Affiliation:
1. Universidade Salvador, Brasil
2. Fleury Medicina e Saúde, Brasil
3. A.C.Camargo Cancer Center, Brasil
Abstract
SUMMARY The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.
Reference21 articles.
1. Coronavirus: a clinical update of COVID-19;Cespedes MS;Rev Assoc Med Bras,2020
2. Fair allocation of scarce medical resources in the time of COVID-19;Emanuel EJ;N Engl J Med,2020
3. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity;Jiang X;CMC,2020
4. Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan;Yan L;medRxiv,2020
5. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls;Naudé W;AI Soc,2020
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献