159 resultados para COMPREHENSIVE HEALTHCARE
Resumo:
Objective To understand differences in the managerial ethical decision-making styles of Australian healthcare managers through the exploratory use of the Managerial Ethical Profiles (MEP) Scale. Background Healthcare managers (doctors, nurses, allied health practitioners and non-clinically trained professionals) are faced with a raft of variables when making decisions within the workplace. In the absence of clear protocols and policies healthcare managers rely on a range of personal experiences, personal ethical philosophies, personal factors and organizational factors to arrive at a decision. Understanding the dominant approaches to managerial ethical decision-making, particularly for clinically trained healthcare managers, is a fundamental step in both increasing awareness of the importance of how managers make decisions, but also as a basis for ongoing development of healthcare managers. Design Cross-sectional. Methods The study adopts a taxonomic approach that simultaneously considers multiple ethical factors that potentially influence managerial ethical decision-making. These factors are used as inputs into cluster analysis to identify distinct patterns of influence on managerial ethical decision-making. Results Data analysis from the participants (n=441) showed a similar spread of the five managerial ethical profiles (Knights, Guardian Angels, Duty Followers, Defenders and Chameleons) across clinically trained and non-clinically trained healthcare managers. There was no substantial statistical difference between the two manager types (clinical and non-clinical) across the five profiles. Conclusion This paper demonstrated that managers that came from clinical backgrounds have similar ethical decision-making profiles to non-clinically trained managers. This is an important finding in terms of manager development and how organisations understand the various approaches of managerial decision-making across the different ethical profiles.
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The book begins with an overview of the use of biomaterials in contemporary healthcare and the process of developing novel biomaterials; the key issues and challenges associated with the design of complex implantable systems are also highlighted. The book then reviews the main materials used in functional biomaterials, particularly their properties and applications. Individual chapters focus on both natural and synthetic polymers, metallic biomaterials, and bio-inert and bioactive ceramics.
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It is crucial to advance understanding of the concept of successful aging at work to guide rigorous future research and effective practice. Drawing on the gerontology and life-span developmental literatures, I recently proposed a definition and theoretical framework of successful aging at work that revolve around employees increasingly deviating from average developmental trajectories across the working life span. Based on sustainability, person–job fit, and proactivity theories, Kooij suggested an alternative perspective that emphasizes the active role of employees for successful aging at work. In this article, I compare the 2 approaches and attempt a partial integration. I highlight the importance of a precise definition, comprehensive model, and critical discussion of successful aging at work. Furthermore, I suggest that person–environment fit variables other than person–job fit (e.g., person–organization fit) and adapting to person–environment misfit may also contribute to successful aging at work. Finally, I argue that proactive behaviors must have age-differential effects on work outcomes to be considered personal resources for successful aging at work.
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Background The objective is to estimate the incremental cost-effectiveness of the Australian National Hand Hygiene Inititiave implemented between 2009 and 2012 using healthcare associated Staphylococcus aureus bacteraemia as the outcome. Baseline comparators are the eight existing state and territory hand hygiene programmes. The setting is the Australian public healthcare system and 1,294,656 admissions from the 50 largest Australian hospitals are included. Methods The design is a cost-effectiveness modelling study using a before and after quasi-experimental design. The primary outcome is cost per life year saved from reduced cases of healthcare associated Staphylococcus aureus bacteraemia, with cost estimated by the annual on-going maintenance costs less the costs saved from fewer infections. Data were harvested from existing sources or were collected prospectively and the time horizon for the model was 12 months, 2011–2012. Findings No useable pre-implementation Staphylococcus aureus bacteraemia data were made available from the 11 study hospitals in Victoria or the single hospital in Northern Territory leaving 38 hospitals among six states and territories available for cost-effectiveness analyses. Total annual costs increased by $2,851,475 for a return of 96 years of life giving an incremental cost-effectiveness ratio (ICER) of $29,700 per life year gained. Probabilistic sensitivity analysis revealed a 100% chance the initiative was cost effective in the Australian Capital Territory and Queensland, with ICERs of $1,030 and $8,988 respectively. There was an 81% chance it was cost effective in New South Wales with an ICER of $33,353, a 26% chance for South Australia with an ICER of $64,729 and a 1% chance for Tasmania and Western Australia. The 12 hospitals in Victoria and the Northern Territory incur annual on-going maintenance costs of $1.51M; no information was available to describe cost savings or health benefits. Conclusions The Australian National Hand Hygiene Initiative was cost-effective against an Australian threshold of $42,000 per life year gained. The return on investment varied among the states and territories of Australia.
