[Supporting medical and nursing activities with AI: recommendations for responsible design and use]. / Unterstützung ärztlicher und pflegerischer Tätigkeit durch KI: Handlungsempfehlungen für eine verantwortbare Gestaltung und Nutzung.

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have a negative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research.

Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals' preferences and concerns.

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.

Causes of adverse events in home mechanical ventilation: a nursing perspective.

BACKGROUND: Adverse events (AE) are ubiquitous in home mechanical ventilation (HMV) and can jeopardise patient safety. One particular source of error is human interaction with life-sustaining medical devices, such as the ventilator. The objective is to understand these errors and to be able to take appropriate action. With a systematic analysis of the hazards associated with HMV and their causes, measures can be taken to prevent damage to patient health. METHODS: A systematic adverse events analysis process was conducted to identify the causes of AE in intensive home care. The analysis process consisted of three steps. 1) An input phase consisting of an expert interview and a questionnaire. 2) Analysis and categorisation of the data into a root-cause diagram to help identify the causes of AE. 3) Derivation of risk mitigation measures to help avoid AE. RESULTS: The nursing staff reported that patient transportation, suction and tracheostomy decannulation were the main factors that cause AE. They would welcome support measures such as checklists for care activities and a reminder function, for e.g. tube changes. Risk mitigation measures are given for many of the causes listed in the root-cause diagram. These include measures such as device and care competence, as well as improvements to be made by the equipment providers and manufacturers. The first step in addressing AE is transparency and an open approach to errors and near misses. A systematic error analysis can prevent patient harm through a preventive approach. CONCLUSION: Risks in HMV were identified based on a qualitative approach. The collected data was systematically mapped onto a root-cause diagram. Using the root-cause diagram, some of the causes were analysed for risk mitigation. For manufacturers, caregivers and care services requirements for intervention offers the possibility to create a checklist for particularly risky care activities.

Secondary use of health care data and left-over biosamples within the 'Medical Informatics Initiative' (MII): a quasi-randomized controlled evaluation of patient perceptions and preferences regarding the consent process.

BACKGROUND: Data collected during routine health care and ensuing analytical results bear the potential to provide valuable information to improve the overall health care of patients. However, little is known about how patients prefer to be informed about the possible usage of their routine data and/or biosamples for research purposes before reaching a consent decision. Specifically, we investigated the setting, the timing and the responsible staff for the information and consent process. METHODS: We performed a quasi-randomized controlled trial and compared the method by which patients were informed either in the patient admission area following patient admission by the same staff member (Group A) or in a separate room by another staff member (Group B). The consent decision was hypothetical in nature. Additionally, we evaluated if there was the need for additional time after the information session and before taking the consent decision. Data were collected during a structured interview based on questionnaires where participants reflected on the information and consent process they went through. RESULTS: Questionnaire data were obtained from 157 participants in Group A and 106 participants in Group B. Overall, participants in both groups were satisfied with their experienced process and with the way information was provided. They reported that their (hypothetical) consent decision was freely made. Approximately half of the interested participants in Group B did not show up in the separate room, while all interested participants in Group A could be informed about the secondary use of their routine data and left-over samples. No participants, except for one in Group B, wanted to take extra time for their consent decision. The hypothetical consent rate for both routine data and left-over samples was very high in both groups. CONCLUSIONS: The willingness to support medical research by allowing the use of routine data and left-over samples seems to be widespread among patients. Information concerning this secondary data use may be given by trained administrative staff immediately following patient admission. Patients mainly prefer making a consent decision directly after information is provided and discussed. Furthermore, less patients are informed when the process is organized in a separate room.

The generic Informed Consent Service gICS®: implementation and benefits of a modular consent software tool to master the challenge of electronic consent management in research.

BACKGROUND: Defining and protecting participants' rights is the aim of several ethical codices and legal regulations. According to these regulations, the Informed Consent (IC) is an inevitable element of research with human subjects. In the era of "big data medicine", aspects of IC become even more relevant since research becomes more complex rendering compliance with legal and ethical regulations increasingly difficult. METHODS: Based on literature research and practical experiences gathered by the Institute for Community Medicine (ICM), University Medicine Greifswald, requirements for digital consent management systems were identified. RESULTS: To address the requirements, the free-of-charge, open-source software "generic Informed Consent Service" (gICS®) was developed by ICM to provide a tool to facilitate and enhance usage of digital ICs for the international research community covering various scenarios. gICS facilitates IC management based on IC modularisation and supports various workflows within research, including (1) electronic depiction of paper-based consents and (2) fully electronic consents. Numerous projects applied gICS and documented over 336,000 ICs and 2400 withdrawals since 2014. DISCUSSION: Since the consent's content is a prerequisite for securing participants' rights, application of gICS is no guarantee for legal compliance. However, gICS supports fine-granular consents and accommodation of differentiated consent states, which can be directly exchanged between systems, allowing automated data processing. CONCLUSION: gICS simplifies and supports sustained IC management as a major key to successfully conduct studies and build trust in research with human subjects. Therefore, interested researchers are invited to use gICS and provide feedback for further improvements.