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The Institute DigiHealth of the University of Applied Sciences Neu-Ulm is planning ELSI guidelines and legal policies on data protection and making ethics requests for the CARE REGIO project, founded by the Bavarian State Ministry for Health and Care. CARE REGIO’s joint project aims at reforming the care system with digital solutions.

Robotic assistance systems offer new therapeutic perspectives for patient mobilization. This work aims to investigate the chances and risks of robotic assistance systems in early neurological rehabilitation. Nine professionals working in physiotherapy and nursing were interviewed on their opinion on robotic assistance systems. The experts were recruited in three different clinics, one of which has already established robot-assisted rehabilitation. 171 individual codes were extracted from the interviews. Based on the professionals’ statements and the literature, the most significant added value of robotic assistance systems is seen in the expected relief of employees. The study results and the literature confirm the potential of robotic systems for early neurological rehabilitation.

Mobile Health (mHealth) applications have seen strong growth in recent years, but they are often not systematically evaluated. A Delphi survey was conducted to identify key elements for the evaluation of mHealth applications. Sixteen experts participated in the study, and the study yielded a list of 79 key elements with expert consensus. Thirty-two elements were in the category of structure quality, 29 in process quality, and 18 in outcome quality. The number of key elements highlights the complexity of conducting systematic evaluations of mHealth applications.

Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.

Even though the “user characteristics” (UC) can influence “acceptance” and “the continuance intention to use” of information systems such as electronic medical records (EMR), the effect of UC has not been adequately evaluated in post-adoption models. This study seeks to examine the effect of UC on post-usage of EMR using the expectation confirmation model (ECM). A total of 450 questionnaires was collected by a survey to extend ECM by integrating UC using structural equation modeling (SEM). Data were analyzed using LISREL through confirmatory factor analysis. A path analysis test was also performed to fit and confirm the model fit. The UC affects the “confirmation of expectations” directly and the “continuance intention to use” indirectly. The findings of the present study can provide a new path for further research on the extending of the ECM model. Moreover, end users’ characteristics should be considered in the preparation and training phases of EMR implementation.

Digital technologies have the potential to improve the quality of nursing care. CARE REGIO is a Bavarian joint research project for digital transformation and technology in nursing care. The project goals are supporting the nursing staff, saving time, improving the quality of care as well as increasing the quality of life and safety of those in need of care. In Phase 1 of the project, literature and stakeholder analyses, and qualitative surveys were carried out. Subsequently, central fields of action were defined for Phase 2 of the project. CARE REGIO can make a significant contribution to evaluating existing digital solutions, developing new solutions, and accelerating their implementation into practice.

Much hope is placed in the use of assistive technologies (AT) to support older adults and retain their independence and quality of life. AT could also be a way to help fulfill the increasing requirements of caring for an aging population. While many devices are freely available, successful and broad adoption is progressing slowly. In a qualitative analysis, we investigated the popularity of AT and the willingness to and obstacles for use. Eleven older adults aged 62–85 participated in three groups, each focusing on a specific age-related case example. Using content analysis, statements have been categorized into three levels: the affected person, their surroundings, and supportive devices. Information (knowledge, consultation) was the topic spoken about the most (n=101), followed by statements focusing on everyday life and the use of assistive devices (n=99). Comments on economic topics carried the least weight (n=28). The biggest obstacles for the reluctant adoption of AT don’t seem to be acceptance issues but rather a lack of knowledge of AT and where to get counseling. Thus, the provision and promotion of coherent information material and accessible consultation for users and relatives should be prioritized. AT training for healthcare professionals can provide further opportunities.

Potential benefits of mHealth applications are large for chronic diseases. To get an overview of how these applications are being evaluated, a scoping review is being conducted. First results show that single factors are most commonly assessed. The results of clinical outcome measures are the most common. Economic factors are the least common. A uniform framework that specifies different factors and metrics for the evaluation is not apparent.