Emergency departments (EDs) often experience overcrowding, staff shortages, and long waiting times, which put a strain on clinicians and negatively impact patients’ experiences. Although AI is commonly proposed to optimize clinical workflows, the perspectives of patients are often overlooked in AI design. We report on a four-phase study at a university hospital combining preparatory fieldwork, co-creation workshops, design phase, and storyboard-guided interviews. Our findings reveal a misalignment: clinicians framed the contribution of AI around triage efficiency, while patients emphasized reassurance, empathy, and guidance. Addressing both needs, we developed and evaluated a concept of an ephemeral AI companion for the ED waiting area, designed to provide orientation, support reflection, and prepare patients for consultations without substituting human contact. We contribute: empirical evidence of patients’ needs, concerns, and expectations for AI support, design principles for an ephemeral AI companion concept, and findings from a study conducted with patients during their ED waits.

Robot-assisted surgery (RAS) has raised concerns within human-computer interaction, particularly regarding the socio-material configurations of robotic systems and their impact on surgical practices. To investigate these configurations in the context of the da Vinci robotic system, we used a research-through-design approach. Through this approach, we developed six Visual RAS (ViRAS) scenarios derived from RAS observations. These scenarios represent different configurations of interacting surgical team members and the robotic system in distinct adverse RAS events. In ViRAS-guided interviews with experienced RAS surgeons, we found that ViRAS scenarios help reflect on surgical practices and the specific material and spatial properties of RAS. Our findings indicated that the team’s cognitive engagement during surgery could be improved by providing sensory augmentation to facilitate task perception and individual skill development. Through our research, we show how ViRAS scenarios, as a tool for reflection, can reveal opportunities for designing socio-material configurations in RAS and beyond.

In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary research purposes, presents a promising resource for such datasets. Yet, current consent user interfaces mediating data-sharing decisions are found to favor data collectors' values over those of data subjects. Seeking to establish value-centered data collection practices in digital health, we investigate the design of consent user interfaces that support end-users in making value-congruent data-sharing decisions. Focusing our research efforts on the situated context of health data donation at the psychosomatic unit of a university hospital, we demonstrate how a human-centered design can ground technology within the perspective of a vulnerable group. We employed an exploratory sequential mixed-method approach consisting of five phases: (1) Participatory workshops elicit patient values, informing the (2) design of a proposed Value-Centered Consent Interface . An (3) online experiment demonstrates our interface element's effect, increasing value congruence in data-sharing decisions. Our proposed consent user interface design is then adapted to the research context through a (4) co-creation workshop with domain experts and (5) a user interface evaluation with patients. Our work contributes to recent discourse in CSCW concerning ethical implications of new data practices within their socio-technological context by exploring patient values on medical data-sharing, introducing a novel consent interface leveraging reflection to support value-congruent decision-making, and providing a situated evaluation of the proposed consent user interface with patients.

The secondary use of routinely collected patient data made possible by the broad consent form is seen as a prerequisite for developing data-driven health technologies. In Germany, relevant stakeholder groups (e.g., ethics committees and data protection authorities) specified the broad consent form; however, only one group of patient representatives was consulted, which may indicate asymmetries in engagement. This situation informed our research on medical data donation and emphasized foregrounding patient values. Drawing on participatory design, value sensitive design, and emerging research on value-led participation, we propose a method consisting of (1) a workshop concept for participatory value elicitation composed of four carefully coordinated phases and (2) an analysis procedure to examine the empirical data collected. This analysis allowed us to derive design requirements for medical data donation user interfaces. We conducted three workshops with patient advocates of vulnerable groups and patients in residential care of a psychosomatic unit. Our findings provide new directions to improve user interfaces for medical data donation: First, user interfaces need to enhance patients' reflective thinking about the potential consequences of their data donation; second, a decision facilitator supporting patients' value-based decision-making (e.g., by providing simple language or tailoring descriptions to patient needs); and finally, a data intermediary relieving patients' decision-making and giving them control over their data after donation. Moreover, we emphasize the need to increase the use of participatory approaches in health technology development.

Every day, we have to decide multiple times, whether and how much personal data we allow to be collected. This decision is not trivial, since there are many legitimate and important purposes for data collection, for examples, the analysis of mobility data to improve urban traffic and transportation. However, often the collected data can reveal sensitive information about individuals. Recently visited locations can, for example, reveal information about political or religious views or even about an individual's health. Privacy-preserving technologies, such as differential privacy (DP), can be employed to protect the privacy of individuals and, furthermore, provide mathematically sound guarantees on the maximum privacy risk. However, they can only support informed privacy decisions, if individuals understand the provided privacy guarantees. This article proposes a novel approach for communicating privacy guarantees to support individuals in their privacy decisions when sharing data. For this, we adopt risk communication formats from the medical domain in conjunction with a model for privacy guarantees of DP to create quantitative privacy risk notifications.