommended price list, practice costs, and supply and demand factors including perceived competition and practice location. Specialists were opposed to price transparency websites and lacked motivation to participate because of the complexity of fee setting, concerns over unintended consequences, and feelings of frustration they were being singled out. If price transparency websites are to be pursued, specialists' lack of motivation to participate needs to be addressed. • Patient, specialist, and system factors influence fees among medical specialists in Australia. • Gender bias and ethical considerations influence fee setting. • Medical specialists practice some degree of subsidization of fees and price discrimination. • Medical specialists oppose price transparency websites and are unlikely to list fees voluntarily. [ABSTRACT FROM AUTHOR]

Abstract Background This is the tenth in a series of papers reporting a program of Sustainability in Health care by Allocating Resources Effectively (SHARE) in a local healthcare setting. After more than a decade of research, there is little published evidence of active and successful disinvestment. The paucity of frameworks, methods and tools is reported to be a factor in the lack of success. However there are clear and consistent messages in the literature that can be used to inform development of a framework for operationalising disinvestment. This paper, along with the conceptual review of disinvestment in Paper 9 of this series, aims to integrate the findings of the SHARE Program with the existing disinvestment literature to address the lack of information regarding systematic organisation-wide approaches to disinvestment at the local health service level. Discussion A framework for disinvestment in a local healthcare setting is proposed. Definitions for essential terms and key concepts underpinning the framework have been made explicit to address the lack of consistent terminology. Given the negative connotations of the word ‘disinvestment’ and the problems inherent in considering disinvestment in isolation, the basis for the proposed framework is ‘resource allocation’ to address the spectrum of decision-making from investment to disinvestment. The focus is positive: optimising healthcare, improving health outcomes, using resources effectively. The framework is based on three components: a program for decision-making, projects to implement decisions and evaluate outcomes, and research to understand and improve the program and project activities. The program consists of principles for decision-making and settings that provide opportunities to introduce systematic prompts and triggers to initiate disinvestment. The projects follow the steps in the disinvestment process. Potential methods and tools are presented, however the framework does not stipulate project design or conduct; allowing application of any theories, methods or tools at each step. Barriers are discussed and examples illustrating constituent elements are provided. Conclusions The framework can be employed at network, institutional, departmental, ward or committee level. It is proposed as an organisation-wide application, embedded within existing systems and processes, which can be responsive to needs and priorities at the level of implementation. It can be used in policy, management or clinical contexts.

Background: The rate of immediate breast reconstruction (IBR) following mastectomy for breast cancer in Australia is low and varies between regions. To date, no previous Australian studies have examined IBR rates between all hospitals within a particular jurisdiction, despite hospitals being an important known contributor to variation in IBR rates in other countries. Methods: We used cross‐classified random‐effects logistic regression models to examine the inter‐hospital variation in IBR rates by using data on 7961 women who underwent therapeutic mastectomy procedures in New South Wales (NSW) between January 2012 and June 2015. We derived IBR rates by patient‐, residential neighbourhood‐ and hospital‐related factors and investigated the underlying drivers for the variation in IBR. Results: We estimated the mean IBR rate across all hospitals performing mastectomy to be 17.1% (95% Bayesian credible interval (CrI) 12.1–23.1%) and observed wide inter‐hospital variation in IBR (variance 4.337, CrI 2.634–6.889). Older women, those born in Asian countries (odds ratio (OR) 0.5, CrI 0.4–0.6), residing in neighbourhoods with lower socioeconomic status (OR 0.7, CrI 0.5–0.8 for the most disadvantaged), and who underwent surgery in public hospitals (OR 0.4, CrI 0.1–1.0) were significantly less likely to have IBR. Women residing in non‐metropolitan areas and attending non‐metropolitan hospitals were significantly less likely to undergo IBR than their metropolitan counterparts attending metropolitan hospitals. Conclusion: Wide inter‐hospital variation raises concerns about potential inequities in access to IBR services and unmet demand in certain areas of NSW. Explaining the underlying drivers for IBR variation is the first step in identifying policy solutions to redress the issue. [ABSTRACT FROM AUTHOR]

OBJECTIVE:Our study examined the psychological outcomes associated with failed ART treatment outcomes in men and women. SEARCH STRATEGY:A systematic search for studies published between January 1980 and August 2015 was performed across seven electronic databases. INCLUSION CRITERIA:Studies were included if they contained data on psychosocial outcomes taken pre and post ART treatment. DATA EXTRACTION AND SYNTHESIS:A standardised form was used to extract data and was verified by two independent reviewers. Studies were meta-analysed to determine the association of depression and anxiety with ART treatment outcomes. Narrative synthesis identified factors to explain variations in the size and directions of effects and relationships explored within and between the studies. MAIN RESULTS:Both depression and anxiety increased after a ART treatment failure with an overall pooled standardised mean difference (SMD) of 0.41 (95% CI: 0.27, 0.55) for depression and 0.21 (95% CI: 0.13, 0.29) for anxiety. In contrast, depression decreased after a successful treatment, SMD of -0.24 (95% CI: -0.37,-0.11). Both depression and anxiety decreased as time passed from ART procedure. Nonetheless, these remained higher than baseline measures in the group with the failed outcome even six months after the procedure. Studies included in the narrative synthesis also confirmed an association with negative psychological outcomes in relation to marital satisfaction and general well-being following treatment failure. CONCLUSION:Linking ART failure and psychosocial outcomes may elucidate the experience of treatment subgroups, influence deliberations around recommendations for resource allocation and health policy and guide patient and clinician decision making.