Contemporary society faces unprecedented challenges—from rapid technological evolution to climate change and demographic tensions—compelling organisations to anticipate the future for informed decision-making. This case study aimed to design a digital system for end-users called the Time Machine, which enables a generative artificial intelligence (GAI) system to produce prospective future scenarios based on the input information automatically, proposing hypotheses and prioritising trends to streamline and make the formulation of future scenarios more accessible. The system’s design, development, and testing progressed through three versions of prompts for the OpenAI GPT-4 LLM, with six trials conducted involving 222 participants. This iterative approach allowed for gradual adjustment of instructions given to the machine and encouraged refinement. Results from the six trials demonstrated that the Time Machine is an effective tool for generating future scenarios that promote debate and stimulate new ideas in multidisciplinary teams. Our trials proved that GAI-generated scenarios could foster discussions on +70% of generated scenarios with appropriate prompting, and more than half included new ideas. In conclusion, large language models (LLMs) of GAI, with suitable prompt engineering and architecture, have the potential to generate useful future scenarios for organisations, transforming future intelligence into a more accessible and operational resource. However, critical use of these scenarios is essential.

New tools are needed to facilitate the involvement of health professionals in healthcare participative processes, partially because a relevant segment of healthcare knowledge and decision-making is capillary distributed among them. A collaborative design strategy has been applied to the creation of an Internet tool to produce digitally adapted Delphi for healthcare purposes. During the period 2012-16 the prototype of the tool has been gradually improved through its application to 18 real cases. It is proposed the model Health Consensus as a digitally adapted Delphi supported by the various capabilities of Internet. The authors agree that Health Consensus is a useful and expandable tool for participative processes. The Internet provides several opportunities to overcome many of the limitations of conventional Delphi, as well as improving the final studies with new functionalities.

Background: There is no consensus in the literature about what analytes or values should be informed as critical results and how they should be communicated. The main aim of this project is to establish consensual standards of critical results for the laboratories participating in the study. Among the project’s secondary objectives, establishing consensual procedures for communication can be highlighted. Methods: Consensus was reached among all participating laboratories establishing the basis for the construction of the initial model put forward for consensus in conjunction with the clinicians. A real-time Delphi, methodology “health consensus” (HC), with motivating and participative questions was applied. The physician was expected to choose a numeric value within a scale designed for each analyte. Results: The medians of critical results obtained represent the consensus on critical results for outpatient and inpatient care. Both in primary care and in hospital care a high degree of consensus was observed for critical values proposed in the analysis of creatinine, digoxin, phosphorus, glucose, international normalized ratio (INR), leukocytes, magnesium, neutrophils, chloride, sodium, calcium and lithium. For the rest of critical results the degree of consensus obtained was “medium high”. The results obtained showed that in 72% of cases the consensual critical value coincided with the medians initially proposed by the laboratories. Conclusions: The real-time Delphi has allowed obtaining consensual standards for communication of critical results among the laboratories participating in the study, which can serve as a basis for other organizations.

Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). At its core, IF operationalises Personalised Innovation Techniques (PInnTs)—adaptive variations of established methods tailored to project genetics and team profiles, generated dynamically through a GenAI-based system. Unlike traditional IMMs that rely on static toolkits and expert facilitation, Innovation Flow (IF) introduces a dynamic, GenAI-enhanced system capable of tailoring techniques in real time to each project’s characteristics and team profile. This adaptive model achieved a 60% reduction in ideation and prototyping time while maintaining high creative performance and autonomy. IF thus bridges the gap between human-centred design and AI augmentation, providing a scalable, personalised, and more inclusive pathway for managing innovation. Using a mixed-methods design that combines grounded theory with quasi-experimental validation, the framework was tested in 28 innovation projects across healthcare, manufacturing, and education. Findings show that personalisation improves application fidelity, engagement, and resilience, with 87% of cases achieving high efficacy. GenAI integration accelerated ideation and prototyping by more than 60%, reduced dependence on expert facilitators, and broadened participation by lowering the expertise barrier. Qualitative analyses emphasised the continuing centrality of human agency, as the most effective teams critically adapted rather than passively adopted AI outputs. The research establishes IF as a scalable methodology that augments, rather than replaces, human creativity, accelerating innovation cycles while reinforcing motivation and autonomy.

New educational models are necessary to update learning environments to the digitally shared communication and information reality. Collective intelligence is an emerging field that already has a significant impact in many areas and will have great implications in education, not only from the side of new methodologies but also as a challenge for education, currently more focused on the individual than in the collective. This paper proposes an approach to a collective intelligence model of teaching using Internet to combine two strategies: idea management and real time assessment in the class. A digital tool named Fabricius has been created supporting these two elements to foster the collaboration, empowerment and engagement of students in the learning process. As a result of the research we propose a list of KPI trying to measure individual and collective performance in a course. We are conscious that this is just a first approach to define which aspects of a class following a course can be qualified and quantified. We finally discuss the need to connect research and innovation in this field.

Clinical documentation, particularly the hospital discharge report (HDR), is essential for ensuring continuity of care, yet its preparation is time-consuming and places a considerable clinical and administrative burden on healthcare professionals. Recent advancements in Generative Artificial Intelligence (GenAI) and the use of prompt engineering in large language models (LLMs) offer opportunities to automate parts of this process, improving efficiency and documentation quality while reducing administrative workload. This study aims to design a digital system based on LLMs capable of automatically generating HDRs using information from clinical course notes and emergency care reports. The system was developed through iterative cycles, integrating various instruction flows and evaluating five different LLMs combined with prompt engineering strategies and agent-based architectures. Throughout the development, more than 60 discharge reports were generated and assessed, leading to continuous system refinement. In the production phase, 40 pneumology discharge reports were produced, receiving positive feedback from physicians, with an average score of 2.9 out of 4, indicating the system’s usefulness, with only minor edits needed in most cases. The ongoing expansion of the system to additional services and its integration within a hospital electronic system highlights the potential of LLMs, when combined with effective prompt engineering and agent-based architectures, to generate high-quality medical content and provide meaningful support to healthcare professionals. Hospital discharge reports (HDRs) are pivotal for continuity of care but consume substantial clinician time. Generative AI systems based on large language models (LLMs) could streamline this process, provided they deliver accurate, multilingual, and workflow-compatible outputs. We pursued a three-stage, design-science approach. Proof-of-concept: five state-of-the-art LLMs were benchmarked with multi-agent prompting to produce sample HDRs and define the optimal agent structure. Prototype: 60 HDRs spanning six specialties were generated and compared with clinician originals using ROUGE with average scores compatible with specialized news summarizing models in Spanish and Catalan (lower scores). A qualitative audit of 27 HDR pairs showed recurrent divergences in medication dose (56%) and social context (52%). Pilot deployment: The AI-HDR service was embedded in the hospital’s electronic health record. In the pilot, 47 HDRs were autogenerated in real-world settings and reviewed by attending physicians. Missing information and factual errors were flagged in 53% and 47% of drafts, respectively, while written assessments diminished the importance of these errors. An LLM-driven, agent-orchestrated pipeline can safely draft real-world HDRs, cutting administrative overhead while achieving clinician-acceptable quality, not without errors that require human supervision. Future work should refine specialty-specific prompts to curb omissions, add temporal consistency checks to prevent outdated data propagation, and validate time savings and clinical impact in multi-center trials.