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10.1111/dewb.12315

http://scihub22266oqcxt.onion/10.1111/dewb.12315
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33934463!7614740!33934463
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suck abstract from ncbi


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pmid33934463      Dev+World+Bioeth 2022 ; 22 (1): 34-43
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  • Ethical heuristics for pandemic allocation of ventilators across hospitals #MMPMID33934463
  • Palacios-Gonzalez C; Pugh J; Wilkinson D; Savulescu J
  • Dev World Bioeth 2022[Mar]; 22 (1): 34-43 PMID33934463show ga
  • In response to the COVID-19 pandemic philosophers and governments have proposed scarce resource allocation guidelines. Their purpose is to advise healthcare professionals on how to ethically allocate scarce medical resources. One challenging feature of the pandemic has been the large numbers of patients needing mechanical ventilatory support. Guidelines have paradigmatically focused on the question of what doctors should do if they have fewer ventilators than patients who need respiratory support: which patient should get the ventilator? There is, however, an important higher level allocation problem. Namely, how are we to ethically distribute newly obtained ventilators across hospitals: which hospital should get the ventilator(s)? In this paper, we identify a set of principles for allocating newly obtained ventilators across hospitals. We focus particularly on low and middle income countries, who frequently have limited pre-existing intensive care capacity, and have needed to source additional ventilators. We first provide some background. Second, we argue that the main population healthcare aim during the COVID-19 pandemic should be to save the most lives. Next, we assess a series of potential heuristics or principles that could be used to guide allocation: allocation to the most densely populated cities, random allocation, allocation based on the ratio of patients to ICU personnel, prioritisation in terms of intrahospital mortality, prioritisation of younger populations, and prioritisation in terms of population mortality. We conclude by providing a plausible ranking of the principles, while noting a number of epistemological challenges, in terms of how they best further the aim of increasing the probability of saving the most lives.
  • |*COVID-19/epidemiology/therapy[MESH]
  • |*Pandemics[MESH]
  • |Health Care Rationing[MESH]
  • |Heuristics[MESH]
  • |Hospitals[MESH]
  • |Humans[MESH]
  • |SARS-CoV-2[MESH]
  • |Triage[MESH]


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