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Africa
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Staying with the trouble
Published in Wendy A. Rogers, Jackie Leach Scully, Stacy M. Carter, Vikki A. Entwistle, Catherine Mills, The Routledge Handbook of Feminist Bioethics, 2022
Some may, however, and with good reasons, object that healthcare needs are too voluminous and resources too limited to adopt the approach we advocate, especially with the emphasis on slowness, and particularly in the context of neoliberalism and austerity politics. We live in and face constant contingency, and healthcare consultations and decisions are often time constrained. In many countries demand for healthcare often exceeds resources and many people struggle to access or face non-negligible delays in accessing care. However, care practiced in non-person-centered care ways may be suboptimal (Stewart et al. 2000). For example, studies on prescribed medicine show that a significant proportion of this ends up in the trash (e.g. Nunes et al. 2009; Madden 2013; Hyrkas and Wiggins 2014) and that person-centered approaches that include getting to know the person and what matters to them could improve healthcare decision-making and reduce waste (e.g. Nunes et al. 2009; Hyrkas and Wiggins 2014).
Introduction
Published in Mickey C. Smith, E.M. (Mick) Kolassa, Walter Steven Pray, Government, Big Pharma, and the People, 2020
Mickey C. Smith, E.M. (Mick) Kolassa, Walter Steven Pray
The ability of the client to control and manipulate her demands on the market is clearly diminished by the great extent to which illness is involuntary and unpredictable. These characteristics are what justifies health insurance and makes it actuarially feasible. The fact that, in most cases, medical care is a necessary and urgent need imposes additional constraints on the capacity of the consumer to manipulate demand.
Alternative Quality Management Strategies in Managed Care
Published in A.F. Al-Assaf, Managed Care Quality, 2020
The emerging science of “demand management” emphasizes the use of technology to reduce consumers’ need for and use of the most expensive health care services, thereby controlling costs and even improving overall health status of a defined population. (Montrose, 1995) The managed care failure of “supply-side” efforts (precertification, peer review, etc.) to manage costs has resulted in innovative strategies to alter the demand-side of the health care equation. At the heart of the concept of demand management is a radical redistribution of information resources and decision making responsibility between providers and patients. Patients are empowered through timely information and training in making appropriate use of health care services, thereby reducing the need for, and use of, costly and/or unnecessary interventions.
Series: Public engagement with research. Part 1: The fundamentals of public engagement with research
Published in European Journal of General Practice, 2023
Steven Blackburn, Megan Clinch, Maarten de Wit, Albine Moser, Jette Primdahl, Esther van Vliet, Christine Walker, Fiona Stevenson
Increasingly, there is more demand for primary healthcare which is evidenced based [5]. We need to do relevant research that will make a difference in peoples’ lives. As such, there are ethical and democratic reasons why engaging the public in the research process is warranted [6]. These include:A democratic right: Public money funds most research, so the public has a right to have a say on what research is done and how it is conducted.An ethical right: As health research involves human participants and/or their data, the public should have a say on how people take part safely in studies, plus how their data is accessed and used.Public accountability: Ensuring that research is value for money and beneficial to society by including public members in research commissioning and governance.
The economic evaluation of ALS care: quality and cost
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2023
Mustafa Çoban, Uğur Bilge, Hale Balseven, Hilmi Uysal, Betül Artut
The aging population and increased patient demand for healthcare services, technologies, and drugs, all contribute to the continuing increase in healthcare expenditures. Health economics studies provide information to decision makers for efficient use of resources for maximizing health benefits. Economic evaluation is one aspect of health economics, and it is a tool for comparing costs and consequences of different interventions (1). According to the Committee on the Quality of Health Care in America that was formed in June 1998, care must be delivered by systems that are carefully and consciously designed to provide care that is safe, effective, patient-centred, timely, efficient, and equitable (Institute of Medicine, 2001). Research on the quality of care reveals that health care systems frequently fall short in their ability to translate knowledge into practice, and apply new technology safely and appropriately. During the last decade alone, more than 70 publications in leading peer-reviewed journals have documented serious shortcomings in quality (2).
Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities
Published in Expert Review of Ophthalmology, 2023
Priyal Taribagil, HD Jeffry Hogg, Konstantinos Balaskas, Pearse A Keane
Ophthalmology is one of the busiest outpatient specialties in the UK and forms a major component of healthcare worldwide. The National Healthcare System (NHS) is one of the largest healthcare providers in the world and noted to have over 7.9 million ophthalmology clinic attendances per year [1]. Demand in healthcare workforce and clinical services is predicted to rise by a further 30–40% over the next 20 years [2]. Contributing factors include aging population, increased prevalence of complex, chronic conditions, and added pressure on timely detection and diagnosis. Capacity pressures have significantly worsened since the COVID-19 pandemic [3], further emphasizing the clinical need for a more efficient system. Artificial intelligence (AI) has the potential to help address these challenges, by augmenting or automating various processes across the healthcare ecosystem. Integration of AI platforms and systems into clinical workflows provides opportunities to increase clinical efficiency and productivity, enabling the consistent delivery of higher quality care despite growing demand.