RT Journal Article
SR Electronic
T1 Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals’ preferences and concerns
JF Journal of Medical Ethics
JO J Med Ethics
FD BMJ Publishing Group Ltd and Institute of Medical Ethics
SP 6
OP 11
DO 10.1136/jme-2022-108814
VO 50
IS 1
A1 Florian Funer
A1 Wenke Liedtke
A1 Sara Tinnemeyer
A1 Andrea Diana Klausen
A1 Diana Schneider
A1 Helena U Zacharias
A1 Martin Langanke
A1 Sabine Salloch
YR 2024
UL http://jme.bmj.com/content/50/1/6.abstract
AB 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.Data may be obtained from a third party and are not publicly available. The data are not publicly available as they might contain information that could compromise research participant privacy and consent.