The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...) the agent responsible for the implementation of diagnosis, therapy, and care is unable to access the generation of findings and recommendations. There is widespread agreement that, generally, a complete traceability is preferable to opaque recommendations; however, there are differences about addressing ML-based systems whose functioning seems to remain opaque to some degree—even if so-called explicable or interpretable systems gain increasing amounts of interest. This essay approaches the epistemic foundations of ML-generated information specifically and medical knowledge generally to advocate differentiations of decision-making situations in clinical contexts regarding their necessary depth of insight into the process of information generation. Empirically accurate or reliable outcomes are sufficient for some decision situations in healthcare, whereas other clinical decisions require extensive insight into ML-generated outcomes because of their inherently normative implications. (shrink)

Developments in Machine Learning (ML) have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and (...) care, has no or insufficient insight into how such recommendations are reached. The following paper aims to make understandable the specificity of the deliberative model of a physician-patient relationship that has been achieved over decades. By outlining the (social-)epistemic and inherently normative relationship between physicians and patients, I want to show how this relationship might be altered by non-traceable ML recommendations. With respect to some healthcare decisions, such changes in deliberative practice may create normatively far-reaching challenges. Therefore, in the future, a differentiation of decision-making situations in healthcare with respect to the necessary depth of insight into the process of outcome generation seems essential. (shrink)

Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to potential changes of responsibility and decision-making (...) authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees’ reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians’ responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS. (shrink)

Zusammenfassung Die Forschungs- und Regulierungsebene bei datenintensiver Forschung in der Medizin liegen auseinander. Ein heterogenes Feld aus regulierenden Institutionen mit regional ungleichen Regelungen, sowohl hinsichtlich der Dichte als auch der Restriktivität von Regelungen, steht einer globalen Entwicklung der Technologien entgegen. Trotz oder gerade wegen mangelnder global-gültiger Regulierungen können auch unverbindliche oder nur bedingt verbindliche normative Vorgaben der Orientierung dienen. Doch wie soll eine solche normative Regulierung angesichts datenintensiver Forschung in der Medizin ausgestaltet werden und woran soll sie sich orientieren? Die (...) Frage, wie konkret und detailliert man eine solche Forschung regulieren sollte, lässt sich nur im Einzelfall für bestimmte Situationen und Institutionen beantworten. Ob dabei neue Normen oder Gewichtungen von Normen vorgenommen werden sollen, oder ob die Normen und deren Gewichtung weiterhin bestehen bleiben sollen, lässt sich nur mit Verweis auf starke normative Argumente beantworten. An zwingenden Argumenten für neue Normen angesichts der datenintensiven medizinischen Forschung mangelt es bisher. Dennoch ist eine Umsetzungsarbeit von Normen, egal ob neue oder alte, und deren Anwendung auf eine neue Methode oder Technologie allemal zu erbringen. Es bedarf daher – wie schon bisher – beider: sowohl etablierter und transparent kommunizierter Normen als auch des unabhängigen Urteils zur Anwendung dieser Normen. Angesichts der besonderen Eignung für diese Aufgabe sollten auch weiterhin Ethik-Kommissionen als prozedurale Instanz bei der Bewilligung von datenintensiver Forschung in der Medizin gefordert und befähigt werden, die als unabhängige Institution ein Urteil im Einzelfall anhand vorgegebener Normen fällen. Nichtsdestotrotz können sich die veränderten Praktiken datenintensiver Forschungsprojekte auch in neuen Anforderungen an Ethik-Kommissionen niederschlagen, denen mit Reformen der Arbeitsweise begegnet werden sollte. (shrink)

Inequities shape the everyday experiences and life chances of individuals at the margins of societies and are often associated with lower health and particular challenges in accessing quality treatment and support. This fact is even more dramatic for those individuals who live at the nexus of different marginalized groups and thus may face multiple discrimination, stigma, and oppression. To address these multiple social and structural disadvantages, intersectional approaches have recently gained a foothold, especially in the public health field. This study (...) makes an empirically informed argument for the merits of increasing the use of intersectional frameworks in the mental health field. In the mental health field, the potential for greater attention to multiple unjustified disadvantages appears to be of particular importance, as many mental health service users already face stigma and discrimination because of their mental health issues and thus may benefit particularly frequently and far-reachingly from effective problem awareness about multiple disadvantages. Intersectional approaches may help address the complexity, interdependence, and mutual constitution of social inequalities better than previous approaches that examined only one category of sociostructural stratification. By helping to identify the needs of those at the greatest risk of poor health, intersectional frameworks and tools can contribute not only to better address the needs of multiple disadvantaged individuals with mental health issues but also to the promotion of equity in the field of mental health, contributing to the reduction of health disparities. (shrink)

