Organizational decisions are usually constrained by policies and rules, sometimes up to the point of completely automated decision making. Policies exist on multiple levels within the organization and require organizational power to be created. They are typically expressed in a policy document that has multiple practical functions. Drawing on and extending the UFO-L core ontology of legal relations, we offer a critical analysis of the relationship between policy and policy document and examine the consequences of the dual nature of policies. We develop a core ontology of organizational policies in the form of four ontological patterns that we claim to be essential for organizations as social and economic phenomena: one for organizational coordination, one for policy documents, one for delegation, and one for community ruling. For validation, the patterns are applied to an example from the university domain and evaluated by means of a critical confrontation with the SBVR business rule standard.

Open Digital Innovation shows how organizations can use contests to drive the development of new digital solutions - from digital services and platforms to mobile apps. By inviting external developers, entrepreneurs, and users to compete and collaborate, organizations can generate solutions that go beyond their internal capabilities and accelerate digital innovation. Taking a hands-on, contest-driven approach, the book explains how to design, manage, and benefit from innovation contests. It introduces the core principles of open innovation and translates them into practical tools for running successful initiatives, from motivating participants and overcoming innovation barriers to transforming promising ideas into real digital services. Written for graduate and undergraduate courses in digital innovation and entrepreneurship, the book is equally valuable for managers and policymakers seeking to use contests as a strategic instrument for digital development. This second edition strengthens the book's scientific foundation while expanding its practical relevance. It explores how generative artificial intelligence is reshaping open digital innovation, how contest-based approaches can support broader digital transformation, and how emerging European data and AI regulations are redefining the conditions for innovation. The book is structured as a clear three-part roadmap - Innovation Foundations, Contest Approach, and Implications - guiding readers from foundational principles to practical application and future challenges.

Digital health in Africa is gaining momentum, yet remains fragmented due to gaps in strategic coherence, capacity, and system interoperability. To address these challenges, this study proposes a Reference Goal Model that captures and structures the key goals and means underpinning digitalization and interoperability initiatives across multiple governance levels-national, regional, and continental. We applied the Business Motivation Model (BMM) to systematically analyze strategic content from three key strategic documents: Rwanda's National Digital Health Strategic Plan, the East African Community's (EAC) Digital REACH Initiative Strategic Plan, and the African Union's (AU) Digital Transformation Strategy. Statements from the three documents were decomposed into atomic goals and means, illustrating how digital health and interoperability are conceived. The resulting Reference Goal Model for Digital Health in Africa (RGMDHA) identifies a set of goals and means for both digitalization and interoperability. By making these goals and means explicit and structured, the RGMDHA facilitates the strategic evaluation of national, regional, and continental plans, enables communication and stakeholder alignment in the digital health domain, and can be used as a tool for guiding the design and analysis of digital health and interoperability projects.

Ontology-driven conceptual modeling (ODCM) utilizes foundational ontology (UFO) to improve the theory and practice of conceptual modeling and building ontologies. ODCM is supported by the language OntoUML. Although OntoUML is widely adopted nowadays and has grown into a de facto standard, its use is not without problems, such as the philosophical terminology of UFO that is hard to grasp for non-experts as well as the size and complexity of the models in complex institutional settings. We argue that OntoUML can become more manageable by separation of concerns. On the basis of an analysis of the aims of ontology-driven conceptual modeling we suggest a distinction between conceptual modeling on the one hand and ontological analysis on the other. We propose a conceptual model view for the first part and three ontological views, focusing on three different ontological dependencies, for the second.

Legal regulations governing digital infrastructures and data spaces, such as the European Health Data Space (EHDS), are becoming increasingly complex and difficult to interpret, particularly for non-experts in law, including citizens and small and medium-sized enterprises. To address this challenge, we propose a method for visualising legal regulations that integrates legal ontology with legal design techniques. The method proceeds through three main stages: an ontology-informed reading of regulatory text supported by large language models (LLMs); the pre-processing of ontological constructs into structured tables enriched with explanations and examples; and the rendering of interactive concept maps using visualisation tools such as Kumu. In order to show the feasibility of the approach, we demonstrate the method using the EHDS as a case. The approach not only supports legal understanding but also contributes to the broader goal of trustworthy digital governance.

Det har traditionellt antagits att informationssystem beskriver den fysiska verkligheten. Denna föreställning håller dock på att bli förlegad i takt med att digitala infrastrukturer i allt större utsträckning blir en del av den sociala verkligheten. Digitala infrastrukturer (allestädes närvarande och skalbara informationssystem) kartlägger inte längre enbart representationer av den fysiska verkligheten till digitala objekt, utan de spelar en allt mer aktiv roll i att skapa, forma och styra den fysiska verkligheten. Vi bevittnar just nu en "ontologisk omvändning", där konceptuella modeller och digitala infrastrukturer förändrar den fysiska verkligheten.

Trots detta kvarstår den grundläggande föreställningen att den fysiska verkligheten är den enda verkliga världen. För att fullt ut förstå konsekvenserna av denna ontologiska omvändning behöver konceptuell modellering genomgå ett "institutionellt skifte" som överger idén om att den fysiska verkligheten alltid har företräde. Den institutionella verkligheten, som inkluderar exempelvis institutionella enheter såsom organisationer, avtal och betalningstransaktioner, är inte bara en del av den fysiska verkligheten frikopplad från digitala infrastrukturer. Digitala infrastrukturer är en del av den institutionella verkligheten.

