The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research

Simulation der Letalität nach verschiedenen Ex-ante- und Ex-post-Triage-Verfahren bei Menschen mit Behinderungen und Vorerkrankungen

Der stetige Anstieg an zu behandelnden Patienten während der COVID-19-Pandemie hat das Gesundheitssystem vor eine Vielzahl an Herausforderungen gestellt. Die Intensivstation ist einer der in diesem Zusammenhang besonders stark betroffenen Bereiche. Nur durch umfangreiche Infektionsschutzmaßnahmen sowie einen enormen logistischen Aufwand konnten in Deutschland selbst in Hochphasen der Pandemie die Behandlung aller Intensivpatienten ermöglicht und eine Triage auch in Regionen mit hohem Patientendruck bei gleichzeitig geringen Kapazitäten verhindert werden. Im Hinblick auf die Pandemievorsorge hat der Deutsche Bundestag ein Gesetz zur Triage verabschiedet, das eine Ex-post-Triage explizit untersagt. Bei einer Ex-post-Triage werden auch Patienten, die bereits auf der Intensivstation behandelt werden, in die Triage-Entscheidung einbezogen und Behandlungskapazitäten nach individueller Erfolgsaussicht verteilt. In der Literatur finden sich rechtliche, ethische und soziale Überlegungen zur Triage bei Pandemien, eine quantitative Bewertung im Hinblick auf verschiedene Patientengruppen auf der Intensivstation gibt es hingegen nicht. Der Fokus der Arbeit liegt auf dieser Forschungslücke, und es wird eine quantitative simulationsbasierte Evaluation von Ex-ante- und Ex-post-Triage-Politiken unter Berücksichtigung von Überlebenswahrscheinlichkeiten, Beeinträchtigungen und Vorerkrankungen durchgeführt. Die Ergebnisse zeigen, dass eine Anwendung von Ex-post-Triage, basierend auf Überlebenswahrscheinlichkeiten in allen Patientengruppen, zu einer Reduktion der Mortalität auf der Intensivstation führt. In dem Szenario, das der realen Situation wohl am nächsten kommt, wird eine Reduktion der Mortalität auf der Intensivstation um ca. 15 % schon bei einer einmaligen Anwendung der Ex-post-Triage erreicht. Dieser mortalitätsreduzierende Effekt ist umso größer, je mehr Patienten auf eine intensivmedizinische Behandlung warten

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results

"Triagegesetz" – Regelung mit fatalen Folgen

With the coming into force of § 5c of the Infection Protection Act (IfSG), the so-called Triage Act, on 14 December 2022, a protracted discussion has come to a provisional conclusion, the result of which physicians and social associations but also lawyers and ethicists are equally dissatisfied. The explicit exclusion of the discontinuation of treatment that has already begun in favor of new patients with better chances of success (so-called tertiary or ex-post triage) prevents allocation decisions with the aim of enabling as many patients as possible to beneficially participate in medical care under crisis conditions. The result of the new regulation is de facto a first come first served allocation, which is associated with the highest mortality even among individuals with limitations or disabilities and was rejected by a large margin as unfair in a population survey. Mandating allocation decisions based on the likelihood of success but which are not permitted to be consistently implemented and prohibiting, for example the use of age and frailty as prioritization criteria, although both factors most strongly determine the short-term probability of survival according to evident data, shows the contradictory and dogmatic nature of the regulation. The only remaining possibility is the consistent termination of treatment that is no longer indicated or desired by the patient, regardless of the current resource situation; however, if a different decision is made in a crisis situation than in a situation without a lack of resources, this practice would not be justified and would be punishable. Accordingly, the highest efforts must be set on legally compliant documentation, especially in the stage of decompensated crisis care in a region. The goal of enabling as many patients as possible to beneficially participate in medical care under crisis conditions is in any case thwarted by the new German Triage Act.</p