A pandemic caused by a newly discovered coronavirus (SARS-CoV-2) is raising dilemmas worldwide in relation to health-resource limitations. To address the possible shortage of acute medical-care capacity, national and international medical societies have drawn up guidance for dealing with scarcity. In everyday medical practice, a therapeutic decision to admit a patient to the intensive care unit (ICU), including various stages of care escalation (intubation, circulatory support, dialysis, extra-corporeal membrane oxygenation), requires a medical indication and incorporates the patient's will. Where resources are not limited, this decision is generally made with a focus on the potential benefit to the individual patient, unless the patient opts out of the treatment. If a

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Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts.

In view of the globally evolving coronavirus disease (COVID-19) pandemic, German hospitals rapidly expanded their intensive care capacities. However, it is possible that even with an optimal use of the increased resources, these will not suffice for all patients in need. Therefore, recommendations for the allocation of intensive care resources in the context of the COVID-19 pandemic have been developed by a multidisciplinary group of authors with the support of eight scientific medical societies. The recommendations for procedures and criteria for prioritisations in case of resource scarcity are based on scientific evidence, ethicolegal considerations and practical experience. Medical decisions must always be based on the need and the treatment preferences of the individual patient. In addition to this patient-centred approach, prioritisations in case of resource scarcity require a supraindividual perspective. In such situations, prioritisations should be based on the criterion of clinical prospect of success in order to minimize the number of preventable deaths due to resource scarcity and to avoid discrimination based on age, disabilities or social factors. The assessment of the clinical prospect of success should take into account the severity of the current illness, severe comorbidities and the patient’s general health status prior to the current illness.

ZusammenfassungUngeachtet der sozialgesetzlichen Vorgaben existieren im deutschen Gesundheitssystem in der Patientenversorgung nebeneinander Unter‑, Fehl- und Überversorgung. Überversorgung bezeichnet diagnostische und therapeutische Maßnahmen, die nicht angemessen sind, da sie die Lebensdauer oder Lebensqualität der Patienten nicht verbessern, mehr Schaden als Nutzen verursachen und/oder von den Patienten nicht gewollt werden. Daraus können hohe Belastungen für die Patienten, deren Familien, die Behandlungsteams und die Gesellschaft resultieren. Dieses Positionspapier erläutert Ursachen von Überversorgung in der Intensivmedizin und gibt differenzierte Empfehlungen zu ihrer Erkennung und Vermeidung. Zur Erkennung und Vermeidung von Überversorgung in der Intensivmedizin erfordert es Maßnahmen auf der Mikro‑, Meso- und Makroebene, insbesondere die folgenden: 1) regelmäßige Evaluierung des Therapieziels im Behandlungsteam unter Berücksichtigung des Patientenwillens und unter Begleitung von Patienten und Angehörigen; 2) Förderung einer patientenzentrierten Unternehmenskultur im Krankenhaus mit Vorrang einer qualitativ hochwertigen Patientenversorgung; 3) Minimierung von Fehlanreizen im Krankenhausfinanzierungssystem gestützt auf die notwendige Reformierung des fallpauschalbasierten Vergütungssystems; 4) Stärkung der interdisziplinären/interprofessionellen Zusammenarbeit in Aus‑, Fort- und Weiterbildung; 5) Initiierung und Begleitung eines gesellschaftlichen Diskurses zur Überversorgung.