Följaktligen ställer vi forskningsfrågan: Vilka är de grundläggande byggstenarna i utformningen av digitala infrastrukturer som utgör och omvandlar institutionell verklighet? När vi besvarar denna fråga utvecklar vi en grund för konceptuell modellering, vilken vi illustrerar genom att modellera institutionen för öppen bankverksamhet och dess tillhörande digitala infrastruktur.

I artikeln identifierar vi digitala institutionella enheter, digitala agenter reglerade av mjukvara och digitala institutionella handlingar som kritiska byggstenar för att modellera digitala infrastrukturer i institutionella sammanhang. Genom detta visar vi hur konceptuell modellering kan förbättra vår förståelse av den digitala transformationen av institutionell verklighet och den normativa rollen för konceptuell modellering. Vi genererar också teoretiska insikter om behovet av legitimitet och ansvar, vilket fördjupar studier och praktik av design och konsekvenser av digitala infrastrukturer.

It has traditionally been assumed that information systems describe physical reality. However, this assumption is becoming obsolete as digital infrastructures are increasingly part of real-world experiences. Digital infrastructures (ubiquitous and scalable information systems) no longer merely map physical reality representations onto digital objects but increasingly assume an active role in creating, shaping, and governing physical reality. We currently witness an “ontological reversal”, where conceptual models and digital infrastructures change physical reality. Still, the fundamental assumption remains that physical reality is the only real world. However, to fully embrace the implications of the ontological reversal, conceptual modeling needs an “institutional turn” that abandons the idea that physical reality always takes priority. Institutional reality, which includes, for example, institutional entities such as organizations, contracts, and payment transactions, is not simply part of physical reality detached from digital infrastructures. Digital infrastructures are part of institutional reality. Accordingly, the research question we address is: What are the fundamental constructs in the design of digital infrastructures that constitute and transform institutional reality? In answering this question, we develop a foundation for conceptual modeling, which we illustrate by modeling the institution of open banking and its associated digital infrastructure. In the article, we identify digital institutional entities, digital agents regulated by software, and digital institutional actions as critical constructs for modeling digital infrastructures in institutional contexts. In so doing, we show how conceptual modeling can improve our understanding of the digital transformation of institutional reality and the prescriptive role of conceptual modeling. We also generate theoretical insights about the need for legitimacy and liability that advance the study and practice of digital infrastructure design and its consequences.

Health care is a complex domain containing large amounts of data, including clinical and administrative data. Furthermore, the domain includes advanced decision-making utilising the collected data. Various IT systems based on AI technologies, such as machine learning, have been promoted as a way to improve both the quality and efficiency of health care. So far, the focus has been on supporting quite narrow and data-intensive activities carried out by a single actor, such as interpreting X-ray images and performing triage. However, providing health care for a single patient can involve a comprehensive process with numerous actors, ranging from home care and primary care to specialist care. In this paper, we examine how existing AI solutions can support a complex care process involving several collaborating actors. We base the examination on a health care case from Swedish elderly care. The case is used to identify multiple problem areas, which are then compared to existing AI solutions

Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.

Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.

Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.

Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.

Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Patients with long-term cognitive impairments following COVID-19 face significant challenges in their rehabilitation, which involves essential therapeutic procedures, administrative tasks, diagnostic tests, and self-assessments. "Digiphysical" methods, combining digital and physical healthcare interventions, offer promising solutions to enhance these rehabilitation processes. In this paper, we present a newly developed digital rehabilitation prototype based on the digiphysical approach. The prototype is designed to integrate digital and physical interventions, streamline clinical and administrative tasks, support the entire rehabilitation process, and facilitate self-directed rehabilitation. It addresses the rehabilitation needs from both the patients' and the rehabilitation personnel's perspectives. In this paper, we present the results of an initial evaluation of the prototype conducted with key rehabilitation professionals.

Drug-drug interactions (DDIs) pose significant risks to patients, ranging from adverse effects to fatal outcomes. Preventing these issues depends on providing caregivers with timely information on DDIs and offering viable alternative options. Currently, there is a gap in the formal specifications of systems designed to alert caregivers about potential DDIs. This gap hinders the development of further support, such as algorithms that can recommend alternative drugs.

This study adopts the Design Science approach, defining a formal knowledge graph to capture DDIs. Then, algorithms are defined to identify drug interactions and suggest alternative medications with less severe consequences. As a proof of concept, we implemented our approach using Neo4j and Python, transforming data from the Swedish DDIs database.

The implementation was applied to real care session data in the healthcare region of Stockholm for a randomly selected day, focusing on instances where caregivers prescribed drugs with severe DDIs. Validation occurred through expert interviews, discussing the correctness and utility of the approach. Results indicate that our graph-based model effectively supports the development of systems that alert caregivers to potential DDIs and recommend alternative drugs with reduced interactions.

To the best of our knowledge, this paper introduces the first graph-based model serving as a blueprint for developing DDI systems. This model enables systems to i) warn caregivers about the presence of DDIs in prescribed drugs and ii) assess the availability of alternative drugs with less severe interactions, providing recommendations.