Resumo:
Background Australia has one of the highest rates of antibiotic use amongst OECD countries. Data from the Australian primary healthcare sector suggests unnecessary antibiotics were prescribed for self-resolving conditions. We need to better understand what drives general practitioners (GPs) to prescribe antibiotics, consumers to seek antibiotics, and pharmacists to fill repeat antibiotic prescriptions. It is also not clear how these individuals trade-off between the possible benefits that antibiotics may provide in the immediate/short term, against the longer term societal risk of antimicrobial resistance. This project investigates what factors drive decisions to use antibiotics for GPs, pharmacists and consumers, and how these individuals discount the future. Methods Factors will be gleaned from published literature and from semi-structured interviews, to inform the development of Discrete Choice Experiments (DCEs). Three DCEs will be constructed – one for each group of interest – to allow investigation of which factors are more important in influencing (a) GPs to prescribe antibiotics, (b) consumers to seek antibiotics, and (c) pharmacists to fill legally valid but old or repeat prescriptions of antibiotics. Regression analysis will be conducted to understand the relative importance of these factors. A Time Trade Off exercise will be developed to investigate how these individuals discount the future. Results Findings from the DCEs will provide an insight into which factors are more important in driving decision making in antibiotic use for GPs, pharmacists and consumers. Findings from the Time Trade Off exercise will show what individuals are willing to trade for preserving the miracle of antibiotics. Conclusion Research findings will contribute to existing national programs to bring about a reduction in inappropriate use of antibiotic in Australia. Specifically, influencing how key messages and public health campaigns are crafted, and clinical education and empowerment of GPs and pharmacists to play a more responsive role as stewards of antibiotic use in the community.
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Globally, the main contributors to morbidity and mortality are chronic conditions, including cardiovascular disease and diabetes. Chronic disease is costly and partially avoidable, with around 60% of deaths and nearly 50% of the global disease burden attributable to these conditions. By 2020, chronic illnesses will likely be the leading cause of disability worldwide. Existing healthcare systems that focus on acute episodic health conditions, both national and international, cannot address the worldwide transition to chronic illness; nor are they appropriate for the ongoing care and management of those already dealing with chronic diseases. As such, chronic disease management requires integrated approaches that incorporate interventions targeted at both individuals and populations, and emphasise the shared risk factors of different conditions. International and Australian strategic planning documents articulate similar elements to manage chronic disease, including the need for aligning sectoral policies for health, forming partnerships, and engaging communities in decision-making. Infectious diseases are also a common and significant contributor to ill health throughout the world. In many countries, this impact has been minimised by the combined efforts of preventative health measures and improved treatment methods. However, in low-income countries, infectious diseases remain the dominant cause of death and disability. The World Health Organization (WHO) estimates that infectious diseases (including respiratory infections) still account for around 23% (or around 14 million) of all deaths each year, and result in over 4.6 billion episodes of diarrhoeal disease and 243 million cases of malaria each year (Lozano et al. 2012, WHO 2009). In addition to the high level of mortality, infectious diseases disable many hundreds of millions of people each year, mainly in developing countries, with the global burden of disease from infectious diseases estimated to be around 300 million DALYs (disability-adjusted life years) (WHO 2012). The aim of this chapter is to outline the impact that infectious diseases and chronic diseases have on the health of the community, describe the public health strategies used to reduce the burden of those diseases, and discuss the historic and emerging disease risks to public health. This chapter examines the comprehensive approaches implemented to prevent both chronic and infectious diseases, and to manage and care for communities with these conditions.
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The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.
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Objective: To identify key stakeholder preferences and priorities when considering a national healthcare-associated infection (HAI) surveillance programme through the use of a discrete choice experiment (DCE). Setting: Australia does not have a national HAI surveillance programme. An online web-based DCE was developed and made available to participants in Australia. Participants: A sample of 184 purposively selected healthcare workers based on their senior leadership role in infection prevention in Australia. Primary and secondary outcomes: A DCE requiring respondents to select 1 HAI surveillance programme over another based on 5 different characteristics (or attributes) in repeated hypothetical scenarios. Data were analysed using a mixed logit model to evaluate preferences and identify the relative importance of each attribute. Results: A total of 122 participants completed the survey (response rate 66%) over a 5-week period. Excluding 22 who mismatched a duplicate choice scenario, analysis was conducted on 100 responses. The key findings included: 72% of stakeholders exhibited a preference for a surveillance programme with continuous mandatory core components (mean coefficient 0.640 (p<0.01)), 65% for a standard surveillance protocol where patient-level data are collected on infected and non-infected patients (mean coefficient 0.641 (p<0.01)), and 92% for hospital-level data that are publicly reported on a website and not associated with financial penalties (mean coefficient 1.663 (p<0.01)). Conclusions: The use of the DCE has provided a unique insight to key stakeholder priorities when considering a national HAI surveillance programme. The application of a DCE offers a meaningful method to explore and quantify preferences in this setting.