Definition of the problemHealth Apps are becoming increasingly important for a preventive and responsible orientation of the health system. Currently, most of these digital health applications (DiGA) are based on so-called self-tracking technologies which record physiological and psychological data via sensors, usually combined with personalized everyday information. In the last few years, these digital developments have launched an intense and clearly polarized debate about the opportunities and dangers of self-tracking in healthcare.ArgumentsAfter a brief overview of medical self-tracking, this essay wants (...) to systematize and check the polarized discourse on the basis of its decisive individual and social potentials. It turns out that self-measurement techniques have long been an integral part of medical practice in diagnostics and therapy.ConclusionThe area of tension between individual empowerment and potential loss of control by the users will be emphasized as being especially important for the implementation of health Apps in primary health care. Finally, possible implications for the current design of the new German Digital Care Law (Digitale-Versorgung-Gesetz) are shown and some existing “blind spots” are highlighted. (shrink)

Zunehmend gewinnen Health Apps an Bedeutung für eine präventive und eigenverantwortliche Ausrichtung des Gesundheitssystems. Die meisten dieser digitalen Gesundheitsanwendungen basieren derzeit auf sog. Selftracking-Technologien, mit deren Hilfe physiologische und psychische Daten sensorgestützt aufgezeichnet und diese zumeist um personalisierte Alltagsinformationen ergänzt werden. Die digitalen Entwicklungen dieser Art lösten in den letzten Jahren eine intensive und deutlich polarisierte Debatte über die Chancen und Gefahren von gesundheitlichem Selftracking aus. Ziel dieser Arbeit ist es, nach einem kurzen Überblick über das Feld des medizinischen Selftracking (...) den polarisierten Diskurs zunächst anhand seiner entscheidenden individuellen und gesellschaftlichen Potenziale zu systematisieren und zu prüfen. Es stellt sich heraus, dass Selbstvermessungstechniken keineswegs ein neues Phänomen, sondern vielmehr seit langem fester Bestandteil medizinischen Wirkens in Diagnostik und Therapie sind. Als für die Implementierung von Health Apps in die medizinische Regelversorgung besonders bedeutend wird das Spannungsfeld zwischen individuellem Empowerment und potentiellem Kontrollverlust der Nutzenden herausgestellt. Abschließend werden mögliche Implikationen für die aktuelle Ausgestaltung des neuen Digitale-Versorgung-Gesetzes aufgezeigt und auf einige bestehende „blinde Flecken“ hingewiesen. (shrink)

Giorgia Pozzi’s feature article1 on the risks of testimonial injustice when using automated prediction drug monitoring programmes (PDMPs) turns the spotlight on a pressing and well-known clinical problem: physicians’ challenges to predict patient behaviour, so that treatment decisions can be made based on this information, despite any fallibility. Currently, as one possible way to improve prognostic assessments of patient behaviour, Machine Learning-driven clinical decision support systems (ML-CDSS) are being developed and deployed. To make her point, Pozzi discusses ML-CDSSs that are (...) supposed to deliver physicians an accurate estimation of the likelihood of narcotic, sedative and stimulant opioid misuse by a given patient (like, e.g., ‘NarxCare’). Regarding cases of deviating assessment between human evaluators and automated systems, the medico-ethical discussion has so far mainly focused on disagreement between clinicians and Machine Learning (ML) algorithms,2 for example in ‘second opinions’ that have been reconstructed as disagreements between physician testimony and ML-CDSS testimony.3 Examples such as those of PDMPs, by contrast, draw attention to situations in which patient testimony potentially contradicts ML-CDSS testimony. Pozzi examines decision-making situations in which ML-CDSS testimony factually overrides the testimony of the person affected. Thus, in the presented case of patient Kathryn, the testimony of her own physical and mental condition was insufficient to overturn the unfavourable outcome for …. (shrink)