The ongoing transition towards electric vehicle (EV) mobility creates a need for charging that drives the development of mobility ecosystems for charging, introducing new actors and a new value creation process. We know little about how value is created and captured in charging mobility ecosystems. The complexity of relations between actors needs to be explained. We therefore ask: What characterizes the value creation process for charging in mobility ecosystems? The study builds on qualitative interviews with different actors in the mobility ecosystem to identify the role of actors in the value creation process for charging. We identify eight value sources and discuss their role for value creation and value realization. In addition, we identify four types of resource combinations in the orchestration of value creation in charging mobility ecosystems.

The project Open and Self Organizing Mechanisms for Sustainable Mobility as a Service (OSMaaS) ran between 2020 and 2024, hosted by Halmstad University and funded by The Knowledge Foundation. The project was a collaboration between researchers from service design, design ethnography, business model innovation, and intelligent systems, and the companies Volvo Cars, WirelessCar, Polestar, and Devoteam. One of the project’s outputs is the OSMaaS Service Design Framework that integrates research from the different activities in the project into a toolkit for service designers. This booklet provides a guide for how to apply the framework. Each canvas can be used standalone or in any order, but our experience is that the framework is most powerful when following the design process presented here. The canvases can be downloaded from the OSMaaS webpage and are free to use. 

Objectives: To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.

Methods: Semi-structured interviews were conducted with twenty-five respondents—nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI. The transcribed interviews were analysed in an iterative coding process from open coding to theoretically informed thematic coding.

Results: We identified four aspects of trust that relate to reliability, transparency, quality verification, and inter-organizational compatibility. These aspects fall under the categories of substantial and procedural requirements.

Conclusions: Development of appropriate levels of trust in AI in healthcare is complex and encompasses multiple dimensions of requirements. Various stakeholders will have to be involved in developing AI solutions for healthcare and radiology to fulfil these requirements. Clinical relevance statement: For AI to achieve advances in radiology, it must be given the opportunity to support, rather than replace, human expertise. Support requires trust. Identification of aspects and conditions for trust allows developing AI implementation strategies that facilitate advancing the field.

Key Points:

• Dimensions of procedural and substantial demands that need to be fulfilled to foster appropriate levels of trust in AI in healthcare are conditioned on aspects related to reliability, transparency, quality verification, and inter-organizational compatibility.  

• Creating the conditions for trust to emerge requires the involvement of various stakeholders, who will have to compensate the problem’s inherent complexity by finding and promoting well-defined solutions. © 2023, The Author(s).

AI-based assistants, such as conversational agents (CAs) and social robots, are becoming increasingly important in healthcare organizations. CAs provide a scalable and cost-effective platform for organizations supporting their employees by retrieving, structuring, and analyzing information to assist work processes. This study targets how knowledge graphs as ontological models manage CA that help improve the patient flow processes and reduce patients’ waiting time in the emergency departments (EDs). We tailored the design thinking (DT) method with modelling workshops employing conceptual modelling (CM) techniques to address these issues. We incorporated a hybrid formal approach of Methontology and Tove methodologies to build design artifacts, develop a goal-oriented interactive conversational system between humans and machines, and support information systems (IS). As a result, this ontology-driven approach contribution helps developers build value-added CAs to facilitate healthcare practitioners and patients. It is helpful for quality care delivery experience and improves bottlenecks in information flow within Eds.

Mobility as a Service (MaaS) combines various modes of transportation to present mobility services to travellers based on their transport needs. This paper proposes a knowledge-based framework based on Artificial Intelligence (AI) to integrate various mobility data types and provide travellers with customized services. The proposed framework includes a knowledge acquisition process to extract and structure data from multiple sources of information (such as mobility experts and weather data). It also adds new information to a knowledge base and improves the quality of previously acquired knowledge. We discuss how AI can help discover knowledge from various data sources and recommend sustainable and personalized mobility services with explanations. The proposed knowledge-based AI framework is implemented using a synthetic dataset as a proof of concept. Combining different information sources to generate valuable knowledge is identified as one of the challenges in this study. Finally, explanations of the proposed decisions provide a criterion for evaluating and understanding the proposed knowledge-based AI framework. © 2023 by the authors.

The study presents an innovative approach to incorporating AI-driven conversational agents (CAs) or social robots technologies into healthcare information systems (HISs) and revolutionizing healthcare delivery systems. The study aims to improve accessibility and personalization, and minimize adverse risks, especially in the emergency departments (EDs). The study investigates patient-related experiences, long waiting times, and overcrowding issues during peak hours in EDs. Design science research methodology (DSRM) principles were tailored with modelling workshop method to capture domain contextual knowledge and include practitioners' cognitive-tacit knowledge-ability into HIS to address the above-mentioned issues. The developed social robot artifact incorporates an artificial intelligence markup language (AIML) technique as a model to restore domain knowledge of EDs, which serves as a foundation for developing goal-oriented interactive conversational system artifact between humans and machines. As a result, the study contributes that CAs, considered value-added AI-driven applications such as CAs or social robots, serve as a coworker to facilitate healthcare practitioners and patients, catering to patients' needs and communication to enhance care delivery experience and improve information flow processes using interactive services within EDs. The research presents a promising solution to improve patient outcomes, reduce waiting times, and enhance communication between patients and practitioners in EDs.

This study focuses on using AI systems, specifically conversational agents (CAs), to improve information flow during peak hours in healthcare emergency departments (EDs). We customized a Cross Industry Standard Process for Data Mining CRISP-DM approach to a CRISP-Knowledge graph (CRISP-KG) for overall design research. We use a knowledge graph approach to create an intelligent knowledge base (KBs) for CAs, which can enhance their reasoning, knowledge management, and context awareness abilities. We employ a collaborative methodology and ontology design patterns to develop a formal ontological model. Our goal is to build intelligent KBs for CAs that can interact with end-users and improve care quality in EDs, using Semantic Web Rule Language (SWRL) for inference. The KG approach can assist healthcare practitioners and patients in managing information flow more efficiently in EDs, ultimately improving care outcomes. © 2023 CEUR-WS. All rights reserved.