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Background: Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it.Discussion: This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. [ABSTRACT FROM AUTHOR]

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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.

Background: Individualized Medicine aims at providing optimal treatment for an individual patient at a given time based on his specific genetic and molecular characteristics. This requires excellent clinical stratification of patients as well as the availability of genomic data and biomarkers as prerequisites for the development of novel diagnostic tools and therapeutic strategies. The University Medicine Greifswald, Germany, has launched the "Greifswald Approach to Individualized Medicine" (GANI_MED) project to address major challenges of Individualized Medicine. Herein, we describe the implementation of the scientific and clinical infrastructure that allows future translation of findings relevant to Individualized Medicine into clinical practice., Methods/design: Clinical patient cohorts (N > 5,000) with an emphasis on metabolic and cardiovascular diseases are being established following a standardized protocol for the assessment of medical history, laboratory biomarkers, and the collection of various biosamples for bio-banking purposes. A multi-omics based biomarker assessment including genome-wide genotyping, transcriptome, metabolome, and proteome analyses complements the multi-level approach of GANI_MED. Comparisons with the general background population as characterized by our Study of Health in Pomerania (SHIP) are performed. A central data management structure has been implemented to capture and integrate all relevant clinical data for research purposes. Ethical research projects on informed consent procedures, reporting of incidental findings, and economic evaluations were launched in parallel.