Open Access

Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.

Digitalisierung und Roboterisierung sind Entwicklungen, die das Gesundheitswesen insgesamt, in besonderer Weise aber die Pflege herausfordern. Pflege ist in fundamentaler Weise Beziehungsarbeit und so gewinnt die Frage nach der Gestaltung der Beziehung zu den Robotern eine besondere Bedeutung. Roboter sind keine einfachen Werkzeuge mehr oder Maschinen, die wir nach unseren eigenen Anforderungen einsetzen. Roboter, wie sie für die Pflege aktuell entwickelt werden, sind komplexe technische Gegenüber, die in die soziale Interaktion mit dem Menschen eintreten, wobei noch nicht klar ist, welchen sozialen und folglich welchen normativen Status wir diesen Erscheinungsformen zuerkennen sollen. Der Artikel bietet einige Orientierungsmarken für diese Diskussion aus einer ethischen und anthropologischen Perspektive.

Background Health information systems have developed rapidly and considerably during the last decades, taking advantage of many new technologies. Robots used in operating theaters represent an exceptional example of this trend. Yet, the more these systems are designed to act autonomously and intelligently, the more complex and ethical questions arise about serious implications of how future hybrid clinical team–machine interactions ought to be envisioned, in situations where actions and their decision-making are continuously shared between humans and machines. Objectives To discuss the many different viewpoints—from surgery, robotics, medical informatics, law, and ethics—that the challenges of novel team–machine interactions raise, together with potential consequences for health information systems, in particular on how to adequately consider what hybrid actions can be specified, and in which sense these do imply a sharing of autonomous decisions between (teams of) humans and machines, with robotic systems in operating theaters as an example. Results Team–machine interaction and hybrid action of humans and intelligent machines, as is now becoming feasible, will lead to fundamental changes in a wide range of applications, not only in the context of robotic systems in surgical operating theaters. Collaboration of surgical teams in operating theaters as well as the roles, competencies, and responsibilities of humans (health care professionals) and machines (robotic systems) need to be reconsidered. Hospital information systems will in future not only have humans as users, but also provide the ground for actions of intelligent machines. Conclusions The expected significant changes in the relationship of humans and machines can only be appropriately analyzed and considered by inter- and multidisciplinary collaboration. Fundamentally new approaches are needed to construct the reasonable concepts surrounding hybrid action that will take into account the ascription of responsibility to the radically different types of human versus nonhuman intelligent agents involved.