Dynamic modulation of the processing of unpredicted technical errors by the posterior cingulate and the default mode network.

The pervasive use of information technologies (IT) has tremendously benefited our daily lives. However, unpredicted technical breakdowns and errors can lead to the experience of stress, which has been termed technostress. It remains poorly understood how people dynamically respond to unpredicted system runtime errors occurring while interacting with the IT systems on a behavioral and neuronal level. To elucidate the mechanisms underlying such processes, we conducted a functional magnetic resonance imaging (fMRI) study in which 15 young adults solved arithmetic problems of three difficulty levels (easy, medium and hard) while two types of system runtime errors (problem errors and feedback errors) occurred in an unexpected manner. The problem error condition consisted of apparently defective displays of the arithmetic problem and the feedback error condition involved erroneous feedback. We found that the problem errors positively influenced participants' problem-solving performance at the high difficulty level (i.e., hard tasks) at the initial stage of the session, while feedback errors disturbed their performance. These dynamic behavioral changes are mainly associated with brain activation changes in the posterior cingulate and the default mode network, including the posterior cingulate cortex, the mPFC, the retrosplenial cortex and the parahippocampal gyrus. Our study illustrates the regulatory role of the posterior cingulate in coping with unpredicted errors as well as with dynamic changes in the environment.

Palliative care as a digital working world (PALLADiUM) - A mixed-method research protocol.

BACKGROUND: In Palliative Care, actors from different professional backgrounds work together and exchange case-specific and expert knowledge and information. Since Palliative Care is traditionally distant from digitalization due to its holistically person-centered approach, there is a lack of suitable concepts enabling digitalization regarding multi-professional team processes. Yet, a digitalised information and collaboration environment geared to the requirements of palliative care and the needs of the members of the multi-professional team might facilitate communication and collaboration processes and improve information and knowledge flows. Taking this chance, the presented three-year project, PALLADiUM, aims to improve the effectiveness of Palliative Care teams by jointly sharing available inter-subjective knowledge and orientation-giving as well as action-guiding practical knowledge. Thus, PALLADiUM will explore the potentials and limitations of digitally supported communication and collaboration solutions. METHODS: PALLADiUM follows an open and iterative mixed methods approach. First, ethnographic methods - participant observations, interviews, and focus groups - aim to explore knowledge and information flow in investigating Palliative Care units as well as the requirements and barriers to digitalization. Second, to extend this body, the analysis of the historical hospital data provides quantitative insights. Condensing all findings results in a to-be work system. Adhering to the work systems transformation method, a technical prototype including artificial intelligence components will enhance the collaborative teamwork in the Palliative Care unit. DISCUSSION: PALLADiUM aims to deliver decisive new insights into the preconditions, processes, and success factors of the digitalization of a medical working environment as well as communication and collaboration processes in multi-professional teams. TRIAL REGISTRATION: The study was registered prospectively at DRKS (Deutsches Register Klinischer Studien) Registration-ID: DRKS0025356 Date of registration: 03.06.21.

Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing.

Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI's potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

How to prevent technostress at the digital workplace: a Delphi study.

Technostress is a rising issue in the changing world of digital work. Technostress can cause severe adverse outcomes for individuals and organizations. Thus, organizations face the moral, legal, and economic responsibility to prevent employees' excessive technostress. As technostress develops over time, it is crucial to prevent it throughout the process of its emergence instead of only reacting after adverse outcomes occur. Contextualizing the Theory of Preventive Stress management to technostress, we synthesize and advance existing knowledge on inhibiting technostress. We develop a set of 24 technostress prevention measures from technostress inhibitor literature, other technostress literature, and based on qualitative and quantitative contributions from a Delphi study. Based on expert feedback, we characterize each measure and, where possible, assess its relevance in addressing specific technostressors. Our paper contributes to research by transferring the Theory of Preventive Stress Management into the context of technostress and presenting specific measures to prevent technostress. This offers a complementary view to technostress inhibitors by expanding the theoretical grounding and adding a time perspective through the implementation of primary, secondary, and tertiary prevention measures. For practice, we offer a comprehensive and applicable overview of measures organizations can implement to prevent technostress.

Understanding the evaluation of mHealth app features based on a cross-country Kano analysis.

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

Assessing healthcare service quality using routinely collected data: Linking information systems in emergency care.

Emergency departments need to continuously calculate quality indicators in order to perform structural improvements, improvements in the daily routine, and ad-hoc improvements in everyday life. However, many different actors across multiple disciplines collaborate to provide emergency care. Hence, patient-related data is stored in several information systems, which in turn makes the calculation of quality indicators more difficult. To address this issue, we aim to link and use routinely collected data of the different actors within the emergency care continuum. In order to assess the feasibility of linking and using routinely collected data for quality indicators and whether this approach adds value to the assessment of emergency care quality, we conducted a single case study in a German academic teaching hospital. We analyzed the available data of the existing information systems in the emergency continuum and linked and pre-processed the data. Based on this, we then calculated four quality indicators (Left Without Been Seen, Unplanned Reattendance, Diagnostic Efficiency, and Overload Closure). Lessons learned from the calculation and results of the discussions with staff members that had multiple years of work experience in the emergency department provide a better understanding of the quality of the emergency department, the related challenges during the calculation, and the added value of linking routinely collected data.

Stratified prevention: opportunities and limitations. Report on the 1st interdisciplinary cardiovascular workshop in Augsburg.

Sufficient exercise and sleep, a balanced diet, moderate alcohol consumption and a good approach to handle stress have been known as lifestyles that protect health and longevity since the Middle Age. This traditional prevention quintet, turned into a sextet by smoking cessation, has been the basis of the "preventive personality" that formed in the twentieth century. Recent analyses of big data sets including genomic and physiological measurements have unleashed novel opportunities to estimate individual health risks with unprecedented accuracy, allowing to target preventive interventions to persons at high risk and at the same time to spare those in whom preventive measures may not be needed or even be harmful. To fully grasp these opportunities for modern preventive medicine, the established healthy life styles require supplementation by stratified prevention. The opportunities of these developments for life and health contrast with justified concerns: A "surveillance society", able to predict individual behaviour based on big data, threatens individual freedom and jeopardises equality. Social insurance law and the new German Disease Prevention Act (Präventionsgesetz) rightly stress the need for research to underpin stratified prevention which is accessible to all, ethical, effective, and evidence based. An ethical and acceptable development of stratified prevention needs to start with autonomous individuals who control and understand all information pertaining to their health. This creates a mandate for lifelong health education, enabled in an individualised form by digital technology. Stratified prevention furthermore requires the evidence-based development of a new taxonomy of cardiovascular diseases that reflects disease mechanisms. Such interdisciplinary research needs broad support from society and a better use of biosamples and data sets within an updated research governance framework.